01 · Leadership signal
Senior leaders can describe what the company looks like when AI is embedded in daily work.
Vague mandates do not change behavior. Specific vision does.
AI Experts digital diagnostic
Find the AI adoption bottleneck before it becomes an expensive ritual.
A software-first readiness scorecard for leadership teams moving from scattered experiments to a repeatable AI operating system. No deck. No binder. No dog-trash ceremony.
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Score
36
EmergingThere are pockets of progress, but scaling is fragile.
AI EXPERTS // READINESS MAP LEADERSHIP LAB CROWD [████░░]
02 · Leadership signal
Executives personally use AI in visible work, meetings, or decision preparation.
C-level use creates permission and urgency.
03 · Workflow redesign
We can name three workflows where AI could save time, improve quality, or increase capacity.
Readiness starts with real work, not tool shopping.
04 · Workflow redesign
At least one department has tested AI on production work with real constraints.
Toy examples do not expose adoption friction.
05 · Governance and trust
Employees know what data they can and cannot put into AI tools.
Clear guardrails reduce shadow use and unblock experimentation.
06 · Governance and trust
There is a defined review path for higher-risk AI use cases.
Governance should route risk, not bury it.
07 · Data and systems
Priority workflows have accessible documents, systems, or data sources.
Agentic work stalls when the inputs are trapped or unreliable.
08 · Data and systems
We know which integrations, permissions, or approvals block the first automation ideas.
A blocker list is more useful than a brainstorm list.
09 · Champions and fluency
We know which employees are already pushing AI forward informally.
The best lab members are often hiding in plain sight.
10 · Champions and fluency
Managers can coach AI use inside actual workflows, not just share prompting tips.
Adoption lives in team routines.
11 · Value measurement
Each pilot has a baseline and a measurable success criterion.
If value is not measured, pilots become theater.
12 · Value measurement
We have a repeatable process to scale, revise, or stop AI pilots.
The goal is a learning system, not a pile of experiments.
12 to 32 points
Experimenting
Curiosity exists, but the operating system is missing.
33 to 48 points
Emerging
There are pockets of progress, but scaling is fragile.
49 to 60 points
Ready to scale
The ingredients are present. Now the work is orchestration.
Research spine
Built for the messy middle between pilots and operating model.
The scorecard tests whether AI is becoming an organizational learning system, not just another software rollout.
Ethan Mollick frames AI transformation as Leadership, Lab, and Crowd: leaders set vision and incentives, the crowd discovers useful work, and the lab scales what works.
Gallup found U.S. workplace AI use nearly doubled in two years, while clear organizational plans and guidelines lag behind adoption.
Deloitte 2026 research points to workforce skills, governance, data, risk, and talent readiness as key constraints on moving from ambition to activation.
Microsoft and CMU maturity models converge on the same point: agentic AI requires strategy, process transformation, governance, data foundations, organizational readiness, and continuous measurement.
Recommended next move
Stand up a small AI lab that turns crowd discoveries into reusable workflows, then measures time, quality, and adoption lift.
Strongest signal: Value measurement. Weakest signal: Leadership signal.