Overall Readiness Analysis
Moderate Health, High Potential
Example Financial Services shows an emerging AI readiness foundation with meaningful progress in leadership alignment and vendor oversight. The most important gaps are policy maturity, risk tiering, evidence collection, and monitoring practices that can support responsible scaling.
This sample output illustrates how the completed assessment will translate user responses into an executive-ready readiness view. The organization appears ready to move from informal AI activity toward a more governed operating model with clearer ownership, controls, and roadmap sequencing.
62
Key Recommended Actions
Governance Focus
Formalize AI decision rights, policy ownership, and approval standards before expanding use cases.
Risk Focus
Prioritize risk assessment, data governance, and model monitoring controls for high-impact AI workflows.
Roadmap Focus
Create a 90-day roadmap that converts governance gaps into accountable workstreams.
Domain Breakdown
Findings and recommendations by category
Strategy & Leadership
Domain score
74
Analysis
Leadership alignment is forming, but board-level reporting and executive ownership need to become more explicit.
Recommended Actions
Name an executive owner for AI strategy, risk acceptance, and roadmap accountability.
Add AI readiness, risk, and performance updates to recurring leadership or board reporting.
Policy & Standards
Domain score
48
Analysis
AI usage standards appear early and may not yet give employees clear direction on acceptable use, approvals, and documentation.
Recommended Actions
Create a practical AI acceptable-use policy for employees and business teams.
Define approval expectations for internal tools, customer-facing AI, vendor AI, and GenAI use.
Risk Management
Domain score
55
Analysis
Risk practices are emerging, but AI use cases need consistent assessment, tiering, and escalation thresholds.
Recommended Actions
Introduce an AI risk intake and tiering process before new use cases go live.
Connect AI risks to the enterprise risk register with clear ownership and escalation rules.
Model Lifecycle & Validation
Domain score
58
Analysis
Model inventory and validation controls are partially in place but may not yet provide independent challenge for high-risk models.
Recommended Actions
Create a production AI inventory with owner, version, risk tier, and model card details.
Require independent validation for high-risk models before deployment.
Ethics, Fairness & Equity
Domain score
52
Analysis
Fairness and human review expectations need stronger thresholds, documentation, and customer impact controls.
Recommended Actions
Define fairness testing thresholds for customer-facing or high-impact AI systems.
Document when human review, plain-language explanations, and appeal paths are required.
Data Governance For AI
Domain score
64
Analysis
Data governance provides a useful starting point, but sensitive training data and decision logging need stronger traceability.
Recommended Actions
Inventory sensitive data used for AI training and confirm legal basis, access, and de-identification controls.
Retain AI inputs and outputs at a level that supports auditability and decision reconstruction.
Vendor & Third-Party AI
Domain score
67
Analysis
Vendor AI oversight is improving, though due diligence and public GenAI controls should be made more consistent.
Recommended Actions
Add AI-specific questions, audit rights, and model-change notification expectations to vendor review.
Use approved tool lists, training, and DLP controls to reduce sensitive data exposure in public GenAI tools.
Monitoring & Incident Response
Domain score
50
Analysis
Monitoring and incident response practices need clearer owners, alerts, and tested playbooks before AI scale increases.
Recommended Actions
Define drift, fairness, leakage, and incident monitoring requirements for production AI.
Test AI incident response procedures with business, legal, risk, security, and technology stakeholders.
Prioritized Roadmap
First
Policy and risk foundation
Create AI acceptable-use, approval, and documentation standards.
Launch a risk intake and tiering process for new AI use cases.
Next
Data, model, and vendor controls
Build inventories for production AI systems, sensitive training data, and third-party AI tools.
Define validation, logging, and vendor diligence requirements for high-risk AI.
Then
Monitoring and accountable scale
Implement monitoring requirements for drift, fairness, leakage, and incidents.
Review progress with executive sponsors and convert the roadmap into an operating rhythm.
Next Step
Ready for a deeper dive assessment and discovery?
Kona Kai can validate these findings with stakeholders, review supporting evidence, and convert the roadmap into an execution plan.