What a first AI pilot should leave behind

A first AI pilot should leave your team with enough evidence to make the next decision. The final handoff should show the workflow before and after, the sources the system used, the results against agreed acceptance criteria, the misses and exclusions, the prototype or recommendation produced, and the decision an owner can make next.

Without that handoff, a polished demonstration can create momentum without answering whether the workflow is worth funding, ready to expand, or better left manual. Asking for these materials before the work begins gives the pilot a practical end point: a decision your team can explain and evidence it can inspect.

A pilot earns its value by supporting the next decision

A pilot is a bounded test of one useful workflow. It should reduce uncertainty about the work, the data, the implementation path, or the value of continuing. That makes the end-of-pilot review more important than the demonstration itself.

The review should help the accountable owner choose among a small set of real options: expand the workflow, revise the scope, run it under review for longer, keep the process manual, or stop. A demo that prompts only “What should we build next?” has skipped the harder question of what the pilot established.

You can see the same evidence discipline in useful public case studies. A credible proof story names the constraint, the artifact that changed, the result that can be shared, and the details that remain private. Your internal pilot handoff should be at least as clear, even when none of it will be published.

The handoff starts with the workflow before and after

The first artifact should be a readable map of the work. It does not need to document an entire department. It needs to show one workflow from trigger to outcome, including the owner, inputs, systems, wait points, decisions, and exceptions that mattered to the pilot.

The “after” view should identify the steps the system prepared, the work that stayed with a person, the point where a reviewer approved, rejected, or edited the result, and the system that received the final action, if any. A buyer should be able to compare the two views without decoding model architecture or vendor vocabulary.

This map prevents the team from judging the pilot against a moving target. If the original process was never written down, a faster-looking demo may reflect a cherry-picked input, hidden manual cleanup, or a different workflow from the one employees actually run.

The map also makes the handoff useful beyond the pilot. An implementation team can see the stable parts worth building. An operator can see the handoffs that still create work. A finance or technical owner can see whether the proposed next phase addresses the original constraint.

The source boundary shows what the result can be trusted to say

Every pilot uses a set of sources, whether that means documents, database fields, customer messages, website content, policies, images, or application events. The handoff should name those sources and state which systems or fields stayed outside the test.

This is the source boundary: the practical record of what the pilot was allowed to read and use. It should also capture credentials or permission assumptions, retention questions, vendor or model exposure, and any unresolved source-quality problems. A source boundary is useful even when the pilot never reaches production because it shows what a future build would need to protect or clean up.

Exclusions belong beside the allowed sources. If the pilot used redacted examples instead of live customer records, say so. If it did not test payment data, attachments, multilingual inputs, old records, or conflicting policy documents, list those limits. Clear exclusions stop a narrow success from being repeated later as a broad capability claim.

For a field-by-field version of this handoff, use the AI pilot proof guide. It covers the workflow baseline, source boundary, reviewer evidence, misses, rollback path, value signals, and decision memo in more detail.

Acceptance evidence includes the misses

Before the pilot starts, the team should agree on the evidence that will count. The criteria may cover output quality, source accuracy, review effort, handoff completion, exception handling, or a measured sample of cycle time. The right criteria depend on the workflow; a draft content assistant, an intake classifier, and a system that proposes account changes should not share the same acceptance bar.

The final handoff should show representative test cases and reviewer decisions. It should include examples that passed, examples that needed edits, examples that failed, and cases that stayed manual. A single model score or a folder of best outputs leaves too much hidden.

Misses are especially useful when they are grouped by cause. Missing source context points to a retrieval or data problem. Repeated reviewer corrections may expose an unclear policy or acceptance rule. A correct answer that takes more effort to verify than the original task may show that the workflow is poorly scoped, even if the output looks impressive.

Early value signals should remain tied to their sample and baseline. Review load, cleaner intake, fewer handoffs, or a shorter cycle-time sample can guide the next decision. They should not become a general ROI or savings promise without enough verified evidence to support one.

The deliverable may be a prototype or a decision memo

Some pilots should end with a working prototype or a narrow production asset. Others should end with a recommendation to defer the build, clean up the process, change the data source, or keep a high-consequence decision with a person. Both outcomes can be useful if the evidence supports them.

When a prototype is part of the handoff, the team should receive enough context to inspect and continue it. That may include the code or configuration, test notes, integration assumptions, known failure modes, deployment or access notes, and the owner responsible for the next phase. A screen recording alone does not tell another team what was built or what remains fragile.

When the work should not continue yet, the decision memo becomes the main deliverable. It should state the recommendation, the evidence behind it, the unresolved risks, the work that was deliberately deferred, and the condition that would justify another review. This is how a pilot can save the team from a larger commitment without being labeled a failure.

The format should match the work. An AI consulting engagement may leave an opportunity map and a recommended first workflow. A process-automation pilot may leave a trigger-to-outcome map, integration notes, exception cases, and monitoring requirements. A custom software pilot may leave a narrow prototype, acceptance notes, architecture direction, and a first-release boundary.

Public proof should make the artifact and boundary visible

BaristaLabs’ current public proof shows why the handoff should vary by engagement. These are examples of published project evidence, not a claim that every project below was an AI pilot.

The CartWheels case study can name the stalled vendor path and the website, app interface, and customer communication channels that shipped. The Stilson Greene portfolio case can show an owner-managed site and CMS workflow, supported by a named testimonial. The anonymized AKS case can describe the network and VM-capacity blockers, the remediation path, and zero application downtime while keeping the client and infrastructure details bounded.

The artifacts differ because the work differed. What stays consistent is the proof structure: constraint, shipped or recommended artifact, evidence, exclusions, and a boundary around what the public story can support. The portfolio is useful for scanning public work; the case-study hub provides stronger context for judging a specific constraint and outcome.

Your pilot handoff should apply the same discipline internally by naming what changed, showing what supports the claim, and keeping private or untested details inside a clear boundary. One successful sample should not become a promise about the full workflow.

Use the handoff to choose the next move

The last page of the handoff should state the next decision in plain language. “Continue exploring” is too vague. A useful recommendation says whether to expand, revise, shadow longer, keep the workflow manual, or stop, and it names the evidence and prerequisites behind that choice.

An expansion decision should identify the next source, action, user group, or integration entering the workflow instead of simply authorizing more activity. It should also preserve the human review point, the evidence that must stay visible, and the owner who can pause the work if the new scope changes the risk.

A revision decision should name what needs to change. The source material may need cleanup. The workflow may need a clearer owner. The acceptance bar may need to separate ordinary edits from business-changing errors. The first AI role may need to shrink from sending or updating to drafting or preparing evidence.

A stop decision should explain what the team learned and what work, if any, remains useful. The pilot may have shown that the process is too inconsistent, the data too costly to prepare, the review burden too high, or the available value too small for the build. That conclusion protects future budget and gives the next project a better starting point.

Put the handoff requirements in the pilot scope

The easiest time to improve a pilot handoff is before the work begins. Put the expected materials in the scope: a workflow map, source boundary, acceptance evidence, known exclusions, a prototype or decision memo, and the owner of the next decision.

Agree on who will review each item, which materials the team will receive, and which details must remain private. That gives the final review a defined purpose: decide whether to expand, revise, shadow longer, keep the workflow manual, or stop.