AI discovery sprint: how to accept the handover

An AI discovery sprint should hand over enough evidence to decide whether to build, integrate, change direction or stop. If you own the product budget, accept the package when your receiving team can trace the recommendation to repeatable tests, data access, cost assumptions and named ownership—not when the demo merely looks convincing.

Use the worksheet below at the final review. It separates acceptance of the discovery work from permission to put an AI feature in front of customers.

Decide what accepting the sprint authorises

Accepting discovery means the agreed uncertainty has been investigated well enough to make the next investment decision. It need not mean the idea passed. A well-supported recommendation to stop can be a useful result.

Write the decision before reviewing the slides: “Can we fund an assistant that drafts support replies from approved product documentation, with a human support agent checking every draft?” That is a narrower question than “Can we automate support?”

Then distinguish three approvals:

• Discovery accepted: the evidence answers the agreed question, including an unwelcome answer.
• Next phase funded: a bounded build or integration has an owner, scope and conditions.
• Release authorised: the implemented system meets the checks for its actual deployment environment.

Keep these decisions separate in the meeting notes. A prototype can help answer the first question without satisfying the last one.

A handover acceptance worksheet to copy

For each row, record an evidence link, the receiving owner and one outcome: accepted, return for clarification, or outside the agreed scope. Agree the evidence needed when commissioning the sprint, rather than introducing new obligations at the final review.

1. User outcome

• Evidence to request: One workflow, its current baseline and the change worth funding.
• Return for clarification when: “Use AI” is the objective, with no observable user or operational outcome.

2. Data access

• Evidence to request: Source inventory, representative examples, access boundaries and unresolved permissions.
• Return for clarification when: the experiment used data the intended users cannot access.

3. Evaluation

• Evidence to request: Test inputs, expected behaviour, scoring rules, recorded runs and failed cases.
• Return for clarification when: a score is shown without its test set or definition.

4. Technical route

• Evidence to request: Proposed data flow, integration points, alternatives and assumptions.
• Return for clarification when: the receiving engineers cannot identify where existing systems must change.

5. Operating cost

• Evidence to request: Usage assumptions, model/tool costs, human review and support effort.
• Return for clarification when: the estimate counts model calls but omits the work needed to check outputs.

6. Risk and fallback

• Evidence to request: Consequences of failure, escalation, manual path and stop conditions.
• Return for clarification when: nobody can explain what happens when a source is missing or a dependency fails.

7. Ownership and next step

• Evidence to request: Decision owner, delivery owner, unresolved work and conditions for funding.
• Return for clarification when: “Proceed to MVP” has no named owner or bounded scope.

The purpose is not to collect seven documents. A short decision memo can link to the evidence. What matters is whether someone outside the sprint team can use it.

Ask for a repeatable test, not a universal accuracy score

For an LLM-based feature, require a way to rerun the assessment. OpenAI’s evaluation guidance recommends defining an objective, assembling a dataset, choosing metrics, running comparisons and continuing evaluation as the system changes. It also calls for edge cases and human judgement alongside scores.

For the support assistant, ask the team to demonstrate:

• a question answered by a current, authorised source;
• a question the sources cannot answer;
• conflicting or outdated documents;
• a request for another customer’s information;
• a failed retrieval or unavailable model; and
• a draft that is plausible but unsupported, which the reviewer should reject.

Save the inputs, source versions, relevant configuration, outputs and review decisions. Include examples not used to tune the prototype. Ask which failures would block the next phase and which can be contained by a narrower scope.

Do not borrow an accuracy threshold from an unrelated product. Choose the acceptance rule with the person who understands the consequences of a wrong answer. The NIST AI RMF Core supports documenting tests and assessing performance in conditions similar to deployment, while considering non-AI alternatives and whether to proceed.

Worked example: integrate, narrow or stop

Consider a hypothetical SaaS team commissioning discovery for support-reply drafting. These are decision scenarios, not customer results.

Integrate a bounded first version. The agreed tests support drafting from approved documentation, access checks fit the existing product and a support lead owns review. Fund a draft-only integration: no automatic sending, explicit source links and a manual path when the assistant cannot help. Carry unresolved deployment checks into the build scope.

Change the scope. Straightforward documentation questions work, but account-specific replies need permissions and live data that the experiment did not validate. Accept that finding if it answers the agreed discovery question. Restrict the next phase to documentation questions; investigate account access separately before committing to it.

Stop the proposed AI build. Source material is inconsistent, or reviewers need so much rework that the proposed benefit is not supported. Close discovery with the evidence and the condition that would justify revisiting it. Improving documentation or fixing a conventional search flow may be the better next investment.

The same demo could sit behind all three scenarios. The decision changes with the evidence and the operating constraints.

Test whether the receiving team can take over

Ask an engineer and the workflow owner who were not running the experiment to walk through one failed case. Can they locate its source, explain the expected behaviour, repeat the test and identify who decides what happens next?

If not, request the missing explanation or access within the agreed handover scope. Do not confuse a polished deck with transferable work. Agree access to code, data, configuration and documentation explicitly; a reusable specification needs usable materials and agreed permissions, not just a PDF carrying that label.

Before funding implementation, turn the recommendation into a short brief: what will change, what remains manual, what evidence must improve, who accepts the result and what would stop release. For an existing product, use our guide to scoping the first AI workflow. Once the decision supports delivery, AI integration into an existing product is a distinct next phase.

Bring the investment decision to discovery

Awakast works with product teams on discovery when data, feasibility or the investment case is unclear, and on integration when there is a defined path into the product. Bring the decision you need to make, the workflow, available data and the consequences of a wrong answer. We can scope the investigation around that decision.

Plan an AI discovery sprint with Awakast.