Model shopping is the easy part. The hard part is deciding what "good" means in a business process, who owns failure modes, and how you will measure lift without fooling yourself with vanity demos.
Start with a narrow workflow where latency, cost, and error tolerance are known. Define human fallback. Instrument before you scale prompts across the company Slack.
Data readiness kills more pilots than model quality. If your CRM is a landfill and your product events are inconsistent, the model will confidently automate the mess.
Governance is not bureaucracy for its own sake. It is how you keep customer data out of the wrong context window and how you explain a bad automated decision to a board.
Vendor lock-in arrives as convenience. Abstraction layers and evaluation harnesses are cheaper early than after every team embeds a different SDK.
Success criteria should be economic: hours saved, conversion lift, defect reduction — not "employees used ChatGPT." If you cannot name the metric, you are funding theater.
Strategy before tools means you can change models without changing the operating system of the company. That is the only AI adoption that compounds.
Related
AI & Strategy · 4 min
Your AI Coding Tools Are Lying to You (And Your Engineers Don't Know It Yet)Security & Compliance · 4 min
SOC 2 Compliance: What Every Technology Leader Needs to Know (And How to Get There Without Losing Your Mind)Leadership · 4 min
The CTO Skill Nobody Trains For: Saying "No"