Strategy · 4 minute read
AI changes what’s worth building
Build what differentiates. Buy what earns its place. Connect what works.
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Trace where your AI workflow processes and stores information
Map an AI workflow’s sources, processing routes, saved results and operational copies. Check the actual configuration against agreed handling requirements.
Read articleChoose a first AI workflow worth testing
Compare specific business tasks, observe the current work and define a test with a clear result, review effort and operating owner.
Read articleHand work between agents with an explicit contract
Define agent handoff tasks, sources, permissions and completion evidence. Enforce authority, handle uncertain outcomes, and test failures before expanding.
Read articleStart with a common business vocabulary
Agree on business definitions, owners, source mappings and change rules. Add formal relationships when a use case needs them, and test how the terms are used.
Read articleMake an executive briefing useful for a decision
Write an executive briefing around a specific choice. Compare evidence, costs, options and unresolved questions, then record the decision and follow-up.
Read articleBuild a small evaluation set before changing models
Compare models against real business tasks. Define expected results, check failures, record uncertainty, and plan a limited release before switching.
Read articleFeatured perspective
Engineering · 4 minute read
From useful work to lasting capability
Turn a successful first solution into something your team can rely on and improve.
Read the articleProducts · 4 minute read
When useful tools need to work together
Recognize when separate AI solutions need shared context, coordination and control.
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