Large Language Models Use Cases: How to Become a High Performer With AI Help

The buzz around generative AI and LLM use cases has reached a fever pitch this year and for a good reason. The industry has the potential to add over $4 trillion to the global economy annually. Billed as a tipping point of AI, large language models are poised to reinvent business processes, bringing unmatched value, opportunities, and cost-efficiency to your table.

But, all hype aside, what is the real business value of ChatGPT-like solutions for your company? Let’s find out.

What is LLM? And what does it have to do with machine learning, neural nets, and gen AI?

Large language models are a new breed of generative artificial intelligence designed to understand, process, and generate human-like language. They glean knowledge from vast amounts of training data to perform a variety of natural language processing tasks. 

Originally, language models were designed to tackle text-based tasks. However, SaaS companies are now bringing us multimodal LLMs that go beyond text to images, videos, and audio.

The application area of LLM is massive — from content creation to text summarization and analysis. Unlike conventional task-driven NLP tools, large language models are much more versatile and can recognize a more complex set of data patterns in human language.

Along with NLP techniques, language models also have neural networks (usually, transformers) as their underlying architecture to process and spot dependencies in multi-format data. Supervised and unsupervised machine learning techniques are used to train and fine-tune a model for a specific business use case.

Key benefits of LLM for enterprises (if they get it right)

One of the main game-changers that put language models on the pedestal in the AI race is their versatility. Not only do they support an unlimited number of tasks, but they can also process and generate different data formats. Let’s see how this superpower can deliver high-value benefits for global companies, irrespective of the industry or LLM use case.

Co-piloting employees to reduce operational costs

Accurate information retrieval is one of the many gains that has put LLMs on the radar of companies. Each day, your employees comb through heaps of financial, transactional, and operational data. This takes a toll on their productivity and eats into corporate time. By automating time-consuming job functions, large language models can drive down operational costs by 30%.  

However, to reap this benefit, you have to fine-tune the model with internal company data and connect the model to the operational datasets. This sets the context for LLMs and allows employees to easily extract relevant information via natural language queries. 

Improving decision-making and spotting new opportunities

Language models can quickly scan and process vast amounts of both internal and third-party data, including reports, news articles, and customer feedback, to identify patterns and trends. 

Equipped with a rundown of insights, companies can then take the guesswork out of their strategies and make informed decisions, whether it’s the development of a new product or novel market segmentation.

Also, fine-tuned models can drill down into historical enterprise data to make predictions and estimate the potential risks of a new initiative. Businesses can either use the findings for immediate response or query them later via language prompts. 

Automating grunt tasks and increasing productivity

Recurring, mundane business processes are another specialty area for language models. Trained on the company’s data, smart models can do the heavy lifting of repetitive tasks, while freeing up the company’s resources for more strategic decision-making. Delegating such tasks as content generation, customer support, or data entry to machine intelligence also helps companies reduce labor costs.

Improving data quality and consistency

Data-heavy companies (aren’t we all?) can use ChatGPT-like models to automatically validate and cleanse reference and transactional data to improve its accuracy and consistency across systems. They can suggest entries in forms, match account numbers, and fill in missing data fields.

Supporting employee training, upskilling, and onboarding

In corporate environments, LLMs can support training programs for employees, allowing trainees to access new knowledge in a way that feels natural for them. Following the needs and learning preferences of the employees, language models can enable tailored learning of trainees and smooth onboarding of new hires.

Don’t wait, automate: 7 large language model use cases across business functions

According to research, over half of organizations are already testing the waters of gen AI-driven tools, LLMs being chief of them. But to make inroads into gen AI, it’s crucial to know the pragmatic, utilitarian use cases for large language models in the business landscape. Let’s see how large language models support workloads across different business functions.

1. Customer operations
   Generative AI and LLMs, in particular, have the potential to transform the entire customer operations function, boosting customer satisfaction and agent productivity. According to McKinsey, LLM use cases in this business function can increase productivity by up to 45% of current function costs.
   
   By fine-tuning the LLM on its customer chats, customer data, and sector-specific questions and answers, companies can automate interactions with customers and take the workload off the agents. Virtual assistants and human-like chatbots can analyze customer inquiries and provide real-time responses across multiple channels.
   
   
2. Sales and marketing
   Language models have taken off in marketing and sales functions to streamline communications with customers and drive personalization at scale. Gen AI models can generate tailored messages in multiple languages based on the demographic and purchasing data of your customers. Everything from social media posts to brand messaging can be produced with the mighty capabilities of LLMs.
   
   Beyond generic applications, language models can take over sentiment analysis, which is the driving force behind social media listening. Unlike targeted sentiment analysis tools, LLMs can better understand more complex nuances of customer sentiment. Also, LLMs can be used for market research, distilling insights from text data to look into consumer behavior and analyzing user preferences.
   
   Moreover, companies can harness the power of gen AI to automate sales. LLMs can move the leads down the sales funnel, facilitate lead scoring, and estimate the number and amount of future sales.
   
   
3. Product research and development
   LLM-enhanced smart applications have emerged as powerful tools for product ideation and brainstorming. They can provide research proposals, accelerate interdisciplinary research, and store the collective knowledge of researchers for easy retrieval. The technology can also assist researchers with exploratory data analysis, hypothesis testing, and predictive modeling, enabling them to improve their research outcomes. 
   
   Multimodal LLMs have raised the bar even higher. Not only can they provide product design recommendations, but they can also select cost-efficient production materials, optimize existing designs for manufacturing, and automate the design creation process.
   
   
4. Supply chain management 
   The application of LLMs is also transforming supply chain management by ushering in more predictability and control over supply-demand balances. Procurement teams rely on generative AI to select vendors, analyze spending data, and gauge supplier performance. 
   
   By reaching across datasets, LLMs can provide companies with on-the-fly inventory or demand analysis and present findings in digestible formats like graphs and narratives. 
   
   Thanks to its contextual learning capability, generative AI can cast its nets even wider by feeding on multiple variables and local context factors to produce detailed, localized forecasting of chain performance in a given environment.
   
   
5. Corporate risk management 
   With a veritable zoo of data sources and a large cohort of customers in multiple markets, risk management and compliance monitoring have become a formidable task for enterprises. By prioritizing risks based on the impact and custom criteria, enterprise LLMs enable proactive decision-makers in companies and tackle the heaps of paperwork related to risk assessments.
   
   The model churns out the specs of financial, operational, and reputational risks, possible control measures, and metrics to track potential vulnerabilities. The output serves as a baseline for risk managers to build from and evolve. The unmatched analytics capabilities of LLMs also allow them to identify suspicious patterns or anomalies and warn compliance officers about potential regulatory violations.
   
   
6. Digital product development
   Although software development is not the main forte of language models, they can still assist developers in writing boilerplate code, crafting effective user stories, and even troubleshooting the system. The potential of this LLM use case is still untapped, but growing with pioneers like Microsoft launching GPT-based coding tools that turn natural language queries into code.
   
   
7. Fraud detection and cybersecurity
   Fraud management is yet another of the many LLM enterprise use cases where the model emulates synthetic data to train fraud detection ML models. Also, large language models can identify potential fraud by ferreting out anomalies in historical data. 
   
   On the same line, LM-based fraud detection systems can create new possibilities for high-stakes functions such as claims management, where real-time fraud monitoring is essential to ensure the safety of sensitive customer data and the eligibility of the claimant.
   
   Cybersecurity experts have also made ChatGPT-like tools part of their stack to automate tasks like source-code analysis and vulnerability detection. Thanks to their pattern-finding features, LLMs can analyze threat patterns and generate response scripts.

Zooming in on the use cases for LLM across industries

Use cases of LLM are distributed unevenly across the business landscape, requiring leaders to carefully assess the potential benefits of gen AI for their specific vertical needs. Let’s explore the field notes of how different industries gain from the advent of gen AI and large language models. 

Healthcare

With an increasing cost of care, admin workloads, and labor shortages healthcare is a vibrant testing ground for AI-based automation technologies. In particular, LLM use cases in healthcare show promise to transform clinical practice by allowing HCPs to spend more time with their patients. 

Here’s where LLMs have made a compelling case:

• Back-office automation — Gen AI models can take the admin burden off healthcare professionals by drafting appeal letters, streamlining patient data entry, and categorizing incoming claims and billing.
• Patient assistance — LLM-based chatbots and virtual assistants can guide patients through ambulatory care, manage medication schedules, track health metrics, and support communication needs of patients.
• Automated compliance management — Gen AI can assist compliance managers in keeping track of regulatory changes and estimating compliance risks.
• Medical diagnosis assistance — Along with automating routine tasks, language models can inform medical diagnosis by analyzing patient symptoms based on the medical records analysis.
• Clinical trials — Training on raw protein sequences allows the AI to make inferences about molecular and protein structures. 

Banking and finance

In April 2023, language models made a distinctive mark on the finance world when Bloomberg released its GPT-based tool trained on and designed specifically for financial data. The tool outperforms similar LLMs by significant margins, enabling fast, accurate, and easy financial analysis. 

Besides financial data analysis, LLM business use cases in finance include personalized trading assistance, chatbots, efficient onboarding of new customers, and market predictions. The analytics engine of LLMs also helps generate reports at scale and supports smart wealth management. 

For example, Morgan Stanley has made a prominent case for LLM-driven financial analysis. The financial services company has launched a gen AI assistant that helps financial advisors comb through a huge database of financial data and extract relevant data in minutes.

Retail and ecommerce

Large language models have revealed the powerful capabilities of AI in retrieving information about products and assets. With jillion of SKUs, product descriptions, and marketing campaigns, retail and ecommerce, more than other industries, stand to reap the greatest benefits from the unprecedented search capabilities of gen AI.

In particular, LLMs fare well in automatically extracting relevant information from custom behavior and demographics data, sentiments, and feedback categories. 

The research area of gen AI can be as limited as the company’s internal data or as wide as social media data to enable always-on social media monitoring and listening.

And it doesn’t end there. LLM-enhanced customer support systems improve user satisfaction, boost sales, and offer 24/7 support to customers. The application area of language models in retail also stretched to procurement management. By digging into seasonality data and customer behaviors, LLMs can predict future product demand, thus reducing stockouts and excess inventory. 

Education

Learning and education are one of the areas where a tailored approach is key to improve performance and enable better learner engagement. By creating a unique conversation environment, LLMs can bring new ways of personalized learning where each program, quiz, and test is cut out for individual students’ needs, interests, and learning styles. 

The model can also become a force multiplier for teachers by taking over menial tasks such as grading and lesson plan development. On a higher level, LLMs promote inclusive, equitable learning opportunities for students of all backgrounds by eliminating language barriers and providing multilingual education. Apps like Duolingo and Babbel also demonstrate the vast potential of LLMs in language learning and language translation.

Media and entertainment

Generative AI models have also opened up a new set of opportunities for the creative industry. Besides textual data generation, multimodal LLMs can create custom sounds and short-form videos, improve editorial workflows, and fine-tune content of any type to better match the expectations of the intended audience.

They can also empower interactive storytelling to take the audience on an engaging and personalized journey, whether it’s in gaming or in advertising. 

Automotive

Recently, language models have also made it into a vehicle’s infotainment system to take voice control to a whole new level. Mercedes-Benz, the undeniable automotive leader, has integrated a GPT-powered model into the voice control system to improve its natural language understanding and level up its responses.

Moreover, gen AI tools can be used in intelligent vehicle production where they co-develop automotive software applications, analyze production data, and brief production employees on safety protocols. Language models can also enable autonomous vehicles to digest complex environment data and make safe driving decisions.

What does it take to adapt LLMs to a vertical need?

When companies choose the gen AI path, they are presented with two options: adapting the model as is with little customization or fine-tuning it on the company’s data. In the first case, adopters can create a tailored user interface or a search index to customize the model and enable it to grasp the company-specific context. 

However, to make the model a native extension of the company’s stack, adopters have to train it on their own, proprietary data. 

This way, the model can deliver accurate outputs for a complex, downstream task across the company. Based on the use case, the training data set can include customer, transactional, operational, and third-party data. 

Challenges and considerations of LLM enterprise use cases

As a part of the AI realm, language models pose familiar, age-old risks that have always been inherent in smart applications. However, as a fine specimen of trailblazing tech, gen AI models also bring new, less researched risks to the table that must be accounted for if you’re aiming for a responsible use of generative artificial intelligence.

Hallucination

The unstructured nature of input being fed into ChatGPT tools brings inherent risks of generating irrelevant and off-topic content. A poorly-trained LLM is prone to producing false, functional knowledge not supported by any training data — as an extrapolation from your prompt. 

The reasons for this lapse of judgment are many — from overfitting to poor prompts to complete datasets. That’s why, the quality, integrity, and completeness of training data are vital to set the model straight.

Biases 

Similar to other natural language processing applications, LLMs can inadvertently amplify biases present in the training data. This happens when the data doesn’t represent the entire population, causing your model to produce unreliable results. This, again, puts a special emphasis on accurate curation of training data and ensuring its completeness and versatility.

Data privacy concerns 

A large number of open-source LLMs store and process data on the provider’s servers, which goes against enterprise data protection regulations. To introduce a language model into your corporate environment, you have to deploy the model locally via APIs. This way, your sensitive data will be kept under lock and key.

Does an LLM make it worth a candle for your business?

We’ve seen it multiple times: companies get underway with hyped technologies and not getting their investment paid off. LLM-based innovation, like any other type of AI, calls for careful analysis of your business case and price-value ratio for your company. 

The rule of thumb would be estimating the human effort to complete a task manually against the effort spent on fact-checking the gen AI output. Typically, generative AI brings the greatest difference in use cases where human effort is high, while the validation of the output is easy. Mind that LLMs aren’t fire-and-forget innovations, they require your constant upkeep and human assistance to succeed.

Harnessing the value of LLM goes beyond merely spotting the right scenario

Ignoring generative AI can put you behind in the productivity race, but adopting LLMs just for the sake of it won’t take you anywhere either. 

You have to prepare for the leapfrog opportunities of LLMs by putting a solid strategy, tech foundations, and a dedicated team in place.

Develop a strategy grounded in value

Just like any other type of machine intelligence, gen AI adoption should be strategic. Your generative AI strategy should align with the existing AI mindset and inherit the same principles as your other AI initiatives. AI governance, risk management, and sensitive data handling are the pillars behind a comprehensive innovation strategy. If you have several gen AI projects, you may need a delivery manager to make the most out of them.

Gain guidance from cross-disciplinary teams

The inherent complexity in gen AI projects requires companies to secure cross-disciplinary talent to ideate, develop, and manage the AI lifecycle. Not only will your dedicated team guide you on the gen AI journey, but they will also ensure the right implementation of your data program. Having an experienced tech partner takes the risk out of AI adoption and keeps your gen AI tools compliant with laws and regulations.

Shore up specific tech capabilities 

While gen AI doesn’t require fundamental changes in your tech infrastructure (provided it’s AI-ready), you still need to tailor your data architecture to support a broad number of use cases. This includes the collection and curation of proprietary data and distinctive data capabilities such as vector databases and preprocessing pipelines.

Secure enterprise data

Bringing the LLM to your data is not enough to keep it safe and sound. Your company should also have a strong data governance framework in place that includes data encryption, access controls, and regular auditing. Also, your training data should be anonymized and aggregated whenever possible.

To sum it up, your gen AI strategy should be fortified with a crystal-clear vision, a path to value-realization as well as risk and adoption plans. These pillars should align at the adoption, talent, tech, and organizational level.

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