What is the difference between proof of concept and proof of value?
Workflow, adoption and value
The difference is simple. A proof of concept asks whether an AI approach can work technically or functionally. A proof of value asks whether using it in a real workflow is worth the money, effort, risk, and change required. The first is about feasibility. The second is about business merit. Many organisations need both, but they should not confuse them or use one as a substitute for the other.
Reviewed by Jackie, Head of Learning & Development, Levellers - Last reviewed 8 June 2026
What this means
These terms sound similar because both are forms of early testing. That is exactly why they are so often muddled. In practice they answer two different leadership questions.
A proof of concept is usually narrow. It tests whether the method can do the job at all. Can the model classify documents? Can the assistant draft usable text? Can the system ground answers in an approved knowledge base? The emphasis is technical and functional.
A proof of value comes later, or in some cases instead. It asks whether the use case creates enough practical benefit in live work to justify implementation. That means looking beyond capability to workflow fit, user behaviour, controls, cost, and measurable impact. Levellers.ai covers proof of value and AI pilots in separate depth because they should not be collapsed into one idea.
Why it matters
Getting this distinction wrong creates expensive confusion. If leaders treat a proof of concept as if it has already proven business merit, they can approve projects that look impressive in a lab but disappoint in operations. If they insist on a full proof of value before feasibility is understood, they can overdesign analysis around an approach that may not even work.
The difference also matters for governance. A proof of concept may use synthetic data, controlled prompts, or a contained technical environment. A proof of value usually needs realistic operating conditions and therefore stronger thinking on privacy, security, review, and accountability.
Most importantly, the distinction keeps the decision honest. Leaders should know which question is being answered, what evidence is still missing, and what decision the current stage is actually capable of supporting.
How it works
Begin with the decision, not the label
Before naming an exercise, decide what leadership needs to know.
If the organisation needs to know whether the core AI approach can do the task at all, that is proof of concept territory. If the organisation already believes the technology can function and now needs to know whether the workflow is worth implementing, that is proof of value territory.
This sounds obvious, but it is where many teams slip. They call something a proof of value when it is still only showing technical plausibility. Or they keep running proof of concept work long after the real missing question is commercial.
What proof of concept is for
A proof of concept tests feasibility. It is useful when there is genuine uncertainty about whether the AI can perform the required function with acceptable technical quality.
This may include basic capability questions such as extraction accuracy, retrieval quality, draft usefulness, classification performance, summarisation reliability, or integration with an existing system. The scope is usually limited, the environment is often controlled, and the emphasis is on "can it work?" rather than "does it justify wider change?"
A proof of concept can be brief and highly focused. It can also be the right way to reject a weak idea early. If the core mechanism is not viable, there is no point pretending a commercial case exists.
What proof of value is for
A proof of value tests business merit in context. It asks whether the use case creates enough practical benefit in a real workflow to justify further investment and change.
That means it expands the lens. The question is no longer limited to model quality. It now includes baseline performance, user adoption, review effort, process redesign, governance friction, data readiness, operating cost, and the organisation's ability to capture the claimed benefit.
A proof of value therefore tends to need stronger measurement, more realistic conditions, and more cross functional involvement than a proof of concept. It is closer to a business test than a technical trial.
Where the AI pilot fits
An AI pilot is not another version of these terms. It is often the live test vehicle used to run a proof of value.
A pilot gives the organisation a contained way to put the AI into real work with defined users, tight scope, and clear guardrails. That makes it ideal for answering the value question. A proof of concept can happen without a pilot. A proof of value often needs one.
This is why the terms should not be merged. Proof of concept and proof of value are questions. The pilot is frequently the method used to answer the second question.
When to run both in sequence
Many organisations should run both stages, in order.
First, use a proof of concept when the technical path is meaningfully uncertain. Confirm that the AI can perform the job in principle. Then, if that hurdle is cleared, move to proof of value and test whether the use case is worth implementing in live work.
This sequence is common in workflows with moderate complexity, sensitive data, or high review burden. It prevents leaders from spending heavily on operational testing before the core approach is even viable.
When it can be reasonable to skip the proof of concept
Sometimes a separate proof of concept is unnecessary. If the AI approach is already well established, the task is relatively standard, and the technical uncertainty is low, the organisation may go straight to a tightly scoped proof of value.
That is often true when using mature tools for routine drafting, knowledge retrieval, summarisation, or low risk classification. In these cases, the unanswered question is not whether the technology can function. It is whether it can function well enough in the specific workflow, with this team's data, this level of review, and this expected value threshold.
Skipping a stand alone proof of concept can be sensible when done deliberately. It is only careless when leaders skip it without understanding what uncertainty remains.
When it can be reasonable to stop after proof of concept
In some cases, the organisation may stop after a proof of concept because feasibility did not hold up. That is a healthy result. It saves time and money.
There are also cases where a proof of concept confirms functionality but exposes constraints that make the value stage unattractive. Perhaps the quality is acceptable only with high manual correction. Perhaps the data is too fragmented. Perhaps the integration effort is excessive relative to the likely gain. Perhaps the use case creates governance friction that outweighs the practical benefit. In that case, the organisation may decide not to proceed further.
The evidence standards are different
A proof of concept usually asks for evidence of technical or functional feasibility. That evidence might include benchmark performance, task completion quality, retrieval accuracy, latency, usability in a test environment, or successful connection to a required system.
A proof of value asks for broader evidence. It needs before and after comparison, workflow level measurement, cost visibility, quality tracking under realistic conditions, adoption data, review burden, and explicit decision thresholds.
Put simply, proof of concept evidence says, "it can." Proof of value evidence says, "it is worth doing here."
The common transition mistake
The most common mistake is this. A team runs a proof of concept on neat examples, gets positive reactions, then presents the work as if implementation merit is already proven.
That leap is dangerous because real organisations do not capture value from technical capability alone. They capture value when the workflow changes in a beneficial way and the organisation can repeat and govern that change. That is a different bar.
A good rule is to ask, after every early test, "What question have we answered, and what question remains open?" If feasibility is answered, the remaining issue is value. If value is answered, the next issue is scaling and operating model. The discipline lies in moving one question at a time.
Examples
A procurement team wants AI to extract key clauses from supplier contracts. Its proof of concept checks whether the system can reliably identify termination dates, liability caps, and renewal terms from a varied document set. Its proof of value then asks whether using that capability actually reduces review effort, shortens turnaround, and avoids missed obligations in a way that justifies implementation.
A sales team considers AI support for proposal drafting. A proof of concept checks whether the tool can produce a coherent first draft from approved materials. A proof of value checks whether the wider bid process improves once review time, accuracy checks, rework, and win room pressure are included.
A support desk wants AI to classify incoming tickets. A proof of concept tests whether the model can place messages into the right queue. A proof of value tests whether the live queue moves faster, whether misroutes stay acceptable, whether staff trust the classifications, and whether service quality holds.
A finance team explores AI coding assistance. The proof of concept checks whether the tool can generate acceptable script fragments. The proof of value checks whether the team's release flow improves once code review, testing, rework, security controls, and production support are counted.
Common misunderstandings
Misunderstanding: Proof of concept and proof of value are just two names for the same thing. Reality: one answers feasibility, the other answers worth.
Misunderstanding: A strong proof of concept means we can approve rollout. Reality: it only means the basic approach may be viable.
Misunderstanding: Proof of value is always a later, bigger proof of concept. Reality: it is a different kind of question with different evidence needs.
Misunderstanding: Every AI idea needs both stages. Reality: some mature and low uncertainty use cases can move straight to proof of value, while weak ideas may stop after proof of concept.
Misunderstanding: If the model quality is high enough, value will follow. Reality: value still depends on workflow fit, user behaviour, controls, and cost.
Misunderstanding: A pilot is a third competing term. Reality: the pilot is often the operating vehicle used to answer the proof of value question.
Risks and boundaries
The main risk in this area is false progression. Leaders can believe a project has moved further than it really has. A strong proof of concept may create confidence that is out of proportion to the evidence actually gathered. Equally, a poorly designed proof of value may ask too much of an immature use case and reject it before feasibility has been properly tested.
There is also a proportionality issue. Small, low risk, well understood workflows may not need a formally separated proof of concept and proof of value. Larger or more uncertain workflows often do.
Finally, these stages do not settle everything. Even after proof of value, major questions may remain about enterprise architecture, wider governance, supplier dependence, and long term operating model. The purpose of the distinction is not to simplify AI into neat boxes. It is to stop organisations asking the wrong question at the wrong time.
What to do next
1. Ask what decision you are trying to support right now.
2. If the open question is technical feasibility, run a narrow proof of concept.
3. If the open question is business merit in live work, design a proof of value, often through a tightly scoped pilot.
4. Write down the evidence that would count as sufficient at the current stage and the evidence that would still be missing afterwards.
5. Avoid presenting proof of concept findings as if value is already established.
6. Move to the next stage only when the previous question has been answered clearly enough to justify it.
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FAQs
Which usually comes first, proof of concept or proof of value?
Proof of concept usually comes first if technical feasibility is uncertain. Proof of value usually follows when the organisation needs evidence of practical merit in real work.
Can we skip proof of concept?
Yes, sometimes. If the technology is already proven for the task type and the real unknown is workflow value, a tightly scoped proof of value may be enough.
Can we skip proof of value?
Only if the decision does not depend on proving practical merit, which is rare in business use. Most leaders still need evidence on workflow impact, cost, control, and adoption before wider rollout.
Is proof of concept only for technical teams?
No. Business leaders should still understand what feasibility question is being tested and what limits remain, even if the work is led by technical specialists.
What makes proof of value harder than proof of concept?
It has to deal with live users, real process conditions, baseline comparison, governance, cost, and value capture, not just whether the tool can function.
Does a pilot replace proof of value?
No. A pilot is often the live test method used to generate proof of value evidence.
How do we know a proof of concept is not enough?
If you still cannot say what changes in the workflow, who benefits, what the benefit is worth, or what controls are needed, you have not yet proven value.
What is the biggest mistake leaders make here?
Treating a technically successful test as if it has already established a sound business case.
Sources
The Green Book UK government guidance on appraisal (HM Treasury). Distinguishing appraisal from evaluation, proportionality, and clarifying that different decisions require different evidence.
Beyond pilots: sustainable implementation of AI in public services (European Commission Joint Research Centre). Adoption versus implementation, the gap between testing and durable transformation, and the importance of going beyond isolated pilots.
Artificial Intelligence Risk Management Framework AI RMF 1.0 (NIST). Context specific risk, real world deployment differences, and why laboratory measurement alone is insufficient for deployment judgement.
AI RMF Playbook Measure (NIST). Pre versus post deployment comparison, fit for purpose metrics, and staged decision making as use cases mature.
Extracting value from AI in banking: Rewiring the enterprise (McKinsey). Explicit reference to moving from proof of concept to proof of value, and the argument that narrow isolated use cases rarely unlock material value.
AI ROI: The paradox of rising investment and elusive returns (Deloitte). Evidence that dummy data and isolated proof work can mislead, and that live implementation introduces data, workflow, and attribution challenges.
The Widening AI Value Gap (BCG). Evidence that isolated pilots and narrow use cases rarely create substantial value, and that end to end workflow redesign matters more.
ISO IEC 42005:2025 AI system impact assessment (ISO). Lifecycle impact assessment thinking, including the need to identify evaluate and document impacts beyond early technical function.