09 / AI at Work · Field briefing
Give the pilot somewhere to land
Give the pilot somewhere to land.
A pilot should produce a decision, not an indefinite demonstration. Name the workflow, its users, its owner and the result you want to learn. Keep the experiment small enough that inputs, outputs and failures can be inspected.
What turns an interesting experiment into a maintained workflow?
An explanatory schematic—not a live system or measured performance.
KAF World Digital · Prepared 4 October 2026 · First editorial edition. Worked examples are invented for teaching.
Try the example02 / Choose evidence over dates
Choose evidence over dates
An invented service team wants help preparing enquiry replies. The pilot uses synthetic and approved examples, creates drafts only and records review time. Its success criteria cover correct policy use, no invented promises, useful tone and the effort required to check the draft. Sending remains a separate permission.
First establish the current process. Who prepares a reply, which documents they consult and how long checking takes are part of the baseline. Then test the proposed workflow on representative cases. Finally, review the comparison with the people who will own its day-to-day operation.
Use gates rather than a theatrical countdown. The preparation gate confirms information rights and a reviewer. The evidence gate reviews results and failure types. The adoption gate assigns ownership, support and a rollback plan. The same sequence may take days or months; a calendar does not guarantee readiness.
Document what changed. A new model, revised policy, prompt edit or extra tool may affect behaviour. Keep the versioned configuration and case set so you can repeat the evaluation. Record known limitations where users will see them, rather than leaving them in a development notebook.
Plan recovery before widening use. If the tool is unavailable, can the existing manual process continue? If a draft contains an unsupported commitment, who catches it? If data was incorrectly included, who handles the incident? A workflow is more credible when those questions have owners.
Retire a pilot that does not justify its overhead. Improving the input documents, using a template or narrowing the task may be a better next step than adding autonomy. Success can mean learning that a particular application is not worth adopting.
Evidence for the technical mechanisms: Microsoft · Using Generative AI Responsibly, Anthropic · Building Evals. The worked examples and practical framework are KAF editorial recommendations.
03 / Worked example
Explore the pilot gates
A small experiment should end with a defensible decision. This interactive lesson uses prepared synthetic examples and local calculations.
Use the companion worksheet and the worked example. All assumptions are illustrative.
No account, model API or external service is connected. Inputs are not submitted or stored by this page.
04 / Go deeper
Useful words. Important details.
- Pilot charter
- A short record of the task, owner, scope and decision the experiment should inform.
- Adoption gate
- A point at which evidence and ownership are checked before widening use.
- Rollback
- A planned return to a workable earlier process.
- Re-evaluation trigger
- A meaningful change that requires the tested assumptions to be checked again.
A gate has evidence and an owner
A gate is a decision point, not just a date on a roadmap. Preparation requires input rights and a reviewer; adoption requires observed results, support and rollback ownership.
Keep the tested system identifiable
Record model, prompt, policy version, data scope and tool configuration. Without that record, a result can be hard to reproduce or interpret after an update.
Stopping is a result
A pilot can justify narrowing a task, switching to ordinary software or not adopting the workflow. Record why: review overhead, inadequate evidence, error consequence or lack of useful benefit.
The technical notes qualify the explanation; they are not product-specific assurance or a certification.
05 / Evidence & scope
Inspect the source.
Keep the context.
Sources were accessed on 4 October 2026. These technical and security references support the mechanisms described; they do not validate our fictional examples or establish comparative product performance.
- Microsoft · Using Generative AI Responsibly
Responsible-use principles, representative testing and operational ownership. Technical guidance, not legal advice.
- Anthropic · Building Evals
Task-specific evaluations and code, human and model-based grading. Provider examples are not independent comparative evidence.
- OpenAI Agents SDK · Human-in-the-loop
Approval before sensitive tool execution, paused runs and explicit decisions.
What this briefing does—and does not—establish
The worked examples, suggested questions and worksheets are original KAF teaching material. Numbers are explicitly illustrative. No customer data, observed adoption results, market-size forecast or live provider pricing is presented.
Check the exact product, release, configuration and policy before using a workflow. Source documents can change; this is a dated educational briefing.
Keep a useful copy.
Read the full briefing offline, or use the companion worksheet to apply it.