How to Measure AI Maturity: A 5-Level Framework for Enterprises

Executive Summary

• AI maturity measures how sophisticated your organization's AI capabilities are—distinct from AI readiness, which measures preparation to start
• This framework defines five maturity levels: Initial, Developing, Defined, Managed, and Optimizing
• Each level has specific characteristics across six dimensions: Strategy, Data, Technology, People, Process, and Governance
• Organizations should assess their current level, then focus on the capabilities needed to reach the next level—not skip ahead
• Maturity assessment enables benchmarking against peers, identifying gaps, and prioritizing investments
• Most organizations in Southeast Asia operate at Levels 1-2; reaching Level 3 represents a significant competitive advantage
• Annual maturity assessment helps track progress and adjust strategy

Why This Matters Now

As AI moves from experimentation to operational reality, executives need a way to answer three questions:

1. Where are we today? Honest assessment of current capabilities
2. Where should we be? Target state based on strategy and competitive position
3. What's the gap? Specific capabilities to develop

A maturity framework provides the structure to answer these questions objectively. Without it, organizations rely on gut feel—often resulting in either overconfidence or unnecessary anxiety.

The pressure is real. Organizations operating at higher maturity levels demonstrate 30-50% better outcomes from AI initiatives. They ship faster, fail less expensively, and scale more effectively. Understanding where you stand—and what it takes to advance—is strategic intelligence.

AI Maturity vs. AI Readiness: A Critical Distinction

Before diving into the framework, let's clarify the relationship between maturity and readiness:

If you haven't deployed any AI systems, start with AI readiness assessment. If you've begun implementation and want to benchmark your capabilities, maturity assessment is the right tool.

Organizations sometimes have pockets of maturity (one team at Level 3) while the broader organization remains at Level 1. This framework can be applied at team, department, or enterprise level.

The 5-Level AI Maturity Framework

Level 1: Initial

Characteristics: Ad hoc experimentation with no coordination

At Level 1, AI activity exists but lacks structure. Individual teams experiment with tools like ChatGPT or build isolated proofs of concept. There's no organizational strategy, governance, or coordination.

Typical behaviors:

• Employees use ChatGPT for personal productivity without guidance
• IT discovers AI tools in use during security audits
• No budget specifically allocated to AI
• "AI initiatives" are side projects for enthusiastic individuals

What success looks like: Awareness that AI exists and informal exploration is happening.

Level 2: Developing

Characteristics: Coordinated pilots with basic infrastructure

At Level 2, the organization has moved from ad hoc experimentation to deliberate pilots. There's executive awareness, some budget allocation, and initial attempts at coordination.

Typical behaviors:

• Executive sponsor for AI initiatives identified
• 1-3 formal pilots running with defined success criteria
• IT engaged in AI technology evaluation
• Initial AI training programs offered
• Draft AI usage policy circulated

What success looks like: Successful pilots with documented learnings and a path to scaling.

Level 3: Defined

Characteristics: Standardized practices and repeatable success

Level 3 represents a significant milestone: the organization can reliably deliver AI value. Practices are documented, roles are formalized, and AI is part of operational planning—not just innovation experiments.

Typical behaviors:

• AI governance committee meets regularly
• Standard process for evaluating and approving AI use cases
• Dedicated AI budget line item
• Multiple AI systems in production
• Documented playbook for AI project delivery
• Regular AI training curriculum

What success looks like: Consistent, repeatable delivery of AI value with managed risk.

Level 4: Managed

Characteristics: Quantitative management and continuous improvement

At Level 4, AI performance is measured systematically. Decisions about AI investment are data-driven. The organization optimizes existing AI systems and identifies new opportunities proactively.

Typical behaviors:

• AI ROI measured and reported at board level
• Automated model performance monitoring
• AI systems retrained on schedule or when drift detected
• Cross-functional AI working groups optimize outcomes
• AI governance dashboard with real-time metrics

What success looks like: Quantified AI value with continuous improvement mechanisms.

Level 5: Optimizing

Characteristics: AI-native organization driving industry innovation

Level 5 organizations have AI embedded in their DNA. They're not just using AI—they're advancing the state of practice. These organizations set industry standards and attract top AI talent.

Typical behaviors:

• AI is core to value proposition, not just operational efficiency
• Organization contributes to AI research and standards
• AI talent actively recruits to join
• Industry looks to organization as AI reference
• Sophisticated AI ethics and governance as differentiator

What success looks like: AI as competitive moat and source of industry leadership.

How to Assess Your Maturity Level

Step 1: Gather Evidence

For each of the six dimensions, collect evidence of current practices:

• Documents (policies, strategies, procedures)
• Metrics (KPIs, dashboards, reports)
• Interviews (stakeholders across functions)
• Observations (tools in use, behaviors)

Step 2: Rate Each Dimension

Score each dimension from 1-5 based on the indicators above. Be honest—overrating yourself defeats the purpose.

Step 3: Identify Your Overall Level

Your overall maturity level is typically the lowest of your dimension scores. Why? Because maturity depends on coherence—a Level 4 strategy with Level 1 governance creates risk, not value.

Step 4: Compare to Target

Based on your industry, competitive position, and strategic priorities, determine your target maturity level. The gap between current and target drives your roadmap.

RACI Matrix: AI Maturity Governance

Clear accountability is essential for maturity advancement. Here's a RACI example for key AI maturity activities:

Key:

• R = Responsible (does the work)
• A = Accountable (final decision maker)
• C = Consulted (provides input)
• I = Informed (kept updated)

Common Failure Modes

1. Overestimating Maturity

Organizations often rate themselves higher than reality warrants. Having ChatGPT access doesn't make you Level 2; having one successful pilot doesn't make you Level 3.

Fix: Require evidence for each rating. "We have a governance committee" means nothing if it's never met.

2. Focusing on Technology Only

Maturity requires advancement across all six dimensions. Organizations that invest only in technology plateau quickly.

Fix: Assess all dimensions and address the lowest-scoring areas first.

3. Trying to Skip Levels

Each level builds on the previous. You can't reach Level 4 without Level 3 foundations. Attempting to skip creates capability gaps that undermine AI systems.

Fix: Focus on reaching the next level, not the ultimate destination.

4. One-Time Assessment

Maturity changes over time—ideally improving, but sometimes degrading (key people leave, priorities shift). A single assessment provides a snapshot, not a trajectory.

Fix: Assess annually and track trends.

Checklist: AI Maturity Assessment

Preparation

• Identified assessment scope (enterprise, division, function)
• Assembled cross-functional assessment team
• Gathered relevant documentation and evidence
• Scheduled stakeholder interviews
• Defined target maturity level based on strategy

Assessment Execution

• Rated each of six dimensions with evidence
• Identified overall maturity level (lowest dimension)
• Documented gaps between current and target
• Validated findings with stakeholders
• Identified quick wins vs. longer-term investments

Post-Assessment

• Prioritized improvement initiatives
• Assigned owners to each initiative (see RACI)
• Established metrics for tracking progress
• Scheduled follow-up assessment (6-12 months)
• Communicated findings and roadmap to leadership

Metrics to Track

Frequently Asked Questions

Next Steps

Understanding your maturity level is the starting point. The value comes from closing the gap between current state and strategic target.

If you're uncertain about your current level or how to advance, a formal assessment provides clarity and actionable recommendations.

Book an AI Readiness Audit with Pertama Partners to benchmark your maturity and develop a practical advancement roadmap.

References

1. Carnegie Mellon Software Engineering Institute. "Capability Maturity Model Integration (CMMI)." Framework reference.
2. McKinsey & Company. "AI Maturity Framework." 2023.
3. MIT Sloan Management Review. "Winning with AI." Research Report, 2024.
4. Singapore Infocomm Media Development Authority. "Model AI Governance Framework." Second Edition, 2020.

Related Reading

• What Is an AI Readiness Assessment? A Complete Guide
• AI Readiness Checklist: 25 Questions
• How to Prevent AI Data Leakage