Free resource
The AI Integration Readiness Checklist
Are you actually ready to ship AI, or just ready to start?
Most "add AI to the product" projects don’t fail on the model. They fail on the six months of infrastructure, data, and scope decisions nobody made before the first prompt got written.
Answer these ten questions honestly. If you’re scoring low, that’s not a reason to stop — it’s a reason to bring in someone who’s done this before you spend the budget finding out the hard way.
1. Data readiness
Do you know exactly which data the AI feature needs, and do you have clean access to it?
"We have a database" is not an answer. RAG systems, agents, and fine-tunes all live or die on whether the underlying data is structured, deduplicated, and queryable at the latency your feature needs. If your data lives in six systems that don’t talk to each other, that’s the actual first project.
2. The scoped use case
Can you describe the AI feature in one sentence that includes what happens when it’s wrong?
"AI-powered customer support" is a vision, not a spec. "Draft a first-response email a human reviews before sending" is a spec. If you can’t state the failure mode and who catches it, the feature isn’t scoped yet.
3. Build vs. buy vs. integrate
Have you priced out the foundation-model API cost against a self-hosted or fine-tuned alternative at your actual expected volume?
The economics of AI features flip hard between "a few hundred requests a day" and "every user, every session." Teams that skip this step either overpay for a prototype-scale integration in production, or over-invest in infrastructure for a feature that never gets traffic.
Latency budgets, evaluation, ownership, cost observability, data boundaries, team fluency and leadership bandwidth — plus how to read your score. Enter your email and the rest of the checklist opens on this page.
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