AI Readiness Checklist

Can your business teams agree on the definition of your top five KPIs?

Why it matters: AI amplifies whatever definitions are in your data. Conflicting definitions produce conflicting AI outputs.

Is there a documented, authoritative source for your core business entities — customers, products, transactions?

Why it matters: AI agents and models need a single trusted source to ground their outputs. Multiple conflicting sources produce unreliable results.

Do you have a data quality process that monitors, measures, and remediates data issues in production?

Why it matters: AI that runs on unmonitored data degrades silently. Quality must be a running operational process, not a one-time cleanse.

Do you have a formal policy defining acceptable AI use, prohibited use cases, and who can approve AI deployments?

Why it matters: Without a policy, employees fill the gap with consumer AI tools, creating shadow AI risk and IP exposure.

Is there a named business owner — not IT — accountable for each AI initiative and its outcomes?

Why it matters: AI without a business owner does not get adopted. Adoption requires someone whose job depends on the change working.

Do you have an audit or oversight mechanism for high-stakes AI decisions — hiring, credit, compliance, clinical?

Why it matters: Regulators in most jurisdictions now require human oversight for AI-assisted decisions in sensitive domains. The exposure for getting this wrong is significant.

Before each AI initiative launches, do you capture baseline metrics so you can measure the change after?

Why it matters: Without pre-deployment baselines, you cannot prove ROI. You cannot defend the budget, prioritize the next initiative, or build the business case for scaling.

Can your finance team see the cost of AI — model API usage, inference compute, fine-tuning, and tooling — in your operating budget?

Why it matters: AI costs scale with usage in non-linear ways. Organizations that cannot see AI costs cannot manage them. This becomes a CFO problem quickly.

Have you mapped the workflows where AI could create measurable value — and ranked them by effort versus impact?

Why it matters: Without a prioritized opportunity map, AI investment is driven by vendor pitches and internal politics rather than business value.

Do your frontline teams understand what AI will and will not do in their workflows — and do they trust its outputs?

Why it matters: AI adoption is the outcome, not deployment. If the people who use the outputs do not trust them, the technology has no business value regardless of its accuracy.