Building an AI Proof of Concept isn’t just about coding fast - it’s about testing if your idea works in the real world.

Creating a good AI Proof of Concept (PoC) is more than just quickly building a working demo. It’s a process where technology meets business reality – full of iterations, hypothesis testing, and checking if the client’s data, processes, and users are ready to collaborate with an intelligent solution.

Over the past months, we’ve had the chance to work on several diverse PoCs: from automating document handling, through image analysis, to predicting customer churn. Each of them taught us something new – not only about AI but, above all, about how to create value with this technology. Here are the three most important lessons we’ve learned from these experiences.

Client data can be powerful – once you understand and shape it

In one of our projects for a platform supporting financial workflows, the goal was to explore how AI could help automate financial management tasks for small business owners. The team had access to various data sources, including invoices, bank transactions, and payment histories.

Challenge

At first glance, the data seemed promising – rich in content and relevant to the business case. But as we dug deeper, we encountered the usual suspects: inconsistent formats, OCR results that varied between document types, and banking data that lacked standardization or had critical gaps.

Lesson learned

Raw data rarely flows smoothly into an AI pipeline. Before building any models, it’s essential to map out data sources, evaluate completeness and quality, and confirm whether the available inputs are enough to produce meaningful output. Fast, lightweight data exploration and clear input checklists go a long way in preventing surprises down the line.

A good PoC tests not only technology but also the process

In a churn prediction project for a B2B SaaS client, our goal was to build a model predicting customer churn based on user activity.

Challenge

We had access to historical data, but the client had never “closed the loop” – no actions were taken based on signals of declining engagement. There were no processes in place to use the prediction.

Lesson learned

The model alone isn’t enough. A PoC needs to simulate or test the real business process where AI is supposed to add value – e.g., who and when reacts to the churn prediction? How fast? How do we measure effects? A PoC without embedding it in an operational context risks becoming a “model on the shelf.”

Don’t measure everything – but measure what matters

In the previously mentioned project, the goal was to check whether users need a financial management tool and if AI can actually simplify their work. We built a prototype with automatic invoice categorization, bank integration, and a financial dashboard.

Challenge

The client had no predefined KPIs but wanted to verify the monetization potential and usability of the tool.

Lesson learned

Even if a PoC doesn’t have to deliver hard financial results, it’s worth setting simple success metrics such as:

• Time saved by users
• Accuracy of automatic predictions/categorizations
• User activity in tested features
• NPS or qualitative feedback from tests

Well-chosen metrics helps to avoid situations where “everyone is happy, but no one knows why”.

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Why You Should Start AI Projects with a Proof of Concept?

What's a difference between a well-executed PoC from a failed one?

Real data, even if imperfect

You don’t need perfectly cleaned data to start, but you do need real data - from the company’s actual operations, not from test data generators. This data shows what AI will really have to handle. Sometimes it means gaps, errors, and hellish formats - and that’s good. It’s better to see it upfront than to build a model on a shiny but unrealistic dataset.

Fast feedback from users

If you don’t show the prototype to users, you can be sure some assumptions will be wrong. That’s why it’s worth running usability tests early on - even with just a few people. Sometimes a “Think Aloud” session reveals that what seemed obvious isn’t. The faster you get feedback, the cheaper and easier it is to make changes.

Test the process, not just the technology

A PoC shouldn’t just prove that something can be done technically. It should answer: what exactly will the company do with this solution? Who will use it? How often? For what purpose? Without a clear process to simulate or plan, even the best technology won’t deliver value.

Hypotheses to disprove, not just confirm

The goal of a PoC is not to prove the idea is brilliant. It’s to check if it works better than the current solution and what might fail. A well-designed PoC expects failure and provides room to learn from it. This healthy mindset allows making decisions based on facts, not enthusiasm.

Shared understanding that it’s an experiment

A PoC is not an MVP, let alone a finished product. It requires openness and flexibility - on both sides. When the client and delivery team share the understanding that the PoC is an experiment meant to test something, cooperation flows better and results are more reliable. Without this shared awareness, frustration and unmet expectations are easy to come by.

Summary

A good PoC is not a promise of AI but a value verification. Our experience shows that even a small prototype can quickly answer questions like: “Is it worth investing further?”, “Does the data make sense?”, “Do users actually need this?”

If you’re planning to implement AI in your company – start with a well-defined PoC that tests not only technology but also the real business context.

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