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AI Adoption Readiness Checklist
This is a 25-point checklist for judging whether your company is ready to adopt AI, before you spend on tools or consultants. It covers the five areas where adoption actually succeeds or fails: leadership, data, people, process and measurement. Tick what is true today, count your score, and the bands at the end tell you what to do next.
It takes about ten minutes with the right two or three people in the room. No email gate; print it, share it, argue about it.
Published 2026-07-08 · Updated 2026-07-08 · By Keyur Patel, AI adoption advisor
1. Leadership & strategy
AI adoption fails at the top more often than at the keyboard.
A named owner is accountable for AI adoption, with time and budget, not just enthusiasm.
Leadership can state the top three business problems AI should help with, in plain words.
There is agreement on what AI will not be used for, at least for now.
Success is defined in business terms (hours saved, cycle time, revenue), not tool logins.
Leadership has personally used at least one AI tool on real work in the last month.
2. Data & security
Most AI risk is data risk wearing a new badge.
You know which categories of data must never enter a public AI tool.
Customer and employee personal data handling meets your privacy obligations (for India, the DPDP Act).
The documents your teams would want AI to read are findable and reasonably current.
Someone can answer where each approved AI tool stores and trains on your data.
There is a route for approving a new AI tool that takes days, not quarters.
3. People & skills
Licences do not create capability. Practice does.
You know roughly how many employees already use AI tools, officially or not.
At least one person per function is genuinely fluent, a possible champion.
Engineers have tried an agentic coding tool (Claude Code, Cursor or Codex) on the real codebase.
Non-technical teams have seen AI applied to their own tasks, not just a generic demo.
There is time set aside for learning; adoption is not expected to happen after hours.
4. Process & workflow
AI pays off inside workflows, not beside them.
You can name five recurring tasks that eat hours and follow a describable pattern.
At least one workflow has been mapped end to end as a pilot candidate.
Review points are defined for AI-produced work: who checks it, and against what.
Someone owns each pilot with a deadline and a definition of done.
There is a plan for what happens after a successful pilot, so wins do not stall.
5. Measurement & follow-through
If you cannot see the change, it did not happen.
A baseline exists for the workflows you want to improve (time, volume or cost).
Usage of approved tools is visible enough to spot both adoption and abandonment.
There is a cadence (monthly is fine) for reviewing what is working and what is not.
Wins are shared internally so adoption spreads by example, not by mandate.
You have decided in advance what evidence would make you stop or change course.
How to read your score
20–25 checked
You are ready. The work now is sequencing and depth, not readiness. A prioritised roadmap will compound what you already have.
12–19 checked
Ready to pilot, not to roll out. Pick one function and one workflow, fix the gaps this checklist exposed there first, and expand from evidence.
Under 12 checked
Buying tools now would waste money. Start with leadership alignment and one mapped workflow; most companies can move up a band in 4–6 weeks.
Want a second pair of eyes on your score?
Bring your filled-in checklist to a free 30-minute discovery call and I will tell you what I would do first, and what I would skip. If AI is not the right answer yet, I will say so.