Expert insight
When AI belongs in your product—and when it is theater
AI is useful when it changes a workflow metric. It is expensive theater when it only changes a slide deck.
Start from the workflow, not the model
The right question is not “Can we use an LLM?” It is “Which repeated decision or triage step burns time, money, or quality today?”
Strong AI candidates: support triage, document extraction, internal search, scoring, and assisted drafting inside an existing product loop. Weak candidates: features users did not ask for, bolted on after a competitor announcement.
Architecture and risk before the demo
Ship AI behind clear boundaries: data access rules, evaluation harness, human override, and cost controls. Security by design matters more when models touch customer data.
MOVE UP builds AI features as part of the Build pillar—after Strategy clarifies where automation pays off. That avoids a quarter spent polishing a chatbot nobody activates.
Takeaways
- AI must move a workflow metric, not a pitch slide
- Evaluate data access, cost, and override paths before launch
- Strategy first: where automation pays, then Build
Questions buyers ask
Do we need a dedicated AI team?
Not at the start. You need a clear workflow target, evaluation criteria, and an architecture that keeps data and cost under control.
What is the fastest way to test AI value?
Pick one high-volume internal or product workflow, define a success metric, and ship a constrained assistant with human override—then measure activation and time saved.
Make the next step concrete
Unclear risk? Start with a Product & Architecture Diagnostic—then Build or Scale with a plan.