The AI Runtime & Governance Readiness Diagnostic™

The AI Runtime Readiness Diagnostic is designed to be referred by engineers and completed by leadership.

It helps enterprises:

• Assign runtime ownership
• Define escalation and stop rules
• Approve autonomy by risk level
• Make assumptions explicit before they fail quietly

Engineers receive documented decisions and constraints — so they can build and operate without governance guesswork.

Engineers implement decisions. They should not be forced to invent them.

This assessment is for engineers and technical teams who are being asked to build, integrate, or scale AI without clear ownership, guardrails, or decision authority.

👩‍💻 Engineers & Technical Leads

• Backend, platform, data, ML, or full-stack engineers
• Staff / Principal Engineers responsible for system design decisions
• Engineers supporting AI pilots, demos, or production systems

🧠 Architects & Platform Owners

• Enterprise, solution, or cloud architects
• Platform teams defining runtime, orchestration, or integration patterns
• Teams responsible for long-term system health, not just delivery

⚙️ AI & Automation Builders

• Teams building copilots, agents, or workflow automations
• Engineers integrating LLMs into existing business systems
• Developers navigating API limits, cost volatility, or vendor constraints

🚧 Engineers Stuck in the Middle

This is especially for teams who:

• Are blocked by unclear ownership or risk decisions
• Are asked to “just make it work” without policy clarity
• Are absorbing governance, compliance, or escalation risk by default
• Are expected to move fast while guessing where the boundaries are

This diagnostic should be completed by the people who own decisions, not the people who implement them.

The assessment is designed for enterprise leadership and decision owners, including:

• Executive sponsors of AI initiatives
  (CEO, COO, CIO, CTO, CDO, CAIO)

• Product and platform owners
  Responsible for AI-enabled systems, copilots, agents, or automation platforms
   
• Risk, compliance, and governance leaders
  Accountable for oversight, escalation, and control once systems are live
   
• Operations and finance leaders
  Who own cost, performance, and sustainability after launch
   

These roles are best positioned to answer questions about ownership, escalation, autonomy, and stop rules.

• Engineers and delivery teams
• Architects and platform teams
• AI/ML practitioners building or integrating systems
   

Engineers often refer the diagnostic when they hit governance or decision boundaries — but they should not be expected to answer leadership-level questions.

This is not a technical skills test or a tooling evaluation.

It does not:

• Recommend specific LLMs, vendors, or frameworks
• Score your engineering team’s capability or maturity
• Audit code, architecture diagrams, or infrastructure
• Replace security, compliance, or risk reviews
• Tell engineers how to implement a solution
   

It is also not:

• A generic AI maturity score
• A “how ready are you for AI?” quiz
• A demo checklist or pilot validation
• A leadership vision exercise without operational follow-through
   

This assessment deliberately avoids surface-level metrics and model comparisons — because those are not where AI failures originate.

Most AI initiatives don’t fail because of bad models or weak engineering.

They fail because critical decisions are never made — and engineers are left to absorb the risk.

This assessment exists to surface and resolve the runtime decisions that usually stay implicit, including:

• Who owns the system after launch
• What the system is allowed to do — and when it must stop
• How risk, cost, and escalation are handled in real usage
• Where autonomy is acceptable — and where human control is required
• Which assumptions must be revisited once reality changes

In many organizations, these decisions:

• Are assumed instead of approved
• Live in people’s heads instead of policy
• Are discovered only after costs spike or behavior drifts
   

This diagnostic creates a decision checkpoint before engineers are asked to implement or scale.