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AI Readiness Assessment
Equip your company operations with a structured framework to diagnose AI maturity, prioritize remediation, and track progress—without external supervision.
The AI Value Gap
Decision-makers across industries are increasingly recognizing artificial intelligence (AI) as a pivotal lever for unlocking operational efficiency, driving innovation, and achieving sustainable value creation. Despite this growing awareness and enthusiasm, the reality on the ground reveals a significant readiness gap.
To bridge this gap, organizations need more than isolated pilot programs or ad hoc experimentation—they require a comprehensive, strategic framework to assess and strengthen their AI capabilities. That’s where our self-contained AI readiness assessment tool comes into play.
This tool is designed to deliver a holistic and actionable evaluation by synthesizing best-in-class industry frameworks with your organization’s unique value-creation priorities.
Seven Dimensions of AI Readiness
Strategic Alignment
Board-approved AI strategy linked to key KPIs and value-creation plan
Data Readiness
Data completeness, accuracy, timeliness, and lineage with unified data fabric
Technology Infrastructure
Cloud-native architecture with MLOps pipeline for versioning, testing, and rollback
Organizational Capabilities
C-suite sponsorship, clear accountability, and enterprise-wide change management
Talent & Skills
Production-grade ML engineers and upskilling programs mapped to competency frameworks
Governance & Ethics
Responsible-AI charter aligned to ISO 42001/GDPR with model-risk validation procedures
Implementation
Reusable code templates, reference architectures, and automated performance monitoring
Five Maturity Levels
Level 1: Unprepared
Ad-hoc initiatives with no formal strategy, limited data infrastructure, and minimal AI awareness across the organization.
Level 2: Developing
Emerging capabilities with initial pilots, basic data governance, and growing executive awareness of AI potential.
Level 3: Defined
Standardized approach with MLOps sandbox, AI metrics in operational reviews, and formal governance frameworks.
Level 4: Managed
Scaled implementation with cross-BU leverage, predictive AI solutions, and real-time model monitoring.
Level 5: Leading
Industry-leading capabilities with monetized AI assets, regulatory influence, and premium valuation multiples.
Tailored Recommendations by Tier
Unprepared
  • Appoint an executive AI sponsor and assign clear accountability
  • Launch urgent data-clean-up sprints for critical processes
  • Initiate board-level AI awareness sessions
  • Complete baseline data lake or warehouse MVP
Developing
  • Stand up a Centre of Excellence (COE) for AI
  • Execute 2-3 rapid pilots in high-impact functions
  • Formalise data governance basics
  • Document and communicate at least one pilot ROI case study
Defined
  • Deploy an MLOps "sandbox" for standardising experimentation
  • Embed AI metrics into regular operational reviews
  • Formalize model risk and governance frameworks
  • Enable 30% of mission-critical workflows with AI
Advanced Maturity Actions
Managed
  • Share and replicate successful use cases across the organization
  • Implement predictive and prescriptive AI solutions beyond core automation
  • Establish real-time model monitoring and agile retraining processes
  • Achieve 5% or greater EBITDA uplift attributable to AI
Leading
  • Monetize proprietary AI assets or algorithms
  • Lead industry consortia and achieve regulatory influence
  • Adopt latest-generation AI with rigorous risk controls
  • Realize premium valuation multiples at exit linked to AI assets
How To Use Assessment Results
Gap Analysis
Review heat-map outputs or radar charts for lowest dimension scores; prioritise investments and interventions in these areas.
Quarterly Reassessment
Repeat the assessment regularly; expect to "move up" at least one tier per year with focused effort.
Resource Allocation
Use scoring as a tool for budget planning, talent development, and technology investment.
Cross-Portfolio Application
Share success patterns and avoidable pitfalls between portfolio companies to raise baseline performance.
Strategic Impact on Value Creation

A rigorous AI readiness assessment delivers clarity, direction, and accountability not just for today's initiatives, but for ongoing, sustainable, and resilient value creation.
This assessment framework provides organization-wide visibility into AI readiness, aligns remediation with value-creation goals, and standardizes reporting across diverse operating companies.
Consistent application accelerates AI maturity curves, supports higher valuation, and embeds data-driven culture across the fund. It transforms AI from an abstract ambition into a core driver of competitive advantage and exit success.