Updated October 14, 2026
An AI pilot should answer a bounded operational question: can this workflow produce an acceptable result, at an acceptable cost, under the conditions in which people will actually use it? A polished demonstration answers only part of that question.
Design the pilot so the final decision includes quality, operating effort, user behaviour, security and ownership. Some work can remain provisional, but the path from experiment to supported service should be visible before live use begins.
Write a pilot charter
Keep the initial charter short enough for the business owner, delivery team and users to discuss together. Include:
Problem: the task, current baseline and reason to change it.
Scope: users, eligible cases, systems and excluded actions.
Hypothesis: the improvement the pilot will test.
Boundaries: permitted data, access and human approvals.
Evidence: evaluation cases, measures and acceptance thresholds.
Ownership: business, technical, operational and decision contacts.
Exit: when and how the team will proceed, revise, pause or stop.
Use a clear resource limit and decision date. Allow the scope to narrow if necessary to answer the main question. Adding features should require an explanation of which uncertainty they resolve.
Build evaluation before polishing the interface
Create a representative evaluation set using appropriately authorised material. Include common tasks, ambiguous inputs, missing information and known exceptions. Keep a separate set of cases for checking later changes rather than repeatedly tuning against every example available.
Define what a good result means for the task. A summary might require accurate facts, coverage of key points and no unsupported assertions. An extraction workflow might require correct fields, reliable missing-value handling and an appropriate review route. An action-taking workflow also needs checks on permissions, parameters and consequences.
Measure end-to-end performance, including retrieval, review, correction, waiting and downstream rework. Record results by case type so an overall average cannot conceal a serious weakness. Document the limits of a small sample; it cannot establish that rare failures will never occur.
Put permissions and failure handling into the design
Agree which data the workflow may access and where it may be processed, stored and logged. Use the minimum access needed for the pilot. Review data retention, supplier settings and the information exposed through logs with the appropriate internal owners.
Treat retrieved documents, messages and model output as untrusted inputs. Test how the workflow behaves when content attempts to redirect it, requests information outside the user’s access or produces an unexpected instruction. Keep permission checks and consequential action controls outside the model’s discretion.
Define when the system should stop, request clarification or hand work to a person. A human review step needs a reviewer with adequate information, time and authority. A button labelled “approve” is only part of that arrangement.
Deliver in small, usable steps
Begin with offline testing where possible. Then consider shadow operation, in which outputs are assessed without controlling the live process. A restricted live pilot can follow once the evidence supports it.
At each stage, involve the people who perform and support the work. Show them complete tasks, including awkward cases. Record what changed, why it changed and which evaluations were rerun. Keep versions of prompts, rules, model settings and source material identifiable so a result can be investigated later.
A hypothetical internal drafting assistant might first generate drafts from a fixed reference set. Later steps could add retrieval and a review screen. Sending messages automatically would be a separate scope and permission decision, requiring its own evaluation and approval.
Use explicit release gates
Before production, ask the accountable owners to confirm:
Value: the observed benefit remains worthwhile after review, support and running costs.
Quality: agreed thresholds are met across important case types, with limitations documented.
Security: access boundaries and relevant failure scenarios have been tested.
Operations: monitoring, support, incident escalation and change ownership are in place.
People: users understand eligible tasks, required checks and escalation.
Recovery: fallback, pause and rollback procedures have been exercised.
A manual fallback must have enough capacity to handle the work routed to it. Identify who can pause the workflow and what happens to queued or partially completed tasks. Test recovery as well as the stop control.
Treat release as the next operating stage
Start with a defined production boundary. Monitor quality, exceptions, latency, usage and cost against the pilot evidence. Rerun relevant evaluations when the model, prompt, data source or business process changes. Assign an owner to decide when a change is material enough to require renewed approval.
The pilot’s final deliverable is a decision supported by evidence and a workable operating plan. That may justify production, a narrower scope or further investigation. Each is more useful than an open-ended experiment without an accountable next step.
Explore Daly Ventures’ AI Strategy & Readiness approach to define a bounded pilot and the evidence needed for an implementation decision.
