Resource
AI Strategy Checklist:
AI Strategy Checklist:
40 Questions Before You Build
Most AI programs fail because of decisions made — or skipped — before the first line of code is written. This checklist covers every dimension of AI readiness: strategy, data, governance, talent, and delivery. Use it before you commit budget, before you select a vendor, and before you launch a pilot.
AI Readiness
Program Planning
Governance Checklist
Data Assessment
Talent Gaps
Vendor Evaluation
Use Case Validation
Risk Review
Business Case
Pre-Launch Review
AI Readiness
Program Planning
Governance Checklist
Data Assessment
Talent Gaps
Vendor Evaluation
Use Case Validation
Risk Review
Business Case
Pre-Launch Review
77%
of enterprise AI projects that skipped a formal readiness assessment failed to reach production — Gartner, 2024
$1.4M
average cost of an enterprise AI program that fails after reaching the build phase — McKinsey Global Institute, 2024
83%
of AI failures are traceable to gaps identifiable before build begins — data, governance, sponsorship, or scope — IBM IBV, 2023
2.6×
higher success rate for AI programs that completed a structured pre-launch checklist vs. those that did not — Deloitte, 2024
The Checklist
40 Questions Across Eight Dimensions of AI Readiness
Work through each category before committing to a vendor, a platform, or a build timeline. Every "no" or "unsure" is a risk that needs a mitigation plan before you proceed.
1
Strategic Alignment
Executive sponsorship confirmedA named C-suite owner with authority to clear roadblocks and sustain investment over 12–24 months.
Business problem clearly definedThe use case is tied to a measurable business outcome — not a technology capability.
Success metrics establishedKPIs are defined in business terms — cost reduction, revenue impact, time saved — before build begins.
AI roadmap approvedA 12–24 month investment roadmap has been reviewed and approved by leadership.
Budget confirmed and protectedFunding is allocated and ring-fenced — not subject to quarterly reallocation.
2
Data Readiness
Required data sources identifiedAll data inputs for the target use case are mapped to specific systems or sources.
Data quality assessedA sample analysis confirms data completeness, accuracy, and consistency at acceptable levels.
Data access confirmedLegal, technical, and contractual access to required data is secured — no assumptions.
Data governance in placeOwnership, lineage, and update frequency for key data assets are documented and maintained.
Data pipeline feasibility confirmedThe technical path from raw data to model input has been validated — not assumed.
3
Governance & Risk
AI policy documentedA formal AI policy exists covering acceptable use, prohibited applications, and accountability.
Accountability owner namedA specific individual is accountable for AI governance outcomes — not a committee.
Regulatory requirements mappedApplicable regulations — AIDA, PIPEDA, GDPR, sector rules — are identified and compliance obligations are documented.
Bias and fairness review plannedA structured review for model bias and fairness issues is scheduled before production deployment.
Model monitoring designedPost-deployment monitoring for model drift, performance degradation, and unexpected outputs is defined.
4
Talent & Team
Core team roles filledAI program lead, technical lead, and business owner are confirmed — not placeholder roles.
Talent gaps identifiedSkills missing from the internal team are documented with a plan to fill via hire, train, or consult.
Time allocation confirmedTeam members have protected time for the AI program — not just nominal assignments alongside full workloads.
AI literacy baseline setKey stakeholders and end users have sufficient AI literacy to participate in decisions and adoption.
External support scopedIf using consultants or system integrators, scope, deliverables, and success criteria are defined before engagement starts.
5
Use Case Validation
Use case scored against criteriaEvaluated against business value, data readiness, technical feasibility, and organizational readiness — not just intuition.
Business owner committedThe business unit that will use the system has a named owner who is accountable for adoption outcomes.
Baseline performance measuredCurrent-state performance metrics are documented so post-deployment improvement can be objectively measured.
Scope boundaries setWhat is in and out of scope for the pilot is documented and agreed — with a formal process for change requests.
Build vs. buy decision madeThe decision to build custom, configure a vendor solution, or partner has been made with documented rationale.
6
Vendor & Technology
Vendor evaluation completedVendors assessed against your specific requirements — not just demo performance or analyst rankings.
Integration complexity assessedThe effort to connect the AI system to existing data sources, platforms, and workflows has been scoped — not estimated.
Data security requirements confirmedVendor data handling, storage, and security practices meet your internal and regulatory requirements.
Exit and portability terms reviewedContracts include data portability and termination provisions — not just onboarding terms.
Support model confirmedPost-deployment support, SLAs, and escalation paths are defined before contract signature.
7
Change Management
Impacted stakeholders mappedEveryone whose role or workflow will change has been identified and is part of the change plan.
Communications plan draftedA structured communication cadence is planned — not ad hoc announcements at deployment.
Training plan confirmedRole-specific training is designed, resourced, and scheduled before go-live.
Resistance risks identifiedLikely sources of resistance are named, with a plan to address each before they become blockers.
Adoption metrics definedHow adoption will be measured — usage rates, process compliance, feedback scores — is agreed before launch.
8
Delivery & Scaling
Pilot-to-production path definedThe criteria and process for moving from pilot to full production are documented before the pilot begins.
Milestone register createdA milestone register with owner, due date, and escalation trigger for every key deliverable is in place.
Rollback plan existsIf the pilot fails or the system underperforms, the plan to revert or pause is defined — not improvised.
Scaling criteria establishedWhat performance thresholds must be met before the use case is expanded to additional users or business units.
Lessons learned process plannedA structured post-pilot review is scheduled to capture what worked, what didn't, and what the next use case should do differently.
Interpreting Your Results
What Your Checklist Score Tells You
Count the number of items you can answer "yes" to with confidence. Be honest — "mostly yes" and "we're working on it" count as no until they are resolved.
0–20
Not Ready
Proceeding to build at this stage carries high risk of failure. Prioritize the gaps in strategic alignment, data readiness, and governance before committing budget to a vendor or pilot.
21–32
Conditionally Ready
You can proceed with a tightly scoped pilot, but unresolved gaps must have a documented mitigation plan. Do not proceed to full build until the red items are addressed.
33–40
Ready to Build
Your foundation is strong. Proceed with confidence — but revisit this checklist at each major milestone to ensure gaps don't open as the program scales.
Separating Good from Great
How the Best AI Programs Use a Checklist Differently
Every serious AI program does some version of pre-launch review. What separates the ones that succeed is how rigorously and honestly they use it.
| Dimension | Good Practice | Great Practice |
|---|---|---|
| Timing | Review readiness after the vendor is selected | Complete the checklist before vendor conversations begin — findings shape the RFP |
| Honesty | Mark items green when they are "in progress" | Mark items red until they are fully resolved — optimism here costs millions later |
| Ownership | AI team completes the checklist internally | Business owners, legal, and IT complete their sections independently — then gaps are reconciled |
| Gap Response | Document gaps and proceed anyway | Every red item has a named owner, a resolution date, and a decision point before build proceeds |
| Cadence | Use once at program kickoff | Revisit at each phase gate — readiness gaps open as programs scale |
| Escalation | Gaps flagged in status reports | Unresolved gaps trigger a formal go/no-go decision at the executive sponsor level |
Common Questions
What Leaders Ask When Using an AI Strategy Checklist
Do we need to complete all 40 items before starting anything?
Not necessarily. Some items can be worked in parallel with early-stage activities like vendor research or data profiling. The critical threshold is the strategic alignment and data readiness sections — those must be solid before you commit budget to a build. Governance and change management items should be resolved before you go to production, not necessarily before you start a pilot.
Who should complete this checklist?
It should not be completed by one person. The AI program lead owns the process, but section owners should include the business unit sponsor, the CIO or IT lead, legal or compliance, HR or the people lead, and the data platform owner. Independent completion by each owner — followed by a reconciliation session — surfaces the real gaps far more reliably than a single team filling it out together.
What if our organization scores below 20 — is AI off the table?
No. A low score is a prioritized remediation roadmap, not a veto. The most common gaps — data quality, governance policy, and executive sponsorship — are all fixable in 8–16 weeks with focused effort. Many organizations use the checklist output as the business case for engaging an AI consultant: here are the gaps, here is what it takes to close them, here is what becomes possible when we do.
How is this checklist different from a vendor's readiness assessment?
Vendor readiness assessments are designed to move you toward a purchase decision. This checklist is designed to move you toward a good outcome — which sometimes means slowing down, fixing your data foundation, or choosing a different vendor than the one offering the assessment. The questions a vendor skips are usually the most important ones.
Need Help Working Through the Gaps?
ClarityArc runs structured AI readiness assessments that go deeper than a checklist — giving you a prioritized remediation plan and a clear path to your first successful deployment.