An AI proof of concept (PoC) is a short, tightly scoped experiment that tests one question: can AI solve a specific business problem with your data, your people and your constraints? A good PoC ends with a written decision (go, pivot or stop) based on criteria you set before the first line of code. It does not end with a demo that everyone liked.
This guide is for CEOs, VPs of engineering and engineering directors who need to scope a PoC, protect their data under EU rules and decide what happens next. The examples reflect the Polish market, where adoption is growing fast and many teams are moving from experiments to production.
TL;DR
- A PoC validates feasibility and value on one use case. It is not a product, and it is not a design mockup.
- Define the riskiest assumption, the baseline, the success thresholds and a timebox of a few weeks before you start.
- Start with synthetic or anonymized data. Real personal data needs security and legal sign-off first.
- Calculate ROI on total cost, including integration, maintenance and supervision, not on model fees alone.
- Close the PoC with a written Go, Pivot or Stop decision tied to the original criteria.
What an AI proof of concept should establish
A PoC answers a feasibility question, and in a company it should also answer a value question. Technically: does the model reach acceptable quality on your real inputs? Commercially: is the gain large enough, compared with today’s process, to justify integration, operation and risk?
The context matters. According to GUS data reported by PAP, 8.7% of companies in Poland declared using AI technologies in 2025. Most of them bought a ready-made commercial solution. Once you move past off-the-shelf tools into custom workflows, internal knowledge search or integration with core systems, a PoC is how you reduce uncertainty before committing budget.
A PoC should establish four things:
- Feasibility. The approach works on representative data, not on a curated sample.
- Quality. Output meets a measurable bar that the business owner agreed to in advance.
- Fit. The solution can plug into existing systems, access rules and workflows.
- Economics. The expected benefit exceeds the total cost of running it.
If a PoC only proves the first point, you have a lab result. The remaining three are what decision-makers need.
PoC, prototype and MVP: what is the difference
The three terms are often mixed up, and mixing them leads to wrong budgets and wrong expectations.
| Proof of concept | Prototype | MVP | |
|---|---|---|---|
| Question answered | Is this worth building and technically possible? | How should it look and feel? | Will the market adopt it? |
| Typical audience | Sponsors, architects, risk owners | Designers, users, stakeholders | Early customers |
| Needs to be functional? | Not necessarily, only enough to test the assumption | Ranges from sketches to interactive simulations | Yes, with core features only |
| Production-ready? | No | No | Usable, intended to evolve |
This comparison follows the distinctions in UXPin’s overview of prototypes, MVPs and proofs of concept. In AI projects the sequence often runs PoC first (does the model work on our data?), then a prototype or pilot with real users, then an MVP or production release.
A useful rule: if the biggest unknown is technical or data-related, run a PoC. If the biggest unknown is usability, build a prototype. If it is demand, build an MVP.
How to choose a use case worth testing
The best candidates share a few traits. The process is frequent, so gains compound. Inputs are digital and reasonably consistent. Someone owns the outcome and can say what a good result looks like. A wrong answer is recoverable, because a human can review it.
Typical starting points for enterprise and SaaS teams include:
- Internal knowledge search and question answering over documents (RAG and enterprise search)
- Drafting and summarizing support responses, contracts or reports
- Classification and routing of tickets, invoices or requests
- Code assistance and test generation inside the engineering organization
- Forecasting and anomaly detection on operational data
Avoid use cases where the data does not exist yet, where nobody can define “correct,” or where the first deployment would need full autonomy over high-stakes decisions. Those need a data or discovery phase before any PoC.
Scope the PoC: the checklist
A scoping playbook from the FDE guide on turning AI pilots into paid contracts lists what a PoC scope should contain: in-scope and out-of-scope work, users and expected volume, the riskiest assumption, data ownership and privacy limits, a baseline, the technical approach, a timebox, checkpoints, and go/pivot/stop thresholds. Put each on one page and have the business owner sign it.
Name the riskiest assumption
Every AI idea rests on one or two assumptions that, if false, kill the project. Examples: “our support history is clean enough to ground answers,” or “the model can extract fields from scanned documents with 95% accuracy.” Design the PoC to attack that assumption first, not to build the easy parts.
Measure the baseline
Without a baseline there is no ROI. Record how long the process takes today, the error rate, the cost per unit and the volume. Measure it for a few weeks if you can, and write down the method so the comparison is fair later.
Set the timebox
Keep the PoC short enough that people stay focused and long enough to test real data. For most single-use-case PoCs, a few weeks is a sensible range. Add checkpoints, for example at the end of data preparation, after the first evaluation run and before the final review, so a failing PoC stops early and cheaply.
Prepare an evaluation set
AWS guidance for generative AI PoCs recommends a validated ground-truth evaluation set, data lineage, freshness checks, representative business scenarios and documented versioning. In practice: have domain experts build 100 to 300 realistic questions or cases with correct answers, include the hard and ambiguous ones, and freeze the set so results across iterations are comparable. Data quality work often decides the outcome, which is why we treat data quality engineering as part of any serious AI initiative.
Define success criteria before you build
Write thresholds in three groups, each with a Must and a Nice-to-have level.
- Technical: accuracy or task success rate on the evaluation set, latency, cost per request, failure behavior.
- Commercial: time saved per case, reduction in errors, or revenue effect, converted into złoty or dollars.
- Organizational: whether users actually adopt it, whether the process owner supports it, whether security and legal accept the architecture.
Example: “Answers on the evaluation set are correct and cite the right source in at least 90% of cases. Average handling time drops by 30% against baseline. Security approves the data flow.” Numbers like these are illustrative. Your thresholds should come from your baseline and your risk tolerance.
Data protection and security in a Polish AI PoC
A PoC is not exempt from the law. Companies in Poland operate under the GDPR, and the regulator, UODO, has been explicit about AI. Interviewees in the UODO bulletin on AI stress that the GDPR remains fully applicable, with emphasis on transparency, data minimization, accountability, risk-based analysis, data protection by design and a DPIA where warranted.
What to do before the first data upload
UODO’s 2026 checklist for AI projects is a practical starting point. It says to:
- identify the business owner, the compliance owner and the data protection officer early
- check whether data is transferred outside the EEA
- apply privacy by design and by default
- use access controls and encryption proportionate to risk
- cover AI-related events in your breach procedures
The same checklist names model-output disclosure, prompt injection and unauthorized access to query history as AI-specific security risks. It also reminds organizations that reportable personal data breaches must be notified without undue delay and no later than 72 hours where the GDPR conditions apply.
Use synthetic or anonymized data first
AWS advises that a generative AI PoC should normally start with synthetic or fully anonymized data, and that real sensitive or personally identifiable data needs explicit information-security and legal approval before ingestion. This keeps the PoC fast. You can begin in days instead of waiting for a full legal review, then widen data access once the approach proves itself.
If you work in a regulated domain, the requirements go further. For healthcare teams in the EU and Poland, we cover the policy side in our guide to policy-ready AI in healthcare. For regulated data more broadly, this explainer on HIPAA-compliant LLM deployment shows how legal and contractual considerations should be settled before an AI solution scales. For governance of models you intend to ship, see our AI governance framework.
Contract and ownership questions
The Polish law firm JDP recommends that AI implementation agreements address GDPR and AI Act requirements, training-data sources and their legal status, model use and rights, responsibility for quality and effects, and entrusted data processing. From the legal perspective in the same newsletter, AI output is probabilistic and evolving, so agile, iterative contracting with an explicit PoC phase is particularly useful before a full implementation commitment. Settle intellectual property early too: our white paper Who Owns the Code? walks through the questions to answer in a development contract.
Measure results and estimate ROI
Keep ROI arithmetic simple and honest. Annual ROI equals savings plus new revenue, minus total implementation cost, divided by total implementation cost, multiplied by 100%. The discipline is in the cost side. Include model inference, integration with existing systems, maintenance and human supervision, not only the PoC build.
A worked example with illustrative numbers: a support team handles 6,000 tickets a month at 12 minutes each. A PoC shows that a drafting assistant cuts handling time to 8 minutes on the evaluation set. That saves 4 minutes on 6,000 tickets, or 400 hours a month. Multiply by your loaded hourly cost, subtract the full run cost, and you have a monthly figure you can compare with the build and integration investment to find the break-even point.
To keep the measurement credible:
- track metrics per unit (per ticket, per document), not only in aggregate
- use a control group where possible, so seasonal changes do not inflate the result
- watch control indicators for unrelated changes, and keep a change log
- report before/after results, hours and currency delta, total costs and a planned break-even date
Be careful about extrapolating. Results from 200 test cases are a signal, not a forecast. State the confidence you have and what the pilot phase will confirm.
Decide: Go, Pivot or Stop
The PoC is worth its cost only if it forces a decision. Compare measured results against the criteria agreed up front and record one of three outcomes in a short written report.
| Decision | When | What happens next |
|---|---|---|
| Go | All Must thresholds are met and the ROI case holds | Plan pilot or production: architecture, security review, rollout, support |
| Pivot | Results are promising, but a specific fix or different approach is needed | Revise scope or method, define new criteria, revalidate in a short second round |
| Stop | The value is not validated or a blocking constraint has no credible resolution | Document learnings, release budget, revisit when conditions change |
The report should pair each criterion with its measured result and pass/fail status, add the rationale for the decision, and name owners and deadlines for next steps. Stopping is a legitimate and valuable outcome: you spent a few weeks of budget instead of a year.
From PoC to production
A Go decision starts a different kind of work. Production needs the things a PoC deliberately skipped: reliable data pipelines, monitoring and evaluation in production, access control, logging, fallback behavior, cost controls, DevOps and a support model. Teams that plan for this during the PoC avoid rewriting the solution afterward.
If your target is more autonomous, multi-step workflows, the path from pilot to enterprise-wide change is longer. Our article on agentic AI from pilot projects to enterprise transformation covers it. People matter as much as models, and adoption suffers without change management, as we describe in AI-powered change management.
Common mistakes in company AI PoCs
- No baseline. Without it, you cannot prove improvement.
- Success defined after the demo. Criteria written afterward drift toward whatever the demo showed.
- Clean demo data. Real inputs are messy. Test on representative cases.
- No business owner. Technical success without an owner rarely turns into production.
- Security and legal at the end. Involve the data protection officer and compliance owner from the first week.
- Counting only model costs. Integration, maintenance and supervision often decide the economics.
Perspective: why we scope PoCs around the decision
We see the same pattern in enterprise and SaaS teams: the technology question is rarely the hard part. The hard part is agreeing what “good enough” means, who owns the result and what happens when the numbers come in.
That is why we approach an AI PoC as an engineering and business exercise together. We start from the use case and the baseline, define thresholds with the process owner, and design the experiment to test the riskiest assumption first. Our approach is built around integrating with your internal team, so the knowledge stays with you.
We also think about production from day one. Secure LLM integration, data quality, DevOps and quality engineering are part of how we work, so a successful PoC has a realistic route to a system that is reliable, secure and scalable.
A short, well-framed PoC is the most efficient way to commit to AI with confidence, or to redirect the budget early.
Vlad
How DevPulse can help
DevPulse helps businesses build, modernize and scale digital products, with a dedicated focus on AI-powered solutions. For PoCs, our teams cover generative AI development, RAG and enterprise search, secure LLM integration, enterprise LLM fine-tuning, agentic AI and predictive modeling.
If you are still shaping the idea, our product discovery and technical audit services help you define the use case and the riskiest assumption before you commit. You can find them under technology consulting. When you are ready to scope a PoC for your own use case, request a consultation with our team.
FAQ
How long should an AI PoC take?
There is no fixed rule, but a single-use-case PoC typically runs a few weeks, with checkpoints along the way. If scope keeps growing, the PoC is turning into a project and you should reset the criteria. Set the timebox before you start and hold to it.
Who should be involved on the company side?
At minimum: a business owner who defines value, a domain expert who builds the evaluation set, an engineering lead for integration, and the compliance owner and data protection officer. UODO’s checklist recommends naming these roles early in any AI project.
Can we use real customer data in a PoC?
Only after information-security and legal approval. A safer path is to start with synthetic or fully anonymized data, then extend access with approval once the approach works. Check whether any data leaves the EEA.
What is the difference between a PoC and a pilot?
A PoC tests whether the idea works and is worth building, often with limited data and a small group. A pilot puts a more complete solution in front of real users in real conditions to confirm results before a full rollout.
How do we estimate ROI from a PoC?
Measure results against a baseline, convert time and error reduction into money, then subtract total costs: inference, integration, maintenance and supervision. Present the result with a planned break-even date and state how confident the numbers are.
Sources
- GUS data on AI use in Polish companies, reported by PAP
- Prototype vs MVP vs Proof of Concept (UXPin)
- How to Scope an AI Proof of Concept: the FDE Playbook
- Architecting a successful generative AI proof of concept (AWS Prescriptive Guidance)
- UODO bulletin 03/01/24
- UODO checklist for AI projects, 2026
- Best contract for AI implementation? (JDP newsletter)
- HIPAA-Compliant LLM: What It Means and How to Deploy One
