AI READINESS CHECKLIST
Is your business actually ready to ship AI?
A pragmatic 30-point checklist covering strategy, data, infrastructure, team, and governance — the same one we use in client discovery. Built to surface real readiness gaps, not to flatter you into hiring us.
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Built for teams that can't afford to guess.
A pragmatic 30-point checklist covering strategy, data, infrastructure, team, and governance — the same one we use in client discovery. Built to surface real readiness gaps, not to flatter you into hiring us.
Strategy & use case clarity
Have you defined what AI is supposed to do — beyond 'use AI'?
Data readiness
Do you have the data, in usable form, with the rights to use it?
Infrastructure & engineering
Can you build, deploy, and keep something running?
Team & ownership
Is there a human who can own this past launch?
Governance, risk & compliance
Do you understand what could go wrong, who's accountable, and your obligations?
Fill it out three ways
Score it yourself, then have two colleagues score it. The disagreements are the signal.
The projects that ship usually score well on most of these. The ones that stall almost always have a low score in a category they thought was fine.
1 · Strategy & use case clarity
Have you identified at least one outcome AI should improve — beyond 'we want to use AI'?
Can you describe success in concrete terms (hours saved, revenue gained, error rate reduced)?
Have you ranked candidates by ROI rather than by enthusiasm or visibility?
Is there an executive accountable for the outcome, not just for shipping the project?
Have you honestly asked whether the problem could be solved without AI?
Can you say in one sentence what changes for users when this ships?
2 · Data readiness
Is the data the AI will use in a form a system can actually read?
Or is it a 2-year-old snapshot nobody updates?
And have you confirmed they'll give you access?
Do you know where PII, PHI, and financial data sits and what your obligations are?
Is your data labeled, or do you need labeling before you can evaluate?
Do you have 50+ realistic example inputs to evaluate against?
3 · Infrastructure & engineering
Will AI live in your existing product or as standalone tools — and is the architecture compatible?
Do you have monitoring you can extend to AI components, or are you starting from scratch?
Have you modeled per-query vs. fixed-infrastructure cost at 10× current volume?
or will every release be a surprise?
Do you know the cost of integrating with your existing systems?
4 · Team & ownership
Is there a person who can read AI outputs and judge whether they're good?
not just for the launch?
Have you allocated time for end-users to learn and form opinions?
Will you actually hear about it from production users when something goes wrong?
Has leadership been told what AI will and won't do, including the wrong answers?
Communication, correction, and escalation paths?
5 · Governance, risk & compliance
Do your DPAs, customer contracts, and policies permit the AI use case?
Is it documented what AI can decide autonomously vs. what needs a human?
Have you specified the audit trail you'll need for AI-influenced decisions?
refund, correction, escalation?
Is the risk of AI being publicly wrong acceptable to leadership?
confirmed compliant?
What your score means.
Count the boxes you confidently checked. Partial credit doesn't count.
Build-ready
You've done the unsexy work. Ready to scope a real build — most engagements at this level ship on the first attempt.
Workable, with gaps
You can build, but two or three categories need attention first. A Discovery engagement closes the gaps before the build.
Pre-readiness
Earlier than you think. Close the gaps first — usually data, ownership, and use-case clarity — before any AI code.
Ready to talk to a human?
Bring your scored checklist. We'll spend 30 minutes on what you actually need to do next — not on a sales pitch.
Ready to build?
Let's build your next intelligent platform.
Share your goals — we'll recommend a model, timeline, and team that fits Aanandi Technosoft.