AI strategy that
survives contact
with reality.

1. Discover the business opportunity

Understand the business objective, existing workflow, friction points, constraints, and baseline metrics. Prioritize opportunities where a measurable outcome is plausible and the workflow is ready for change.

2. Architect the end-to-end solution

Define system dependencies, data needs, security and privacy considerations, integration requirements, and operating ownership. Decide whether to buy, build, or prototype before making a large commitment.

• Buy: evaluate vendors, run proofs of concept, validate integration and operating fit.
• Build: prototype internal solutions, test workflow assumptions, and prepare an engineering handoff.
• Compose: connect existing systems and AI capabilities where the value comes from the workflow, not one model alone.

3. Prove value with evidence

Set success criteria before the proof of concept. Evaluate technical quality alongside business fit, user adoption, reliability, cost, and the operational changes required to realize value.

4. Scale and measure

Move validated solutions into the teams and systems that own the workflow. Track adoption and outcomes after launch, and establish guardrails for quality, privacy, and customer experience.