A proof of concept is meant to answer a question, yet many end with a demo that impressed a meeting and then stalled. The usual reasons are predictable: the test used clean sample data instead of real inputs, nobody agreed in advance what result would count as success, and nobody planned how the system would connect to the tools people already use.
An AI proof of concept succeeds when it is designed backwards from production: it uses real data, measures results against success criteria agreed before work starts and tests the hardest part of the problem first.
This article explains how an AI proof of concept differs from a prototype, a pilot and an MVP, sets out a six-week plan, lists success criteria worth agreeing on and shows how .wrk projects moved from proof to production. It does not cover academic research projects, and it does not compare AI platforms.
Key takeaways
- A proof of concept answers one question: can this approach solve this problem, on this data, well enough to be worth building?
- Agree on measurable success criteria and a go or no-go threshold before the first line of code.
- Use real, messy production data, since clean samples hide the problems that stop projects later.
- Test the hardest part of the problem first, not the easiest part to demonstrate.
- Most proofs of concept never reach production: IDC found that only 4 of every 33 AI proofs of concept made it.
- A focused AI proof of concept typically takes four to six weeks and costs $20,000–$60,000.
Proof of concept, prototype, pilot or MVP?
These terms are often used interchangeably, but each answers a different question and needs a different budget.
| Stage | The question it answers | Who uses it | Typical length |
|---|---|---|---|
| Proof of concept | Can this approach work on our data, well enough? | The project team and decision makers | 4–6 weeks |
| Prototype | What will it look like and how will people use it? | Selected users, for feedback | 2–6 weeks, often alongside the proof of concept |
| Pilot | Does it work in real operations with real users? | A limited group of users or sites | 1–3 months |
| MVP | Will people use and pay for a first production version? | Real customers or employees | 2–4 months |
Skipping the proof of concept is reasonable when the approach is already proven for your type of data, such as standard invoice extraction. It is essential when the data is unusual, the accuracy bar is high or nobody on the team has built something similar before.
Decide what you are trying to prove
Write the question as one sentence before anything else. Good examples are specific:
- "Can a language model extract the 12 fields our claims team needs from scanned medical records with at least 95% field accuracy?"
- "Can an assistant answer our top 50 support questions from our documentation, with sources, well enough that agents accept its draft in most cases?"
- "Can we transcribe and score every recorded call on premises, within our hardware budget?"
Weak examples ask whether "AI can help with support" or "improve efficiency". They cannot be proven or disproven, so the project cannot end with a clear decision.
Success criteria to agree on before day one
A proof of concept needs a scorecard that the business owner signs off before work begins. Typical criteria include:
- Quality: field accuracy, answer acceptance rate or error rate on a defined test set, compared with the current process.
- Coverage: the share of real cases the system handles without falling back to a person.
- Speed: time per case or response time, if users will wait for the answer.
- Cost per case: model usage, infrastructure and review time, projected to production volume.
- Risk: what happens on failure, and whether low-confidence cases are caught reliably.
- Fit: whether the result can connect to the systems and workflow people already use.
Set a threshold for each one and agree in advance what happens if results fall in between. A clear "no" after six weeks is a useful result, and far cheaper than a failed production project.
Want to test an AI idea before committing a full budget? We run proofs of concept on your real data with success criteria agreed upfront.
Explore AI servicesA six-week AI proof of concept plan
Week 1: frame and collect
Confirm the question, the success criteria and the go or no-go thresholds. Collect real data from production, including messy, incomplete and unusual cases, and agree how it may be used and stored.
Week 2: build the test set and baseline
Prepare 50–200 cases with verified answers. Measure how the current process performs on them, whether manual or automated, so there is something to compare against.
Weeks 3–4: build and iterate on the hardest part
Build the smallest version that tests the core question, starting with the part most likely to fail. Run it against the test set after every significant change and record the results.
Week 5: test the edges
Run adversarial and unusual cases, measure cost and speed at expected volume and check how the system behaves when it is unsure. Sketch the integration path into existing systems.
Week 6: decide
Present results against each criterion, the projected cost at production volume, the main risks and an estimate for the production build. Make a go, adjust or stop decision.
Why most AI proofs of concept stall
The pattern shows up across independent research.
- 4 of 33. IDC research for Lenovo found that for every 33 AI proofs of concept launched, only four reached production, with 88% failing to advance. IDC pointed to gaps in data, processes and infrastructure, unclear return on investment and a lack of in-house expertise.
- 46% scrapped. S&P Global Market Intelligence reported that the average organization scrapped 46% of its AI proofs of concept before production, and that 42% of companies abandoned most of their AI initiatives in 2025, up from 17% a year earlier. Cost, data privacy and security risks were the top obstacles.
- 30% abandoned. Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs and unclear business value.
The causes repeat: data that does not reflect reality, value that was never defined and integration that was never planned. Each one can be addressed inside the six weeks.
How .wrk proofs of concept moved to production
Proving that the simple approach fails
A US personal injury law firm wanted AI to prepare insurance opinions from case bundles of about 250 PDF pages of medical records, police reports and handwritten notes. The first step was to test the obvious approach, a single prompt to a general chatbot, on real bundles. It could not handle the volume or keep topics separate. That result shaped the production design: five specialized assistants, each tested against attorney-approved sample opinions. In production, model usage costs about $3–4 per case bundle, and attorney throughput improved about 1.5 times. Read the legal document processing case study.
Building the test set from production failures
An SEO software vendor's AI assistant suggested links to pages that did not exist and titles that were too long. .wrk started by building a taxonomy of failures from real production samples, then tested new prompt versions against those edge cases before release. Restricting suggestions to an approved URL list and adding hard title limits fixed both problems without changing the product's architecture. Read the AI SEO tool case study.
Going straight to a working first version
When a US cybersecurity publisher's newsletter vendor subscription was about to expire, there was no time for a separate proof of concept. .wrk scoped an emergency MVP with the editorial team and delivered a working AI newsletter system in 10 working days, with an editorial review step kept in place before every send. Read the Aesop case study.
Moving from proof to pilot in shadow mode
For a regional telecom operator, the voice agent was tuned on the operator's own call recordings and then run in shadow mode on real traffic before handling limited live calls, and only then went into full production on two pilot sites. Read the voice AI case study.
What an AI proof of concept costs
A focused AI proof of concept typically costs $20,000–$60,000 and takes four to six weeks, depending on data preparation, the number of systems involved and how strict the accuracy requirements are. A proof of concept for a single document type usually sits at the lower end. Our guide to AI development cost explains how these figures relate to full production budgets.
To keep the budget useful, require three outputs from any proof of concept, whoever builds it: the test set with results, a projected running cost at production volume and an estimate for the production build. Without them, the next decision is a guess.
Checklist: from proof of concept to production
- Results meet the agreed thresholds on real data, not samples.
- The test set is versioned and will be re-run on every future change.
- Running cost has been projected at production volume.
- Low-confidence cases are routed to a person reliably.
- The integration path into existing systems is designed and estimated.
- Data handling, access rights and security have been reviewed.
- A business owner has committed to the production budget and the rollout plan.
For the steps after the proof of concept, our guide on how to implement AI in your business covers rollout, governance and scaling.
Frequently asked questions
What is an AI proof of concept?
An AI proof of concept is a short, time-boxed project that tests whether an AI approach can solve a specific business problem on real data, against success criteria agreed in advance. It ends with a go, adjust or stop decision.
How long does an AI proof of concept take?
A focused AI proof of concept usually takes four to six weeks, including data collection, building a test set, iterating on the approach and preparing the decision.
How much does an AI proof of concept cost?
Most focused AI proofs of concept cost $20,000–$60,000. The main cost drivers are data preparation, integration with existing systems and the accuracy level required.
What is the difference between an AI proof of concept and a pilot?
A proof of concept tests whether the approach works on real data. A pilot tests whether it works in real operations, with real users, over weeks or months.
Why do AI proofs of concept fail to reach production?
The most common reasons are unrepresentative test data, success criteria that were never defined, unclear business value, unplanned integration and rising cost or risk concerns. Research from IDC found that only 4 of every 33 AI proofs of concept reached production.
To sum up
An AI proof of concept is worth running when it answers a specific question on real data, against success criteria agreed before work starts. Test the hardest part first, project cost at production volume and plan the integration path, so a positive result leads straight to a production decision.
.wrk runs AI proofs of concept and takes them into production for US and European companies, from document processing and knowledge assistants to voice agents and editorial automation.
Have an AI idea you want to test properly? Share the problem and a sample of your data, and we will propose a proof of concept with clear success criteria and a budget.
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