There is a question that comes up in nearly every boardroom conversation about artificial intelligence: Where do we actually stand?
Not where leadership hopes the company stands. Not where the AI vendor's pitch deck suggests. Where the organization genuinely sits in terms of AI capability, readiness, and production deployment.
The answer, for most Asian small and medium businesses, is earlier than they think. According to the Pertama Partners AI Maturity Model, 73% of Asian SMBs currently sit at Stage 1 (AI Aware) or Stage 2 (AI Experimenting) — meaning they have either discussed AI or run a few pilots, but have not deployed a single AI system into production (Pertama Partners, AI Maturity Model for Asian Businesses, 2026). Globally, the picture is similarly humbling: only 1% of leaders describe their organizations as truly mature in AI deployment, and for Asian SMBs specifically, that figure drops below 0.5% (Pertama Partners, AI Maturity Model for Asian Businesses, 2026).
The gap between AI awareness and AI maturity is not just an academic distinction. It is a revenue gap. Companies at Stage 3 and above report 2.5 times higher revenue growth than peers stuck at Stage 1-2 (Pertama Partners, AI Maturity Model for Asian Businesses, 2026). Every month an organization spends at the wrong stage without a clear plan to advance is a month its competitors may be pulling further ahead.
This post introduces the Pertama AI Maturity Scorecard — a 20-point diagnostic tool that gives you an honest, structured assessment of where your organization sits today, and a clear picture of what it takes to move forward.
The Five Stages of AI Maturity
Before diving into the assessment, it helps to understand the framework. The Pertama AI Maturity Model defines five progressive stages that describe how organizations adopt and operationalize AI.
Stage 1: AI Aware (38% of Asian SMBs)
The organization knows AI exists as a business capability. Leadership has attended conferences, read reports, or engaged consultants. Individual employees might use ChatGPT for personal productivity. But there are no formal pilots, no AI budget, and no structured activity. AI appears in strategic conversations but not in operational plans.
Stage 2: AI Experimenting (35% of Asian SMBs)
One to three pilot projects are underway. The company has subscribed to AI SaaS tools. Someone has been designated as the AI lead, usually on top of their existing job. Pilot results are anecdotal rather than measured against clear KPIs. There is energy and enthusiasm, but nothing is in production.
Stage 3: AI Implementing (19% of Asian SMBs)
This is where the gap opens. At least one AI system runs in production — meaning it directly affects business operations, customer interactions, or decision-making on an ongoing basis. Outcomes are measured against business KPIs. A formal AI budget exists. Data governance is in place. The organization has crossed from experiment to operational reality.
Stage 4: AI Scaling (4% of Asian SMBs)
Multiple AI systems operate across different business functions. The organization has internal governance, repeatable deployment processes, and the ability to launch new AI use cases without starting from scratch. AI is no longer a single project — it is a cross-functional capability.
Stage 5: AI-Native (<1% of Asian SMBs)
AI is embedded in the organization's core operating model. The business could not function at its current level without AI. This stage is exceptionally rare — these are effectively AI companies that happen to operate in traditional industries.
The numbers paint a stark picture. Only 4% of Asian SMBs have reached Stage 4 or beyond (Pertama Partners, AI Maturity Model for Asian Businesses, 2026). The vast majority are clustered in the first two stages, where AI enthusiasm has not yet translated into production value.
The Five Assessment Dimensions
The Pertama AI Maturity Scorecard evaluates organizations across five dimensions, with four questions per dimension. Each question is scored from 1 to 4, producing a total score between 20 and 80 that maps directly to a maturity stage.
Here is what each dimension measures and why it matters.
Dimension 1: Strategy
Strategy assesses whether AI has moved from a talking point to an operational priority. It examines leadership commitment, budget allocation, use case identification, and competitive awareness.
Sample questions:
- AI Vision and Leadership Commitment: Does your organization have a formal AI strategy approved by leadership, with defined objectives, timelines, and budget? Or is AI still discussed in general terms during planning sessions?
- AI Budget and Investment Allocation: Is there a dedicated AI budget line item with projected ROI expectations? Or is AI spending ad hoc and untracked?
- AI Use Case Prioritization: Does a structured process exist for identifying, evaluating, and prioritizing AI use cases based on business value, feasibility, and data availability? Or are potential use cases discussed informally without evaluation?
What to watch for: A company might score 3 or 4 on Strategy questions because leadership is enthusiastic and a budget exists — but score 1 on Data or Technology. This is the most common imbalance among Asian SMBs. Ambition without infrastructure is a recipe for failed pilots.
Dimension 2: Data
Data is the foundation that every AI system rests on. This dimension evaluates data availability, quality, governance, and whether the organization has data assets suitable for AI model training.
Sample questions:
- Data Availability and Accessibility: Is key business data consolidated in a centralized system with cross-functional access? Or is data siloed in departmental spreadsheets and disconnected systems?
- Data Quality and Consistency: Have data quality standards been defined for AI-relevant data sets, with automated validation? Or is data cleaning done ad hoc before specific reports?
- Data Governance and Privacy: Does a data governance framework exist covering data classification, access controls, retention, and privacy compliance for AI use cases?
What to watch for: Software licenses account for only 30-50% of total AI implementation costs. Integration, data preparation, and technical implementation consume the remaining 40-50%. If your Data dimension scores are low, any AI investment you make will be more expensive and less effective than it should be.
Dimension 3: Technology
Technology measures whether the organization has the infrastructure to run AI workloads, the tools to deploy them, and the security to protect them.
Sample questions:
- Cloud and Infrastructure Readiness: Does your cloud infrastructure support AI workloads? Is there at least one cloud AI/ML platform in use?
- AI Tool and Platform Adoption: Are AI tools integrated with core business systems like CRM and ERP, or are they standalone departmental subscriptions?
- Security and AI Risk Management: Are AI-specific security controls implemented — access management, data encryption for AI workloads, monitoring for model integrity?
What to watch for: Many Stage 2 companies have subscribed to several AI SaaS tools but lack the integration layer that makes those tools useful in production. A chatbot that is not connected to your CRM is a demo, not a deployment.
Dimension 4: People
People evaluates the human side of AI adoption — literacy across the organization, technical talent, change management capability, and responsible AI awareness.
Sample questions:
- AI Literacy Across the Organization: Has a structured AI literacy program been delivered to at least 50% of employees? Or is AI understanding confined to leadership and IT?
- AI Technical Talent: Does the organization have access to AI technical talent sufficient to deploy and maintain AI systems in production — whether internal hires, contractors, or managed service providers?
- Change Management for AI Adoption: Does structured change management accompany every AI deployment — stakeholder analysis, training, feedback loops, and dedicated support?
What to watch for: 75% of Asia-Pacific employers struggle to find the AI talent they need (Pertama Partners, AI Maturity Model for Asian Businesses, 2026). If your People score is low, do not assume you can solve it by hiring. Consider managed AI service providers, fractional roles, and structured upskilling programs.
Dimension 5: Process
Process measures whether AI is managed as a disciplined capability with clear project methodologies, performance measurement, and continuous improvement — or whether it is a collection of ad hoc experiments.
Sample questions:
- AI Project Management and Delivery: Does a defined AI project methodology exist covering discovery, data assessment, development, testing, deployment, and monitoring?
- AI Performance Measurement: Are KPIs defined for each AI initiative and tracked regularly, with business impact reported to leadership?
- AI-Augmented Decision-Making: Are AI-generated insights a standard input to major business decisions — pricing, inventory, hiring, marketing spend?
What to watch for: Process is where Stage 3 companies often stall on their way to Stage 4. The first AI system worked because a talented team delivered it. The second and third require repeatable processes, templates, and playbooks. Without them, every new use case feels like starting over.
How to Interpret Your Score
Once you have answered all 20 questions and totaled your score, map it to a maturity stage:
| Total Score | Maturity Stage | What It Means |
|---|---|---|
| 20-30 | Stage 1: AI Aware | No meaningful AI capability. Focus on education, strategy development, and data readiness. |
| 31-42 | Stage 2: AI Experimenting | Early experimentation is underway. Focus on selecting the right first production use case and building data foundations. |
| 43-55 | Stage 3: AI Implementing | AI is in production. Focus on measuring impact, expanding to additional use cases, and building repeatable processes. |
| 56-68 | Stage 4: AI Scaling | AI operates cross-functionally. Focus on optimization, governance at scale, and advanced capabilities. |
| 69-80 | Stage 5: AI-Native | AI is core to the business. Focus on continuous innovation and market leadership. |
Beyond the Total: Reading Your Dimension Scores
The total score tells you your stage. The dimension breakdown tells you why you are at that stage and what is holding you back.
Calculate the average score for each dimension (sum of 4 question scores, divided by 4). Then look for imbalances.
The most common pattern among Asian SMBs is high Strategy (3.0-3.5 average), moderate People (2.0-2.5), and low Data and Technology (1.5-2.0). This reflects the fact that AI awareness and strategic intent have outpaced foundational capability building. The organization wants to use AI and has allocated budget and leadership attention — but the data infrastructure, cloud readiness, and technical integration required to move AI into production are not yet in place.
Other patterns to watch for:
- High Strategy, Low Process: The organization makes bold AI plans but does not have the project management discipline to execute them. Pilots start but do not finish.
- High Technology, Low People: The tools are in place, but nobody knows how to use them effectively. AI systems are deployed but underutilized or abandoned because change management was neglected.
- High People, Low Data: The team is enthusiastic and trained, but the data they need to work with is fragmented, dirty, or inaccessible. This is common in retail and professional services.
What to Do Next Based on Your Stage
Knowing your stage is valuable only if it leads to action. Here is what the research says about progression timelines, investment levels, and critical success factors at each stage.
If You Scored Stage 1 (AI Aware)
Timeline to Stage 2: 3-6 months Estimated investment: USD 10,000-30,000
Your priorities are straightforward: appoint an AI lead (even part-time), audit your data, run one AI literacy workshop for leadership, subscribe to 1-2 AI SaaS tools, and identify your first pilot use case. Do not try to do everything. Constrain ruthlessly. The goal is to generate the organization's first structured experience with AI.
If You Scored Stage 2 (AI Experimenting)
Timeline to Stage 3: 6-14 months (average 14 months for those who succeed) Estimated investment: USD 30,000-150,000
This is the most dangerous transition. 60% of Stage 2 companies never reach Stage 3 (Pertama Partners, AI Maturity Model for Asian Businesses, 2026). The key is to commit to a production deployment from the start — not a pilot that might become production, but a system designed for production from day one. Budget 40% of the project for data preparation. Define success metrics before deployment. Commit to a 12-month evaluation period.
If You Scored Stage 3 (AI Implementing)
Timeline to Stage 4: 12-24 months Estimated investment: USD 150,000-500,000
Your first AI system is in production. Now build a repeatable deployment process before launching use case number two. Invest in a unified data platform. Establish formal AI governance. Secure sustainable AI talent — either internal or through a managed services partner. The Stage 3-to-4 transition is an infrastructure investment disguised as a scaling decision.
If You Scored Stage 4 (AI Scaling)
Timeline to Stage 5: 24-36+ months Estimated investment: USD 500,000+ annually
Consider whether Stage 5 is actually your target. Not every organization needs to be AI-native. For many Asian SMBs, Stage 4 — AI operating effectively across the business — is a strong, defensible position. Stage 5 is appropriate when AI capability is the primary competitive differentiator and the market environment supports it.
The Revenue Case for Advancing
If the assessment reveals your organization is at Stage 1 or 2, the business case for advancing is not theoretical. Companies at Stage 3 and above report 2.5 times higher revenue growth and 34% lower operational costs in AI-augmented functions (Pertama Partners, AI Maturity Model for Asian Businesses, 2026). BCG research shows that AI-mature companies achieve USD 3.70 in value for every dollar invested, with top performers reaching USD 10.30. Meanwhile, 91% of SMEs using generative AI report measurable efficiency gains — the challenge is not whether AI delivers value, but whether your organization can move past the experimentation stage to capture it (Pertama Partners, AI Maturity Model for Asian Businesses, 2026).
The cost of inaction is compounding. As more competitors advance through the maturity stages, the penalty for remaining at Stage 1-2 increases. AI adoption among firms globally has more than doubled from 8.7% in 2023 to 20.2% in 2025. In Asia-Pacific, the pace is accelerating. The window for catching up is not infinite.
Access the Full Scorecard
This post has introduced the five dimensions and provided sample questions from each. For the complete framework including the full 20-point scorecard, stage-by-stage playbook, and tool recommendations, read AI Maturity Model for Asian Businesses. The full paper includes all 20 questions with detailed scoring rubrics, industry-specific advancement pathways, budget guidance for every stage transition, and an analysis of the four "death valleys" that cause most organizations to stall.
Take the Next Step
A self-assessment gives you a baseline. A facilitated assessment gives you a plan. The difference is the difference between knowing you are at Stage 2 and knowing exactly what to do to reach Stage 3 — with realistic timelines, budget ranges, and a prioritized action plan tailored to your industry and market.
Ready to advance your organization's AI maturity? Book a consultation with Pertama Partners.
Frequently Asked Questions
Use the 20-point scorecard across five dimensions: data readiness (quality, accessibility, governance), talent and skills, technology infrastructure, organisational culture, and strategic alignment. Each dimension is scored 1-4, from ad-hoc to optimised.
Most Asian SMBs score between Level 1 (Ad-hoc) and Level 2 (Developing) on the maturity scale. Common gaps include inconsistent data practices, limited AI skills beyond IT, and absence of a formal AI strategy linked to business objectives.
Prioritise the dimension with the largest gap relative to your AI ambitions. Typically, data readiness and talent are the first bottlenecks to address. Create a 90-day improvement plan targeting your weakest dimension before investing in AI tools.