What is AI Maturity Model?
An AI Maturity Model is a framework that assesses an organization's current level of AI capability across dimensions like data readiness, technology infrastructure, talent, and governance, helping leaders understand where they stand and what steps are needed to advance.
What Is an AI Maturity Model?
An AI Maturity Model is a structured framework that helps organizations assess how advanced they are in adopting and leveraging artificial intelligence. It evaluates multiple dimensions of AI capability — including data infrastructure, technology, talent, processes, and culture — and places the organization at a specific maturity level, from beginner to fully optimized.
The purpose is not to label your company but to provide a clear picture of where you are today and a practical roadmap for where you need to go. It turns the abstract question "how good are we at AI?" into a concrete, actionable assessment.
The Five Levels of AI Maturity
While different frameworks use different terminology, most AI maturity models follow a progression similar to this:
Level 1: Aware
The organization recognizes that AI exists and could be valuable, but has not taken meaningful action. There are no AI projects in production, and data infrastructure is fragmented or undeveloped.
- Typical signs: Conversations about AI happen informally; no budget allocated; data sits in spreadsheets and siloed systems
Level 2: Exploring
The company is actively investigating AI opportunities. Small teams may be running experiments or evaluating vendors, but efforts are not coordinated or strategic.
- Typical signs: One or two pilot projects underway; some data consolidation efforts; AI champion identified but not empowered
Level 3: Operational
AI is deployed in at least one business function and delivering measurable value. The organization has established basic data pipelines, some AI talent, and governance policies.
- Typical signs: Production AI systems running; dedicated AI budget; cross-functional collaboration beginning; ROI being tracked
Level 4: Systematic
AI is integrated across multiple business functions with standardized processes for development, deployment, and monitoring. The organization treats AI as a core business capability.
- Typical signs: AI Center of Excellence established; reusable platforms and tools; strong data governance; AI embedded in strategic planning
Level 5: Transformational
AI fundamentally shapes the company's business model and competitive strategy. The organization continuously innovates with AI and uses it to create new products, services, and market opportunities.
- Typical signs: AI-driven business models; continuous experimentation culture; industry leadership in AI adoption; data is a strategic asset
Dimensions of Assessment
A thorough AI maturity assessment evaluates your organization across multiple dimensions:
- Data — Quality, accessibility, governance, and integration of your data assets
- Technology — Infrastructure, cloud capabilities, AI platforms, and development tools
- Talent — Skills, roles, training programs, and access to AI expertise
- Process — How AI projects are identified, prioritized, developed, and maintained
- Culture — Leadership commitment, organizational openness to change, and innovation mindset
- Governance — Policies for ethics, bias, privacy, security, and regulatory compliance
Why Maturity Models Matter for Southeast Asian SMBs
In ASEAN markets, AI maturity varies significantly not only between companies but between countries. Singapore tends to lead in AI adoption, while businesses in Indonesia, Vietnam, and the Philippines are often in earlier stages but advancing rapidly.
Understanding your maturity level helps you:
- Set realistic expectations — A Level 1 company should not attempt a Level 4 initiative
- Prioritize investments — Spend resources on the capabilities that will create the most progress
- Benchmark against peers — Understand how you compare to competitors in your market
- Communicate progress — Give leadership and investors a clear framework for measuring advancement
- Avoid costly mistakes — Skipping maturity stages often leads to failed projects and wasted investment
How to Conduct a Maturity Assessment
A practical AI maturity assessment typically involves:
- Stakeholder interviews with leaders across business functions
- Data infrastructure audit to assess quality, accessibility, and governance
- Technology inventory of existing systems, tools, and platforms
- Skills gap analysis to identify talent needs
- Process review of how decisions are made and work gets done
- Scoring and benchmarking against industry standards
- Roadmap creation with specific actions to advance to the next level
For CEOs and CTOs, an AI maturity model provides the objective, structured assessment needed to make smart investment decisions. Without understanding your current maturity level, you risk either over-investing in advanced AI capabilities your organization cannot support or under-investing in foundational capabilities that everything else depends on.
The business case is compelling: organizations that align their AI investments with their actual maturity level see significantly higher success rates on AI projects. Those that try to skip stages — deploying advanced machine learning before building proper data infrastructure, for example — almost always fail and waste significant resources.
In the competitive ASEAN market, where digital transformation is reshaping industries from fintech to logistics to retail, knowing your AI maturity level is strategic intelligence. It helps you identify the fastest path to competitive advantage and ensures your AI roadmap is grounded in reality rather than aspiration. Many forward-thinking companies in Singapore, Indonesia, and Thailand are using maturity assessments to guide their multi-year AI investment strategies.
- Conduct a formal maturity assessment before creating your AI strategy or allocating AI budget
- Assess all dimensions — data, technology, talent, process, culture, and governance — not just technology
- Be honest about your current level; overestimating maturity leads to failed projects
- Focus on advancing one level at a time rather than trying to leap from Level 1 to Level 4
- Reassess maturity every 6 to 12 months to track progress and adjust your roadmap
- Use the assessment to identify your biggest bottleneck — often it is data quality or organizational culture, not technology
Frequently Asked Questions
Where do most SMBs in Southeast Asia fall on the AI maturity scale?
Most SMBs in the ASEAN region are at Level 1 (Aware) or Level 2 (Exploring). They recognize AI is important and may have run small experiments, but have not yet deployed AI in production. Companies in Singapore and parts of Malaysia tend to be further along, while businesses in emerging markets are often in the early awareness stage. This represents a significant opportunity for early movers.
How long does it take to move up one maturity level?
Moving up one level typically takes 6 to 18 months depending on the organization size, starting point, and level of investment. Moving from Level 1 to Level 2 can happen relatively quickly with the right leadership commitment. Moving from Level 2 to Level 3 usually takes longer because it requires building data infrastructure and deploying production AI systems.
More Questions
Attempting to skip levels is one of the most common causes of AI project failure. Each level builds foundational capabilities that the next level depends on. For example, you cannot successfully deploy production AI systems (Level 3) without having solid data infrastructure and at least basic AI talent (Level 2). The fastest path to advanced AI maturity is to build each stage thoroughly.
Need help implementing AI Maturity Model?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how ai maturity model fits into your AI roadmap.