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Expert Guide on Implementing an AI-based Knowledge Management System

Posted on January 29, 2026September 22, 2026 by Artsyman Lizaveta

McKinsey’s recent survey shows that AI knowledge management (KM) is emerging as a key focus for implementation and scaling of intelligent agents. And it makes sense: somewhere between SharePoint and Teams, there’s a mountain of document wrangling, summarization, cleanup, and other tedious-yet-unavoidable routine tasks just waiting to be automated. AI is already capable enough to take them off everyone’s plate, giving employees hours back for higher-order work, so the business can actually move faster and more efficiently.

Think your company’s knowledge is a fertile ground for agentic AI perks? It probably is. This guide on implementing an AI-based knowledge management system will show you how to get started and make it work.

Key highlights

With knowledge management tools enhanced by AI capabilities, employees access hidden knowledge and get accurate answers instantly. Automating routine tasks in KM reduces expert workload and builds a clear competitive advantage.
Some of the key agentic automation areas of KM include intelligent content ingestion, semantic discovery, autonomous curation, and the deployment of multi-agent systems where specialized AI agents handle distinct sub-processes like compliance checks or real-time synthesis.
The success of AI and knowledge management depends on a crawl-walk-run approach: audit knowledge sprawl, build a single source of truth, choose fit-for-purpose technologies, and embed governance from day one.

What is AI-powered knowledge management?

AI in knowledge management enables a fundamentally different – compared to traditional knowledge management – level of navigating the vast amounts of information sprawled across a company.

By facilitating interaction through human language, AI helps capture knowledge intelligently, find relevant information fast, and extract key insights from the knowledge base. This draws on advances in:

generative AI and large language models that understand context,
natural language processing that parses human queries accurately,
machine learning that detects patterns across documents,
and agentic AI that can autonomously connect, update, and act on organizational knowledge across systems.

Speaking of the most common AI-powered knowledge management software in enterprises, it usually takes three forms:

AI agents embedded as add-ons in enterprise software that employees already use: CRMs, ERPs, or other systems,
Conversational AI chatbots integrated into collaboration tools like Slack or Teams, or websites to answer routine questions, guide workflows, and surface relevant documentation,
Centralized knowledge hubs or portals enhanced with AI-powered search and recommendation engines.

Agentic AI for knowledge management: key automation areas and use cases

While generative AI for knowledge management has served as a smarter way to find relevant search results, agentic AI turns it into something more ambitious: a system that can act on your behalf. Some KM operations practically beg for this kind of automation.

Content curation

Manual knowledge assets curation burdens every employee’s move or decision with cognitive overhead from the outset. AI absorbs that load.

Automated knowledge capture from different kinds of unstructured data, such as meetings, resolved support tickets or internal Q&A chats, change logs in product/engineering systems, etc.
Automated content tagging and classification. NLP is used to read, understand, and automatically classify new and existing content, ensuring consistency.
Maintenance. AI identifies outdated, redundant, or missing content, flagging it for review or suggesting updates.

Intelligent search and information delivery

Not exactly breaking news – searching for information has changed a lot in the last couple of years. So why make your team members stumble through random AI chatbots, or worse, feeding them with your internal docs, when they could get what they want instantly, all within the boundaries of your knowledge ecosystem?

NLP-based semantic search moves beyond keywords to understand natural language queries, providing contextually relevant answers.
Summarization condenses long documents or multiple sources into quick summaries.
Personalized content delivery recommends relevant articles or snippets to users based on their role, behavior, and current context (e.g., during a support call).

Proactive support and self-service insights

Knowledge that once required digging through documents or asking the right person can now reach the people who need it, as soon as they need it.

Generative responses and smart suggestions. Through AI chatbots and virtual agents, organizations can provide 24/7 assistance to customers and answer their FAQs instantly, reducing support load.
Knowledge gap analysis. LLMs identify themes in queries that reveal missing or unclear content.
Trend and pattern discovery. AI algorithms analyze large datasets to surface hidden knowledge insights.

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Proven benefits of AI in knowledge management, backed by real-life examples

AI-powered knowledge management pulls multiple levers at once. What your team actually gains depends on the concrete use case, but these are some enterprise-wide wins that have already made a habit of appearing across organizations.

Benefit	Example
Enhanced employee productivity	An Australian startup partnered with IBM to build an AI-driven enterprise KM platform aimed at content generation. After one year of internal use, their 5-person team plus an AI assistant (KIRA) accumulated ~2,000 articles (~500K words) inside their enterprise knowledge base. Usage stats are striking: on average each employee reads ~9.3 articles and writes ~0.9 articles per day, enabled by having every aspect of business documented. It’s been reported *a 3.8x increase in employee productivity* since deploying the platform.
Improved knowledge discovery and reuse	The electric vehicle maker Rivian has Gemini integrated with Google Workspace, enabling employees to *conduct instant research, master complex topics quickly, and accelerate skill-building*.
Faster decision-making	The use of NotebookLM by, again, Rivian, shortens decision loops. By reducing repetitive FAQs and quickly aggregating needed information, employees spend less time gathering facts. This means decisions – from technical troubleshooting to design planning – can be made faster because the underlying knowledge is immediately accessible.
Time and cost efficiency	Handing support ticket triage to a multi-agent AI system allowed a US online retailer to *slash processing time by 4x and cut first-response times by 75%*, all without adding extra customer support staff.
Faster onboarding and training	A luxury fashion retailer, Tapestry, created an internal AI knowledge assistant based on AWS Bedrock/Titan models and Claude 3. The solution is now used by six teams and around 300 people, who can quickly access information through a single interface instead of hunting across multiple documents and portals. This effective knowledge management system reduces the load on subject matter experts by handling repetitive questions and *empowers both new hires and employees switching teams to get up to speed independently.*

Case in point: how we automated knowledge management with agentic AI for ourselves

The appeal of automating knowledge-intensive work was too strong to ignore, so at *instinctools, we built a solution that dramatically simplifies one of the most tedious tasks in IT services and consulting – resource management.

Using the GENiE™ platform, our proprietary solution accelerator for building custom AI agents, we’ve developed a Resource Management chatbot, which is basically an AI-powered assistant integrated into Microsoft Teams, designed to automate and streamline resource management, staffing, and team coordination. It serves as a centralized, intelligent interface for tasks like finding available employees, parsing CVs, scheduling meetings, collecting feedback, and more, all through natural language chat interactions.

The platform consists of eight specialized agents, each handling distinct aspects of the resource management value chain:

Chat context agent enables our Resource Management platform to understand and retain conversation context, especially when files are shared, allowing it to answer questions based on uploaded documents.
Team composition agent helps generate CVs, match skills to roles, align CV formatting, parse job descriptions, and suggest team structures based on historical data.
Resource availability agent finds available employees by skills, time periods, or project needs using data from internal availability sheets (e.g., Google Sheets).
Meeting creation agent automates the scheduling of meetings by finding free time slots and creating calendar events in MS Teams.
History cleanup agent cleans chat history and resets conversation context when the bot is removed or re-added to a chat.
Feedback agent collects user feedback automatically and logs it into a structured file for developers and stakeholders.
Logging of failed requests agent logs errors, access issues, and out-of-scope requests for troubleshooting and improvement.
CV Parser Agent parses uploaded CVs into a standardized company format and allows queries based on CV content.

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How to automate enterprise knowledge management with AI

The shortcut to disappointment is thinking of AI knowledge management projects as crafting a dumbed-down ChatGPT version with your logo slapped on it and deployed in your corporate IT ecosystem. Achieving a positive ROI, regardless of the use case you pursue, calls for a solution architected for your unique operational realities, grounded in your proprietary data, and implemented with expert oversight throughout.

Step 1. Assess the current state

Start with an audit. Is there already some level of knowledge management automation that AI can extend? Or are knowledge sharing practices undefined, with information scattered and processes improvised? If it’s the latter, take a closer look at where your knowledge assets live. Review collaboration tools, shared folders, and even the informal networks built around a few experienced employees.

For our clients, this work usually unfolds over a two-day AI adoption workshop. Beforehand, participants fill out a short brief that gives us a quick snapshot of AI readiness across data, technology, and talent while highlighting the pressure points. During the live strategy workshop, either in-person or online, we identify knowledge managementareas where AI can truly drive impact, anchor them in concrete use cases, and outline a direction that reflects current constraints. From there, we work through technical feasibility and shape a roadmap with defined budgets, timelines, and validation steps.

– Chad West, Managing Director USA, *instinctools

Step 2. Prepare your data

This is the unglamorous, yet critical, foundation. Garbage in,gospel truth out is a fantasy. A rigorous data preparation process consists of collecting, labeling, cleaning, and, sometimes, augmenting your raw information. Our experience shows this step often consumes 70-80% of the AI-powered knowledge management automation effort but dictates 100% of the eventual output quality.

If your data already sits in one place – a data warehouse, a data lake, or, even, if you’ve taken it further with a modern data platform – you are definitely ahead of the game. However, just because your data is consolidated doesn’t mean it’s ready for AI. So don’t skip this step if you expect those much-coveted insights to be not just actionable but truly reliable.

Step 3. Choose the best-fit AI tech stack

While the specific stack can vary depending on whether your solution is a set of lightweight, context-aware agents bolted onto existing tools or a centralized, standalone conversational application, the key technological pillars remain similar:

The foundational AI model (e.g., OpenAI’s GPT, Anthropic’s Claude, open-source Llama/Mistral) that powers reasoning and language understanding.
Orchestration framework, acting as an architectural layer (e.g., LangChain, LlamaIndex, Semantic Kernel) that manages workflows, tools, and multi-step interactions with the LLM.
Knowledge base and retrieval, representing where your company data lives, combined with a system to find it. This is typically a vector database (e.g., Pinecone, Weaviate) for semantic search paired with traditional storage.
Application integration layer, aka the interface users interact with (e.g., a web app, chatbot in Slack/Teams) and its backend infrastructure (e.g., FastAPI, cloud functions).

This stage is one of the most time-consuming and demanding, as it calls for deep AI expertise that must be continuously built up and kept current as new bells and whistles roll out. Businesses that do not focus on AI development and lack a strong bench of AI specialists are unlikely to pull this off on their own.

To speed up the development and delivery of AI agents and get more out of them in practice, we’ve brought our hands-on experience and a solid, battle-tested methodology together in our GENiE™ solution accelerator. It sits on top of your existing software foundation, works with what you already have, and avoids locking you into a broad set of expensive add-ons.

Step 4. Train and govern your AI models

The AI models you choose don’t magically know your business. They require guardrails before they touch your employees’ workflows and need to be trained on your operational nitty-gritty.

At this stage, you decide whether to go for model fine-tuning or rely on retrieval augmented generation (RAG).

The choice is usually driven by cost and technical fit: fine-tuning makes sense when you have a stable, well-defined dataset and you need the model to behave in a very specific way, but it can be expensive and time-consuming because every update requires re-training and redeploying.

RAG, on the other hand, is often cheaper and faster to maintain because you can keep the model general and simply update the knowledge base as new information arrives, though it may require more engineering work around indexing, retrieval, and ensuring the system stays reliable when the source documents change.

Either way, the decision shapes how your AI interacts with users and how governance and monitoring are implemented downstream.

Next, set up governance. Define who owns the models and approves changes, and how updates get validated. Track confidence scores and error rates on critical knowledge tasks, and log outputs for auditing. Without this, even a technically capable model becomes a liability.

Step 5. Roll out, monitor, and support

Start small, with a pilot group that’s willing to poke holes in the system and say out loud when something feels off. Watch closely how comfortable people feel using it and whether everyday work actually speeds up or just shifts shape. Besides, track how often the AI confidently gets things wrong. Adjust the system according to early feedback and let it eventually earn its place. Then scale. And, never skimp on employee training.

AI knowledge managementis as much a change in habits and trust as it is a technical rollout. You’re asking people to rethink how they move work forward. Build this new habit with engaging education formats like interactive workshops, hands-on simulation sandboxes, dedicated help desk channels for real-time support, etc.

– Chad West, Managing Director USA, *instinctools

Challenges of knowledge management automation with AI

Even the most carefully planned projects from the technical perspective can bump into either operational friction or the inherent constraints of underlying AI technologies. Yet, professional AI engineering and consulting teams keep building their chops to push right past them.

LLM hallucinations or inaccuracy

For all their brilliance, LLMs are masters at dressing up authoritative-sounding nonsense as facts, which is a headache for enterprise knowledge systems. Key engineering practices to combat this and polishing up model performance include:

implementing RAG architectures to ground outputs in verified sources,
establishing comprehensive guardrail and validation frameworks for output filtering,
maintaining continuous human-in-the-loop review processes,
and applying meticulous prompt engineering alongside fine-tuning on domain-specific, high-quality corpora.

Need for governance

AI might surface a piece of information that is technically correct but is inappropriate for a specific user, a sensitive internal situation, or a regulated context. Well-planned governance to prevent this is built on practices such as:

model update management, prompt governance, and monitoring for unintended behavior,
training and awareness programs to ensure users understand responsible AI use rules,
role-based access control to limit who sees what,
content classification to flag sensitive or confidential data,
automated compliance checks to enforce regulations,
AI outputs accuracy, relevance, and suitability checks and approvals (if needed),
bias checks and safeguards against discriminatory or harmful content,
and audit logs to track what was shared, when, and by whom.

Cost management

Workloads used to power up AI-powered KM systems can scale unpredictably, when underlying models and data retrieval workloads grow. Cloud compute, storage, and API token usage all contribute to variable costs that are difficult to forecast without controls.

Managing this process is possible with specialized tools such as AWS Auto Scaling for compute, Datadog or Prometheus for monitoring usage spikes, Kubernetes or Docker Swarm to orchestrate containerized workloads efficiently, and cost-alerting dashboards in platforms like Azure Cost Management or GCP’s Cloud Billing to maintain financial visibility and efficiency.

Change management

If there’s one thing that can derail even a flawlessly automated knowledge management process, it’s resistance from the people who are supposed to use it.

Smooth AI knowledge management adoption isn’t just a matter of training. It’s about preparing teams for new workflows, communicating value, addressing concerns, and securing buy-in from all levels, with top management leading the charge to ensure the system is trusted and integrated into daily work.

Automate enterprise knowledge management with agentic AI

AI changes the equation for how organizations capture, share, and apply what they know. Its payoffs show up in distinct, measurable ways: support tickets that deflate, projects that move without waiting for information, and decisions made with full context at hand. The journey towards implementing agentic, or any other kind of AI in your knowledge management strategy should start with a clear-eyed assessment of your company’s knowledge landscape. From there, it’s a matter of engineering the foundation, assembling the right digital team of AI agents, and guiding your human team to work alongside them.

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FAQ

What is AI in knowledge management?

It’s the application of artificial intelligence, specifically machine learning, natural language processing, and agentic automation, to intelligently capture, organize, retrieve, and maintain an organization’s knowledge. Static document repositories serve as a basis for interactive and proactive AI-powered systems that understand and act on information.

What is the 30% rule in AI?

A pragmatic guideline, suggesting that to see a 30% improvement in a key metric (e.g., process speed, cost reduction), you typically need to automate about 70% of the process steps with high reliability. It underscores that partial automation can yield significant, but not infinite, returns.

What is the 10-20-70 rule for AI?

A framework for AI investment allocation: roughly 10% of effort/resources on the AI algorithms and models themselves, 20% on the technology and data infrastructure, and 70% on business process integration, change management, and fostering adoption among people. It highlights that the technical model is the smallest piece of the puzzle.

How to measure ROI of AI in knowledge management?

You can measure AI ROI in knowledge management by looking at time saved on searching for the information and support, improved productivity and customer satisfaction, fewer mistakes from outdated data, and lower costs from reduced manual work, all translated into financial value.

How to Build a Modern Data Platform? A Data Engineer’s Perspective for 2026

Posted on January 22, 2026September 22, 2026 by Artsyman Lizaveta

The surge of interest in artificial intelligence has elevated the importance of building the modern data platform. And here’s why.

As AI sets higher expectations for how businesses use their data, many are growing more uncertain about the strength of their data foundations. Companies still struggle with issues such as integration, security, and data quality and the pace of improvement has not matched the increasing demands of AI/ML initiatives.

Today, a striking 84% of global data and analytics executives agree their data strategies need a ground-up rethink before innovative AI-related undertakings and advanced analytics can live up to their promise. And the surest way towards this is building a modern data platform. In this article, our data experts explain how to build a data platform right.

Key highlights

Advanced data analytics and AI/ML initiatives can only succeed on a foundation of clean, well-organized data. A modern data platform, developed in line with a clear data platform strategy, delivers exactly that.
Core layers of a modern data platform architecture include data ingestion, data storage, data transformation, data processing, data consumption, and data governance.
Without strong governance, even the most elaborate data platforms risk inconsistencies, compliance issues, and limited trust in the insights derived.

What is a modern data platform?

A modern data platform (MDP) is a unified, enterprise-wide data ecosystem of tools that enables the collection, storage, transformation, and consumption of data under transparent governance. Its goal is to move beyond a set of loosely connected components and their chaotic usage toward a cohesive modern data infrastructure that oversees the full data lifecycle end to end. It can be reached either by a collection of best-of-breed, cloud-native tools for data tasks (such as dbt, Fivetran, etc.), commonly referred to as a modern data stack, or a more integrated and often self-service platform built around those modern data ecosystem components.

Why do businesses need a modern data platform?

In fact, 82% of companies are either planning or already implementing a data platform. There’s nothing new about the business goals they are trying to achieve with solutions like this. What is new is how effectively a modern data platform enables organizations to reach them, tipping the balance in its favor over legacy, fragmented, and semi-manual data management environments that offer nothing but slow, brittle, expensive, and hard-to-scale band-aids.

Building a modern data platform dramatically shortens time-to-value and boosts efficiency across the sought-after AI/ML initiativesand augmented analyticsproducing real-time, actionable insights. Other benefits of having well-organized data platform infrastructure include:

Lower costs. Even with a solid upfront investment, building a big data platform saves money in the long run by reducing spend on data team headcount needed to manage scattered data sources, as well as on licensing fees for disparate tools.
Saving engineering time. To create a new pipeline, there’s no need for intensive coding work as templates and reusable components can be replicated across different use cases.
Democratized usage. Beyond data analysts, the platform’s user-friendly ecosystem makes trusted and governed data accessible to a broader team of business users across the organization.
Frictionless data delivery. Data doesn’t get stuck in isolated silos or require complex handoffs between tools. Besides, with standardized schema and governance, different teams can access and interpret the same data without extra cleaning or mapping.

However, a modern data analytics platform is only as effective as the vision behind it. Without a clear data platform strategy, businesses end up duplicating efforts across teams, fragmenting their data ecosystem, and slowing every transformation initiative, whether in business intelligence, advanced analytics, or AI.

When is it better to opt for custom data platform development? Isn’t a ready-made enterprise data platform enough?

Off-the-shelf platforms like Microsoft Fabric or Google BigQuery are fine for fast launch at relatively lower upfront costs or standard needs.

But if you want a data foundation that’s built for your unique playbook, one that scales exactly when and how your business scales, delivers long-term savings, and eventually turns into a genuine competitive edge, you need custom data platform development, also called data platform engineering or data platforming.

Besides, ready-made solutions often come packed with features you don’t need. Or, worse, data platform features that aren’t designed for your actual needs, leaving you to hack your way around their limitations. Those workarounds eat up time and budgets.

With custom data platforming, on the other hand, you:

get exactly what you need to achieve your goals
gain full control over your data platform architecture and your usage model, which is especially crucial when your data becomes a strategic digital asset
have the freedom to rapidly test and deploy advanced AI functionalities, like autonomous AI agents or semantic understanding before they are available in commercial platforms. Plus, these can be tailored exactly to your needs, something ready-made solutions allow only in a very limited way.

How to build core data platform layers?

As a rule, a modern data platform architecture is built on four core layers, including ingestion, storage, processing, and consumption. Each is made up of its own set of tools and technologies. Collectively, components of a modern data ecosystem aim for one simple goal: getting the right data, to the right people, at the right time, in the right shape.

Data ingestion

Data ingestion is the first step in extracting value from the massive volumes of structured and unstructured data businesses amass from corporate systems like ERP or CRM, financial platforms, third-party providers, social media, and others.

When data ingestion is well-planned, all relevant data sources are identified and properly integrated, and data flowing into the modern data platform is validated and formatted for reliable storage and efficient downstream processing. Engineers have to wrestle chaos into order, carefully deciding how to handle formats, missing or duplicated data, and temporal alignment, since errors here cascade into analytics, reporting, or AI models, depending on the business use case for the data.

Today, this is made possible by tools like Fivetran, Apache Kafka, and CDC technologies such as Debezium.

Data storage

The choice of a data storage system depends on an organization’s requirements and a variety of data users, and performance expectations. Modern storage architectures can be deployed in both cloud and on-premises environments while leveraging high-resilience databases for modern data platforms to support AI workloads and large-scale data processing. When building a data analytics platform, the following storage options are commonly considered:

Data warehouses. A data warehouse is the right choice when the required datasets are well defined, their structure is known, and data-reliant initiatives are already clear.
Data lakes. When organizations expect analysis patterns to evolve and need to work across heterogeneous data, a data lake provides the necessary room to explore.
Data lakehouses. Pioneered by Databricks, the lakehouse concept makes it possible to use data management features inherent in data warehousing on the raw data stored in a low-cost data lake owing to its metadata layer.

Data processing and modeling

Stored data only becomes useful once it’s been properly transformed. The data processing layer is where cleansing, combining, and structuring data happens to ensure its quality, consistency, and readiness for planned initiatives. Depending on your needs, we integrate different data platform tools in this stage. Just a few examples are:

For high-speed, large-scale batch data processing, we suggest using Apache Spark and its integrated modules for SQL, streaming, and machine learning.
When a fully managed, serverless ETL service is needed, AWS Glue automatically discovers, prepares, and combines data for analysis.
Apache Kafka (with Kafka Streams) powers real-time streaming applications that demand high scalability and fault tolerance.
To perform stateful, low-latency computations on unbounded data streams, Apache Flink provides exactly-once processing guarantees.
dbt transforms data directly within the warehouse using SQL-centric modeling and documentation.
Complex data pipelines are programmatically authored, scheduled, and monitored as directed acyclic graphs (DAGs) with Apache Airflow.

Data consumption

This is where data becomes actionable. From the powerful outputs of machine learning models to sleek, interactive dashboards, all the data platform features you’ve wanted from your development initiative come served on a silver platter.

Business intelligence. Curated datasets from the storage system are consumed via drag-and-drop interfaces or direct SQL, producing BI dashboards and reports that inform daily operational decisions and strategic reviews.
Machine learning and data science. Here, data fuels predictive engines. Data scientists access organized feature stores and massive datasets to train models, running iterative experiments to deploy services that can, for example, forecast inventory demand or score transaction risk in real time. The toolkit includes solutions like Databricks ML, SageMaker, or Vertex AI.
Data as a product. Cleaned, aggregated data can be exposed to other internal systems or customer-facing applications through secure, documented APIs, such as a REST endpoint or an internal GraphQL API.

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Building trust in your data pipeline through effective governance

Data platform implementation isn’t complete without making the whole system observable, secure, and trustworthy, while keeping all the workflows traceable.

The bitter truth is, no single platform covers all aspects of data governance. To serve the goal, it must be composed of multiple, well-chosen data platform tools. This is how modern data platforms work.

Data catalog and metadata management

Effective data governance is impossible without knowing where the data is and what it means. Think of catalog and metadata management as the map, legend, and compass for your data ecosystem. Metadata tools like DataHub or Unity Catalog act as the Google for your data, indexing schemas, owners, descriptions, and usage stats, while semantic layers (dbt Docs, Cube, Looker semantic model) and tagging engines (Atlas-based catalogs, Purview classifiers) add shared meaning and classify sensitive data.

Data lineage

Data lineage shows how data flows from raw ingestion to consumption. It answers practical governance questions:

Where did this data come from?
What transformations were applied?
What depends on this table or column?

Lineage is captured via tools like OpenLineage, dbt, Spark, Airflow, and Unity Catalog, and visualized in catalogs such as DataHub. This traceability enables impact analysis, root-cause debugging, and safe change management, and is critical for regulatory compliance.

Data monitoring

When data arrives late, duplicates appear, or the data isn’t structured the way the pipeline requires, observability tools surface the issue before users notice.

With monitoring and alerting tools (Monte Carlo, Bigeye), data quality becomes measurable and enforceable, answering the question “can we trust this data?”.
Freshness, volume, and schema-drift checks (Great Expectations, Soda) reveal when data stops behaving as expected.
To keep the data platform fast, reliable, and cost-efficient as usage scales, query performance monitoring tools like Snowflake Query History, or Databricks metrics are added.

That’s how you configure data quality management across your pipelines.

Data security

No business data platform is complete without security built in. This is where you ensure the right people access the right data, all the needed policies are enforced automatically, and sensitive information is protected, while multiple teams can work safely and compliantly. Your security stack should enforce a set of practices, including:

Policy enforcement. Define and automate policies that govern who can access data and under what conditions. Policy engines like Apache Ranger, AWS Lake Formation, or Azure Purview can be helpful.
Access control management. Apply role-based (RBAC) or attribute-based (ABAC) models to strictly govern user permissions.
Data protection. Mask or tokenize sensitive information to minimize exposure while enabling safe data use for teams.
Schema integrity control. Apply rules and constraints at the data schema level to prevent unauthorized or invalid data modifications. Thinks Delta Lake constraints or BigQuery policies.

Here’s how to make a data platform matter for the long haul: best practices

For every modern cloud data platform implementation, going live is only the starting line. The expected system’s operation and performance depend on a set of carefully orchestrated follow-up measures. These data platform best practices come from real-world implementations across industries, including high-volume analytics, ML pipelines, and modern cloud-native architectures designed to survive the next AI wave.

Run a controlled pilot. Select a single use case or a defined user group for the initial rollout. The objective is to test your core assumptions about usability and utility in a real, but contained, environment. Gather specific feedback on bottlenecks and areas of confusion, then address them.
Educate your employees. Embed learning into their workflow rather than forcing formal training. Provide context-sensitive guides, templates, and self-serve notebooks so your team can explore data safely. Pair this with hands-on workshops tied to real projects and create a feedback loop where early adopters mentor others, gradually making usage the default behavior instead of an optional skill.
Monitor impact. You need a balanced approach. Tie platform metrics to business outcomes: track adoption, query performance, data freshness, and error rates alongside revenue, product usage, or operational efficiency gains.
Scale smartly. Treat your modern data platform as a living system. Adapt it as your business needs evolve and as new data technologies emerge.

Optimizing performance, reliability, and business value is an ongoing effort. Without continuous oversight and iteration, even a once-perfect and efficient data platform can become a drag on progress.

Build a data platform ready for today’s needs and tomorrow’s innovations

Before jumping into building a data platform, get clear on what you really need

It’s always better to step back and define exactly what your use case requires because the wrong architecture or technology choice can lock you into inefficiencies for years.

Different business needs require fundamentally different solutions. For instance, if your goal is to build real-time recommendation engines, the platform must handle streaming data and low-latency inference, whereas predictive analytics on historical sales data demands batch processing and scalable storage for structured datasets. Each of these needs dictates different architecture choices, and your data platform strategy overall, so defining them early on ensures the platform actually supports your goals and prevents costly redesigns.

At Instinctools, we conduct a discovery phase to reveal the precise requirements upfront and make sure you commit to the right solution, one that delivers today and tomorrow, without resorting to retrofits or overcomplication.

– Ivan Dubouski, AI Lead Engineer, Instinctools

During discovery sessions dedicated to data platform design for our clients, we usually look at a few core things:

Defining business objectives and concrete use cases
Auditing the state of existing data pipelines
Tying goals to success criteria
Validating technology fit for data types, scale, and workloads
Mapping governance, security, and regulatory compliance requirements
Assessing organizational capabilities, skills, and data maturity

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What makes a good (and AI-ready) data platform

Sustainable, scalable, AI-optimized architecture of a modern data platform is formed thanks to a set of guiding principles:

Unified access. Providing a single, consistent access layer for raw data, derived data, and AI services to reduce fragmentation and operational friction.
Semantic context. Embedding business meaning and relationships into data through a rich semantic layer (often powered by knowledge graphs) to make data understandable and actionable.
Multimodal by default. Supporting all data types – structured data, text, images, video, audio, and their AI-native derivatives (e.g., embeddings) – as integral components of the platform.
Productized data as a foundation. Treating data as reusable, well-documented products with rich metadata to accelerate AI development and enable scalable reuse.
Continuous adaptation. Refining data and data products based on system feedback and changing needs, enabling ongoing improvement and new data derivations.
Governed and trusted by design. Ensuring all data is secure, compliant, explainable, and validated to build lasting trust and reliability.

How AI gives a nudge to modern data platform development

Rather than acting solely as a consumer of data, AI can also serve as an enabler of more mature data platform design and management practices. It helps establish and maintain semantic consistency, governance controls, and trust across use cases through:

Reducing duplicate definitions and improving consistency by automatically identifying similar business terms, recommending standard definitions, and suggesting the right owners for key data assets.
Making data classification more accurate and maintainable by combining existing classifications with context from related data, lineage, and past decisions.
Assessing the impact of changes before they happen by predicting which reports, applications, or teams may be affected by schema or pipeline updates, and identifying the most likely source of issues.
Speeding up data quality issue resolution by connecting anomalies to business impact, responsible teams, and downstream dependencies.
Strengthening data governance by detecting where sensitive data restrictions should be applied, flagging uncertain classifications, and highlighting areas that need review.

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Start your data platform off on the right foot

What sets a modern data platform apart from traditional data architectures is that its design is dictated by each specific business task at hand. If your AI or advanced analytics initiatives need a strong, custom-built data foundation to take flight, make sure it’s there for you, crafted from the best technologies and tools the market offers and pieced together by a reliable engineering partner.

From data platform strategy and first production deployments to data platform transformation initiatives, our data engineering services company has guided companies through every stage of modern data platform implementation.

FAQ

What is the modern data platform?

A modern data platform is an integrated set of tools and technologies that supports an enterprise’s data across its entire lifecycle.

What is data platform engineering?

Data platform engineering involves designing, building, and maintaining the infrastructure, pipelines, and tools that enable organizations to collect, store, process, and consume data at scale. It combines software engineering principles with data management expertise to create reliable, scalable data systems.

What does a modern data platform look like?

Often a cloud-native, serverless platform that ingests, stores, transforms and serves data on demand. It typically follows a lakehouse architecture, combining the structured performance of a data warehouse with the flexible storage of a data lake. It also features a centralized governance layer, and self-service access points for data scientists and business users.

What is an example of a data platform?

A good modern data platform example is Microsoft Azure Data Platform, which unifies data ingestion (Azure Data Factory, Event Hubs), data storage (Azure Data Lake, Azure SQL), data processing (Azure Databricks, Synapse Analytics), data governance (Microsoft Purview), data analytics (Power BI) and AI/ML (Azure Machine Learning). Other examples include Google Cloud data platform and AWS data platform.

What are the major data platforms?

The market is dominated by Snowflake, Databricks, and the native stacks from “Big Three” cloud providers: Google BigQuery, Amazon Redshift, and Microsoft Azure Synapse/Fabric. Each offers integrated tools for data engineering, warehousing, and machine learning.

What are the layers of a modern data platform architecture?

The architecture rests on five pillars: ingestion (ELT tools like Fivetran), storage (data lakes / lakehouses / data warehouses), processing (transformation and modeling tools like dbt), consumption (business intelligence tools, AI/ML platforms, APIs), and governance (observability, security, lineage, and cataloging).

How do modern data platforms work?

Modern data platforms combine ingestion, storage, processing, and consumption layers into a unified system. Data flows from operational sources through pipelines into storage (data lakehouse, warehouse), gets transformed by processing engines, then serves analytics, BI tools, and AI/ML workloads. Governance and metadata management run across all layers.

How long does it take to build a modern data platform?

Building a basic modern data platform takes 3-6 months for an MVP and 12-18 months for full enterprise deployment with governance, AI-readiness, and multiple consumption paths. Timeline depends on team size, complexity of data sources, and whether you use ready-made components or go fully custom.

How to modernize your data platform?

Data platform modernization starts with assessing the specific parts of your data stack that create bottlenecks, risks, or unnecessary costs. Every environment is different, so priorities vary. The right approach may involve upgrading tools, redesigning pipelines, improving data architecture, or replacing legacy components to match your business and technology goals.

AI Trends 2026: Where and How to Attain Enterprise Impact in the AI Post-Hype Era

Posted on December 24, 2025September 22, 2026 by Artsyman Lizaveta

After several years of runaway hype, a fair dose of AI disillusionment has set in. Businesses are recalibrating AI’s role in terms of what it can realistically do and how to leverage it for measurable results.

Which industries are seeing the greatest impact? What methods are proving most effective? And which tools are quietly powering this transformation behind the scenes? Our exploration of these questions has led to a clear set of AI and machine learning trends that define the practical boundaries of today’s technology and will shape the path forward in the coming years.

Curated by AI practitioners, backed by premier advisory perspectives: our enterprise-centric lens on AI trends 2026

In this article, we’ve handpicked the latest and most impactful trends of AI technology. Instead of trying to cover every innovation out there, we’re zeroing in on the technologies that matter most for medium and large organizations that have moved past the AI testing phase.

The emerging trends we’re highlighting are based on a survey of experts from our AI Center of Excellence, along with valuable input from leading consulting firms like Deloitte, McKinsey, BCG, S&P, and KPMG.

Quick recap: 2023-2025 breakthroughs leading into 2026

The last few years have been filled with genuine “wow” moments. All of them together have calibrated business expectations towards AI results.

The foundation-model shifts

When OpenAI released ChatGPT in late 2022, it shifted the trajectory of the entire AI industry, and, in many ways, global economies. Throughout 2023, a wave of next-gen generative models from multiple labs followed, grabbing business attention. Besides, the first attempts to put guardrails around AI emerged.

Multimodal capabilities rise

Alongside text-only models, multimodal AI made strides in 2024. With LLMs increasingly capable of jointly processing text, images, video, and audio, a larger range of applications and more complex use cases started to appear in enterprises across industries.

Agentic AI and its enterprise adoption

Organizations began deploying virtual AI agents to automate processes traditionally handled by human workers, to let the latter focus on higher-level tasks. Over time, it became clear that for complex workflows, coordinated networks of agents deliver greater precision, driving the shift toward multi-agent systems (MAS). Although enterprises are all in on the potential of MAS, foundational constraints in legacy systems and data architectures, as well as governance hurdles stand in the way of full-scale adoption.

Trend 1. Enterprises having hard times choosing from a myriad of AI models

If anything has been certain about AI so far, it’s that as soon as one vendor upgrades, others are hot on their heels.

For example, Claude Opus 4.5 by Anthropic has impressed many technology leaders with a step change in AI-assisted coding and long-horizon reasoning. Against the backdrop of increasingly capable models like Opus, Claude Sonnet, and Google’s Gemini 3, OpenAI went into code-red mode and pushed forward with ChatGPT 5.2, its “best model yet” (as of December 2025), promising notable progress in general intelligence and higher tool-calling performance. All of this happened in just a few weeks, leaving little time for anyone to catch their breath.

With no shortage of top-tier generative AI models to choose from, it might seem that enterprises can simply pick any one and hit the gas. But in practice, the flood of new releases tends to blur the decision rather than sharpen it. Public benchmarks, meant to guide those choices, rarely help. High scores look great on paper, but in practice? Not so meaningful.

For worthwhile results, enterprises need to conduct domain‑specific tests and evaluate models under conditions that closely mirror their production environments.

Before building the agentic pipeline for a global insurance aggregator, our AI team ran extensive tests across multiple large language models. Those were executed using reverse-engineered examples from existing API adapters to see which model would truly meet the client’s requirements. Out of GPT, Gemini, Grok, and Anthropic’s Opus and Sonnet, we found Claude Opus 4.1 to be the most reliable and production-ready, especially when paired with structured prompts and step-by-step checkpoints.

– Pavel Klapatsiuk, AI Lead Engineer, *instinctools

Trend 2. Ever-evolving AI capabilities are fanning the flames of AI obsession

Amid the decision paralysis that seems to grip so many enterprises, there’s also a certain awe surrounding what foundation models can now do today. The fascination is fueled by recent breakthroughs in multimodal capabilities. 2025 wraps up as a year where a significant shift occurred in how LLMs can perceive, reason over, and act on information across text, images, audio, and video.

The release of Sora 2 at the end of September was the ‘GPT-3.5 moment’ for video generation. Likewise, built on the Gemini 3 Pro system, Nano Banana Pro is unnervingly excellent at image generation, bringing it to an entirely new, somewhat scary, level. And now OpenAI has caught up with its latest release, ChatGPT Images 1.5. The pace of progress in multimodal AI isn’t slowing anytime soon.

– Ivan Dubouski, AI Lead Engineer, *instinctools

However, the picture is not all rosy. You’ve probably heard of a huge backlash against AI‑generated ads. Take the recent McDonald’s case, which pulled a “creepy,” AI‑produced Christmas commercial after widespread viewer criticism. Similar consumer pushback affected Coca‑Cola and Valentino campaigns with AI-generated content. But despite these viral incidents, the bottom line for business is that companies are rapidly integrating multimodal AI into workflows and products. According to Gartner, trends in enterprise software point toward 80% of applications being multimodal by 2030.

Trend 3. AI agents proving their value across business functions

Agent-first workflows redesign and multi-agent systems are grabbing headlines as major agentic AI trends 2025. Down on the enterprise floor, the picture is less flashy: while 62% of organizations surveyed by McKinsey are experimenting with AI agents, no more than 10% report scaling them.

The slow pace of adoption tells you a lot about the challenge: it’s one thing to have a capable agent and quite another to plug it into decades-old enterprise infrastructure. Legacy systems create choke points that stop agents from functioning as they’re intended.

Similarly, the architecture of many organizations’ data repositories isn’t set up to let AI agents consume the data smoothly. Deloitte’s 2025 survey shows just how common this is: nearly half of companies say searchable data is a sticking point, and 47% hit walls when trying to reuse it for agentic automation… Which is yet another signal that careful data preparation is non-negotiable.

Want your AI agents to actually do their job? Focus on modernizing your core legacy systems and cleaning up your data first.

Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value, or inadequate risk controls.

Even so, enterprises don’t appear likely to curtail their efforts. Over half of executives now rank agentic AI as their top AI investment priority for 2026. Once adoption barriers are knocked and initiatives are executed thoughtfully, agentic AI projects can move far beyond pilots with blurry potential.

Using GENiE, our proprietary agentic solution accelerator, to build an autonomous AI worker, an Australian consulting firm processed 20% more leads, boosted upselling and cross-selling by 19%, and cut cost per lead by 15%.

– Vitaly Dulov, AI Solutions Lead, *instinctools

Deloitte predicts that if enterprises orchestrate agents better and thoughtfully address the adoption challenges and risks, the autonomous AI agent market could reach as high as $45 billion by 2030.

Trend 4. Agentic software engineering marks the beginning of the end for the traditional SDLC

Who’s even coding in late 2025? Jokes aside, recent AI trends in software engineering have really pushed developers out of the trenches of hand-written boilerplate and onto the high ground of system design and careful governance of multi-agent systems.

Truth is, the SDLC isn’t what it used to be. Changes started from AI tools being seamlessly integrated into more and more of its stages.

The way our dev team’s toolkit looks now shaves serious time off our clients’ projects. With AI-driven prototyping, we validated a business idea in just four days for a French startup, cutting demo costs by 60%. That’s the power of the right setup and the right tricks.

– Ivan Dubouski, AI Lead Engineer, *instinctools

Then, vibe coding took both business and engineering communities by storm. To cut through its chaos, spec-driven development (SDD) emerged as an approach to responsible AI development. Here, specifications serve as a single source of truth guiding what’s being built, boundaries, and how it’s all verified. With SDD in place, forward-thinking teams are finding power in agent swarms – fleets of specialized agents tackling complex engineering problems through decentralized, collaborative effort under human oversight.

No wonder companies across industries are itching to integrate this into their development workflows to achieve more with less. But as with all innovations, the hard truth is that results come only when teams understand the craft. In inexperienced hands, most projects almost never leave the prototype land.

Developing enterprise-grade production systems in a new way demands an understanding of the nuances, the pitfalls – things that you reveal through vast experimentation, testing, and refining your approach before overhauling your pipeline.

Our high-end AI practitioners at *instinctools has been doing what is essentially agentic programming since 2024. We’ve set up swarms of agents to back the SDLC, keeping an eye on every move they make without human intervention. Governance is built in. Everything AI does is reviewable, reversible, and compliant from the start.

– Pavel Klapatsiuk, AI Lead Engineer, *instinctools

So, the software development future is being written right before our eyes. Your edge in it depends on how fast you can adapt to new ways of building. The easiest way to get there is by teaming up with AI engineers, who are blazing the trail every day on real projects.

Get professional guidance on operationalizing vibe coding

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Trend 5. AI awakening a once-stagnant robotics industry

Thanks to advances in multimodal foundation models and cutting-edge chips, robots are now able to perceive, learn, and operate autonomously in complex environments. Adaptable general-purpose humanoid robots, autonomous vehicles, industrial robots, and drones are coming to life from science fiction books and movies.

Waymo continues its rollout of robotaxis.
Figure 02 robots have contributed to the production of 30000+ BMW X3 cars.
XPeng’s humanoid robot IRON went viral after the company literally cut it open on stage to prove it wasn’t human.
R1 from China-based Unitree Robotics, an ultra-agile humanoid robot, caters to researchers, educators, and software developers testing AI and robotics projects.
Tesla is preparing to unveil the Optimus Gen 3 as a production-intent prototype in Q1 2026.

In fact, humanoid robots are expected to enter the mainstream in 2026, thanks in large part to Nvidia, which made several significant breakthroughs in the field this year, from rolling out the Jetson Thor platform to expanding the Omniverse platform for industrial AI simulation.

Looking ahead, the next wave of robotics may bring revolutionary developments like quantum robotics and bio-hybrid robots.

Trend 6. Industry-specific AI trends

Hyper-digitized, data-packed finance, healthcare, automotive, energy, and consulting are at the frontier of applied AI in 2025. Here’s a peek at AI-powered, intelligent systems already being applied across those industries.

Healthcare

The Future Health Index 2025 survey commissioned by Philips reports that 62% of healthcare professionals associate AI adoption with gains in efficiency, diagnostic accuracy, readmission reduction, and overall patient outcomes.

Leading medical systems, including Mayo Clinic, Northwell Health, Johns Hopkins Medicine, and UNC Health, are scaling the Abridge ambient AI platform to convert patient-clinician conversations into structured clinical notes embedded directly in the EHR.
Heavy use of conversational AI is seen across the industry through countless use cases, from appointment scheduling and remote patient monitoring to medication management.
Hospitals and imaging centers globally deploy centralized AI platforms to orchestrate and govern multiple imaging algorithms across CT, MRI, X-ray, and ultrasound.
Health systems scale AI-driven predictive analytics to unify clinical, claims, and operational data for better population health management, risk prediction, and operational efficiency.
Pharmaceutical companies apply AI to whole-genome cancer analysis to identify personalized treatment targets and accelerate drug discovery pipelines.
Gen AI tools significantly shorten R&D timelines, enabling faster hypothesis testing, trial design, and molecule optimization.

Automotive and transportation

Leading automakers are keeping pace with AI adoption to drive business growth. According to Volkswagen Group, human-AI collaboration established inside the corporation aids in the development of more competitive vehicles, enhances customer service, and improves production efficiency through better use of energy and materials, lowering costs and carbon emissions. Here are other AI trends in the automotive industry:

Automakers are using digital twins to mirror vehicles and production systems in software so they can design, test, and optimize before anything gets built.
Suppliers like ZF have rolled out AI‑based solutions such as TempAI, which uses machine learning to model internal temperatures in electric motors more precisely than conventional methods.
Thanks to hardware advances and lightweight models that enable low-latency inference directly in the vehicle, edge AI is gaining ground, reshaping the market for automotive semiconductors. The latter power infotainment and vehicle comfort systems, end-to-end ADAS systems, and battery electric vehicles (BEVs).

Consulting

Business models are becoming leaner, with smaller, more focused teams. Generative AI tools, predictive algorithms, and synthetic research platforms now handle the tedious research, modeling, and analysis tasks that once took consultants weeks to complete. Initially, human workers were ambivalent about AI, but consulting firms report that it has eventually freed up time for higher-value work. Large-scale partnerships with multiple AI vendors support this shift:

Deloitte is going to roll out Anthropic’s Claude to its 470,000 global employees. Companies will co-create compliance products and features for regulated industries including financial services, healthcare, and public services.
Deloitte also plans to create different AI agent “personas” to represent the different departments within the company, including accountants and software developers, according to reporting from CNBC.
KPMG adopted the Microsoft AI stack to integrate AI into daily workflows and enable enterprise-wide agent development.
Almost 90% of the BCG’s employees use GENE, a GPT-4o-powered chatbot, and about half use it daily.

Finance and insurance

Faster underwriting and claims processing. Fraud detection and risk modeling. Improving customer service and engagement. These are some of the most widely recognized ways AI has been recently applied in financial services. Others include:

A European bank deployed an AI-powered chatbot capable of handling complex inquiries about accounts, loans, and transactions, reducing the load on human agents and improving response times.
For a client, we’ve built an agentic system that automates partner integration for a global insurer, handling document parsing, adapter creation, and testing in a guided interface.

Energy

AI’s impact in the energy sector continues to grow, especially in oil and gas. Notable AI applications include:

AI agents used to oversee complex workflows across drilling, production, and logistics and autonomously schedule maintenance.
Platforms like Methane.AI identify and quantify emissions sources across operations, enabling upstream companies to implement targeted, cost-effective reduction strategies using drones, sensors, and AI analytics.
ExxonMobil leverages machine learning algorithms to simulate refining reactions, optimize output, and minimize waste.
BP applies AI for emissions tracking and predictive maintenance, supporting sustainability objectives and operational performance.

Find more examples of how enterprises successfully adopt AI

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Trend 7. Moving from a gray zone toward certainty in AI regulation, though fragmentation remains

Many legal frameworks are moving beyond voluntary guidelines, but policies differ by region.

United States

While there is still no comprehensive federal AI law, agencies are enforcing existing statutes and implementing earlier safety and disclosure mandates from the 2023 “safe, secure, and trustworthy AI” executive order. In late 2025, the federal government issued a new executive order to assert a unified national AI policy and curb stricter state rules, even as states like California move ahead with targeted frontier-model transparency laws such as SB 53.

European Union

The EU’s AI regulation is grounded in the EU AI Act, which is already in force with bans on certain “unacceptable risk” uses. Governance and general-purpose AI rules were activated in August 2025, with full applicability expected in 2026. The Commission has also proposed a “Digital Omnibus” to streamline overlapping digital rules and delay high-risk AI obligations until 2027-2028, in response to implementation challenges and industry feedback.

United Kingdom

UK oversight in 2026 will only change significantly if the proposed Artificial Intelligence Bill creating an independent “AI Authority” actually passes, which remains uncertain as of late 2025. If enacted broadly in its current form, the document would centralize and coordinate AI supervision across sectoral regulators and enforce economy‑wide obligations for higher‑risk and frontier AI systems.

Asia

China enforces strict rules like the Interim Measures for Generative AI Services, which require service registration, security reviews, content labeling, and data compliance since 2023. South Korea’s AI Basic Act mandates risk assessments, disclosures, and human oversight for high-impact systems starting in 2026, while Japan maintains a voluntary principles-based approach. India and Australia rely on sectoral laws and privacy rules amid developing frameworks.

Still in flux, AI regulations have moved beyond the “Wild West,” becoming far more enforceable than they were a few years ago. The debate over AI ethics shows no signs of slowing down, evolving as fast as the technology itself. Risks persist and new ones come up daily. IBM put together an entire atlas featuring dozens of potential ones. Tech giants understand that and act accordingly. OpenAI and Microsoft team up with state law enforcers on the AI safety task force. More cooperation is expected in that regard.

Opportunities are still ahead

Shiny new foundational models and enterprise agentic AI systems grab attention, but taking them beyond pilots and trials requires work that doesn’t make headlines: data preparation, workflow integration, governance, and compliance. Even though experiences of 2025 have brought many organizations far up the learning curve, gaps are still glaring when we talk about adoption and scaling.

Anyways, with a better sense of the key technology trends, it’s easier to see potential areas where AI initiatives can help you meet your ambitious business goals.

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FAQ

What is the future of AI in 2026?

The future of AI trends 2026 is all about business-scale transformation, with agentic AI stepping in to handle complex, multi-step workflows on its own. On top of that, advanced multimodal AI will supercharge automation, efficiency, and decision-making across industries, while robotics powered by LLMs and cutting-edge semiconductors are finally going mainstream.

What are the latest trends and issues in information technology?

The latest trends in information technology include AI-driven process automation, cloud-native solutions, cybersecurity advancements, computer vision, edge computing, and the growing adoption of quantum computing, all reshaping how businesses operate and secure data.

Preparing Data for AI and Machine Learning: A Production-Ready Playbook

Posted on October 30, 2025September 22, 2026 by Artsyman Lizaveta

Key highlights

Machine learning data preparation is a mandatory part of any ML initiative aiming to avoid the ‘garbage in, garbage out’ trap.
Getting data from raw to AI-ready can take up to 80% of the ML project timeline, but the effort ensures high accuracy of the model outcomes.
Well-thought-out data collection, hybrid labeling, and cleaning are the compulsory steps of the data preparation pipeline, data augmentation is an optional one.

Data is the backbone of any analytical system. Nothing has changed in this regard with the industry-wide adoption of AI technology. Drawing on our hands-on experience in delivering AI solutions across industries, this playbook walks you through every step of preparing data for AI, from collection and labeling to cleaning and augmentation, to help you build a reliable dataset that powers accurate, bias-free models.

What does data preparation mean for AI and machine learning?

Data preparation for machine learning and AI means collecting raw data from internal and external sources, labeling it, and carrying out data quality improvement to produce a well-calibrated, bias-free dataset for training an ML model. It’s not a one-off step but a continuous process, since each time new data arrives, it must be labeled, cleaned, and checked for bias.

Why prepare data for machine learning and AI?

Data preparation is the most time-consuming part of any ML project, taking up to 80% of the overall timeline. But this initial investment in data discovery pays off manifold.

Grounded confidence in your data. At the AI scale, the old garbage-in-garbage-out adage evolves and takes the form of “garbage in, beautifully phrased/formatted/visualized garbage out.” Putting data preparation on the front burner saves you from falling into a trap of false confidence in the model’s outcomes without noticing that “something is rotten in the state of Denmark.”
Highly precise decision-making. Clean, bias-free, use-case-relevant data leads to well-thought-out business decisions.
Ability to deliver hyper-personalized user experience. In highly competitive domains, say, streaming services or ecommerce websites with AI-driven recommendation systems at their core, the level of data preparedness directly influences user experience, helping companies win new customers and retain existing ones.

Data readiness levels for AI & ML

ML models are only as good as the data they’re fed. And that data needs to go from messy to clean and purpose-ready.

Raw data. Unstructured data in multiple formats and from various internal and external sources. It’s consolidated in one place, usually a data lake or a lakehouse, but hasn’t undergone any checks.
Clean data. Structured data that has been freed of duplicates, outliers, and missing values, making it usable for various projects. Clean data is typically stored in a data warehouse for easier access and management. At this stage, the intended use of the dataset isn’t yet defined.
AI-ready data. Once the task is defined, data scientists get the clean and labeled data and ensure it fits the use case. For instance, they eliminate irrelevant data, such as dog images in a dataset for training a fare‑evasion detection model. At this point, they also determine whether the dataset needs to be reduced or artificially augmented with synthetic data.

Scanning your dataset for duplicates and missing values is a shortcut to understanding how much your data is messed up. For instance, you can use Python libraries like Pandas and Great Expectations to run an auto check. Even more than 3% of exact duplicates in your dataset is strong evidence that it’s nowhere near AI-ready.

— Pavel Klapatsiuk, AI Lead Engineer, *instinctools

How to prepare data for machine learning and AI

Behind every thriving AI model is a lot of unglamorous preparation work. Here are our practice-proven tips on how to make each step of that groundwork count.

1. Data collection

The first thing to do for successful data collection is getting an experienced data scientist on board. Once the purpose of your ML project is clear, they will determine the right strategy to collect the data and prevent potential bias from slipping into a training dataset.

Say, for a global online retailer that wants to analyze customer behavior, a data expert can anticipate the WEIRD bias (oversampling data from Western, Educated, Industrialized, Rich, and Democratic populations) and head it off by diversifying data sources to include inputs across regions, cultures, income groups, etc.

— Pavel Klapatsiuk, AI Lead Engineer, *instinctools

The same goes for data noise, which has to be filtered out in advance. For instance, in churn-prediction work spanning website, CRM, and ad platforms, not every event belongs in training. You’ll have to sift out the noise, such as test accounts, marketing email previews that look like real opens, competitors’ clicks, price-checkers’ activity, and other artifacts.

If your business involves IoT devices, the physical world writes itself into your data (mechanical vibration, temperature spikes, electrical hum), turning real-world noise into data noise. In one of our oil and gas projects vibrations from drilling rigs were making it tricky to identify meaningful signals. Our data scientist had to go through the data fields filled in according to the info from sensors to determine the most informative ones and down-weight the rest to lower their noisy impact.

So where to collect the data from?

Internal sources, such as databases and business operational systems (ERP, CRM, inventory software, etc.).
External sources, such as public databases, social media platforms, third‑party datasets, publicly available or purchased reports and statistics, etc.

If you’re a startup without rich internal data, check for valid publicly available datasets. Even if there’s no exact match, you can still resort to web scraping and assemble a solid dataset from free public sources.

— Pavel Klapatsiuk, AI Lead Engineer, *instinctools

Also remember to put a premium on data lineage from the very start of machine learning data preparation. When you can trace the path of any data point within your dataset end-to-end, fixing errors and auditing becomes a walk in the park.

2. Data labeling

After collecting the raw data, you need to specify its context for the ML models by labeling it. The labels, or annotations, make data more consumable for a model and enable it to interpret the information correctly, contributing to the overall accuracy of the outputs.

While data labeling can be automated, our hands-on experience proves that if you want the ML model to masterfully imitate human perception, thinking, and judgment, at least some part of the labeling should be done by humans.

— Pavel Klapatsiuk, AI Lead Engineer, *instinctools

Here’s how to make the most out of the hybrid labeling approach while not spending a fortune:

Create a ‘golden’ seed set. Have three human annotators cross-label 5-10% of the dataset (size-dependent). Use a brief guideline, measure inter-annotator agreement, and resolve disagreements. You don’t need senior data scientists here – trained annotators are enough.
Train the auto-labeler, then loop. Use the golden set to train an AI-assisted labeling tool (passive learning), auto-label the rest, and spot-check samples. Route uncertain/low-confidence items back to humans (active learning) until quality stabilizes.
Pick the right tooling. Available options range from open-source platforms like CVAT and Label Studio to SaaS solutions like SuperAnnotate and LabelBox.
Run a final human check. Annotators from the first step validate auto-generated labels to ensure consistently high precision throughout the dataset.

3. Data cleaning

Once the whole dataset is labeled, clean it from duplicates, outliers, missing data, irrelevant or incorrect records. As we’ve mentioned earlier, you can leverage Python libraries like Pandas and Great Expectations to detect and flag all issues automatically.

However, sometimes you do need to enrich your dataset with inconsistent and incorrect inputs on purpose. It applies to the conversational AI chatbots of all kinds, from general customer support bots to specialised ones like flight booking assistants, financial advisors, etc. You have to take into account user queries with typos and misspellings, syntax and grammar errors, to improve intent recognition rates.

Further decisions like “should the outliers and missing values be removed, imputed, or corrected using domain knowledge?” require human judgment.

Don’t rush to anonymize data at this stage! While encryption is a vital data protection mechanism, if applied to an uncleaned dataset, it only complicates spotting irrelevant and incorrect entries. It’s better to double down on sensitive data anonymization after you get a noise-free, clean dataset.

— Pavel Klapatsiuk, AI Lead Engineer, *instinctools

4. Data augmentation

It may happen that after all the cleaning, you’re left with too little data to train the ML model (“too little” being a spectrum that varies from tens of patient records for a niche medical research to thousands of user interactions for an ecommerce customer study). That’s where data augmentation comes in handy.

For example, a dermatology R&D lab is building AI-powered software to make a preliminary diagnosis based on skin photos. For a rare cancer like cutaneous T-cell lymphoma, early signs can resemble eczema or psoriasis, and examples are scarce. In this case, a data scientist can resort to image augmentation (zoom, flip/mirror, rotate, crop, slight lighting shifts) to expand the dataset. In less regulated contexts, synthetic images can be generated based on the originals as part of the machine learning data preparation.

If you still have data preparation-related questions, find an AI and ML consulting services provider to cooperate with.

AI/ML data preparation checklist

Here’s a short recap of the data preparation work that prevents rework. Do this before the modeling starts:

Engage a data scientist early to design collection, cut noise, and preempt bias
Apply a hybrid data labeling approach: create a human ‘golden set’ → train an auto-labeler → spot-check low-confidence items
Automate the first pass of data cleaning, then apply human judgment to drop, impute, or correct with domain rules
Anonymize sensitive data after labeling and cleaning it
Augment image and text data if the training dataset ended up being too small after the previous AI data preparation steps

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Data preparation is the heavy lifting that accelerates every next step

Data preparation is like getting the soil ready before you plant. If the soil is full of rocks and weeds, the seeds won’t take. It’s the same with AI: clean, unbiased, balanced data gives your model the fertile ground it needs to perform well.

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FAQ

What is data preparation in AI and machine learning?

Data preparation for machine learning and AI is the process targeted at cleaning the data, eliminating the bias it may contain, and ensuring the data is relevant to your AI use case.

How clean is “clean enough” for machine learning data?

Data without missing values, duplicates, and outliers is clean enough for training an ML model. The catch is that cleanliness alone doesn’t signal the end of data preparation for machine learning.

Do I need to label data for unsupervised learning?

The primary purpose of unsupervised learning is analyzing and clustering unlabeled datasets to uncover meaningful patterns in data. You don’t need to label data, the algorithm will generate its own labels for the human labelers to interpret.

What are the key stages of AI/ML data preparation?

Stages of preparing data for AI & ML include pre-collection data assessment, data collection, labeling, cleaning, and augmentation or reduction if needed.

Why is data preparation crucial?

Prepared data is a mandatory prerequisite for getting an accurate, bias-free ML model and, thus, precise decision-making. Invest in modeling with unprepared data as a foundation, and you’ll end up with a harmful solution producing inaccurate outputs.

Vibe Your Way to Viable Outcomes: Our AI Engineers’ Guide on Vibe Coding for Enterprises

Posted on September 29, 2025September 7, 2026 by Artsyman Lizaveta

Key highlights

Vibe coding is the next phase of AI-assisted development with AI agents now handling the full coding workload end-to-end.
Its use cases quickly evolved from experimenting with disposable prototypes to building scalable enterprise systems.
Sustained success with vibe coding apps still requires engineers responsible for agent onboarding, orchestration/coordination, context and prompt engineering, agent-specific tooling, guardrails, and integration.
Vibe coding hasn’t been standardized yet, but a growing set of field-tested practices can make it safer, more predictable, and auditable.

Vibe coding is like a tree that’s judged by its fruits. However, the quality of those fruits can vary wildly. The grower’s knowledge and hands-on expertise make all the difference. An amateur can only get as far as the simplest disposable experiments. Senior engineers, on the other hand, can cultivate abundant harvests, such as stable and scalable prototypes and feature-rich enterprise software.

This guide offers an insider’s perspective on using vibe coding for full-scale product development, straight from *instinctools’ AI Center of Excellence. Dive in and learn how to use the new programming approach to your business’s benefit, while others are still figuring out where it fits.

What is vibe coding?

Vibe coding is a way to build apps without manual programming. Unlike traditional development, you describe intent in natural language and an LLM-driven agent turns that intent into code, tests, and repo-wide changes.

It differs from AI-assisted tools like GitHub Copilot in two ways: lower initial barrier and higher proactivity. Vibe coding apps like Claude Code, Cursor, Windsurf, Jules, and others can plan work, create or refactor multiple files, run commands, read errors, and propose diffs or pull requests. In practice, they behave like junior pairs who can scaffold features quickly, while a senior engineer sets direction, enforces constraints, and owns the merge.

How do AI agents fit into vibe coding?

AI agents interact with the databases, code repositories, staging and dev environments, and external APIs to execute tasks on the user’s behalf. In a vibe coding workflow, the agentic setup is what actually gets things done when you prompt the AI vibe coding tool. Without agents, AI tools would remain suggestion-only.

Three ways in which vibe coding reshapes the SDLC

When vibe coding emerged in February 2025, only half of the companies trusted agentic AI to author, review, and submit code. However, just three months later, this number spiked to 82%. And there’s a good reason behind it. Vibe coding marks a paradigm shift in the way software is developed and brings:

Higher speed-to-value. Vibe coding empowers companies to progress from idea to MVP to full-scale product times faster. For instance, within the traditional approach, development teams used to spend weeks turning a vague idea into a prototype. With vibe coding, it’s only several days away.
Lesser business risk. With agent-led rapid prototyping, businesses can test many ideas in parallel and move on with the most promising option.
Lower cost. As of autumn 2025, you can run a full vibe coding setup with the core AI coding tool of your choice, plus any additional automation and monitoring tools for a fraction of a single FTE. Exact spend varies by model usage and repo size. The key is elastic capacity that scales with demand, not headcount.

Vibe coding for software development is to teams what a turbocharger is to a car engine. It delivers a burst of acceleration, letting you cover SDLC miles in record time. But unlike the turbocharger, it doesn’t blow up your budget.

In the right hands, vibe coding safely hits the gas on resource-intensive engineering work. Experienced developers who equip their vibe coding AI tools with clear security and quality guardrails, entrust AI to:

Create and update the project documentation. Under deadline pressure, development teams tend to put project documentation on the back burner. That’s where AI agents can pick up the slack: draft a clear README file, thoroughly comment on source code, and keep the documentation in sync as the codebase evolves. These automated efforts help new team members to grasp the project’s purpose and structure at a first glance.
Build an MVP faster and smarter. With a traditional approach, it’d occupy a team of developers full-time for up to three months. Vibe coding enables one or two software engineers to cover the same scope in 4-8 weeks.
Modernize outdated systems. Renovating software written in some opaque programming language like COBOL or Algol looks challenging for humans. First, you’ll need to find engineers well-versed in these languages. Then they’ll need months to reconstruct intent from decades-old code. AI-driven software development practices flip the script. Trained on large datasets of legacy patterns, ML models are of huge help with an initial comprehension pass, including source code comments, module summaries, and a modernization plan, compressing what used to be months of discovery into hours.

Human engineers can’t be written off, and here’s why

While vibe coding can be approached as ‘writing software without a plan,’ there’s more to it than that. You can’t achieve the outcomes we’ve mentioned earlier by freestyling from scratch on ‘feel’ alone. Anything beyond a one-off prototype demands years of hard-won engineering instincts. Seasoned humans still have to orchestrate agents, steer the lifecycle, and preempt risks. As they say, first crawl, then walk, and eventually run.

Instinctools’ senior AI engineer named four responsibility areas developers should cover to successfully use vibe coding for more than disposable prototypes.

1. AI agents onboarding

Think of AI agents as junior developers joining mid-sprint. For them to carry out the tasks hitch-free, a human lead has to make sure that the newbies are informed on the project context.

Explain the workflow. Clarify the issue-tracking process and which tools are allowed.
State the development approach. Specify whether the method is feature-, test-, or domain-driven.
Establish boundaries up front. For example, allow read-only access to production infrastructure and restrict access entirely to files with security keys.
Point to current coding standards. OWASP and CERT Coding Standards are solid baselines. Include any internal guidelines and linters.
Set the quality bar. For instance, make it mandatory that at least 90% of the codebase has to pass unit testing.
Create a lightweight plan artifact. Start each feature or initiative with a PLAN.md at the repo root (and nested PLAN.md files for larger components when needed). Capture naming conventions, responsibilities, boundaries, feature order, design notes, and include simple visuals when helpful. Keep this file up to date and have the agent update it after each change or commit, since this becomes the anchor for shared context and a quick way for agents to “restore” what we decided last time.
Share project history. For ongoing work, give AI agents access to Git commits, ADRs, and documentation so they come to speed faster.

It all boils down to providing an AI agent or multi-agent system with an unambiguous project context. It may seem like a lot of work, and it is. You can work with barebones AI frameworks, but setting up an infrastructure middleware around the AI coding app of your choice is way more productive in the long run.

My practical experience proves that if this infrastructure middleware layer covers testing, security, and efficiency checks, you can sail smoothly through SDLC stages without looking into the code, which is the whole point of vibe coding.

— Vitaly Dulov, AI Solutions Engineer, *instinctools

2. Continuous context engineering

Setting up a clear context once and for all would be great, but the reality is different. Context engineering and management remain one of the core ongoing tasks for humans to deal with.

Every prompt for vibe coding apps should be context-rich. Compare the prompt examples below:

The outcome quality of vibe coding is entirely down to the quality of your instructions and the depth of the project context you provided initially.

Another vital part of context engineering is memory management. As prompts pile up, the working context bloats and quality degrades (“context rot”). The challenge can be tackled by updating the memory file after every pull request. A simple prompt like “Read project_summary.md before every task and update it in the end” will do the trick.

3. Agent engineering

Will a style guide and references make agents run exactly as you want them to? Not yet. As of 2025, agentic AI still needs targeted oversight.

Here’s an example. Declaring a specific development approach as you start AI vibe coding isn’t enough to ensure agents actually practice it. Build a lightweight supervisory agent that audits outputs against your chosen method.

To stay on the safe side, I usually create a specific supervising AI agent responsible for checking whether core agents work in line with the established approach. Say, if the development is test-driven, I’d build a ‘TDD-checker agent.’

— Vitaly Dulov, AI Solutions Engineer, *instinctools

4. Agentic pipeline monitoring

Just like context can rot, agentic pipelines can regress, manifesting in broken dependencies, a lower pass rate in unit tests, etc. So don’t wait to notice it in prod. Instead, constantly run pipeline regression checks. Tools like Promptfoo added to your infrastructure middleware layer help automate the task.

Worried about vibe coding? Here’s how your doubts can be settled

Leaders are bullish on AI vibe coding. But, at the same time, they’re just as worried about the complications it can bring. Here’s an overview of the top concerns, paired with pragmatic guardrails to address each one.

Overreliance on the vibe coding apps makes software upkeep challenging

This concern stems from the idea that AI-generated code will be maintained by humans. That’s not how the future unfolds.

First of all, vibe coding reimagines solution upkeep, shifting it from manual to managed. Just as it frees developers from writing code, it takes over mundane maintenance, drawing on the rules and guardrails set up by humans. Secondly, when ‘vibe maintaining’ doesn’t work anymore, it’s often cheaper to instruct agents to re-generate a conformant replacement than to modernize legacy code.

AI can replicate existing security vulnerabilities and bad practices from its training set

Sure, it can. But look at it this way: all AI tools come with a “may make mistakes” warning, which doesn’t stop people from using them productively. The same applies to vibe coding apps. If you stay one step ahead, they’re safe to use.

Having seasoned ML engineers by your side also helps, as they know potential failure points as the back of their hand and how to lock them down. Guardrails we standardize:

Using secure-by-design backend systems with built-in tools for checking the codebase for vulnerabilities
Running the model locally (in a private cloud or on your hardware) if the software has high security requirements
Establishing strict access limitations for AI agents across data repositories, tools, and documents
Enriching your infrastructure middleware with tools for automated security checks, such as Semgrep and CodeQL
Setting up an automated renewal of API keys and service credentials every 30/60/90 days, or add a tool like HashiCorp Vault for dynamic secrets management to the infrastructure middleware

Vibe coding adds prompt injection as a whole new attack class

New tech brings new headaches, and vibe coding is no exception. In case of prompt injection, attackers smuggle manipulative instructions into what looks like legitimate prompts to tweak model behavior, extract sensitive data, transmit malware, or spread misinformation.

We suggest combining several tactics to protect your AI/ML pipeline:

Locking down permissions. An agent is allowed to write code, but not deploy it in a staging or production environment.
Sandboxing code. Run all AI-generated code in a safe environment separated from stage and prod.
Enforcing injection-aware guardrails. You can hardcode commands like “Never follow instructions from non-whitelisted tools.”
Testing before trusting. Automated unit tests, dependency checks, and security scans will catch unsafe code right away.

AI-generated code fuels technical debt

When you hear that vibe-coded solutions are tricky to debug, consider the reasons behind this challenge:

Spaghetti code
High coupling of software components
Inconsistent naming and formatting
Hallucinated APIs or phantom dependencies

Those problems can be solved with an upfront comprehensive agent onboarding. Follow the practices we’ve listed earlier: workflow transparency, agreed development approach, controlled access, documented standards, and a clear quality bar.

Keep in mind that vibing isn’t just about building. You can also vibe refactor and vibe clean up. I’d say that vibe fixing tech debt is just around the corner and will be applied not only to AI-native solutions, but also to the tech-debt-heavy software from the pre-genAI era.

— Vitaly Dulov, AI Solutions Engineer, *instinctools

Tap into vibe coding with practice-grounded confidence

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Our field-tested practices and off-menu hacks for vibe coding to rise to your bar

As we are all still in the early days of vibe coding software development, there’s no universally accepted playbook yet. However, based on their experience in building agentic setups and continuous monitoring of vibe coding software engineering trends, engineers from our AI center of excellence have shaped routines for high-quality vibe coding results.

Create a configurable middleware infrastructure

Even top tools like Claude Code, Cursor, Windsurf, and others still leave gaps for vibe coding. For instance, there’s no built-in monitoring of token consumption. And you may want to add tools for automated security checks, dynamic secrets management, etc. The more monitoring and automation tools you use, the more time you’ll spend integrating them with your core AI vibe coding app during the initial setup.

Now imagine if you had a unified, technology-agnostic platform, where all the connections between potentially useful tools are pre-established. You’d be up and running right away instead of spending hours wiring things together.

At *instinctools, we created our own configurable middleware infrastructure to speed up the AI setup configuration stage on the projects where we vibe code. It proved its worth, since now the initial orchestration takes minutes.

Speed is only half the win. A solid middleware backbone raises confidence in code quality without constant babysitting.

— Vitaly Dulov, AI Solutions Engineer, *instinctools

Use one agent that takes on different roles instead of several agents

When working with a multi-agent system, you have to coordinate agents’ collaboration, which adds 8-16 working hours to the initial agent onboarding.

I mostly work in Claude Code and find using one agent in several tabs with different “role settings” to be more efficient than operating a multi-agent system. Say, this single agent starts as a coding agent. Once that’s done, I switch to the next tab with the same agent acting as a QA engineer and instruct it to check the code for spaghetti code, dependency conflicts, feature creep, security vulnerabilities, etc.

— Vitaly Dulov, AI Solutions Engineer, *instinctools

Manage agents’ context, but don’t overcomplicate it

For instance, retrieval-augmented generation (RAG) is a valid practice for keeping the context up-to-date. But you only need it if the project documentation you use to contextualize the agents swells past 200 pages. Until then, a well-structured markdown is enough.

Set up limitation rules where necessary

AI agents aim to be perfect, and can loop endlessly on an unsolvable task, only cluttering the context. To prevent it, set up a rule like “If you can’t solve a problem, stop after three cycles and alert me.”

Another scenario when a human-imposed rule is necessary is when agents create a sub-task you didn’t ask for and switch to it instead of doing the main task. Here, you can limit it with “Don’t take on a new task until you finish the current one.”

Choose an appropriate communication protocol

Two AI communication protocols dominate today: MCP and A2A. The choice depends on your intent.

MCP is a go-to option if the focus is on the AI agents connecting to various tools.
A2A works best when the agents need to talk to each other.

If the idea of using both crosses your mind – don’t. Mixing the two gets messy fast and leads to schema drift.

— Vitaly Dulov, AI Solutions Engineer, *instinctools

Automate token consumption tracking

Last but not least practical tip is tracking token consumption to prevent unintended cost creep, as every request, no matter how simple, invokes the whole model. Use tools like Langfuse and OpenTelemetry for easy token consumption per request monitoring. You can also set up custom token usage alerts to avoid exceeding a specified threshold.

The only case when vibe coding won’t do

Have you ever tried asking an AI tool the same question twice? Unless it was solving a simple two-plus-two equation, the answers never matched word-for-word, did they? This pattern is also inherent in AI vibe coding tools. They can’t produce the exact same output and behavior for a given set of inputs without any randomness or variation. Therefore, they aren’t suitable for building deterministic software, such as firmware for safety-critical systems used in automotive, aerospace, and medical devices. In this case, traditional software engineering would be the only option.

Ready to vibe?

It used to take a village to build software. Today, the ‘village’ is a set of AI agents: powerful, fast, but unforgiving if left unchecked. The real advantage now comes from experienced developers who can orchestrate those agents across the SDLC, see through the risks beforehand, and take measures to prevent them.

With AI accelerating every bit of software development and business processes around it, the edge you can gain from vibe coding won’t last forever. Seize the moment before your competitors wake up to it.

Vibe with us on top of two decades of practical experience

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FAQ

How is vibe coding different from traditional coding?

Traditional coding implies manually writing lines of code in a specific programming language like Python, Ruby, Java, C++, etc. Meanwhile, with vibe coding, humans only write natural language prompts in vibe coding apps, and AI agents deliver fully functional code.

How does vibe coding change the development process?

Vibe coding boosts development speed and lowers its cost while keeping risks in check. Traditionally, building a new product, enhancing an existing one, or modernizing legacy systems requires a whole team of cross-functional specialists. But with a responsible vibe coding approach, a single senior engineer can orchestrate and oversee an agentic setup that replicates project team roles at just a fraction of the cost of one full-time employee.

Does vibe coding make everyone a programmer?

Not exactly, more like vibe coding makes software development more accessible for non-programmers. Look at it like this: earlier, to validate a business idea, you needed a whole team of software engineers, solution architecture, DevOps engineers, and UX/UI designers. That was the only way to transform a vague idea into a prototype. Now, thanks to AI vibe coding apps that can simulate those roles, non-technical users have an opportunity to experiment with testing their ideas before real engineering begins.

What can you build with vibe coding?

Non-technical users can build simple prototypes mostly to validate their idea’s viability. But with vibe coding apps in software engineers’ hands, you can get pretty much everything from scalable prototypes to MVPs to enterprise-grade solutions and legacy software modernization.

What are the limits of vibe coding?

The limits of vibe coding are set by the expertise gaps of a person using AI tools. For someone without a tech background, vibe coding becomes a low-risk sandbox to test ideas, most of which won’t move past brainstorming experiments. For professional developers, it’s a serious tool that enables them to build the same solutions they would build with traditional programming, only times faster.

Autogen vs LangChain vs CrewAI: Our AI Engineers’ Ultimate Comparison Guide

Posted on July 31, 2025September 7, 2026 by Artsyman Lizaveta

Do you even need frameworks for AI agents in the first place? Not necessarily. You can build a capable AI agent from scratch: one that uses an LLM, performs complex tasks, and interacts with other modules. With Python, queues, async logic, direct calls to vLLM, it’s all possible.

But the moment you need to move fast, let’s say, ship a prototype, plug in retrieval, manage agent coordination, or just avoid reinventing the wheel, frameworks start pulling their weight. They give you ready-made pieces: memory modules, agent logic, chains, integrations… Everything that makes your life a whole lot easier.

Still, one question hangs in the air: which framework is worth using? Our AI engineers put CrewAI vs LangChain vs AutoGen head to head to answer that.

At-a-glance overview of AI agent frameworks: LangChain vs AutoGen vs CrewAI

All three frameworks are designed to take the pain out of AI agent development, but each of them takes a drastically different route to get there.

AutoGen lends itself well to structured multi-agent collaboration.
LangChain hands a huge, flexible toolbox to developers, which fares well in complex, multi-step workflows, but can get bloated fast.
CrewAI keeps things lean, which is a good match for rapid prototyping or small-to-mid-scale agent setups.

Quick AI agent framework overview
Feature	AutoGen	LangChain	CrewAI
Best for	Multi-agent conversations	LLM apps and agent chains	Multi-role automation crews
Multi-agent support	Yes	Enabled by LangGraph	Native
Open-source	Yes (MIT)	Yes (MIT)	Yes (MIT)
Commercial license	No	Yes	Yes
Enterprise suite	No	Yes	Yes

Now, let’s zoom in on CrewAI vs AutoGen vs LangChain, breaking down their architecture, core capabilities, and trip-ups.

AutoGen: the engine behind multi-agent capabilities

In our work developing multi-agent systems, we’ve found AutoGen to be one of the most flexible and developer-friendly agent frameworks. It’s conversation-centric, comes with a low-code interface for agent prototyping, and it’s cut out for building multi-agent systems.

AutoGen allows developers to compose conversational agents that chat with each other to complete tasks. What’s unique about this framework is that agents turn out to be both highly customizable and well-suited for natural interaction, which enables them to run across modes and integrate with LLMs, human inputs, and tools. Thanks to their nature, AutoGen agents can operate both in deterministic and dynamic, LLM-driven workflows.

On the flip side, building with AutoGen doesn’t eliminate orchestration, which means the developer has to manually design the way agents interact and take care of the decision flow between them.

LangChain: a multitool with a learning curve

When you first do a spike on LangChain, it looks like a set of pretty basic abstractions. In practice, LangChain is more like a universal, modular SDK that gives developers building blocks for linking LLMs to tools, APIs, memory, retrievers, and structured reasoning flows.

Recently, the ecosystem has been supplemented with LangGraph and LangSmith. LangGraph allows developers to define agent workflows as stateful graphs, which steers the framework towards multi-agent systems, iterative refinement loops, and deterministic task orchestration. LangSmith is a debugging and tracing layer for when your project grows beyond a prototype.

Overall, LangChain is a Swiss army knife of AI agent frameworks – yet, it has no prescribed workflows, which leaves the developer to design the agent logic or flow. Also, it tends to overengineer simple tasks, unnecessarily pushing them through all the layers of abstractions.

CrewAI: the new kid on the block that keeps it simple

CrewAI is a shiny new framework that has gained traction thanks to a lower learning curve and extensive documentation. Called an enabler of multi-agent automation, it’s made to let developers engineer teams of intelligent agents that work in tandem.

Unlike LangGraph, CrewAI runs at a higher level of abstraction, allowing developers to double down on role assignment and goal specification. The multiagent orchestration framework also comes with a set of built-in functionalities for task delegation, sequencing, and state management.

Architecture and design differences

The way the framework structures the interaction and the level of developer control are different for LangChain vs AutoGen vs CrewAI. AutoGen gives you the bricks, LangChain puts a toolkit on the table, and CrewAI lends you the crew and a mission briefing.

AutoGen’s architecture consists of a low-level Core for event-driven messaging and orchestration and a high-level AgentChat interface for developing conversational agents. AutoGen’s design prefers conversation orchestration over structured flowcharts, which adds flexibility, but at the cost of growing complexity. AutoGen agents own outcomes, while developers watch and refine.

Initially, LangChain was a modular framework with two core orchestration modes, including Chains and Agents. Thanks to LangGraph, the architecture became graph-based, enabling multi-agent workflows where each node is an agent with its own prompt, tools, and logic. This addition delivered finer control and outcome ownership, but backfired in terms of state management overhead for developers.

CrewAI uses a two-layer architecture, consisting of Crews and Flows, which balances out high-level autonomy with low-level control. Crews are responsible for dynamic, role-based agent collaboration, while Flows ensure deterministic, event-driven task orchestration. In other words, developers can start with simple agent teams and layer in control logic as they progress.

Integrations capabilities

Among all contenders, AutoGen stands out thanks to its impressive flexibility at the tool and LLM level. LangChain lives up to its ‘Swiss army knife’ label with broad integrations out of the box. Striking the middle ground, CrewAI features both canned tools for common use cases and an easy way to define custom ones, plus Python function calls.

AutoGen is known for its mix-and-match ability, letting developers easily combine agents using different LLMs (OpenAI + Claude), supplement them with tools (Code Exec + DB Access + Web Surfing), and even include human input. AutoGen offers essential pre-built extensions (OpenAI, Docker execution, WebSurfer), but its library is younger compared to LangChain.

LangChain has over 600+ integrations and can connect to virtually every major LLM, tool, and database via a standardized interface. The framework easily beats other frameworks due to the sheer breadth of ready-to-use integrations.

CrewAI takes a hybrid approach to integration. On the one hand, CrewAI offers the Tools package with ready-made tools. On the other hand, CrewAI’s Flows allows for more complex integrations through custom logic, branching, and external Python functions.

Performance, scalability, and flexibility

Microsoft AutoGen vs Langchain vs CrewAI each takes a different approach to managing concurrency, orchestration, and runtime efficiency, which impacts the way they scale in real-world deployments.

The core philosophy of AutoGen is centered around scalability, with an asynchronous event loop and RPC extensions to back up low-overhead, high-throughput multi-agent workflows. Although there are no exhaustive hard numbers to support its resilience, AutoGen has already proven its durability in production use cases. For example, at Novo Nordisk, AutoGen powers production-grade agent orchestration in data science environments, with the team extending it to meet strict pharmaceutical data compliance standards.

LangChain pulls its weight within basic, straightforward flows. However, the overhead is inevitable once you start chaining multiple agents or tools. The LangGraph extension makes up for the setback with stateful agent loops and more efficient graph execution. For enterprise-grade deployments, you’ll want to either go with the hosted LangChain platform or calibrate your deployment.

Since CrewAI operates with minimal abstractions, it beats other frameworks in raw speed and simplicity. CrewAI runs fast, marries well with async flows, and can handle concurrent agents by default. You can scale it from a local script to a full-on enterprise cluster, with observability and deployment flexibility built in.

Security and reliability

Like with other criteria, the Langchain vs CrewAI vs AutoGen trio each brings a different mindset to safety nets. AutoGen’s autonomy inherently leads to larger potential risks, especially in critical applications, yet baked-in isolation and kill switches stave off the risks. LangChain offers composability with guardrails you build in, and CrewAI pushes for enterprise-grade discipline from the start.

By confining the high-risk code to Docker containers, AutoGen makes sure the main system is surrounded by a moat. Unlike other frameworks, AutoGen lets developers set custom termination conditions for multi-agent loops, so no runaway agent behavior can creep in. Also, the event-driven nature enables fine-grained error handling, though you have to DIY it. Open-source and self-hosted, it leaves security entirely to the developer, but with Microsoft’s backing as a stand-in.

LangChain’s flexibility means your agents are only as safe and reliable as the rules you define. LangChain leans on ecosystem tools like LangSmith for tracing and guardrails, but sandboxing is on the developer. Reliability patterns such as output parsers, retries, and callback hooks are available to the developer, but LangChain doesn’t enforce them.

CrewAI ships with role-based access control, encrypted data, and on-prem deployment options by default. The framework doesn’t sandbox code out of the box, so the developer has to isolate risky operations in their own tools or containers. CrewAI allows for real-time agent monitoring, task limits, and fallbacks, which makes it solid for production and mission-critical workflows.

Pricing

The core orchestration engine of each of the three frameworks is open-source – free to use and ripe for tinkering. However, in some cases, a developer will have to pony up for accessing premium features or multiple tools.

AutoGen. The only out-of-pocket costs a developer covers are for the infrastructure they deploy it on and any API calls to LLM providers. AutoGen is a great option for teams that need deep, no-cost customization as long as they can roll up their sleeves.

LangChain. While the framework core is entirely free, with no usage limits at the library level, developers will have to fork out for LangChain commercial products. Both LangSmith and LangGraph have free tiers but scale with usage or team size. For example, if the team needs more than 5K traces per month, they’ll have to upgrade the pricing from free to around $39/month per seat.

CrewAI. Paid plans start at $99/month for 100 executions and scale up to Enterprise and Ultra tiers advanced features and heavy usage. Low-frequency tasks like occasional reports fall into the Standard plan with 1,000 monthly executions. However, if your agents run in real-time pipelines or at scale, you will need a higher-tier plan with increased execution limits.

Ease of use: developer experience

Most developers look past LangChain’s complexity because of its unmatched control over the code. AutoGen generally gets high marks from developers for its quick setup and the drag-and-drop interface. The sentiment around CrewAI is somewhat mixed, with documentation gaps putting a damper on the overall experience.

As the most beginner-friendly framework out of the three, AutoGen’s web-based UI makes it easy to experiment with agents, even for those less tech-savvy. The learning curve for AutoGen is moderate, but if you’re a Python developer familiar with async patterns, you’ll have no problem finding your way around the framework. However, the documentation is scattered.
Many developers like LangChain the way they like a starter repository, because it gives a basic foundation for getting from zero to prototype fast. Its learning curve is pretty steep, especially if you’re dabbling in custom agent orchestration, but you can tap community support to get the hang of it. That said, the documentation is ever-evolving. Also, many criticize it for being over-engineered due to excessive dependencies and unnecessary complexity.
CrewAI’s well-documented API and a straightforward developer workflow aim to keep things simple and rookie-friendly, which seems to suffice for rapid prototyping and small-to-mid-scale projects. However, the black-box feel and its relative newness mean that production-grade agents might become a headache to manage.

Where each framework shines (or fails) across use cases

Choosing between multi-agent frameworks comes down to how well the framework’s design philosophy marries with your specific industry demands, workflow DNA (linear, dynamic, or modular), and collaboration patterns (hierarchical, equal-peer debate, human-in-the-loop). Let’s break down the optimal use cases for each framework.

1. Technology

Use cases: developer assistants, CI/CD analyzers, automated testing agents, and release note generation.

AutoGen is ideal for code-heavy tasks, such as developer assistants, thanks to automated code execution, debugging, and multi-agent collaboration. However, you’d want to combine it with LangChain for full CI/CD coverage.
LangChain shines for building API-driven assistants and workloads focused on Retrieval Augmented Generation.
CrewAI’s rigid workflows are more suitable for approval-heavy pipelines, so the framework conflicts with iterative dev workflows.

2. Customer service

Use cases: ticket triage, escalation handling, LLM-powered helpdesk agents, and sentiment-based routing.

AutoGen is not a good fit for customer-facing communication, yet it can be leveraged for internal support automation, such as analyzing error logs submitted via tickets.
LangChain is a top choice for automated FAQ bots, semantic search over knowledge bases, and dynamic response generation, since it easily integrates with third party services and external tools like CRMs and databases.
CrewAI performs well in tiered support systems, where you model agents as Level 1, Level 2, or Supervisor roles.

3. Sales and marketing

Use cases: campaign planning, lead scoring, personalized outreach, content generation, and sales funnel optimization.

AutoGen is not a natural fit for sales and marketing tasks, unless it’s used for internal tooling, like report generation or iterative optimization loops.
Although LangChain falls short in collaborative, multi-agent campaign planning, it can be used for developing standalone content generation apps or research bots that summarize competitors, spot trends, or generate ideas through external APIs.
CrewAI has an edge here thanks to its role-based agent model, which organically aligns with a standard marketing team structure.

4. Human resources

Use cases: Employee onboarding automation, report scheduling, and leave processing bots.

AutoGen can do the heavy lifting of backend HR workflows, such as data parsing or automated reporting, but is a bit of a stretch because of its lower-level orchestration.
LangChain makes sense for building HR assistants that fetch policy information or automate structured requests, but is usually too taxing for approvals or multi-role processes.
CrewAI’s role-based design and built-in task orchestration make it a nice fit for HR workflows that have to do with onboarding, scheduling, and multi-step approvals.

5. Financial services

Use cases: regulatory report generation, data validation, scenario modeling, and automated financial briefings.

AutoGen works wonders in scenario modeling with multi-agent simulations that demand dynamic data validation and iterative analysis.
LangChain can generate reports or pull live data from APIs, but lacks out-of-the-box capabilities for multi-agent validation, auditable workflows, and business rule enforcement.
CrewAI is an organic match for automating regulatory compliance and approval chains.

6. Supply chain

Use cases: shipment tracking bots, demand forecasting, supplier performance comparison, and delay predictions.

Overall, AutoGen misses the mark here, but has a moderate fit for demand forecasting, provided it’s done via Python-based statistical agents.
LangChain plays to its strength in analytical dashboards or assistants that feed on live supply chain data.
CrewAI is a logical choice for tiered, role-based workflows, such as Supplier Analyst → Risk Evaluator → Procurement Approver.

7. Healthcare and life sciences

Use cases: research support, clinical document summarization, internal knowledge agents, and care plan automation.

AutoGen is well-suited for peer-review-style workflows common in life sciences. Also, AutoGen’s support for human-in-the-loop and dynamic back-and-forth uniquely positions it for research-heavy tasks.
LangChain leads in clinical document summarization using RAG on medical databases.
CrewAI hasn’t gained traction in the industry because of absent compliance tooling and fine-grained error validation.

8. Legal

Use cases: contract review, clause extraction, policy drafting, and redline automation.

AutoGen can be used to support interactive, conversation-driven tasks, such as simulating internal consultations.
LangChain is right for the mission when paired with data retrieval tools for advanced search and semantic analysis.
CrewAI is a debatable choice since the framework has fewer ready-made compliance features.

Pros and cons of AutoGen vs CrewAI vs LangChain from our AI engineering team

Summing up, here’s how our artificial intelligence team sizes up each framework after hands-on experience building production-ready multi-agent systems:

Framework	Summary	Pros	Cons
CrewAI	Role-based agent framework with collaborative agents (a “crew”). Built for simplicity.	– Easy to pick up – Role-based abstraction – Beginner-friendly	– Can feel opinionated or rigid – Hidden abstractions make deep control harder
LangChain / LangGraph	Modular agent/toolchain framework with graph-based workflow support. Best for structured workflows with heavy external tool usage.	– Highly flexible – Good for RAGs and DAGs – Ecosystem size – Explicit control and monitoring	– Complexity and steep learning curve – Verbose wrappers lead to developers’ frustration – Overengineering risk for simple tasks – A moving target in terms of tool compatibility
AutoGen	Microsoft-backed multi-agent framework focused on LLM-to-LLM collaboration and orchestration.	– Supports multi-agent chats natively – Good for autonomous multi-agent collaboration and task management – Ideal if you’re deep in the Microsoft ecosystem	– Not beginner-friendly – Challenges with documentation consistency – Needs manual orchestration

FAQ

Can you combine these frameworks in one project?

Yes, you can build a hybrid setup to accommodate complex interactions. For example, in customer service agents, you can use LangChain for sentiment analysis, while CrewAI will be responsible for triage and escalation capabilities. AutoGen can be integrated to enable human escalation with code-backed diagnostics and context-based insights.

Are these tools open-source?

AutoGen, LangChain, and CrewAI are all open-source, but have different levels of commercial licensing and support.

How mature is the community support?

As LangChain is the most adopted framework out of all, it has high ecosystem gravity, backed up with integrations, community support, and community channels. AutoGen benefits from Microsoft’s backing – its GitHub repo is active, but the intel outside the core Microsoft team is limited. CrewAI’s community is still nascent, so when things break, you may have to comb through the code yourself.

How fast are new features released?

LangChain gets updated daily to weekly. AutoGen’s release cadence is slower compared to LangChain – roughly monthly or per milestone. CrewAI gets a facelift every week, with fast iteration on core APIs and bug fixes.

AI Development: Be-All and End-All Leader’s Guide

Posted on May 30, 2025September 7, 2026 by Artsyman Lizaveta

Key highlights

AI development has moved to the stage where it shows measurable results across industries.
Artificial intelligence (AI) can meet your transformational expectations if your data, infrastructure, and workforce are ready.
Machine learning algorithms work better and safer with an AI governance framework in place.

Artificial intelligence is becoming more powerful and omnipresent day by day. 78% of companies already use artificial intelligence in at least one business function to minimize costs, speed up processes, reduce complexity, transform customer engagement, fuel innovation, and unlock new revenue streams. However, only 1% of these organizations describe their AI developing efforts as “mature.”

How to do AI development right on the first try and avoid the AI adoption plateau? This guide summarizes a decade of our hands-on AI expertise, as we were providing our clients with scalable, value-focused AI solutions long before LLMs hit the headlines.

Read this comprehensive AI development guide to get the answers that spark action, and move from small-scale pilots to deploying AI at scale in a way that is sustainable, secure, and aligned with your business goals.

What is AI development?

AI development is the process of creating intelligent systems that can mimic human cognitive skills such as learning, comprehension, reasoning, problem solving, decision making, and creativity. Underpinned by capabilities like natural language processing, image and speech recognition, computer vision, machine learning, deep learning, and generative AI, these systems can create various types of content, analyze data, identify patterns, and make predictions faster than humanly possible. For companies looking to leverage these capabilities, professional artificial intelligence services development can provide the specialized expertise needed to navigate this complex undertaking.

Does AI development pay off? The true return on artificial intelligence investment

While AI technologies have generated years of hype and expectations of high ROI, there was little evidence to prove this promise. In 2026, however, the technology’s potential is backed by hard data.

Yet, unlocking this value is only possible with a thoughtful approach, which starts with identifying relevant business use cases. That’s why, before rushing into AI-based development, companies often choose to invest in AI adoption workshops — intensive exploratory and planning activities which set the right project trajectory from day one.

Where is AI making the biggest impact?

Recent developments in AI empower companies to accelerate and enhance their front, middle, and back office processes by automating repetitive tasks within workflows, enriching them with personalization and problem-solving capabilities, and eliminating human errors.

The shift toward action-oriented AI

In 2023-2024, a new trend started gaining traction — large action models (LAM), better known as AI agents. This marked a fundamental shift from generative to actionable AI, where AI algorithms moved beyond providing output to performing tasks on the user’s behalf.

However, so far, the potential of AI development technologies is still largely untapped — only 11% of companies involved in the development of AI move from piloting to deploying AI agents.

Take the legal world. Our client, a global law firm, wanted to implement AI to analyze stacks of M&A data and extract key points in one click. A multimodal AI agent now interprets legal language, tables, and images, saving the client 47,000 hours of manual work annually.

On the retail side, Amazon is setting the standard, simplifying and streamlining the entire shopping journey. Its AI agents power highly personalized recommendations, automate fulfillment workflows, and even complete purchases across third-party sites via a “buy for me” feature.

Meanwhile, an organization from the travel sector partnered with our chatbots development company to overhaul their booking app by replacing a rule-based chatbot with a proactive virtual assistant that can handle all bookings and payments and track expenses on the user’s behalf. This upgrade spiked the annual retention rate from 28% to 41%.

Predictive equipment maintenance is another area where AI agent development solutions drive significant efficiency gains. Deploying them to orchestrate machinery maintenance for an electronics manufacturer led to a 20% drop in maintenance costs and a 15% boost in production uptime.

Proven high-impact use cases across industries

If you can imagine it, AI can do it. Moreover, chances are someone is already leveraging it. But with all the hype, many use cases can feel more like marketing fiction than practical solutions to real business needs.

Indeed, artificial intelligence (AI) development promises are huge on a full-blown Midas scale, with everything it touches supposed to turn to gold, or rather, a fully autonomous workflow. We’ve cut through the noise and gathered real-world examples of our clients’ projects across industries and functions.

This list isn’t final, as there’s more to AI than meets the eye, and valid use cases keep multiplying, but it offers surefire ways to nail AI development right here, right now.

Ecommerce

IBM survey pinpoints that AI’s contribution to revenue growth in retail will more than double by 2027. The technology has permeated all ecommerce functions to some degree:

Tried and true generative AI applications in ecommerce include:

Hyper-personalization of every step of the customer journey, from custom advertising and recommendations to unique loyalty programs
Virtual try-ons with computer vision and augmented reality under their hood
Human-like intelligent chatbots for accurate 24/7 customer support
Market research with AI combing through the vast amounts of customer data, feedback on social media platforms, competitors’ moves, and other valuable data
ML-powered demand forecasting backed by the EPoS and transactional data for 90%+ accurate predictions
Ad spend optimization by matching best-performing offerings to relevant consumers
Supply chain and inventory data analysis carried by neural networks evaluating suppliers, optimizing logistics routes, improving last-mile delivery, and running what-if scenarios to foresee demand fluctuations
Gen AI-driven pricing based on customers’ behavior, market trends, seasonality, inflation rates, and other variables
Enhanced fraud detection thanks to simulating fraudulent activities and training AI algorithms to detect and counteract them

Technology

Gen AI-powered automation is the primary driver of changes in how software engineering companies deliver their services. Projects that earlier called for niche expertise can now be done automatically and at a way lower cost. Let’s take COBOL as an example. Our experience proves that by using generative AI tools to translate legacy COBOL code into Java, you can cut software modernization costs by 70%.

The range of time-tested AI usage in software development spans:

Writing robust boilerplate code thanks to pattern recognition, contextual awareness, and code suggestion.
Explaining legacy code
Computer code refactoring and modernization
Code translation aligned with the project’s specific coding style, patterns, and software libraries
Early-stage bug detection when fixing anomalies costs next to nothing and doesn’t affect your project budget
Testing where neural networks take over test planning, synthesizing test data, and generating and executing test cases
Preparing comprehensive documentation and keeping it updated

Logistics

The volatility of trade controls and reciprocal tariffs, with consequent supply chain disruptions and ambiguous tax regulations, introduces an uncertain business environment as a new normal.

Our AI center of excellence is developing an AI-driven strategic response to minimize the impact of tariff-associated risks. Here’re two solutions we’ve already tried with our clients:

A bill of materials analyzer built with the use of machine learning techniques can predict potential Harmonized Tariff Schedule (HTS) classifications, flag high-duty components, and recommend duty-efficient alternatives.
Thanks to natural language processing, fine-tuned LLMs can read CAD files and PDF spec sheets and suggest product specification optimizations to help classify items under lower-rate tariff categories. Early adopters of this approach report 3–5 % duty savings.

The implications of AI in the logistics industry aren’t limited to the tariffs’ context. For instance, generative and conversational AI successfully cover the high-impact operational areas:

Inventory management and demand planning, when ML-based predictive data analytics enables highly accurate stock replenishment
Real-time route optimization enabled by deep learning models analyzing the weather conditions, traffic density, and road restrictions
Real-time vehicle route optimization depending on the weather conditions, traffic density, and road restrictions
Customer service with AI chatbots handling routine customer queries
Finance and risk management, where artificial neural network monitors regulatory changes and factors in operational cost trends, such as rising fuel prices and increasing inflation, to suggest relevant budget adjustments

Our client, an Italian transportation company, used conversational AI within their mobile taxi booking app to provide smart, human-like customer support with 97% accuracy of intent recognition. This approach empowered them to resolve 78% of support requests without the involvement of human workers and gain a 4.8-star app rating.

Automotive

75% of automotive manufacturers already use gen AI at all stages of the R&D process and report up to a 30% productivity gain.

Once confined to the pages of science fiction, autonomous vehicles are now a tangible reality, with generative AI and deep learning techniques working in tandem to process vast amounts of sensor data in real time. The rise of self-driving cars has pushed manufacturers to harness gen AI’s ability to create infinite synthetic driving scenarios, allowing models to train on millions of edge cases that would be too dangerous or rare to capture on real roads.

CarMax, the largest used car retailer in the United States, demonstrates another use case. Their GenAI tool scans and summarizes thousands of real customer reviews and updates the related section on the vehicle’s page, enabling buyers to instantly grasp the pros and cons of a particular car highlighted by other drivers.

Finance

Banks, insurance agencies, accounting and tax firms, and mortgage companies benefit from adopting conversational AI tools for front, core, and back-office operations, increasing staff productivity by up to 35% while reducing cost-to-serve by 20%.

For instance, high-impact conversational AI use cases in banking include:

Customer onboarding with AI-powered image recognition taking care of ID validation checks and submitting the customers’ documents
Customer support with 60% of trivial inquiries, such as activating a card, resetting PINs or account passwords, and updating account information, being handled by AI bots
Deep neural networks analyze customer data, identify patterns in saving and expense behavior to distill tailored insights delivered by personalized virtual financial advisors
Assistance to C-level executives to save them from spending ⅓ of their time on chasing down metrics from the management information systems team
Employee onboarding and training with a single AI chatbot trained on the company’s data instead of slogging through the corporate wiki

Manufacturing

AI and machine learning are the driving forces of Industry 4.0, and the speed of their adoption is accelerating by the day.

Common real-world applications of AI in manufacturing cover:

Digital twins allowing for optimizing production lines, supply chains, and whole-factory workflows without disrupting physical assets
Predictive machinery maintenance backed with deep learning models and IoT sensor data prevents failures before they occur, eliminating unexpected downtime
Advanced quality control systems powered by computer vision spot product defects in real time
Mass product customization becoming scalable, with artificial intelligence adjusting product designs on the fly based on customer feedback
Demand forecasting relying on augmented analytics helps maintain optimal stock levels and reduce carrying costs

Healthcare

GenAI-driven solutions, from text-based chatbots to voice-enabled interfaces, reshape user experience for both patients and healthcare providers by making medical care more affordable while driving operational cost-efficiency. For instance, AI-based claims processing speeds up resolution time by 40%, creating a better patient experience. At the same time, delegating this and other administrative, repetitive tasks to AI saves up to 25% of total healthcare spending.

Key use cases for AI in healthcare including conversational tools are:

Proactive appointment scheduling
Medical triaging to take symptoms gathering and identifying diagnoses off the shoulders of over-loaded primary care doctors
Clinical decision support, where even general-purpose LLMs can cut hours of preparing the clinical recommendations down to minutes
Remote patient monitoring
Post-visit patient support and engagement, for instance, outlining care summaries, estimating out-of-the-pocket costs for patients, walking them through the insurance coverage and billing process, and other complex tasks
Medication management with an AI assistant serving as a personalized medication encyclopedia
Reimbursement, where AI prioritizes claims, submits them to insurance providers, monitors payments from providers, and offers guidance on bills to patients
Clerical operations, like churning out post-visit summaries, organizing clinical notes, and creating personalized learning plans for clinicians
Clinical trials with AI handling a broad range of tasks, from candidate screening to checking for missing data points in incoming clinical trial data and lab results
Back-office work and administrative functions, such as finance, staffing, and legal activities

Oil & gas

The margin for error in the oil and gas industry is razor-thin. A delayed maintenance check, a misjudged drill path, or a supply chain hiccup can lead to millions lost. In such a high-stakes environment, AI adoption is your chance to stay on top of your game.

The range of AI use cases in the oil and gas:

Reservoir exploration with AI augmenting human fieldwork by interpreting seismic images and creating geo-models of hydrocarbon reservoirs in hours instead of months
Drilling optimization, when ML algorithms and neural networks are used to prevent drill-bit failures
Automated E&P equipment scanning with computer vision at its core to schedule maintenance on time and decrease operational expenses
Field workers’ support with virtual assistants proves to be more efficient than human-staffed call centers
By using robots with OGI cameras and summaries generated by AI, operators can perform tasks typically done through dangerous manual entries, such as inspecting storage facilities and taking remedial actions
Route planning and adjustments can be done on the go without increasing the planned transit time
Refinery optimization with AI systems monitoring distillation, catalytic cracking, and hydrogenation to spot safety hazards
Quality control done by AI models ensures that fuels and petrochemicals meet key standards, such as ISO, ASTM, and API
Accelerated and cheaper product R&D thanks to AI-based simulations
Supply chain automation, as ML algorithms take over configuring distribution networks, monitoring inventory levels at each facility, and optimizing transportation routes

3 questions to assess your AI readiness

Everyone is talking AI, a medley of use cases prove its efficiency… And here comes the ‘but’: is your data, infrastructure, and employees ready for artificial intelligence?

Business owners tend to feel optimistic hearing that developing artificial intelligence can take something between a few months and a year. However, the reality shows there are quite a lot of things to be taken care of prior to AI technology development, and they take time too.

47% of C-suite respondents believe that overcoming AI adoption barriers, such as data concerns, trust issues, risk management, governance, regulatory compliance, and workforce training, can be achieved within 12+ months. Meanwhile, Deloitte’s AI research indicates a 1–2 year timeline as more realistic, with some challenges extending up to five years.

Is your data AI-ready?

Lack of easy access to data from different systems, incorrect and missing data, bias, and other issues increase the AI development and maintenance costs, not to mention affecting the solution’s quality.

Successful AI adoption starts with improving your data foundation.

Since data is the difference maker, 75% of companies have already increased their investments in organizing, streamlining, and protecting their data. How can you strengthen your data lifecycle management to keep up with them? Building on experience gained through delivering professional data preparation services, we’ve listed data-related challenges standing in the way of AI adoption and shared practical tips for addressing them.

Inadequate data quality

Clean and validate data regularly to spot and remove duplicates and incomplete records before they affect the accuracy of machine learning models. The frequency depends on the data type and its importance for decision-making:

High-velocity data, like financial transactions, should be validated daily.
Operational business data, such as supply chain and inventory records, can be checked weekly.
Customer data, like CRM records and customer profiles, can be reviewed for inaccuracies once a month.

Use resources like the Great Expectations data quality framework, dbt tests, or the Deequ library to automate and schedule validation checks for each type of your data.

Lack of data

If you don’t have enough proprietary data to fine-tune deep learning models or cannot use real data because of privacy concerns, your limited dataset may fail to reflect the reality and result in an algorithmic bias.

Discriminatory outcomes lead to missed business opportunities and severe legal and regulatory penalties, as it was with UnitedHealth Group. The health insurance provider used a faulty AI tool for post-acute care predictions that denied elderly patients coverage for extended care.

To combat these risks:

Augment your existing data with its modified versions if your dataset lacks diversity. Say, you are training a customer sentiment classifier on a limited set of customer reviews. You can diversify the dataset by replacing some words in reviews with synonyms. Changing ‘fast shipping’ to ‘quick delivery’ doesn’t compromise the original review, but is essential for training a highly accurate AI classifier.
Generate synthetic data that mimics the characteristics of the existing data without jeopardizing its privacy. This is a silver bullet for accelerating medtech R&D efforts without exposing patients’ information.

Generating synthetic data is also a go-to option for simulating rare events. For example, a traffic management company may not have enough data on accidents to create a solid AI-driven accident prediction and prevention system. Synthetic data empowers them to immediately get realistic scenarios in any weather and lighting conditions for different road types, traffic density, and driver behavior.

Use bias-detection tools like AI Fairness 360, Fairlearn Aequitas, etc., to ensure you have a diverse, equitable dataset. In cases when there’s no quick way to get more high-quality data on the underrepresented group, you can oversample minority classes to balance the dataset.

Data privacy

With the EU Artificial Intelligence Act going into effect in 2026 and the shifting status of AI-specific legislation in the US (Colorado and Virginia AI Acts), companies have to stay alert about how their AI systems store and use personal data and other confidential information.

Better safe than sorry (and on the front pages) — confront data privacy concerns by embedding privacy-by-design principles in data collection, storage, and usage processes:

Reduce data usage to the essential minimum
Encrypt sensitive data at rest
Anonymize private data before feeding it into AI models
Incorporate human review mechanisms to oversee AI decision-making

No data governance

AI can’t scale without robust governance guardrails. Therefore, the development of artificial intelligence requires an end-to-end data lifecycle strategy, from secure data gathering to its safe disposal.

Implement data quality monitoring procedures
Establish clear data ownership
Impose strict data access rules
Develop data privacy policies to protect data from misuse
Set up templates to enable data traceability
Ensure you have a centralized data storage
Arrange data inventory mechanisms
Enforce clear data disposal practices

Is your infrastructure AI-ready?

Infrastructure to support the AI development process includes cloud services, data storage, and network security. Our AI engineers share insights on optimizing each component.

Cloud services

The type of model you pick directly affects cloud costs and storage needs. And that’s the reason behind 77% of companies using smaller models (13B parameters and below) rather than large ones.

The challenge of using the right tool for the right job is especially valid when choosing between LLMs and SLMs. LLMs shine when it comes to answering general queries. But SLMs can be quickly trained on a small, highly curated dataset to address your specific use cases.

Apart from so-called narrow AI, designed for a specific task or limited set of tasks, organizations can also use industry-specific models tailored to the needs of a particular domain. There’s already a whole range, from BloombergGPT for finance to BioNeMo for biotech to ClimateBERT for climate change research.

— Pavel Klapatsiuk, AI Lead Engineer, *instinctools

There’s also a question of API-based vs. self-hosted models. When accessing AI capabilities via API, you avoid costly infrastructure investments, but lack control. Self-hosting AI models, on the other hand, come with high compute demands but offer complete control over the model and airtight-secure data pipelines.

Data storage

Traditional data lakes and warehouses fall short in supporting the agility, governance, and scalability requirements of AI initiatives. New architectures like data lakehouses, data mesh, and data fabric have brought AI development from hype to reality.

Each data architecture type has its highs and lows, and choosing the right one involves balancing various trade-offs, including limited scalability and flexibility, weaker data governance capabilities, lower data security, and higher cost.

Our AI projects show that a data lakehouse often meets most business needs — single data storage with built-in data governance controls for different kinds of big data, seamless scalability, and adequate functional security.

— Ivan Dubouski, Head of AI CoE, *instinctools

Network security

Last but not least in your infrastructure assessment is network security. Robust policies and controls are vital for protecting your resources (data storage, models, APIs) from external or internal threats, such as data exfiltration, model poisoning, adversarial inputs, unauthorized API access, etc.

Our recommendations for secure AI development include:

Adopting a zero trust security posture with granular access controls and centralized identity management (IAM)
Integrating network security tools (SIEM, SOAR, or XDR) to centralize signals from an automated anomaly detection system and enable fast, coordinated incident response across your AI infrastructure.

Can your staff take on AI roles?

IBM pinpoints that 84% of companies considering AI development lack AI-specific technical competence and resort to augmenting their team as they don’t have months to hunt for and win over top talents in computer science, data science, ML engineering, and other AI-specific areas.

Raising strong in-house AI expertise isn’t a weekend bootcamp. While some professionals can pivot into AI-related roles relatively quickly, upskilling takes time.

For instance, given the widespread use of Python in deep learning, ML, and NLP, your in-house Python developers already have a head start. With focused upskilling, they can transition into roles like prompt engineers or AI/ML engineers. In my experience, the first option will require 3+ weeks of full-scale training, and the second will take 3+ months of full-time learning and hands-on practice.

So the question is: can you afford investing in the employees’ reskilling without compromising the momentum of your current projects?

— Ivan Dubouski, Head of AI CoE, *instinctools

Struggling with data, infrastructure, or talent?

Delegate it

Navigating AI development risks

The same AI software that can increase your revenue by more than 10% can also expose the company to various data, model, operational, and ethics risks. While many consulting firms warn about AI dangers in vague terms, we draw from hands-on project experience and offer targeted, actionable ways to handle them, all aligned with the NIST AI risk management framework.

Cybersecurity threats

Only 24% of AI initiatives are secured against AI-related threats, such as data poisoning, data tampering, API security breaches, model inversion attacks, prompt injections, etc.

Secure all the stages of the AI pipeline to enable the safe development of AI solutions.

Data collection and handling. Data encryption at rest and in transit and strict access controls are the basic best practices.
ML model training. If you access open-source models via APIs, use strong authentication protocols like OAuth, OpenID Connect, etc.
ML model usage. Use a machine learning detection and response (MLDR) solution to monitor the models’ behavior and quickly detect and quarantine or disconnect compromised models.

Data privacy issues

Inform users about data collection practices for your AI system, such as what personally identifiable information (PII) you want to collect, for what purposes, how it’ll be stored and used, Then, let customers decide if they want to share their data.

In highly regulated industries like finance and healthcare, where companies are obliged to comply with specific regulatory acts, such as HIPAA and GLBA, organizations should consider replacing real information with synthetic data.

Intellectual property infringement

Even though AI-centered copyright laws, such as the Generative AI Copyright Disclosure Act in the US, the EU AI Act, and the Generative AI Training Licence in the UK, are still in the legislative process, you’d better play it safe.

To weed out the possibility of intellectual property violation while developing AI systems:

Check your datasets for potential copyrighted content with copyright detection software, such as DE-COP for text, Google Vision AI for images, Audible Magic for audio, etc.
Use publicly available data or data that’s explicitly licensed for use, distribution, modification, and commercial use (for example, has a Creative Commons BY license).

Lack of explainability and transparency

The complex nature of machine learning algorithms is a double-edged sword. On the bright side, it contributes to delivering highly accurate outputs. On the dark side, the logic behind these algorithms is challenging to understand and explain.

If you want neural networks and deep learning algorithms to be an open book, adopt explainable AI techniques tailored to your model type:

Feature importance, LIME, and SHAP for simpler machine learning models, such as decision trees, gradient boosting, and random forests.
DeepLIFT and integrated gradients for more complex deep neural networks with deep learning and neural networks at their core.

Misinformation and manipulation

AI hallucinations are one of the examples of misinformation that damages the reputation of AI systems. Malicious manipulations, like reverse engineering and model hacking, are even more harmful, as attackers can expose sensitive or confidential information or poison your ML model with bias.

Safeguard your AI development process by:

Using high-quality data for model training
Rigorously testing your ML model
Continually evaluating and refining the ML model
Keeping humans in the loop to review and validate the accuracy of the model’s outputs

AI-specific technical debt

Quickly patched data pipelines, rushed model deployments, and poorly documented feature engineering slow down future iterations of your AI software, raise its maintenance costs, and increase the risk of model failures.

To minimize the amount of AI-related tech debt that builds up around data, models, and infrastructure, strengthen all of the weak points:

Set up automated data validation, standardize data pipelines, and track data lineage to get high-quality, consistent, and reliable data.
Use monitoring tools with auto alerts to catch model drift immediately.
Prioritize building solid MLOps pipelines and scalable infrastructure that support deployment, monitoring, and retraining to ensure consistent behavior of the ML model in production.

Can’t wrap your head around all possible AI risks?

We’ve got you covered

Solid AI governance as your clear-cut to risk-free, responsible AI

AI governance should be established from day one rather than tabled and taken care of later. Without well-documented rules and standards for aligning your AI development with ethical and human values, your AI initiatives are doomed to face the aforementioned risks.

Deloitte’s AI research highlights that the lack of a sound AI governance framework is one of the most widespread roadblock companies bump into when adopting artificial intelligence. Another survey pinpoints the chasm between what organizations declare about AI governance and what they actually do. If you’re in the same boat as 79% of businesses that don’t have a robust AI governance framework yet, mind that the boat is rocking, and it’s time to act.

Here’s a set of responsible-by-design AI principles to use as a blueprint for your AI governance framework:

Build an AI ethics code around principles, such as fairness, interpretability, and human oversight.
Keep an eye on local and global AI regulations and align your internal AI policies with new standards before they come into force.
Raise in-house data stewards and risk officers who’ll be in charge of overseeing AI development and deployment.
Create a compliance checklist and run regular audits to ensure policy adherence — quarterly for AI systems used in finance and healthcare, and annually for less regulated cases.
Address AI-specific failure scenarios, such as model bias, drift, misuse, etc., with on-point risk mitigation practices (AI model optimization, pre-deployment bias audit, automated drift detection, detailed audit logs).
Incorporate responsible AI best practices, such as model explainability, data encryption and anonymization, bias monitoring, etc.

These AI development principles should be established from day one. Yet, keep in mind that your AI governance policies aren’t set in stone. You should review and refresh them whenever you add new machine learning models to your tech stack, spot even minor incidents or failures, and if new AI regulations emerge.

— Pavel Klapatsiuk, AI Lead Engineer, *instinctools

Stages of the AI development lifecycle

As tempting as it is to jump straight into the development of AI technology, selecting ML models, and fine-tuning them on your data, the right place to start is by defining your business problem. Only then can you clearly see high-value, low-risk AI use cases capable of moving the needle.

That’s why AI projects should begin with an exploratory and planning workshop focused on the following:

Articulating your business problem to set clear goals and requirements for your AI development project
Identifying low-barrier, high-impact use cases and establishing their success metrics
Creating technology and business risk profiles for selected AI use cases

After strategic preparation is done, move to the development steps:

Selecting an AI model compatible with your existing infrastructure and matching your performance metrics
Customizing the AI model to tailor it to your particular use case
Integrating the fine-tuned model into your infrastructure by connecting it to relevant databases, data pipelines, and APIs
Verifying the model’s performance under production conditions and fine-tuning it further with model distillation techniques if needed
Deploying your AI solution and monitoring its performance in real-world scenarios
Continuously improving the software’s performance by collecting user feedback and retraining or updating the underlying model to enhance output quality and accuracy

Here’s a thing. You don’t need to reinvent the wheel with every new use case. If you invest in robust MLOps practices, you’ll always have a scalable, low-friction AI development process.

— Ivan Dubouski, Head of AI CoE, *instinctools

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How to decrease AI development cost? Bonus cheat sheet from our AI engineers

AI development doesn’t have to break the bank. Our AI development company in the USA has battle-tested tips for building high-performing and accurate AI solutions at half the cost.

Use API-based foundation models instead of self-hosted ones. This way, you pay as you go instead of investing in computing power upfront. If you decide on self-hosting, you can still save by adopting optimized inference engines (vLLM, TensorRT) to slash inference costs by up to 60–80%.
Apply transfer learning instead of full training and use PEFT techniques (LoRA, QLoRA, or QDoRA) for cost-efficient fine-tuning.
Use SLMs whenever possible to pay a lower per-token cost.
Store and reuse model outputs for solutions like AI-powered FAQ bots to avoid paying for the same answer 1000 times. This way, you cut API costs by 30–60% and improve response speed.

AI becomes valuable when it is strategic

Just like the cloud changed the game last decade, artificial intelligence is set to define the next, completely rewriting the rules of how businesses operate. If you’re wondering when to explore AI development, the answer is yesterday. And the next best time is now, with a clear strategy, not scattered experimentation. While tackling individual use cases is a natural starting point, long-term success comes from embedding AI development into your broader business strategy. Adoption at scale isn’t just a tech upgrade, but rather a company-wide transformation spanning data, infrastructure, and workforce.

If you struggle to move from planning and scattered experimentation to structured execution and scaling, it’s time to bring in expert guidance from a trusted artificial intelligence development company.

Ready to start your AI journey?

Let’s strategize

FAQ

Which industries does AI benefit the most?

From our experience, AI development delivers most benefits in ecommerce, finance, healthcare, manufacturing, transportation, energy, media, and telecommunications sectors. However, there are a lot of low-barrier, high-impact AI applications across other industries.

What is the timeline for implementing AI?

Depending on the current state of your data, infrastructure, and workforce readiness, AI implementation takes 12 to 36 months.

How can I accelerate my AI adoption?

To accelerate the development of AI you can use API-based foundation models to kick off your project quickly. But to speed up the evolution of your AI initiative in the long run, you should invest in building solid MLOps pipelines and regular staff reskilling and upskilling programs.

What is the smartest AI right now?

New developments in AI, such as AI agents, are considered the smartest and most advanced AI form, as agentic systems can initiate and perform complex tasks, including multi-step ones, within a diverse software ecosystem without human intervention.

What to expect from AI in the next 5 years?

Recent development in artificial intelligence indicates that AI’s level of responsibility and autonomy will increase. That means that AI agents will keep dominating the AI industry in the foreseeable future, causing a shift from application architecture to AI agent architecture.
Current trends, such as further domain and industry customization of the foundational models and exponential evolution of generative AI, conversational AI, and edge AI use cases, will keep unfolding.

AI Adoption Workshop: From Curiosity to Real Business Value In Just Two Days

Posted on May 19, 2025September 7, 2026 by Artsyman Lizaveta

Key highlights

AI democratization makes the technology more accessible, but AI adoption itself can still be tricky.
An AI adoption workshop is a way to decide how to address your AI pain points right here, right now.
The workshop goes beyond brainstorming with AI experts — you get a realistic, documented plan for technology implementation.

Let’s be honest, AI today can feel like a solution in search of a problem. Endless tools, sudden hype, and scattered experimentation make it hard to know where to begin or how to push beyond a prototype, what’s worth building, and how to make it all work.

That’s why we’ve designed an AI Adoption Workshop that starts from the only place that matters: your business goals. In just two days, *instinctools’ experts transform siloed ideas into structured, ROI-driven plans tailored to your business goals and tech environment. It’s a working session designed to move you from interest to action to value, with realistic planning, cross-functional expertise, and clear deliverables that are ready to implement.

A shortcut to sustainable AI adoption

When you hear “workshop,” you probably think of ready-made templates, generic frameworks, and pointless toy projects. We do it differently: you get direct, high-touch collaboration from a multidisciplinary team centered around your goals, your data, and your constraints.

An AI adoption workshop is a two-day exploratory and planning activity during which your tech partner cooperates with your company’s stakeholders. The aim is to identify how artificial intelligence can serve your current needs, catalog and calibrate relevant use cases.

Led by senior AI practitioners, including the head of our AI Center of Excellence, and digital transformation experts, the workshop brings up high-value, low-risk opportunities for responsible AI adoption. With a detailed action plan, you can either go further with us or choose any other AI service provider.

Our clients want practical advice and concrete steps to confidently release internal and market-facing AI products to their employees and customers sooner, with less risk and waste.

— Ivan Dubouski, Head of AI CoE

Common AI adoption challenges we help you solve

Here’s what we hear from clients before the workshop and how we help them move forward:

1. “We build prototypes, but they never make it to MVP”

94% of companies are good at developing AI prototypes, but only 21% can distill high-potential ones and carry them forward to MVPs.

An AI adoption workshop is a way to break you free from AI limbo and quicken AI development lifecycle, as your tech partner:

Catalogs your current AI prototypes
Evaluates and prioritizes them based on ROI, feasibility, and corporate strategy
Identifies the most promising ones to invest in
Prepares a high-level backlog for the chosen AI prototypes

2. “We don’t know where to start”

You’ve got AI FOMO, but are overwhelmed by all the available AI capabilities and tools. Or maybe you’ve launched some experiments, but nothing has really worked. We help you pinpoint the right starting point based on your business context, available data, and ROI goals.

One of our clients, a Canadian clothing retailer, wanted to replace their Excel-based analytics with an ML-powered system, but a vast selection of suitable ML tools paralyzed their decision-making. We analyzed their current tech stack and opted for Azure ML to keep it consistent and easy to maintain.

3. “We’re working with a tight budget”

You want to explore AI, but need to make every dollar count. We help you validate what’s feasible within your budget by calculating ROI across CapEx and OpEx for each viable use case so you can invest where it pays off most.

For a French eyewear manufacturer and retailer, we pinpointed a high-value, low-cost use case — a virtual frame fitting feature for their app. It enabled them to run a lean AI experiment that immediately set them apart from competitors.

4. “We want to play it safe”

It’s natural to be cautious about AI — 54% of companies are still wary about trusting AI systems for multiple reasons, such as data privacy issues, misinformation, model bias, and cybersecurity threats.

Moreover, these concerns can be topped with the company’s own unique challenges, such as innovation maturity, legacy infrastructure, or operating in heavily regulating industries like healthcare and finance.

The AI adoption workshop is also a quick intro to solid AI governance and risk mitigation frameworks. Thoughresponsible AI isn’t built overnight, you can make meaningful progress in this direction during a two-day workshop.

For example, our client, a Czech bank, wanted to move beyond AI-driven customer support and offer their clients hyper-personalized financial advisors. To do this safely, our team proposed deploying a private instance of GPT-4 within their Azure tenant, ensuring data control and compliance.

5. “We lack AI expertise”

You may have a budget for AI adoption, but without hands-on expertise, your initiatives either wouldn’t move an inch or go south before you realize it. At the workshop, you get access to senior and lead-level experts with practical knowledge of AI development, who not only guide you during the workshop but can support implementation too.

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What happens in the AI adoption workshop? | *instinctools’ experience

At *instinctools, we provide virtual and in-person workshops. Whichever the format and wherever you are on your AI journey (just starting with a single use case, scaling across departments, or introducing AI capabilities to the market), our flexible, modular approach adapts to your timeline, goals, and level of readiness.

Pre-workshop: strategic preparation

We don’t walk in blind. After signing an NDA, we do the homework by:

Diving into your business context by interviewing key stakeholders to see your challenges and opportunities.
Assessing your as-is state, including overall AI readiness and current AI prototypes, if there are any.
Exploring meaningful processes and areas of potential impact where AI can add value
Drafting an initial vision of your future AI-enabled to-be state.

Day 1: finding the right fit for AI

With your business context in hand, we spend the first day:

Translating your business objectives and pain points into visual mind maps to reveal AI opportunities
Presenting our ideas of the to-be state with a chart of relevant high-value, low-barrier AI use cases
Defining clear success criteria for your AI initiative to measure results
Running a validation session to see if everyone is on the same page after a day of discussions

Day 2: making it real

On the second day, we focus on turning ideas into tangible artifacts:

Outlining the required tech environment
Preparing a prioritized project backlog
Drafting an initial architecture vision
Sharing UX/UI concepts
Creating a strategic roadmap
Outlining project timelines and budget
Planning for market or employee validation through a pilot program

And because every organization is different, our workshops are truly agile. We can recalibrate the program on the go to cover what brings value to your unique business, such as staff AI upskilling programs, or the basics of establishing an AI center of excellence if a client has an in-house software development team and wants to raise internal AI expertise. What matters most to you always makes it into the room.

What you’ll walk away with: AI workshop’s deliverables to act on

Even a structured, facilitated AI discussion is of little value if the insights go undocumented. Practical, no-fluff takeaways are what drive companies to turn their AI ambitions into actions.

The list of deliverables may vary from provider to provider. Here’s what *instinctools’ clients get:

Vision&Scope provides an all-encompassing breakdown of your AI-related business problems, objectives and risks, opportunities for AI implementation, success metrics, and the scope and roadmap of your AI initiative.
An architecture overview includes a review of your current tech infrastructure and an outline of an AI system’s architecture. It covers data lifecycle management, ML model training, and integration into your existing software ecosystem.
UX/UI concepts are initial wireframes that can be later used for AI development.
Budget and time estimates comprise a high-level cost of AI adoption (data cleansing, model fine-tuning, etc.) and delivery timeline.

Get your AI wheels turning in just two days

You don’t need another presentation. You need a clear path from where you are to where AI can take you. We’ll help you find it…and build it.

You leave the AI adoption workshop with a clarified vision, evaluated business opportunities, and a realistic, documented plan for technology implementation.

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Large Action Models Walked So AI Agents Could Run?

Posted on October 15, 2024September 7, 2026 by Artsyman Lizaveta

Before AI agents became mainstream, large action models framed the idea of AI that acts – an action AI model capable of moving from user intent to real-world execution. Conceptually, LAM in AI represents a shift from language generation to autonomous execution, bridging the gap between understanding and action. Popularized by the tech company Rabbit, the term large action model (LAM) was never widely adopted across the broader AI community and carried a certain marketing flavor from the start. It emerged before frontier LLMs had multimodal processing, tool use, intent decoding, or task decomposition capabilities.

Still, even as the label itself faded from general AI discourse, the underlying concept became foundational to today’s agentic AI systems. Instinctools’ AI experts revisit this idea to assess which LAM capabilities have proven durable and practical in modern AI agents.

What is a large action model?

A large action model is an AI system capable of understanding natural language intent and autonomously translating it into real-world actions across digital or physical environments. The primary focus of a LAM (large action model) is to autonomously execute actions – completing tasks on behalf of the user. Building on the natural language understanding capabilities of Large Language Models (LLMs), interact with software interfaces, trigger workflows, make context-aware decisions, and adapt based on feedback and observed behavior. Unlike robotic process automation (RPA), which follows rigid, pre-programmed scripts, LAMs adapt dynamically to interface changes and unexpected scenarios.

In enterprise settings, LAM large action models function as autonomous agents that operationalize workflow automation, yielding quantifiable benefits, including reduction of manual cognitive and motor overhead, accelerated task completion, and more.

LAM may not be a fashionable term anymore, and current vendor consensus instead converges on a different framing – these systems are usually LLM-based agents, – but Rabbit, the one that put it on the map, never really left it behind. DLAM is their latest pass at it: a plug-and-play controller that carries out tasks on behalf of a user across their computer’s operating system, browser, and applications. They’ve also added voice integration with OpenClaw, a prominent brand in personal agentic AI assistance.

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Large action model architecture: key capabilities to move from words into action

Similar to an AI robotics system, LAMs go by the hierarchical approach to action representation and execution. To perform tasks, large action models decompose complex actions into smaller, more manageable sub-actions. The latter can then be reused in different contexts, supercharging the flexibility and planning capability of LAMs.

Processing multimodal input

Large action models are activated by user input, which serves as the starting point for their operations. Made possible in large part by the multimodal capacity of generative AI and foundation models, LAMs can process multimodal data like text, voice, video, audio, code, and more simultaneously.

Decoding human intention

Once user input enters the LAM’s bloodstream, the system infers the meaning behind it, leveraging neuro-symbolic AI – a hybrid approach combining symbolic reasoning with neural networks. This fusion enables LAMs to handle both structured logic and ambiguous human intent. Large action models analyze the whole spectrum of cues, such as language, past behavior, external context, and other signals to determine the underlying human intentions behind the input.

Interpreting user interface

To execute complex tasks and effectively interact with interfaces, large action models need to analyze what they see on screen. Thanks to their GUI automation capability, LAMs get a thorough understanding of buttons, fields, and images in application interfaces to accurately identify the purpose and functionality of UI elements within a given application. After that, the system can seamlessly interact with the appropriate element based on what it has learned.

Decomposing the task and performing action sequencing

Once assigned to action oriented tasks, a large action model first breaks them down into steps, creating a hierarchical structure. Symbolic reasoning allows the system to model actions and determine an optimal sequence of actions that will get the model from point A to point B.

Based on the analysis of the input and the identified tasks, the LAM generates precise prompts, augmented by data on prior experiences and codified domain knowledge, that guide the subsequent actions and allow the system to draw upon.

Acting

On its final leg, a LAM can execute actions either independently or by connecting to external systems and tools such as web automation frameworks. API orchestration is central to LAM execution. Large action models can use APIs to communicate with third-party systems, for example, they can access a weather API to analyze the current weather conditions. But most importantly, some LAMs can also send commands to devices, while others can interact with web applications by simulating user actions, such as clicking buttons, filling out forms, and navigating between pages.

Analyzing the results and learning from feedback

The best large action models are lifelong learners, always evolving and responding to feedback. Thanks to reinforcement learning, LAMs can create an iterative learning loop that improves by simulating actions, evaluating their outcomes, and adjusting future behavior accordingly.

Also, large action models allow for human oversight that helps drift the model in the right direction and improve their performance over time by injecting feedback into LAMs.

The inner mechanics of LAMs take after those of AI agent systems. However, in agent systems, there is more of a hierarchical structure, where subagents have specific roles, and a manager subagent assigns and coordinates tasks, whereas LAMs typically handle decomposition and planning within a more unified framework.

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LAM use cases: where the value is already tangible

The use of AI agents as the primary driver behind enterprise automation overall is broadening within organizations across industries. And although many still struggle to scale agentic automation initiatives enterprise-wide, the number of use cases is staggering.

Healthcare

The sheer volume of admin tasks, patients’ and admissions make the healthcare industry clamor for automation – a demand that previous-generation AI was able to partially satisfy.

Large action models can further mend some of the mounting problems faced by healthcare providers, in accordance with applicable regulations, care models, reimbursement approaches, and specific organizational blueprints.

Task execution in EHR processes, documentation, and scheduling is one of those areas where LAM systems can take more clerical tasks off the providers’ shoulders. LAMs can handle dynamic scheduling adjustments based on changing circumstances, factoring in patient preferences, doctor availability, and facility resources.

AI agents can also check on elderly patients outside healthcare facilities, assisting them with minor health issues and booking appointments with healthcare professionals, if necessary.

Besides, large action models can support clinical decisions by providing personalized treatment plans based on the interplay of different factors, including specific treatment guidelines, patient data, and patient preferences. Unlike traditional conversational AI, LAM-style systems can reduce the need for tightly predefined conversational integrations by interacting more flexibly with software interfaces and toolchains, though they still require governed access to systems like EHRs via APIs or compliant connectors.

Finance

40% of investors regret their investment decisions. A highly personalized LAM-based support system can prevent those costly investment mistakes by providing tailored investment recommendations based on an investor’s financial situation, risk tolerances, goals, and market data. It can then bring these recommendations into action, i.e. by making trades or transferring funds on behalf of the investor.

For banks and financial institutions, an agentic system bodes well for enhancing customer service. When human agent resources are stretched too thin, LAMs can engage in complex voice interactions to provide immediate support and offer recommendations based on user preferences and prior interactions.

One of our clients, a Czech bank, experienced first-hand the disruptive potential of AI agents. Our custom AI chatbot that has an LLM and actionable AI at the core, supplemented with pattern identification, speech recognition, and advanced deep learning algorithms, delivered a 60% increase in First Contact Resolution and took 98% of customer queries off human agents’ hands.

Read the full case study here.

Loan underwriting is another process that can benefit from the implementation of LAM solutions. To create a credit memo, relationship managers and credit analysts have to sift through 15+ sources on the borrower, loan type, and other factors, and then, after a few more sweats and back-and-forths, write the document.

Large action models can relieve managers and analysts of extensive data analysis, enhancing productivity and reducing the time spent on credit-risk memo generation. Leveraging agentic AI, a human user can outline the overall workflow, including specific rules, standards, and conditions, through natural language. The ecosystem of AI agents takes it from there by handling the communication with the borrower, gathering documents, calculating financial ratios, and executing the rest of the leg work.

Supply chain management

The current challenges in supply chain management create a breeding ground for innovation, a task LAMs are up to. As SCM systems usually comprise a whole variety of software, including ERP, WMS, TMS, IoT applications, and others, automation solutions require a whole lot of integrations to access and analyze consolidated real-time data.

Conversely, multi-agent systems have no problem integrating with industrial control systems and IoT devices. They can execute actions directly, such as collecting data from sensors or triggering maintenance alerts. Here are potential areas for LAM application in supply chains:

Predictive maintenance: large action models can accumulate data from sensors and other resources to predict equipment failures and send maintenance alerts.
Quality control: using the combination of computer vision, sensor data, machine learning, and reference data, LAMs can flag quality issues and perform immediate corrective actions.
Inventory optimization: not only can LAM systems take over complex data analysis tasks, such as recognizing patterns and anomalies in demand data, but they can autonomously respond to changes in demand or supply by adjusting inventory levels, placing orders, and managing transportation.
Industrial robotics: LAMs can transform human robot interaction, enabling automated systems to understand human intentions and work safely alongside humans.

Along with these real world scenarios, agentic capabilities can improve virtually all logistics processes, from route optimization to transportation resource management and vehicle safety systems. For example, agentic AI systems can dynamically adjust routes based on real-time traffic conditions and TMS data. They can then identify the most optimal mode of transportation according to the analyzed data and assign routes to each vehicle based on factors such as vehicle capacity, location, and driver availability.

Literally any enterprise

There is not a single incumbent that wouldn’t benefit from strategic planning capabilities brought into the fold by LAMs. Large action models delve deeper than any other analytics solution, closing the gap between enhanced decision-making and subsequent action.

Let’s have a look at feasible large action model examples that can flip the script in enterprises:

Customer experience: LAM-enabled chatbots can automate many routine customer service tasks, providing targeted support in real time. By identifying possible equipment failures or customer concerns before they happen, LAMs can automatically initiate tasks like notifying the maintenance crew or placing orders for replacement parts.
Fraud detection: agentic AI systems can detect fraudulent activity in large datasets of transaction data and automatically implement safeguarding measures in case of emergency.
Process automation: LAMs can do the heavy lifting of time-consuming tasks, including automated data entry, payment processing, financial analysis, contract management, and document review.
IT support: action-oriented systems can act as tech co-pilots, solving troubleshooting technical issues and providing necessary user support.
Compliance management: large action models can streamline routine compliance tasks, such as generating reports, conducting audits, and even updating records.

Take a page from our book: three success stories with an agentic AI linchpin

Give it a few years, and multi-agent systems will be standard enterprise AI infrastructure. And if there are still companies cautiously eyeing the agent-led automation trend, the only ones with a real competitive moat will be those who are not sitting back, but actively exploring how to raise the bar on operational efficiency using the technologies already at hand. Just see how it played out for our recent clients.

12× faster partner onboarding in insurance

For one of our clients, a global insurance aggregator, onboarding new partners across regions was slow, fragmented, and heavily dependent on manual engineering effort. We built a UI-first, multi-agent AI system that ingests partner documentation, interprets heterogeneous API formats, and automatically generates working integration adapters with tests and deployment-ready artifacts. The agentic pipeline, supported by structured validation, model governance, and human-in-the-loop checkpoints, cut partner onboarding from 3-6 months to 2 weeks, while facilitating a 10× decrease in operational costs.

Agentic AI sales representative slashing CPL by 15%

An Australia-based consulting firm wanted to automate early-stage sales without losing conversion quality. We developed an autonomous AI virtual worker that engages prospects, qualifies leads, maintains context across conversations, and advances opportunities inside existing CRM and communication tools. The system handles outreach, follow-ups, and basic deal progression with minimal human input, while escalating only high-value cases to sales teams. After deployment, the solutionincreased lead processing capacity by 20%,improved upsell and cross-sell rates by 19%, and reduced cost per lead by 15%.

Delegating customer support ticket triage to multi-agent system

Another client, a US online store, serving over 3 million yearly customers faced critical bottlenecks with 5,000-10,000 daily support requests, leading to 12-minute wait times and low CSAT. By implementing a multi-agent system with six specialized microservices, handling tasks from PII removal to policy compliance, we helped the retailer reduce ticket processing time from 6-12 minutes to just 1-3 minutes. This allowed each support specialist to handle 200-250 tickets daily (up from 50-70), achieving 75% faster first responses and a15% CSAT uplift without increasing headcount.

Giving AI the power to act should only be done with a control layer

LAMs are not immune to errors and biases that can creep into the systems as a result of insufficient prompting, inaccurate data quality, or unforeseen circumstances they were not trained to handle. So before entitling agentic AI to automate workflows, make sure you have a solid AI agent orchestration system in place. One with all the essential safeguards, including well-defined unified data standards, access to complete, accurate, and up-to-date data, and data security guardrails such as data minimization, anonymization, and encryption.

As for LAM-specific risk prevention measures, it’s recommended to isolate LAMs from host systems to protect the infrastructure from unintended consequences and provide a controlled environment for LAM testing and experimentation.

Adversarial testing that simulates real-world attacks on a system and identifies its vulnerabilities, can also shield your company from harmful fallout and make sure the output of actionable AI is free from sensitive data, biases, and inaccuracies.

A trusted engineering partner ensures those best practices are fully implemented, so agents operate safely within clear, well-defined boundaries.

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FAQ

What is the primary focus of a LAM?

The primary focus of a large action model (LAM) is autonomous action execution — translating user intent into real-world or software-based tasks. Instead of only generating text, it understands context, plans steps, and performs actions across interfaces and applications to complete goals with minimal human intervention.

How does a large action model work?

It begins with input processing, followed by intent inference, where the model maps ambiguous user requests into structured goals. Next, the system performs environment grounding, interpreting UI states, available controls, and API surfaces. Based on this, it generates a task decomposition plan, splitting the objective into ordered, executable sub-steps. Each step is translated into concrete actions such as API calls, UI interactions, or system commands. During execution, the model operates in an iterative feedback loop: it observes system responses, validates intermediate outcomes against the target state, and dynamically replans if discrepancies occur. This continuous perception-action cycle enables LAMs to maintain goal alignment while operating across multi-step workflows.

LAM vs LLM – what’s the difference?

A Large Language Model (LLM) is designed to generate and understand language. It predicts the next token based on context, producing outputs like text, summaries, code, or answers. Its role is primarily descriptive and generative, it responds to prompts but does not inherently act on external systems. A Large Action Model (LAM) extends this idea into execution.

What is the architecture of a large action model?

A large action model architecture combines perception, reasoning, planning, action, and feedback layers into a continuous loop.

What are real-world use cases for large action models?

Real-world use cases for large action models include automating healthcare tasks like scheduling and EHR processes, providing personalized financial investment recommendations, optimizing supply chain management, and enhancing enterprise functions like customer service and fraud detection.

How is a LAM different from an AI agent?

A LAM is generally viewed as an action-oriented model that can interact with interfaces, tools, APIs, or software to perform actions, while an AI agent is the broader autonomous system that reasons, plans, maintains memory, adapts to feedback, and decides which actions to take to achieve a goal.

What is the focus of LAM in AI?

The focus of LAM in AI is autonomous, goal-directed action execution. Triggered by the user’s natural language commands, LAM models navigate interfaces, orchestrate APIs, and complete multi-step tasks with minimal supervision.

AI Privacy Concerns: Profiling Through the Risks and Finding Solutions

Posted on July 26, 2024September 7, 2026 by Artsyman Lizaveta

Today, artificial intelligence is billed as a superpower that brings about unprecedented technological advancements in virtually every industry — and rightly so. The advent of gen AI and LLMs have put AI on an even higher pedestal, extending its applications and impact on modern organizations. But with this incredible progress comes a growing concern: is AI infringing on our privacy? AI privacy concerns have been the subject of many debates and news headlines lately, with one clear takeaway: protecting consumer privacy in AI solutions must be a top business priority.

Is your privacy governance ready for AI? Let’s find out.

A pulse check on AI and privacy in 2025

The heady growth of generative AI tools has revived concerns about the security of AI technology. The data chills it triggers have been long plaguing AI adopters — except they’re now exacerbated by unique gen AI capabilities.

Inaccuracy, cybersecurity problems, intellectual property infringement, and lack of explainability are some of the most common generative AI privacy concerns that refrain 50% of organizations from scaling gen AI responsibly.

The worldwide community is echoing a security-focused approach of AI leading players, with a sweeping set of new comprehensive regulations, national policies, focused legislations for specific use cases, etc., advocating for more responsible AI development. These global efforts are initiated by actors ranging from the European Commission to the Organization for Economic Co-operation and Development to consortia like the Global Partnership on AI.

For the first time in history we might be prioritizing security over innovativeness, as we should. Microsoft has finally sorted the wheat from the chaff and started to pay deserved attention to security:

“If you’re faced with the tradeoff between security and another priority, your answer is clear: Do security,” Microsoft CEO Satya Nadella said in a memo issued to his employees last month.

“In some cases, this will mean prioritizing security above other things we do, such as releasing new features or providing ongoing support for legacy systems.“

But although supra-national and region-specific regulations on responsible AI and data protection are underway, it’s still incumbent on individual companies to establish a set of risk-related best practices — especially, if they’re looking to derive value from AI-driven innovation.

The dark side of AI: how can it jeopardize your organization’s data security?

No matter what type of AI solutions you are integrating into your business, prebuilt AI applications or self-built ones, the adoption of AI systems demands a heightened level of vigilance. When left unattended, AI-related privacy risks can metastasize, potentially causing a range of dire consequences, including regulatory fines, algorithmic bias, and other pitfalls.

Lack of control over what happens to the input data or who has access to it

Once an organization’s data enters the gen AI intelligence stream, it becomes extremely difficult to pinpoint how it is used and secured due to unclear ownership and access rights. Along with black box issues, reliance on third-party AI vendors places companies at the mercy of external data security practices that may not always live up to the company’s standards, potentially exposing business data to vulnerabilities.

Unclear data residency

An overwhelming majority of generative AI applications offer little oversight of data storage and processing destinations, which may be an inconvenient circumstance if your organization has strict requirements around data residency. Your company’s legal or regulatory obligations might conflict with relevant data privacy laws in your jurisdiction, potentially putting you at risk of hefty fines.

So unless you indicate a specific preference or turn to regionally hosted models, your AI solution places your data in the red zone.

Reuse of your data for training the vendor’s model

When a company signs up for a vendor-owned AI system, they unknowingly consent to a hidden curriculum. Most third-party models collect data and reuse it to train vendor’s foundational models, not just your specific use case. This may raise significant privacy concerns associated with sensitive data. Data reuse also works in reverse, introducing biases into your model’s output.

Dubious quality of data sources used to fine-tune the model

‘Garbage in, garbage out’ — this adage holds true even for the most advanced AI models. Poor quality of the source data used for fine-tuning can trigger inaccurate outputs. In most cases, there’s little a company can do to head off this pitfall since organizations have limited control over the origin and quality of data used by vendors during fine-tuning.

Personally Identifiable Information (PII) violations

You might think that data anonymization techniques place PII under wraps. In reality, even anonymized and scrubbed of all identifiers data can be effectively re-identified by AI based on users’ behavioral patterns. Not to mention, that some smart models struggle to anonymize information properly, leading to privacy violations and serious repercussions for organizations.

Also, the General Data Protection Regulation, California Consumer Privacy Act, and other bodies set a very high bar, unreachable for most AI models, when it comes to the effectiveness of anonymization.

Security in AI supply chains

Any AI infrastructure is a complex puzzle consisting of hardware, data sources, and the model itself. Ensuring all-around data privacy demands the vendor or the company to introduce safeguards and privacy protections into all components as any breach in the AI supply chain can have a far-flung effect on the entire ecosystem, including poisoned training data, biases, or derailed AI applications.

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Front-page data breaches involving AI: lessons learned?

If there’s one thing we can learn from the tech news is that even high-profile companies fail to effectively protect user data. And with AI technologies, this mission becomes even more formidable due to the expanded vulnerability surface.

Microsoft’s massive data exposure incident that took place in 2023 is one of the many stark reminders, highlighting AI and data privacy concerns. In this incident, Microsoft’s AI research team accidentally exposed 38 terabytes of data from employee workstations. As a result, a wide range of highly sensitive information slipped through the cracks, including personal computer backups, passwords, secret keys, and other data. Consequently, the attacker gained complete control over the system, including the ability to delete and manipulate existing files at will.

Foundational model owners aren’t immune to data bridges either. Recently, OpenAI faced scrutiny after its ChatGPT model made payment-related and other personal information of 1.2% of users visible to some users. This incident underscored concerns from industry experts who have previously criticized OpenAI’s insufficient data security practices.

Undoubtedly, every A-list company that has been exposed to any kind of data breach was quick to take crucial post-breach responses — by patching, adjusting cloud configurations, or taking their applications offline. But considering the ever-high cost of data breaches, no measure is more effective than proactive prevention.

Tech players like Accenture, AWS, and IBM take prevention to a whole new level by shoring up capabilities and processes for responsible AI development and use. While specific points in their blueprints may differ, a common thread runs through their strategies — an unwavering commitment to compliance, data privacy, and cybersecurity.

Navigating legal waters: AI regulatory rules of thumb

Not all AI products are inherently flawed, some popular solutions like Simplifai, Glean, and Hippocratic AI demonstrate that success can be achieved while meeting privacy regulations and paying due diligence to privacy protection. But we get it: the regulatory landscape is changing fast, making AI development an uncharted territory for first-time technology adopters.

The good news is there are key principles that can drift your development efforts in the right direction and save you a lot of headaches down the road.

Reducing data usage to the essential minimum

First and foremost, you can get a lion’s share of AI data privacy concerns out of the way by keeping the amount of training and operating data to the necessary minimum from the get-go.

There’s a lot you can do to achieve minimal data usage in AI applications:

Give your data a good scrub — clean and filter the data to get rid of duplicated input, structural errors, and noisy information before training the model.
Double down on the most relevant variables — leverage feature engineering and distill the most useful patterns from the data.
Piggyback pre-trained models with larger datasets — turn to transfer learning to train your data on a smaller, specialized dataset.
Artificially create new data points — use data augmentation techniques to increase the training dataset without collecting additional input.

Providing understandable explanations of how AI systems function and make decisions

A bad reputation associated with the lack of data privacy demonstrated by AI can be partly attributed to the black-box nature of the latter. Safeguarding data becomes a tall order when there’s little explainability in the decisions of machine learning algorithms and deep learning techniques. That’s why building a transparent and explainable system with clear underlying mechanisms and decision-making processes is crucial for an organization to build trust and confidence in emerging technologies.

Making your AI systems explainable boils down to the main three features, including prediction accuracy, traceability, and decision understanding.

Incorporating human review mechanisms to oversee AI decisions

Different regulations, the GDPR and EU AI Act in particular, set out certain obligations for human intervention or human oversight as a means of preventing decision-making based solely on machine intelligence. To meet the requirement, organizations should employ robust review practices to avoid perpetual biases.

There are four main ways to put a rein on the outputs of the smart solution. The most common one is a human-in-the-loop system that is often used in high-risk applications. In this case, a human reviewer is directly involved in the decision-making process alongside AI algorithms. Organizations can also apply post-hoc reviews and exception-handling rules, to promote more accurate output and make sure the system doesn’t disclose any personal data.

Identifying and understanding different risk levels associated with AI systems

Just as the old saying goes ‘forewarned is forearmed’, knowing the risks and possible doomsday scenarios beforehand allows companies to devise effective mitigation strategies.

While there is no one-size-fits-all for conducting a risk assessment for artificial intelligence tools, most frameworks require companies to assign a category of risk to the system and draw up a risk mitigation strategy based on the risk profile.

Ongoing monitoring and system refinement are other non-negotiables of a holistic risk assessment framework that can give you a heads-up about any emerging risks.

Paying special attention to profiling workloads

Some AI applications like facial recognition software or customer services chatbots scan personal user data to create audience profiles based on user’s behavior, preferences, and other criteria. As this exercise involves processing large amounts of personal information, US companies must make sure their profiling is conducted in line with the CCPA, GDPR, DPDP Act, or any other relevant regulation.

When building a conversational AI chatbot for a leading Czech bank, we needed an LLM capable of handling a flood of customer queries (the client’s app serves over 100,000 users). ChatGPT 4.0 was a go-to option, as it can process 12,000+ inquiries and transactions per second, even in peak hours. However, using the open-source model was off the table in such a heavily regulated banking industry.

To meet compliance standards, we deployed a private instance of GPT-4 within the client’s controllable Azure environment. By connecting their initial platform-based chatbot to the LLM through the API, we delivered a secure, high-performing solution fully aligned with GDPR requirements.

In reality, though, users might not be in the know about their data being used for profiling purposes, which is a hard no for ethical and responsible AI use. Also, profiling datasets may become an easy target for hackers, especially if the system has rickety security controls.

Robust safeguards such as anonymization or pseudonymisation as well as technical and organizational security controls are among the go-to safe nets when it comes to shielding profiling data. Also, transparency around profiling methods and data controls in place is important to alleviate users’ concerns.

Ensuring AI systems operate reliably and do not pose risks to users or the environment

According to ISO 42001:2023, AI systems must behave safely under any circumstances without putting human life, health, property, or the environment at risk. To meet these requirements, smart systems shouldn’t operate in silos — they must be weaved into a broader ethical framework that prevents biases in decision-making and mitigates environmental footprint stemming from resource-intensive model training.

Proactive risk management coupled with the explainability of algorithms and traceability in your workloads empowers organizations to shore up capabilities and safeguards instrumental to building trustworthy AI systems that benefit society as a whole.

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6 practices to wipe out AI data privacy concerns

While some companies grapple with AI risk management, 68% of high performers address gen-AI-related concerns head-on by locking risk management best practices into their AI strategies.

Standards and regulations provide a strong ground zero for data privacy in smart systems, but putting foundational principles in action also requires practical strategies. Below, our AI team has curated six battle-tested practices to effectively manage AI and privacy concerns.

1. Establish AI vulnerability management strategy

Just like any tech solution, an AI tool can have technology-specific vulnerabilities that spawn biases, trigger security branches, and reveal sensitive data to the prying eyes. To prevent this havoc, you need a cyclical, comprehensive vulnerability management process in place that focuses on the three core components of any AI system, including its inputs, model, and outputs.

Input vulnerability management — by validating the input and implementing granular data access controls, you can minimize the risk of the input vulnerability.
Model vulnerability management — threat modeling will help you harden your model by mitigating known documented threats. If you have commercial generative AI models in your infrastructure, make sure to perform close inspection of data sources, terms of use, and third-party libraries to prevent bias and vulnerabilities from permeating your systems.
Output vulnerability management — strip the output of sensitive data or hidden code to ensure it can’t be inferred and to mitigate cross-site vulnerabilities.

2. Take a hard stance on AI security governance

Along with vulnerability management, you need a secure foundation for your AI workloads, rooted in the wraparound security governance practices. Thus, your security policies, standards, and roles shouldn’t be confined to proprietary models but also extend to commercial and open-source models.

Water-tight security starts with a strong AI environment, amplified with encryption, multi-factor authentication, and alignment to best industry frameworks such as NIST AI RMF. Just like vulnerability management, effective security requires continuous attention to three components of an AI system:

Input security — check applicable data privacy regulations, validate data residency, and establish Privacy Impact Assessments (PIA) or similar processes for each use of regulated data.
Model security — make sure you have clear user consent or another reason allowed by law to process data. You can use the PIA framework to evaluate the privacy risks associated with your AI model.
Output security — revisit the regulations to see whether the regulated data is available for secondary processing. Your AI system should also have a way to erase data on request.

3. Build in a threat detection program

To defend your AI set-up against cyber attacks, you should apply a three-sided threat detection and mitigation strategy that addresses potential data threats, model weaknesses, and involuntary data leaks in the model’s outputs. Such practices as data sanitization, threat modeling, and automated security testing will help your AI team to pinpoint and neutralize potential security threats or unexpected behaviors in AI workloads.

4. Secure the infrastructure behind AI

Manual security practices might do the trick for small environments, but complex and ever-evolving AI workloads demand an MLOps approach. The latter provides a baseline and tools to automate security tasks, usher in best practices, and continuously improve the security posture of AI workloads.

Among other things, MLOps helps companies integrate a holistic API security management framework that solidifies authentication and authorization practices, input validation, and monitoring. You can also design MLOps workflows to encrypt data transfers between different parts of the AI system across networks and servers. Using CI/CD pipelines, you can securely transfer your data between development, testing, and production environments.

5. Keep your AI data safe and secure

Data that powers your machine learning models and algorithms is susceptible to a broader range of attacks and security breaches. That’s why end-to-end data protection is a critical priority that should be implemented throughout the entire AI development process — from initial data collection to model training and deployment.

Here are some of the data safeguarding techniques you can leverage for your AI projects:

Data tokenization — protect sensitive data by replacing it with non-sensitive data tokens as surrogates for the actual information.
Holistic data security — make sure you secure all data used for AI development, including at-rest, in-transit and in-use data.
Documented data provenance — create verifiable mechanisms to confirm the origin and history of all data used by the models, especially inference data used for model training. Make sure data lineage and data access in non-production and development regions are in check to stave off data manipulation.
Loss prevention — apply data loss prevention (DLP) techniques to prevent sensitive or confidential data from being lost, stolen, or leaked outside the perimeter.
Security level assessment — continuously monitor the sensitivity of your model’s outputs and take corrective actions if the sensitivity level increases. Extra vigilance won’t hurt when using new input datasets for training or inference.

And by no means, do not use data directly as input for commercial pre-trained gen AI models, unless you intend to put sensitive information into the limelight.

6. Emphasize security during AI software development lifecycle

Last but not least, your ML consulting and development team should create a safe, controllable engineering environment, complete with secure model storage, data auditability, and limited access to model and data backups.

Security scans should be integrated into data and model pipelines throughout the entire process, from data pre-processing to model deployment. Model developers should also run prompt testing locally in their environment and also in the CI/CD pipelines to assess how the model responds to different user inputs and nip potential biases or unintended behavior in the bud.

Balancing innovation and privacy

To remain top of the game amidst the growing competition, companies in nearly every industry are venturing into AI development to tap its innovative potential. But with great power comes great responsibility. As they pioneer AI-driven innovation, organizations must also address the evolving risks associated with AI’s rapid development.

Responsible AI development demands from organizations a holistic risk management and data privacy approach, paired with a mix of risk-specific controls. By partnering with an experienced AI development company and keeping privacy and ethics in AI development and deployment top of mind, you can enjoy the benefits of AI while prioritizing data privacy and promoting trust and accountability in the use of AI technologies.

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