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Scope a Six-Week Pilot
Articles from Sprinklenet on AI governance, AI strategy, enterprise knowledge management, and government contracting.
Federal Buying
Most federal AI pilots fail at transition, not at the demo, because no program office has agreed to own the result. Five questions worth settling before the award, and what to require of the architecture.
Assign responsibility at the points where work changes hands: source approval, access changes, answer review, incident response, and release.
Specify the task, permitted data, system interfaces, acceptance evidence, and operating responsibilities.
Describe one workflow as allowed steps, approval points, and recovery paths. Use narrow permissions for each action and record the approved input.
Pre-release testing evaluates a system against a prepared task set. It does not inspect or guarantee every future answer.
Use one entry per concrete failure scenario. Record the affected workflow, consequence, existing control, evidence, owner, and next review date.
A useful reference architecture records the decisions teams can reuse and the decisions each workflow must revisit.
Treat a model change as an application release. Record the model version, prompt version, tool configuration, retrieval settings, and evaluation results together.
Pilot with a bounded collection of approved folders.
For each connector, document the authenticated identity, permitted records, allowed actions, credential owner, and revocation process. Test access changes and failures.
Measure the cost of completing the work, including review, corrections, integration, support, and model usage.
Many document platforms now offer AI assistants. Evaluate whether the existing platform meets the task before adding another layer.
Use an operating dashboard that connects service health to task quality.
Most weeks I send one short read on enterprise AI: what’s working in real deployments, what’s not, and what I’d do in your seat.
Jamie Thompson, Founder and CEO
