Templates

AI proof of concept (POC) success criteria template

A one-page POC charter that fixes the question, the thresholds and the decision rule before anyone writes code, so the result is a decision, not a debate.

By the Ryz Labs team · Updated October 2026

AI proof of concept success criteria are pass/fail thresholds you agree before the build starts: the quality the model must reach on a defined test set, the cost and latency limits, the data and security conditions, and the business result that would justify production. Pair them with a one-sentence hypothesis, a fixed scope, a time box and a decision rule that says what happens at each outcome. The template below is a one-page POC charter you can fill in and get signed off in a single meeting.

AI POC success criteria template

1. POC charter

Field	Fill in
Question the POC answers	[Can a model draft first-pass answers to vendor security questionnaires from our policy library?]
Hypothesis	[Drafts for at least X% of questions need only light edits, cutting time per questionnaire by Y%]
Business owner	[Name, title]
Technical owner	[Name, title]
In scope	[Questionnaire formats A and B; policy library v3; English only]
Out of scope	[Submission to customers; other languages; integration with the sales CRM]
Data used	[Sources, owners, approval status]
Time box	[Weeks, with a fixed end date]
Budget cap	[Engineering time plus model and infrastructure cost]
Decision date and decision maker	[Date, name]

2. Success criteria (set thresholds before building)

Category	Metric	How measured	Threshold to pass	Result
Quality	[Share of answers accepted with light or no edits]	[Expert review of a held-out set of real questions]	[ ]%	[ ]
Quality	[Rate of unsupported or wrong statements]	[Reviewer flags against source policies]	[Below ]%	[ ]
Business	[Time per questionnaire]	[Timed comparison with today's process]	[Reduction of ]%	[ ]
Cost	[Cost per questionnaire]	[Token and infrastructure cost on the test set]	[Below $ ]	[ ]
Latency	[Time to first draft]	[p95 on the test set]	[Below ] seconds	[ ]
Data and security	[Data access approved; no restricted data in prompts]	[Data owner and security sign-off]	[Pass / fail]	[ ]
Adoption	[Reviewers would use it]	[Short survey of pilot users]	[Majority yes]	[ ]

3. Decision rule

Outcome	Condition	Decision
Go to production planning	All quality and security criteria pass; business and cost within 80% of threshold	Plan production with the gates in the pilot-to-production checklist
Iterate once	Quality within reach with a clear fix (data, retrieval, prompt)	One more time-boxed round with the same criteria
Stop	Quality far from threshold or a security criterion fails without a fix	Stop and record what was learned

Use this template

How to use this POC template

Write the question and hypothesis in one sentence each. If you cannot, the scope is too broad.
Build the test set first: real inputs from the target workflow, with expected outputs or grading notes from a domain expert. Hold some back so you do not tune on the test set.
Fill the thresholds with the business owner before the build starts. Use today's process as the baseline.
Set the time box and budget cap. A POC that keeps extending is a project without a plan.
Build the smallest system that can answer the question, on real data with real access rules.
Run the evaluation, fill in the results column and apply the decision rule in the meeting with the decision maker.

Example: filled-in quality criterion

Metric	Threshold	Result	Note
Answers accepted with light or no edits	70%	64%	Most rejections were questions about controls missing from the policy library. Decision: iterate once after adding the missing policies, same test set.

The threshold and result are illustrative. The pattern to copy is the note: a measured gap, a cause and a decision.

Building the test set

The test set decides whether your POC result means anything. Pull real inputs from the last few months of the target workflow, including the awkward ones: long documents, ambiguous questions, inputs in unexpected formats. Have a domain expert write the expected answer or the grading notes for each, and record why. Keep a held-out portion that nobody looks at while tuning prompts or retrieval, and run it only for the final result. If the business owner would not accept the test set as representative, fix it before building anything.

Common POC mistakes

Criteria written after the demo. Thresholds set after seeing results always pass.
Testing on hand-picked examples. Use a held-out set of real inputs, including the hard ones.
Proving the model instead of the workflow. A model that answers well in a notebook may not fit where users work. Include an adoption check.
No cost measurement. Measure cost per task during the POC; it is much harder to fix later.
Synthetic or sample data. A POC on clean sample data proves little about production. Get access approvals early.
No stop condition. Without one, weak POCs drift into pilots that never end.

Need engineers who build POCs that can become production systems?

Ryz AI pod teams build proofs of concept in your cloud and repos, on your real data, with the eval set and tracing in place from the first week, so a passing POC becomes the base for production rather than a throwaway demo. Pods are senior engineers who work on your team, reporting to your leads, on US business hours. Only the top 1% of the tens of thousands of engineers we interview make it. Talk to us to scope your POC team, or see generative AI development and forward-deployed engineers.

FAQ

What are success criteria for an AI proof of concept?

Pass/fail thresholds agreed before the build for quality on a real test set, error rates, business impact against today's process, cost per task, latency, data and security conditions, and user willingness to adopt. Each criterion has a metric, a measurement method and a threshold.

How long should an AI POC take?

Long enough to answer one question on real data, and no longer. Set a fixed time box at the start and a budget cap. If the question cannot be answered within the box, narrow the scope rather than extending the POC.

What is the difference between an AI POC and a pilot?

A POC tests whether something can work on real data in a controlled setting. A pilot puts a working system in front of real users in their actual workflow to test value and adoption. Pass the POC criteria before starting a pilot.

How do you measure AI POC quality?

With a held-out test set of real inputs graded by domain experts or by calibrated automated graders, compared against today's process. Track both acceptance rate and the rate of wrong or unsupported outputs.

What should happen when an AI POC fails?

Apply the decision rule. If the gap has a clear cause and fix, run one more time-boxed round with the same criteria. Otherwise stop and record what you learned about the data, the task and the users. A clean stop is a useful result.

Questions we didn't answer? Email info@ryzlabs.com.