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Today, every company, irrespective of their focus, is a tech-first company. When technology becomes an integral part of everything your organization does, having the means to master continuous delivery is instrumental to fuelling your business growth. In 2024, AI in software development is billed to become that growth engine, embedding software in your products and services at scale.

As an AI-savvy company, we help our clients leverage the power of artificial intelligence in software development, applying it to specific tasks — and we’re sharing our battle-tested expertise and best practices in this guide.

impact of gen AI on the software industry

Benefits of AI for software development go beyond automation

When AI made its debut into mainstream technology, organizations approached it with a cautious mindset. These days, 31% of global functional spending on gen AI goes to software development, which bears testament to its rising importance in the field. Let’s see what benefits have turned AI software development from faddish to fundamental.

AI coding tools

Cutting costs associated with development efforts

AI-powered software development is one free from time-consuming tasks, such as test cases, exploratory data analysis, small code updates, bug detection, and others. By automating away menial activities, AI tools bring down the number of labor hours, ultimately leading to a more cost-effective development process. Some of our clients wield the sword of machine intelligence to optimize maintenance costs for cloud infrastructure. 

Accelerating developers’ productivity and time-to-value

Software engineers can join forces with an AI pair programmer to tap auto-complete features like code recommendations and context-aware suggestions. The latter eliminates the need to manually search for code snippets or specific function syntax, minimizing context switching for developers.

AI toolbelts

AI toolbelts like Github Copilot also keep developers in the zone by offering integrated development environments, summaries for pull requests, and explanations for code blocks.

According to GitHub Copilot, AI-powered developer assistants can deliver an uptick in developer productivity, with results reaching up 55%

Our client, a prominent bank in the MENAT region, stated: “Embedding AI tools into the development workflows has changed the game for our developers. These tools can produce an entire script based on just a single comment, cutting coding time in half”.

Productivity gains also translate into swifter and more cost-efficient innovation cycles that allow companies to rapidly adapt to shifting customer needs and snap opportunities faster than their competitors.

Improving code quality

AI-powered developer tools leverage natural language processing to dissect context from a user’s existing code and suggest additional lines of code and functions. It means that smart assistants can identify the optimal approach or solution to the development challenge — based on the natural-language prompt from human software engineers. 

AI tools can also improve the debugging process by quickly identifying and rectifying errors, contributing to a higher quality of deliverables. AI-assisted code reviews and static code analysis minimize the code vulnerability surface and improve its consistency. For example, artificial intelligence puts edge cases, which might be hard to spot through manual testing, on sharp display, leading to a more reliable final product. 

Focusing on building and creating vs. repetitive tasks

Over 50% of developers say that artificial intelligence in software engineering allows them to focus on more creative and challenging tasks instead of grinding away at mind-numbing activities like code formatting or documentation updates. Developers then use this headroom to flex their problem solving skills, analyze feedback from end users, and hatch solutions to novel problems.

Speeding up the onboarding process and flattening the learning curve for new hires

Apart from being the wingman for experienced software developers, AI systems can also help junior specialists get up to speed. By training intelligent systems on the company’s software stack, developer teams can integrate new hires into the fold faster than usual, accelerating their modus operandi. 

Also, these tools can improve the availability of mentorship and support for junior developers. Unlike team leads, AI tools, with their never-ending bandwidth, can provide instant feedback or relevant suggestions 24/7 to lead rookie developers in the right direction. This creates a valuable tool — artificial intelligence is always there to support junior developers, while senior developers can leverage AI’s insights to provide targeted support when new hires need it most. 

Need AI software developers to set up smart coding assistants?

7 ways AI can supercharge your software development process 

According to McKinsey, the direct impact of AI on software development efficiency ranges between 20 to 45% of current annual spending on the function. The value produced by the powerful combo of artificial intelligence and software engineering mainly results from reducing time spent on the following seven activities.

Writing boilerplate code

Boilerplate code is a developer’s long-time nemesis, forcing engineers to spend an eternity writing the same tedious sections of code over and over. Artificial intelligence software development can eliminate the associated drudgery by streamlining the process of boilerplate code generation:

  • Pattern recognition — once trained on the existing code base, large language models can identify patterns commonly used in boilerplate code, such as database connections, error handling blocks, and other structures.
  • Contextual awareness — intelligent teammates can figure out developers’ intent and fine-tune the boilerplate code to the unique needs of the project.
  • Code completion — AI-enabled virtual assistants can suggest code snippets based on the surrounding code and auto-complete the boilerplate structure for a specific function.

Code suggestions 

Much like with boilerplate code, AI co-pilots can act as smart code whisperers for developers, offering semantically relevant code snippets based on their deep learning capabilities. These tools can also guide developers toward the right solution by directing them to the relevant libraries and common coding patterns. 

Besides accurate suggestions with comments, developers can pick the AI brains to get their code inline fixed. 

Source: GitHub

Code refactoring and modernization 

Most codebases get messy over time due to constant fixes and changing project requirements. Generative AI models fed with well-refactored code can highlight code smells in your existing codebase and suggest refactoring techniques or alternative code structures for enhanced system performance. 

Developers can also ask AI tools to explain legacy code — and then feed this explanation, supplemented with additional information, back into the system to modernize the code and facilitate technical debt management.

Code translation

Old-school code translation tools fail to understand the underlying purpose of code due to their focus on the code structure. Conversely, AI-enabled models can discern the code semantics to suggest translations that preserve the original functionality. 

Development teams can capitalize on this superpower to streamline code conversion or app modernization projects, such as converting COBOL to Java.

Bug detection 

Development teams can also harness the duo of software engineering and artificial intelligence to automatically catch bugs early in the software development lifecycle. Thanks to built-in pattern recognition and machine learning anomaly detection trained on historical data from similar codebases, AI coding tools can spot subtle anomalies in the existing code that might otherwise go unnoticed.

AI pair programmers can also perform code reviews in the background, bringing potential issues to the fore and suggesting areas for closer inspection. 


Quality assurance and testing have a significant scope for automation due to inherent redundancy. Although AI can’t take over testing tasks completely, it can significantly improve testing speed and coverage by enhancing the following aspects:

  • Test case generation and execution — following software specifications, generative AI can produce test cases to help testers cover a wider range of scenarios.
  • Data generation for testing — artificial intelligence proves effective in generating edge cases and corner scenarios that might be challenging to create manually.
  • Performance and load testing — AI-powered testing tools can produce load for performance and load testing.
  • Test planning — smart coding tools can suggest test cases based on historical data, identify missing test cases, and map out dependencies to identify the right order of test execution.

Artificial intelligence also acts as a collaborative assistant for visual testing, exploratory testing assistance, and maintenance of test cases.

Before introducing AI in the testing workflow, our developers haven’t had enough time to create unit, functional, and performance tests for their code. Bug slipped through the cracks, causing issues in production with missing tests snowballing into tech debt. AI tools have tackled this problem by automating repetitive testing tasks and increasing test coverage without developers doubling the code’, — noted a representative from a consulting company, partnering with us on software development initiatives.

Preparing documentation

Code is never static — it evolves throughout development cycles, making it challenging for developers to keep up with documentation updates. Based on code comments and function signatures, machine learning models can automatically create a document that accurately outlines system behavior and its functionality. These tools can also account for the implemented tech stack and project-specific functionalities to generate comprehensive documentation aligned with the specific codebase.

Source: GitHub

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Features to look for in your code assistant

Which tasks AI for software development is failing at?

Now the main question is: can AI replace software engineers? Not in the foreseeable future. While AI and software development are a power couple, their duo is still a far cry from a fully automated software development process. Artificial intelligence requires human oversight and collaboration to do its best.

Handling a combination of legacy and updated code

AI software solutions have a hard time clawing through a repository that contains both legacy and new code due to the fragmentation and incompatibility caused by the former. Legacy codebases may not integrate well with common frameworks or libraries and often lack structure and adherence to current best practices — which makes it challenging for AI to understand its functionality. 

Contributing to organizational and project context

Canned AI–based coding tools are know-it-alls, so they are not designed to consider the unique requirements of your project or your business context. It means that unless you train it on your proprietary data, AI output might not meet your specific performance or security needs. 

Non-generic architectures

Artificial intelligence coding tools are capable of mustering basic, high-level design blueprints, while more nuanced architectural solutions remain a near-impossible feat for these tools. Also, the innovative potential of AI is limited by the training data, which means that cutting-edge architectural solutions fall under the domain of human ingenuity, not machine intelligence.

Navigating tricky coding requirements

While AI coding tools are all for answering basic prompts, they hit a brick wall when faced with more complicated coding challenges. For example, AI-based tools wrestle with combining multiple frameworks with disparate code logic or generating a code suggestion based on the big picture.

Choosing an AI software development assistant: open-source vs proprietary tools

Standing at the AI crossroads, companies usually face two options. They can either leverage open-source tools such as OpenCV, PyTorch, and Codestral or invest in proprietary solutions such as Microsoft Github Copilot and IBM watsonx Code Assistant.

Open-source tools that put AI algorithms, pre-trained models, and data sets up for public use are favored by the community due to their transparency and limitless options for innovation. Proprietary tools hide the source code from the public eye, yet offer support from the vendor, and potentially more advanced features.

So which one should you opt for? Here’s a table from our experts that summarizes the key differences:

CriteriaOpen-source AI toolsProprietary AI tools
Source code accessFreely availableNot publicly available
CostLower upfront costs + potential hidden costs (additional infrastructure, feature development)Higher upfront costs, lower TCO due to pre-built features
SecurityHigher control over data, yet the vulnerability surface is largerLess transparent, vendor is responsible for security updates
CustomizationHighly customizable, can be fine-tuned to suggest code based on best practices in your repositoriesLimited customization with a few configuration options (custom extensions, plugins)

A new era of development empowerment is underway

As AI continues to push the boundaries of software development, it’s important to remember that it’s not a panacea. Artificial intelligence is an augmentation tool whose true potential lies in its ability to automate menial tasks, enable developers to spend more time on problem-solving and empower them to build high-quality software faster. You can’t expect it to work miracles, but you can embrace the technology to ship software solutions more efficiently than ever before.

Take your software development to the next level with AI

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Anna Vasilevskaya
Anna Vasilevskaya Account Executive

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