AI is a force multiplier when paired with human judgment and clean processes. We treat AI models like interns with superpowers: fast, tireless, literal and improving all the time. The human job is framing, sense-checking, and deciding based on our expertise and experience. We design prompts as reusable checklists, wire tools into existing workflows, and make quality control explicit. Teams learn three skills: articulating intent, iterating based on output, and capturing what works as playbooks. The prize is not flashy demos, it is a compounding edge in research and design, decision preparation, client delivery, and learning loops.

Where do we start?
Pick one high-leverage workflow (e.g., research briefs, risk scans) and build a prompt-plus-checklist around it.

Won’t this slow us down?
Only if you bolt it on. Integrated into cadence and roles, it speeds prep and improves judgment.

How do we manage risk?
Guardrails: source citations, red-teaming, and human sign-off on consequential outputs.

What skills should we train?
Intent framing, constraint prompts, critique loops, and traceable decisions.

How do we measure value?
Saved hours, improved first-pass quality, and lead indicators like faster options analysis.

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