Is your organization actually AI-ready?
Roughly 80% of enterprise AI initiatives stall before production — and almost never because the model was bad. They stall on the six dimensions below. Score yourself honestly: each checked item is a point, 24 possible.
Data Estate
Can you answer, today, where the data for your top use case lives, who owns it, and what its quality is?
- A named owner exists for each critical dataset
- Access takes days, not quarters of committee meetings
- Lineage is traceable for data feeding any model
- PII handling is policy, not tribal knowledge
Infrastructure & Platform
Could a team deploy a model behind an authenticated API this month using approved tooling?
- A paved path exists: registry, CI/CD, serving, monitoring
- GPU/inference capacity is requestable, not heroic
- Environments reproduce — laptop to prod parity
- Cost telemetry per workload exists from day one
Talent & Literacy
Do you have builders, translators, and informed executives — not just one hero hire?
- At least one team has shipped ML/LLM features to production
- Domain experts are budgeted into AI projects, not borrowed
- Leadership can distinguish a demo from a deployment
- A build/buy/partner doctrine exists and is used
Governance & Risk
If a regulator or board member asked how a model decision was made, could you reconstruct it?
- Model inventory exists with owners and risk tiers
- Validation and approval gates scale with risk
- Audit trails capture inputs, versions, and overrides
- An incident path exists for model failures, like outages
Use-Case Discipline
Are initiatives selected on value and feasibility, or on executive enthusiasm?
- Every initiative names a metric and a baseline before build
- A kill criterion is written before the pilot starts
- Adoption is engineered (latency, UX, workflow fit), not assumed
- Portfolio reviews retire zombies quarterly
Operating Model
When a pilot works, does a route to production funding and ownership exist?
- Product, not project, ownership for AI capabilities
- Run-cost budgets survive the fiscal year boundary
- Central enablement + embedded delivery, not ivory tower
- Procurement can evaluate AI vendors on evidence
Build the floor first
Don't fund model work yet. Sequence: data ownership, one paved deployment path, one literate team. A focused two-quarter foundation program beats three stalled pilots — and costs less.
Ship one thing end-to-end
You can support a narrow production win. Pick one use case with a named metric, engineer adoption deliberately, and use it to force the missing governance and platform muscle into existence.
Industrialize the portfolio
Your constraint is throughput, not capability. Invest in platform leverage, portfolio governance, and unit-economics discipline so the tenth use case costs a fraction of the first.