--- title: "How to Run an AI Proof of Concept: 8 Steps" description: "Learn how to run an AI proof of concept in eight steps: one process, one question, a success bar, a time box and a clear go or no-go decision." url: https://nexumautomations.com/how-to/run-an-ai-proof-of-concept updated: 2026-10-31 publisher: Nexum Automations, an AI consulting firm --- > Nexum Automations is an AI consulting firm that builds what it recommends. It finds where AI pays off in a company, builds it on the tools the team already uses and stays until it is live and working. How-to # How to run an AI proof of concept A time-boxed test on real data that answers one question before you commit real money. Follow the steps in order, then decide. Last reviewed October 30, 2026 6 min read The short answer To run an AI proof of concept, pick one process and one question, set the success bar and the go or no-go rule first, check your data, fix a short time box, build the smallest working version, test it with the people who do the work, then decide. Key points * Test one process and one question * Write the success bar before building * Check data and access first * Fix the time box up front * Decide go or no-go, then name an owner Our data ## What buyers see in this search The first page of Google for "ai proof of concept", counted by type of result. | | Results | | ---------------------------------------------------- | ------------------------------- | | Articles by companies that sell a product or service | 5 of 9 | | Social and blog-platform posts | 2 of 9 | | Forums and Q&A threads | 1 of 9 | | Videos | 1 of 9 | | Titles that answer this exact search | 9 of 9 | | Google features on the page | An AI Overview, People also ask | The 9 organic results on the first page of Google, United States, desktop, measured with DataForSEO on October 29, 2026\. Types only: no company or product is named. Step by step ## Eight steps to a clear decision Each step feeds the next. Skip none, or the result will not settle the question. 1. ### Pick one process and one question Choose a single repetitive process and the one thing you need to learn, such as whether AI can handle it accurately enough. 2. ### Set the success bar and the rule Write the numbers that count as a pass and the go or no-go rule before anyone builds anything. 3. ### Check your data and access Confirm you have real examples, usable quality and permission to connect the systems involved. 4. ### Fix the time box Set a short, firm end date. A test with no deadline drifts into a project. 5. ### Build the smallest working version Use your current tools and real data. Skip polish, extra features and anything the question does not need. 6. ### Test it with the people who do the work Let the team run real cases through it and record where it helps, where it fails and what they trust. 7. ### Decide go or no-go Compare results with the bar you wrote in step two. Stop, adjust or move forward on that basis alone. 8. ### Plan the move to production Name an owner, a budget, the operating cost and the next milestone, or the work stalls after a good demo. Side by side ## Phases as the guides present them A summary of what published guides say. Durations vary by firm and by project. | | Typical duration | Go or no-go question | | ---------------------- | -------------------- | ------------------------------------ | | Define the problem | A few days to a week | Is the question worth answering? | | Assess data and access | About a week | Is the data usable and reachable? | | Build the prototype | Two to four weeks | Does it work on real cases? | | Test with users | One to two weeks | Does the team trust and use it? | | Review and decide | A few days | Does value hold after running costs? | ## Proof of concept versus pilot Proof of concept * Can it work at all? * Small, short test * Real data, few users Pilot * Does it hold up daily? * Larger, longer run * Real users, real conditions A proof of concept asks whether an idea can work at all. A pilot asks whether it works for real users in daily conditions. The first is smaller and quicker. Many teams use the two terms loosely, and an AI pilot project often starts as a proof of concept. The full comparison is in our [proof of concept vs pilot](https://nexumautomations.com/compare/poc-vs-pilot) page. ## What a good scope looks like * One process Narrow enough to finish in the time box. * One question Phrased so a number can answer it. * Existing inputs Data and files you already have. * A reviewer Someone who can judge each output. A good scope is narrow. One process, one team and one measurable question. Spreading effort over many workflows is a known way to produce disconnected experiments that return little, as Boston Consulting Group's 2025 study of more than 1,250 firms points out. Pick a process where the work is repetitive, the inputs are digital and a person can check the output quickly. ## Why most tests stall before production 1. Evaluated 60 percent of organizations looked at the tools. 2. Reached pilot 20 percent got as far as a pilot. 3. Reached production 5 percent reached production. Stalling is the norm. MIT Project NANDA's 2025 report on enterprise AI tools found that 60 percent of organizations evaluated them, 20 percent reached the pilot stage and 5 percent reached production. The report ties most failures to brittle workflows, tools that do not learn from feedback and a poor fit with day-to-day operations. That is a finding about enterprise tools in that study, not a prediction for your project. Thoughtworks cites a study claiming up to 90 percent of generative AI projects stay stuck at the proof stage. A test with no path to production is the common thread. ## What buyers see in the guides Guides from consultancies and development firms describe the phases in a similar way. The table below summarizes the pattern, as those guides state it. Read the durations as ranges from the firms that wrote them, not as a standard. ## The go or no-go questions Go * Met the written bar * Value holds after costs * Team wants to use it No-go * Only the demo impressed * Costs erase the gain * No owner named A good demo is not a reason to proceed. SmartDev's guide says a rollout should go ahead only with a credible path to sustainable value. It also notes that a positive result does not justify scaling unless the value holds after production and operating costs are counted. * Did it meet the bar you wrote first? * Does the value survive running costs? * Will the team actually use it? * Who owns it after the test? FAQ ## AI proof of concept questions * ### What is an AI proof of concept? A short, small test that checks whether an AI approach can solve one specific business problem using your real data. It answers a single question before any large investment. * ### What does a pilot project mean? A pilot runs a solution with real users in real conditions for a limited time. It sits after the proof of concept and tests day-to-day fit, not just feasibility. * ### How do I start an AI pilot? Pick one process, define one measurable question, write the success bar and the go or no-go rule, then check your data and access before building anything. * ### How do I scope an AI pilot? Keep it narrow: one process, one team, one question and a fixed end date. Use data you already have and name a person who can judge the output. * ### How long should an AI POC take? A guide from SmartDev, a development firm, says a well-scoped AI PoC commonly takes four to twelve weeks. Fix the end date at the start, because open-ended tests tend to drift. * ### Why do so many AI tests never reach production? MIT Project NANDA's 2025 report found only 5 percent of organizations reached production with the tools it studied. Brittle workflows and poor fit with daily operations were common reasons. * ### What should the success bar include? A number tied to the question, such as accuracy, time saved or error rate, plus a rule that says what result means go and what means stop. * ### Where does an AI POC fit in a wider plan? An AI POC tests one idea inside a larger roadmap. Our [AI strategy consulting](https://nexumautomations.com/services/ai-strategy-consulting) work and the guide to [creating an AI strategy](https://nexumautomations.com/how-to/create-an-ai-strategy) show how the pieces connect. --- Source: https://nexumautomations.com/how-to/run-an-ai-proof-of-concept Free AI diagnosis (30-minute call): https://nexumautomations.com/#book - Services: https://nexumautomations.com/services - Guides: https://nexumautomations.com/guides