AI Executive Checklist
A practical, step-by-step guide to implementing AI in your organization. Review the checklist below, then print it to use in meetings and planning sessions.
AI Executive Checklist
From JAKT.AI - AI Strategy Without the Complexity
How to Use This Checklist
1.Review each section summary to understand the strategic context
2.Check off completed items or write target dates next to each task
3.Use the notes sections to capture decisions and action items
4.Share with your executive team and AI implementation partners
Quick Progress Overview
Key Success Metrics
1. Set ambition and guardrails
Section 1 of 10
Clarify the outcomes you want in the next 12 months and the rules you will not break. This creates focus and prevents tool-first projects.
Action Items
Define 3 to 5 business outcomes: revenue, cost, risk, or cycle time
Name an executive sponsor and product owner for each outcome
Write non-negotiables for privacy, security, and human oversight
Approve a lightweight AI governance model with monthly portfolio review
Notes & Action Items
2. Build a use case portfolio
Section 2 of 10
Source ideas from journeys and bottlenecks, score them consistently, and balance quick wins with strategic bets.
Action Items
Source ideas from customer journeys, operations logs, finance, and supply chain
Score every idea on impact, feasibility, and risk using one rubric
Place ideas on a 2×2: quick wins, scalable wins, strategic bets, deprioritized
Select 3 quick wins and 2 strategic bets with clear exit criteria
Notes & Action Items
3. Choose models and platform
Section 3 of 10
Decide where you need speed versus control. Most teams start hybrid: a managed LLM with retrieval over governed data.
Action Items
Decide buy, build, or hybrid based on control needs, latency, and cost
Select a primary LLM and a backup model with routing rules
Stand up retrieval over governed data with a vector index
Define SLOs per journey: accuracy, latency, and cost per task
Notes & Action Items
4. Architecture and controls
Section 4 of 10
Stand up the minimal rails for safety and operability so pilots can scale without rework.
Action Items
Data layer: sources documented, access controlled, lineage tracked
Orchestration: prompts, tool use, eval harness, rollback plan
Security: identity, secrets, audit logging, policy enforcement
Observability: telemetry, usage analytics, cost caps, feedback loops
Notes & Action Items
5. Delivery method
Section 5 of 10
Ship a thin slice for one role and one task. Keep a human in the loop until quality is proven, then expand scope.
Action Items
Ship a thin slice for one role and one task before expanding scope
Keep human-in-the-loop until SLOs are met and monitored
Add guardrails: input validation, output filters, rate limits, PII redaction
Document purpose, data, risks, and owners for each AI feature
Notes & Action Items
6. Evaluation and quality
Section 6 of 10
Make quality measurable. Use golden sets and weekly evals to prevent regressions and build trust.
Action Items
Create a golden test set for accuracy, safety, and edge cases
Run weekly offline evals and track drift
Pilot with 5–10 users, then 25–50 with A/B tests
Tie every release to one measurable business KPI
Notes & Action Items
7. Upskilling and change
Section 7 of 10
Raise fluency at the top and skills at the edge. Celebrate shipped wins and retire legacy processes.
Action Items
Hold executive sessions on AI economics, risk, product patterns, and ROI
Run a practitioner bootcamp on prompts, retrieval, tools, and evals
Publish simple playbooks and internal examples
Celebrate shipped wins and retire legacy processes
Notes & Action Items
8. Compliance snapshot
Section 8 of 10
Keep a simple register of AI uses and map controls to recognized frameworks. Track timelines for your regions.
Action Items
Maintain a register of AI use cases with risk tier and controls
Map practices to recognized frameworks and standards
Track regulatory timelines that apply to your footprint
Train staff on responsible use and incident handling
Notes & Action Items
9. Metrics and ROI
Section 9 of 10
Agree on the math with Finance before launch. Publish a monthly scorecard with inputs, outputs, and outcomes.
Action Items
Inputs: adoption by role, time on task, automation rate
Outputs: accuracy, latency, completion rate
Outcomes: revenue lift, cost reduction, risk loss avoided, customer NPS
Finance signs off on baselines and the ROI method
Notes & Action Items
10. 8-week pilot plan
Section 10 of 10
A proven tempo to go from idea to scale decision without stalling.
Action Items
Week 1 – Scope and success metric
Week 2 – Data access and thin slice design
Week 3 – Retrieval and tool wiring
Week 4 – Evaluation harness and golden set
Week 5 – Pilot to 5–10 users with human in the loop
Week 6 – Fixes and guardrails
Week 7 – Expand to 25–50 users, measure ROI
Week 8 – Go or no-go with scale plan and de-risking
Notes & Action Items
Implementation Summary
Key Decisions
Next Steps
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