The Enterprise GenAI Adoption Checklist

By Prasanth Sahoo · GenAI & Agentic AI Expert

Most failed AI initiatives don't fail on the technology — they fail on everything around it: unclear goals, the wrong first use case, messy data, or teams that were never brought along. This is the checklist I walk clients through before a single model is deployed. Use it to pressure-test your own readiness.

1. Strategy & alignment

• A clear business outcome the AI initiative is tied to (cost, speed, revenue or experience)
• Executive sponsorship and a named owner accountable for results
• Success metrics defined before you build, with a measurable baseline
• Realistic budget covering build, run and change-management — not just the model

2. Use-case selection

• Started with a high-value, well-bounded use case (not the riskiest one)
• Assessed feasibility vs. impact — and picked something you can ship in weeks
• Confirmed a human-in-the-loop path for anything consequential
• Defined what "good enough" output looks like for this use case

3. Data readiness

• Identified the data the solution needs and confirmed you can access it
• Checked data quality, freshness and ownership
• Classified sensitive data and decided what may never leave your boundary
• A plan for retrieval (RAG) or fine-tuning if the model needs your knowledge

4. Security & responsible AI

• Secrets, keys and PII handling reviewed (OWASP-aligned practices)
• Guardrails against prompt injection, data leakage and harmful output
• Clear policy on which tools and models are approved for which data
• Bias, fairness and transparency considered for user-facing decisions

5. Governance & operations

• Logging and observability so you can see what the AI did and why
• An evaluation harness to catch regressions before users do
• Cost monitoring and rate controls in place
• A rollback plan and clear incident ownership

6. Team enablement

• The people who'll use the solution were involved early
• Role-based upskilling — leaders, engineers and end users each get what they need
• A prompt/playbook library so good practice spreads
• A feedback loop so the solution improves with real usage