Core Components

Rapid Diagnostic Framework

Structured assessment of five maturity dimensions:

1. AI Strategy & Governance

• Is there a board-level AI vision and strategy?

• Is AI positioned as a driver of competitive advantage or a side initiative?

• How is AI linked to strategic goals, investment allocation, and governance?

• How frequently is AI strategy reviewed at board level?

Data Infrastructure & Foundations

• Are core data sets complete, accurate, and trusted?

• Are systems integrated to allow real-time data access?

• Are governance, ownership, and standards clearly defined?

• Are scalable, modern platforms in place, and do they support unstructured data?

Real-Time AI Capabilities

• Can the business process and act on streaming data in real time?

• How many models are deployed in production, and how are they monitored?

• Are insights embedded directly into workflows (CRM, ERP, operations)?

• Is reliability tested under load and supported by resilience mechanisms?

AI Organisation & Talent

• Is the talent mix balanced (data science, ML engineering, product)?

• Is AI expertise centralised, embedded, or hybrid?

• Are training programmes in place to improve AI/data literacy across functions?

• Are agile/DevOps/MLOps practices embedded in AI project delivery?

• Do executives actively sponsor and advocate for AI adoption?

Use Case Portfolio & Value Realisation

• Is there a complete inventory of AI initiatives across the business?

• What % of pilots are successfully scaled into production?

• Is prioritisation based on clear scoring (impact vs. feasibility)?

• Is business value (revenue uplift, cost savings, NPS) being tracked and proven?

• Are lessons learned from failed pilots shared and embedded?

External Benchmarking & Ecosystem

• How do AI capabilities compare to direct competitors and sector leaders?

• Are there active partnerships with vendors, universities, or cloud providers?

• Is intellectual property being generated or protected (patents, IP)?

• Is there systematic horizon scanning and adoption of AI innovations?

• Is the company engaged in AI forums, standards bodies, or policy work?

2. Evidence-Based Validation

Every assessment score is grounded in tangible outputs such as:

• Strategy documents, board decks, investor reports.

• Data quality dashboards, architecture diagrams, governance frameworks.

• Model deployment logs, monitoring dashboards, retraining cycles.

• Org charts, training records, agile sprint boards.

• ROI reports, KPI dashboards, investor packs.

• Benchmarking studies, patents, research outputs.

3. Board & Leadership Interviews

• Structured sessions to test alignment between board, investors, and management.

• Exploration of whether AI is seen as tactical or transformational.

• Evaluation of cultural readiness to adopt and scale AI across the business.

4. Maturity Heatmap & Gap Analysis

• A visual summary of maturity across all six dimensions.

• Identification of strengths, critical risks, and priority gaps.

• Comparative analysis versus sector best practice.

5. Roadmap & Recommendations

• Short-term (0–100 days) actions to build momentum and deliver quick wins.

• Medium-term (12–24 months) initiatives to institutionalise AI capabilities.

• Long-term (24–36 months) strategy to ensure AI maturity drives premium exit value.

Key Takeaways

• A board-ready, evidence-based assessment of AI maturity in just 1–3 days.

• Independent validation of current capabilities versus investor-grade standards.

• Deep assessment of strategy, data, capabilities, organisation, value realisation, and ecosystem.

• Alignment across board, leadership, and management on AI opportunities and risks.

• A practical roadmap that balances immediate proof points with long-term value creation.

• Clear connection between AI maturity and enhanced exit positioning.