GovSight AI Capability Assessment

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Knowledge Layer

Knowledge maturity

How well policies, procedures, documentation, and institutional expertise can become usable AI context.

Governance Layer

Policy and governance

Whether AI use has clear rules, ownership, approval paths, and accountable decision controls.

How mature are your AI, data-use, and decision-governance policies?

No formal policyDraft or informal policy guidanceApproved policies, but uneven adoptionPolicies are approved, operationalized, and actively reviewed

Where do humans approve or override AI-supported work?

Approval points are not definedApproval is informal and depends on the teamKey approvals are defined for high-risk workApproval gates, ownership, and audit trails are built into execution

Execution Layer

Execution maturity

Whether recurring work is stable enough to be modeled, assisted, reviewed, and improved.

How consistent is the recurring work you want AI to support?

Every team does the work differentlyThere is a rough process, but many exceptionsThe execution pattern is repeatable with known variationsThe execution pattern is modeled, measured, and ready for governed assistance

Can you see work status, blockers, and handoffs across execution?

Status is mostly discovered in meetingsSome teams track status, but not consistentlyStatus, owners, and blockers are tracked for major workExecution telemetry feeds review, governance, and improvement loops

Capability Layer

Decision intelligence

How consistently the organization makes, records, and reviews operational decisions.

How are important operational decisions recorded?

They are rarely captured after the factCaptured in notes, but hard to retrieve or reuseCaptured in systems of record for major decisionsCaptured with context, rationale, evidence, and downstream impact

Do similar decisions produce similar outcomes across teams?

No, outcomes depend heavily on who is involvedSome consistency, but no shared decision modelDecision criteria are mostly sharedDecision criteria, evidence, and review patterns are standardized

Learning Layer

Learning systems

Whether outcomes, exceptions, lessons learned, and performance signals improve future work.

What happens to lessons learned after missions, reviews, or exceptions?

They disappear or stay in meeting notesThey are captured, but rarely change daily decisionsThey feed process updates in some areasThey update knowledge, controls, execution patterns, and future recommendations

Can you measure whether AI-assisted work improves decisions or execution?

No clear baseline or performance measuresSome activity metrics, but not decision qualityOperational KPIs exist for major execution patternsKPIs, quality signals, risk events, and adoption measures are reviewed together

Architecture Layer

Enterprise sponsorship

Whether executives, operators, compliance, technology, and domain experts can align around governed AI capability delivery.

Who owns governed AI capability development at the executive level?

No clear sponsorInterested sponsor, but no operating mandateNamed executive sponsor with a defined priority areaExecutive sponsor, budget, operating mandate, and review cadence

How well do operations, compliance, technology, and domain experts collaborate?

Mostly siloedThey collaborate when a mission forces itThere is a working group for major initiativesCross-functional ownership is built into capability delivery

Complete all questions to generate the executive summary.