How to Measure LLM ROI and Achieve Over 90% Prediction Accuracy

Data management isn’t just a technical topic – it’s a core part of business strategy. It impacts regulatory compliance, executive decision-making, and customer trust. Learn how large language models (LLMs) can not only improve how you work with data, but also help reduce costs and drive a cultural shift towards data-driven thinking.

It’s no surprise that more and more companies are asking: how can we justify investing in Data Governance – a comprehensive approach to organizing, protecting, and managing data? But there’s another question that’s becoming just as important: how can modern solutions like LLMs actually improve the way we manage data?

At BlueSoft, we set out to test this in practice. We used an LLM to analyze and document our internal data warehouse, checking how well it could index database structures, generate metadata, and answer natural language questions – all without extra context or documentation input. The results? LLMs can provide real support for business users and analysts – especially in environments without dedicated maintenance teams or where knowledge about data is scattered. This approach not only simplifies access to information, but also helps with system documentation and builds trust in data across the organization.

Read: 7 Reasons Why Data Governance Projects Fail (And How To Avoid It) 

Why are large language models changing the game?

Large language models like GPT, Claude, or Gemini are advanced systems that can “understand” and generate natural language – the kind we use every day. This makes them extremely flexible and useful when working with data. They lower the barrier to entry for Data Governance and help boost data adoption across the organization.

In our view, LLMs represent a new standard for Data Governance projects because they can:

• answer questions about your data without needing knowledge of query languages like SQL
• create and update technical documentation, which is often time-consuming and quickly outdated
• analyze metadata and assign relevant tags or categories
• support regulatory compliance (e.g., GDPR) by automatically identifying personal data

Most importantly, they perform these tasks quickly, effectively, and at scale – no matter how many users or how large the system is, their performance remains high. Of course, the cost grows with usage, but overall it stays relatively low and manageable.mie.

How do LLMs actually improve efficiency and generate savings?

When looking at this topic, we identified several LLM use cases where it’s easy to build a clear business case:

• data self-service – conversational access to your data warehouse without knowing SQL
• automatic data labeling – improved quality and compliance in one
• enriching metadata and glossaries – a foundation for consistency and automation
• technical documentation – always up to date, always accessible

The key point here is that the cost of tokens for any LLM is significantly lower than the cost of manual work hours needed to perform the same tasks. Below is an example of how to calculate ROI for one of these scenarios.

Data self-service – conversational access without knowing SQL

The challenge: In every organization, people like managers, analysts, and product owners need quick answers to data-related questions on a daily basis. Things like: “What does this column mean?”, “Why is there an error here?”, or “Where does this report data come from?”

These types of questions usually end up with the data platform team, which leads to:

• overloaded analysts and data engineers,
• constant interruptions (context switching),
• delays in creating accurate reports – and ultimately, in decision-making.

The solution: With LLMs, users can interact with the data platform through a simple chat interface. They ask questions in natural language, without writing any technical queries. Just like using a search engine – the user types a question, and the system replies based on metadata, embedded business logic, external documentation, or by auto-generating the right query to the database or other data sources.anych, logice biznesowej zawartej w kodzie, dokumentacji zewnętrznej lub automatycznie generując odpowiednie zapytanie do bazy bądź innych źródeł danych.

Example assumptions and figures for calculating ROI:

• The data platform support team consists of 5 people, and there are 50 data users in the organization (people who work with data daily).
• Each user asks an average of 2 data-related questions per week.
• Each question takes about 25 minutes of a specialist’s time.

Over the course of a year, this adds up to more than 2,100 hours spent manually answering questions. Assuming an hourly rate of 135 PLN (based on jooble.pl – Data Engineer), the total yearly cost comes close to 300,000 PLN.

Meanwhile, the cost of running an LLM for text processing shouldn’t exceed €50 per year – just over 200 PLN. Adding the one-time cost of developing the first version of the chat interface – assuming 2 engineers working for 2 weeks – gives us an initial implementation cost of around 20,000 PLN. The result? An ROI of up to 93% in the very first year.

(Note: this calculation doesn’t include the learning curve, ongoing platform maintenance, or the skills required to implement an LLM.)

Additional benefits of using LLMs:

• Answers in 2 minutes instead of 2 days – instant access to information can reduce data wait times by up to 99%.
• More independent users and stronger data literacy – employees better understand the data and can interpret it on their own.
• Less workload for the data platform team – fewer repetitive questions mean more time for strategic work and improving data architecture.

Strategic benefits – beyond cost savings

Implementing large language models (LLMs) is about more than reducing costs. Their impact goes deeper – transforming how teams work with data and helping to build a modern, data-driven organizational culture. What exactly can your organization gain?

• Less distraction, more focus:
  Data engineers and analysts no longer need to answer dozens of questions on Slack or email. LLMs take over these tasks, letting experts focus on creative, high-value work.
• Stronger alignment across teams:
  Automatically generated glossaries and data descriptions help business and technical teams speak the same language. Communication barriers disappear, and decisions are faster and more accurate.
• Greater trust in data:
  When users understand where data comes from and how it’s processed, they’re more likely to use it. Instead of guessing – they know. And that leads to better, more confident decision-making.
• Higher satisfaction in data teams:
  Less repetitive, tedious work and more meaningful tasks. This not only saves time but also boosts engagement and motivation.

Summary

Large language models (LLMs) are more than just a new technology – they’re a real driver of change in how organizations approach data. By enabling automation and natural language interaction, LLMs are transforming how teams describe, share, and use information. They make knowledge more accessible, lower the barrier to working with data, and support the implementation of key Data Governance components.

Key takeaways:

• Real savings of up to 90% in areas like conversational data access, automatic labeling, and up-to-date technical documentation.
• Strategic benefits that are harder to quantify, but essential for business: faster onboarding, stronger user engagement, and better decision-making through improved data understanding.
• Scalability – the bigger your data ecosystem, the higher the potential return on investment.

Let’s talk

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