SEA SMB AI Adoption Index 2026

Executive Summary

Southeast Asia's 70+ million small and medium businesses stand at an inflection point in artificial intelligence adoption. The Pertama Partners SEA SMB AI Adoption Index 2026 — a composite measure synthesizing data from McKinsey, IDC, Stanford HAI, Cisco, Oxford Insights, Salesforce, Deloitte, government agencies, and regional economic reports — places the region's overall SMB AI adoption score at 31 out of 100. This score reflects a market that has moved decisively past the awareness stage but remains overwhelmingly stuck in early experimentation.

Only 18% of Southeast Asian SMBs have progressed beyond pilot-stage AI projects into sustained implementation or integration. The gap between large enterprises and SMBs continues to widen: large firms across the region are approximately twice as likely to have deployed AI at scale compared to their smaller counterparts (McKinsey Global AI Survey, 2025). Yet the opportunity is enormous. SMBs adopting AI report revenue improvements of up to 91%, and those with active AI deployments are 1.8 times more likely to experience growth than non-adopters (Salesforce ASEAN SMB Trends, 2025).

Singapore leads the regional index at 52/100, buoyed by its National AI Strategy 2.0, SG$150 million Enterprise Compute Initiative, and a tripling of SME AI adoption rates from 4.2% to 14.5% within a single year (IMDA, 2025). Vietnam emerges as the fastest mover with a year-on-year AI adoption growth rate of 39%, now covering 18% of all enterprises nationwide. Indonesia represents the largest untapped opportunity — home to over 65 million MSMEs but with only 26% of organizations having implemented AI tools. Malaysia's HRD Corp-funded training ecosystem is creating a distinctive pathway for workforce-led adoption, while Thailand, the Philippines, and Hong Kong each face unique structural challenges that keep adoption below potential.

Across all markets, the barriers are consistent: talent shortages (cited by 55% of firms), cost constraints (40%), integration complexity (38%), and regulatory uncertainty. The most successful SMB adopters share identifiable patterns — they start with clearly defined business problems rather than technology solutions, invest in data foundations before AI tools, and pursue a disciplined "pilot-to-platform" strategy that compounds returns over time.

The region's digital economy, now exceeding USD 300 billion in gross merchandise value (Google/Temasek/Bain, 2025), provides a fertile substrate for AI-driven transformation. With IDC projecting Asia-Pacific AI spending to reach USD 175 billion by 2028 at a 33.6% compound annual growth rate, the question for Southeast Asian SMBs is not whether to adopt AI, but how quickly they can move from experimentation to measurable impact.

Methodology

The Pertama Partners SEA SMB AI Adoption Index

The SEA SMB AI Adoption Index is a composite measure designed to capture the multidimensional nature of AI adoption among small and medium businesses. Unlike single-metric surveys, the Index recognizes that AI adoption is not binary — it is a continuum from initial awareness through full organizational integration.

Six Dimensions of AI Maturity

The Index scores each market across six dimensions, each weighted to reflect its relative importance in driving business value from AI:

Data Synthesis Approach

The Index draws on 17 primary data sources spanning global research firms, government agencies, industry surveys, and regional economic reports. For each market, scores are calculated by:

1. Normalizing data from disparate sources to a common 0-100 scale
2. Triangulating multiple data points for each dimension — no single source determines a score
3. Adjusting for SMB specificity — where data covers all enterprise sizes, discounting is applied based on known SMB-enterprise adoption gaps (McKinsey, 2025: large enterprises adopt AI at approximately 2x the rate of smaller firms)
4. Weighting by recency — 2025-2026 data receives higher weight than older surveys
5. Applying regional calibration — cross-referencing government readiness indices (Oxford Insights), infrastructure readiness (Cisco), and economic indicators (World Bank, ADB) to adjust for structural factors

Limitations

This is a synthesized index, not a primary survey instrument. The analysis is constrained by the availability and recency of source data, which varies significantly by country. SMB definitions differ across markets (ranging from fewer than 50 to fewer than 300 employees depending on jurisdiction). Where primary data is unavailable, informed estimates are derived from proxy indicators and clearly noted.

Regional Overview: Southeast Asia vs. Global Benchmarks

The State of AI in 2026: A Global Context

The global AI adoption landscape has shifted dramatically. According to the McKinsey Global AI Survey 2025, 88% of organizations worldwide now report using AI in some form — a ten-percentage-point increase from the prior year. Generative AI usage specifically surged to 79%. The Stanford HAI AI Index 2025 corroborated this acceleration, showing organizational AI adoption reaching 78% globally as of 2024.

Yet beneath these headline figures lies a critical nuance: adoption is not the same as impact. McKinsey's data reveals that only 7% of respondents indicate AI has been fully scaled across their organizations. Approximately two-thirds of AI-using organizations remain in experiment or pilot mode, with only about one-third reporting genuine scaling. The Deloitte State of AI in the Enterprise 2026 report reinforced this finding, noting that while sanctioned access to AI tools is now available to roughly 60% of workers (up from under 40% a year earlier), "access is not the same as real workers using it for real productivity."

This global pattern — broad adoption, shallow depth — is amplified in Southeast Asia's SMB segment.

Southeast Asia: The Regional Picture

Southeast Asia's digital economy has reached a scale that commands attention. The 10th annual Google/Temasek/Bain e-Conomy SEA report (2025) showed the region surpassing USD 300 billion in gross merchandise value, beating the inaugural forecast from 10 years prior by 1.5 times. Revenue across digital sectors hit USD 135 billion, with both GMV and revenue growing at a steady 15% year-on-year.

The region is home to over 70 million MSMEs (Asian Development Bank SME Monitor, 2025), which account for 88.8% to 99.9% of total enterprises across ASEAN member states. These businesses employ over 140 million people and generate approximately 40% of regional GDP.

Against this backdrop, the SEA SMB AI Adoption Index registers 31/100 for the region as a whole. This places Southeast Asian SMBs approximately 15-20 points below global enterprise averages and 25-30 points below the leading markets of North America and Western Europe on equivalent composite measures.

Regional Score Breakdown

The variance across markets is substantial. Singapore's score of 52 is 2.3 times the Philippines' 23, reflecting deep structural differences in infrastructure, human capital, and government support. However, even Singapore — the region's undisputed leader — scores only slightly above the global midpoint, underscoring how much room for growth exists across the entire region.

Key Regional Metrics

• 75% of ASEAN SMBs report investing in AI (Salesforce, 2025), but only 18% have moved beyond experimentation into implementation
• AI application revenues in Southeast Asia grew 127% from H1 2024 to H1 2025 — the highest increase among global regions (IBM/IDC, 2025)
• Asia-Pacific AI spending reached USD 90.3 billion in early 2025 and is projected to hit USD 175 billion by 2028 at a 33.6% CAGR (IDC, 2026)
• The AI skills gap affects 94% of organizations across the region, with leaders reporting skills shortages of 40-60% (Deloitte, 2026)

Country-by-Country Analysis

Singapore: The Regional Benchmark

Index Score: 52/100 | Maturity Stage: Early Integration

Singapore occupies a distinctive position in the Southeast Asian AI landscape — simultaneously the region's most advanced market and a cautionary example of how even strong government support cannot bypass the fundamentals of organizational readiness.

Government Infrastructure and Investment. Singapore's National AI Strategy 2.0 (NAIS 2.0), launched in late 2023, has matured into a comprehensive ecosystem of support. The government committed SG$150 million to the Enterprise Compute Initiative (ECI), providing SMEs with access to compute resources critical for AI workloads (Budget 2025). IMDA introduced the GenAI Navigator for SMEs, a tool that recommends pre-approved generative AI solutions with accompanying grant support. The Productivity Solutions Grant saw SME adopters report average cost savings of 52% in 2024 (IMDA, 2025). SkillsFuture Enterprise Credit was enhanced with an additional SG$10,000 per company from 2026 for workforce transformation.

Adoption Metrics. The results are measurable. Singapore's SME AI adoption rate tripled from 4.2% in 2023 to 14.5% in 2024 (IMDA, 2025). Among larger enterprises, 62.5% had adopted AI by the same period. Singapore's digital economy now represents 18.6% of GDP, up from 14.9% in 2019. IMDA's Digital Leaders Programme targets 2,000 local digitally mature enterprises for AI adoption support over three years.

Workforce Readiness. Among Singaporean workers surveyed by IMDA, three in four regularly use AI tools, and 85% report that AI makes them more efficient. However, the Cisco AI Readiness Index 2025 found that while adoption approaches 90% broadly, only 13% of organizations across the Asia-Pacific are classified as "pacesetters" — companies pulling ahead on every measure of AI value.

Persistent Challenges. Despite world-class infrastructure, Singapore faces its own barriers. The Deloitte 2026 survey found that among Singapore-based respondents, regulations and compliance (27%), AI skills and knowledge gaps (24%), and high implementation costs (15%) remain the top challenges. Nearly all companies reported an urgency to implement AI, yet only 13% felt fully prepared (EY Singapore, 2025).

Dimension Scores:

Singapore's strength lies in awareness and experimentation, supported by government programs and a digitally literate population. The gap emerges in implementation and beyond, where even well-resourced SMEs struggle to move from pilot projects to production-grade AI systems. The Index score of 52 reflects a market with the right ingredients but still cooking.

Malaysia: The HRDF-Driven Training Pathway

Index Score: 33/100 | Maturity Stage: Advanced Experimentation

Malaysia has charted a distinctive approach to SMB AI adoption, leveraging its established Human Resource Development Corporation (HRD Corp, formerly HRDF) training infrastructure to drive workforce-led transformation. This approach addresses the talent gap head-on but creates a particular adoption profile — strong on awareness and capability building, lagging on production deployment.

HRD Corp and the Training Ecosystem. HRD Corp has sharpened its focus on digital training grants, incentivizing organizations to adopt AI-enabled learning and tools. The MDEC AI Skills Training programme is fully HRD Corp-claimable under the Skim Bantuan Latihan (SBL) scheme, covering AI training with no upfront fees required. The SBL-Khas scheme extends coverage to digital learning solutions, including AI tools and platforms. The National Training Week (NTW) 2025/2026 targeted one million Malaysians across 70,000+ programmes, with partnerships involving over 2,000 HRDF training providers and strong SME participation (HRD Corp, 2025).

Market Context. Malaysia's SME sector contributes approximately 40% of national GDP. AI-related courses — particularly generative AI for marketing, operations, and customer service — have seen surging demand through HRDC-claimable channels. The government's emphasis on digital upskilling as a pathway to AI adoption creates a workforce-led model that differs from Singapore's infrastructure-first approach.

Government AI Readiness. Oxford Insights ranked Malaysia 37th globally on government AI readiness in 2025, positioning it solidly in the second tier. The country's strength lies in its training infrastructure and regulatory clarity, while weaknesses appear in data infrastructure and AI research capacity.

Adoption Profile. Malaysia's adoption pattern is characterized by broad training participation but slower translation into operational deployment. Many Malaysian SMEs have sent employees through AI-related training programmes, gaining familiarity with tools like ChatGPT, Copilot, and industry-specific AI solutions. Fewer have moved to implement these tools in production workflows with measurable business outcomes.

Dimension Scores:

The training-led model creates a distinctive pattern: Malaysia's Awareness and Culture scores are relatively strong, reflecting HRD Corp's reach. The challenge is converting that awareness into implementation — the gap between "we have trained our people" and "we have deployed AI in our operations" remains wide.

Indonesia: The Largest Untapped Opportunity

Index Score: 27/100 | Maturity Stage: Early Experimentation

Indonesia is Southeast Asia's largest economy and home to its largest MSME population — over 65 million businesses that employ over 120 million people. The sheer scale of this market, combined with its early-stage AI adoption, makes it the region's most consequential opportunity.

Digital Economy Growth. Indonesia's digital economy is projected to exceed USD 130 billion by 2026, having nearly reached USD 100 billion in gross merchandise value in 2025 (Google/Temasek/Bain, 2025). AI application revenues grew by 127% from H1 2024 to H1 2025 — the highest growth rate in Southeast Asia. The "Making Indonesia 4.0" industrial strategy and the National Strategy for AI (2020-2045) provide policy frameworks for technology adoption.

Adoption Reality. Despite the growth narrative, Indonesia's actual AI deployment remains limited. Only 26% of organizations have implemented AI tools, according to an IBM study released in 2025. While 85% of businesses report significant operational gains from AI and 93% express confidence in their ability to deploy it, these figures represent sentiment rather than demonstrated capability. Among small businesses specifically, only 63% report having a clear AI strategy, compared to 80% of medium-sized organizations (IBM Indonesia, 2025).

Digital Readiness of MSMEs. In 2025, 63% of Indonesian MSMEs actively use digital tools for daily operations — a significant improvement from prior years but concentrated in basic digital tools (social media, messaging, e-commerce platforms) rather than AI-specific applications. The Cisco AI Readiness Index assigned Indonesia a 23% readiness score, with infrastructure (84%), cybersecurity (55%), and digitally skilled talent (45%) cited as the top barriers.

Oxford Insights Ranking. Indonesia ranked 42nd globally on government AI readiness in 2025, reflecting moderate policy ambition constrained by implementation challenges across a geographically dispersed archipelago of 17,000+ islands.

Dimension Scores:

Indonesia's trajectory is defined by contrast: massive market size paired with early-stage adoption, strong government ambition paired with infrastructure constraints, and high executive optimism paired with limited operational deployment. The market's low base means that even modest acceleration in AI adoption among its 65+ million MSMEs could shift regional averages significantly.

Vietnam: The Fastest Mover

Index Score: 34/100 | Maturity Stage: Advanced Experimentation

Vietnam has emerged as Southeast Asia's most dynamic AI adoption story. The country's combination of a young, technically adept population, competitive labor costs, and decisive regulatory action has created conditions for rapid acceleration.

Adoption Velocity. AI adoption in Vietnam surged in 2024, with 47,000 new enterprises implementing AI, bringing total adoption to nearly 170,000 firms — approximately 18% of all enterprises nationwide. This represents a year-on-year growth rate of approximately 39% in AI adoption (UNDP Vietnam AI Landscape Report, 2025). By 2025, 73% of Vietnamese companies had adopted AI in some form, though the depth of that adoption varies significantly.

Regulatory Leadership. In December 2025, Vietnam's National Assembly passed the Law on Artificial Intelligence — the first comprehensive AI legislation in the country. Taking effect in March 2026, the law spans 35 articles and applies to all organizations and individuals involved in AI research, development, deployment, and use. This regulatory clarity provides a framework that reduces uncertainty for businesses considering AI investments.

Market Growth. Vietnam's enterprise AI market was valued at USD 161.41 million in 2025 and is projected to reach USD 1,834.85 million by 2034, representing a compound annual growth rate of 31.01% (IMARC, 2025). AI solution provision is concentrated in IT (31%), finance and banking (22%), education (17%), and healthcare and e-commerce (15%).

International Recognition. Vietnam ranked 6th in the WIN World AI Index 2025, a notable achievement for a market that many analysts still classify as "developing." The country leads Southeast Asia in AI trust metrics, with Vietnamese consumers showing the highest confidence in AI applications among regional peers.

Challenges. Vietnam's rapid adoption faces bottlenecks. A significant talent gap persists — while the country produces a large number of IT graduates, many lack deep AI expertise. The 55% talent gap rate is among the highest in the region. Infrastructure limitations, particularly outside Ho Chi Minh City and Hanoi, constrain adoption in secondary and tertiary cities where the majority of SMEs operate.

Dimension Scores:

Vietnam's Index score of 34 slightly exceeds Malaysia's, driven primarily by its superior experimentation velocity and the breadth of enterprise adoption. If the country can convert its regulatory clarity and workforce energy into deeper implementation, Vietnam has the trajectory to rival Singapore within this decade.

Thailand: Digital Infrastructure Meets Cultural Caution

Index Score: 30/100 | Maturity Stage: Experimentation

Thailand occupies the middle ground of Southeast Asian AI adoption — strong digital infrastructure and a sizeable economy, but constrained by workforce skill gaps, cultural conservatism around technology change, and a challenging translation gap between pilot projects and operational deployment.

Digital Economy Context. Thailand's digital economy is projected to grow 4.2% in 2026, approximately double the pace of national GDP growth. The country's broad digital GDP is projected to reach 5.6 trillion baht (approximately USD 160 billion) in 2026, driven by tech investment, AI adoption, and data center expansion (The Nation, 2026). Thailand's AI market alone is forecast to reach US$1.16 billion in 2025, with an annual growth rate of 26.24% through 2031.

Adoption Rates. E-commerce merchant surveys across ASEAN found that Vietnam and Indonesia each had AI adoption rates of 42%, with Thailand at 39% (Salesforce, 2025). At the enterprise level, Thailand accounts for 17.1% of AI-adopting organizations in the region, ranking second to Indonesia's 24.6%. The Cisco AI Readiness Index assigned Thailand a 21% readiness score.

Consumer vs. Business Adoption. A distinctive feature of Thailand's AI landscape is the gap between consumer enthusiasm and business deployment. The SCBX Consumer AI Adoption Report 2026 found that over 80% of Thai consumers have used AI in some form — product recommendations, text summarization, translation tools. Additionally, 48.3% of Thais report feeling excited about AI, nearly matching the global average of 48.7%. Yet this consumer familiarity has not translated proportionally into SMB operational adoption.

SME-Specific Challenges. Research from Northern Thailand's SME ecosystem identifies business process maturity, strategic leadership, and technology alignment as the most influential enablers, while financial constraints, workforce skill shortages, and poor data quality represent major obstacles (ScienceDirect, 2025). Most MSMEs use digital platforms for daily operations, but adoption of advanced tools — data analytics, automation, AI-enabled e-commerce — remains limited.

Dimension Scores:

Thailand's Oxford Insights government AI readiness rank of 32nd globally reflects solid institutional foundations. The country's challenge is activating its SME base — converting consumer AI enthusiasm and government digital economy momentum into SMB operational adoption.

Philippines: High Individual Use, Low Organizational Adoption

Index Score: 23/100 | Maturity Stage: Early Experimentation

The Philippines presents a distinctive paradox in the regional AI landscape: among the highest rates of individual AI tool usage in the world, combined with among the lowest rates of organizational AI deployment in Southeast Asia.

Individual vs. Organizational Adoption. According to a 2024 JobStreet report, 46% of Filipinos use generative AI for work and personal tasks at least once a month — higher than the global average of 39%. The Philippines ranked 6th globally for ChatGPT usage in early 2026, with 42.4% of the population using the platform (Meltwater/We Are Social, 2026). An impressive 86% of Filipino professionals report using AI or automation in some form, outpacing global averages. Yet at the organizational level, only 14.9% of Philippine firms use AI tools, with overall enterprise AI adoption at just 3% across industries (PIDS, 2025).

Sector Concentration. What AI adoption exists is heavily concentrated in the ICT and BPO sectors (6-7% adoption), which is unsurprising given the Philippines' position as one of the world's largest business process outsourcing markets. Agriculture trails at 1.5%. The gap between the BPO sector — where global clients drive AI adoption requirements — and the broader MSME economy is stark.

Awareness Gap. The Philippine Institute for Development Studies (PIDS) found that overall awareness of AI and other Fourth Industrial Revolution technologies remains "notably low" among Philippine firms, with only about one in five firms cognizant of these technologies. This low awareness among organizations exists alongside high individual familiarity — a gap that underscores the challenge of translating personal tool usage into organizational capability.

BPO Sector Vulnerability. The Philippines' USD 37.5+ billion IT-BPM industry faces both opportunity and threat from AI. The ASEAN+3 Macroeconomic Research Office (AMRO) has flagged the risk of AI automating BPO functions, potentially displacing workers. Yet the sector's exposure to global technology trends also creates a channel for AI capability transfer into the broader economy.

Market Growth. Despite low current adoption, the Philippines' AI market is projected to reach USD 1.025 billion in 2026, with a CAGR of nearly 28% through 2030. The Philippines climbed seven spots in the Oxford Insights Government AI Readiness Index 2025 to 49th globally (score: 57.76), reflecting improving policy attention.

Dimension Scores:

The Philippines' path to AI adoption runs through its greatest asset — its people. The high individual usage of AI tools represents latent organizational capability that could be unlocked through targeted SME support programmes and awareness campaigns. The country's BPO sector, with its exposure to global AI practices, could serve as a bridge for technology transfer to the broader economy.

Hong Kong: Awareness Without Readiness

Index Score: 29/100 | Maturity Stage: Experimentation

Hong Kong presents perhaps the starkest paradox in this analysis: a sophisticated financial center with near-universal AI awareness that simultaneously registers the lowest organizational AI readiness among 30 global markets surveyed by Cisco.

The Readiness Paradox. The Hong Kong Productivity Council's "AI Readiness in Workplace Survey 2025" found that AI adoption approaches 90% among surveyed organizations — a headline figure suggesting advanced adoption. Yet the Cisco AI Readiness Index 2025 classified only 2% of Hong Kong organizations as "pacesetters" (companies outperforming peers across all AI value measures), compared to a global average of 13%. This is the lowest pacesetter rate among the 30 markets surveyed. The divergence reflects a market where many organizations have experimented with AI tools but very few have embedded them into core operations with measurable impact.

Strategy Gap. While nearly 99% of AI pacesetters globally had defined AI roadmaps, only 32% of all organizations surveyed in Hong Kong had one. This strategy deficit explains much of the gap between awareness and impact — organizations are adopting AI tools tactically and ad hoc rather than as part of a deliberate transformation programme.

SME Financial Context. The current SME business sentiment index stands at 43.9, indicating broadly stable but cautious expectations. The Hong Kong government's 2025-26 budget extended the principal moratorium under the SME Financing Guarantee Scheme, allowing interest-only payments for up to 12 months, and maintained a dedicated loan portfolio exceeding HK$390 billion. This liquidity support reduces one barrier to technology investment but does not address the strategy and talent gaps.

Talent as the Primary Bottleneck. The HKPC survey identified talent shortage as the biggest challenge in AI adoption across Hong Kong enterprises. Hong Kong's position as a financial center creates intense competition for AI-capable talent from banks, hedge funds, and global technology firms — leaving SMBs at a structural disadvantage in the hiring market.

Dimension Scores:

Hong Kong's Index score of 29 understates the market's potential. The city's financial infrastructure, international connectivity, and proximity to the Guangdong-Hong Kong-Macao Greater Bay Area (population 86 million, GDP exceeding USD 2 trillion) create advantages that should accelerate AI adoption once the strategy and talent gaps are addressed. However, until organizations move from "we have AI tools" to "we have AI strategies," Hong Kong will underperform its economic fundamentals.

Industry Breakdown

AI adoption across Southeast Asian SMBs varies dramatically by industry, driven by differences in data maturity, competitive pressure, regulatory requirements, and the availability of industry-specific AI solutions.

Industry Adoption Rates in Southeast Asia

Source: Pertama Partners Analysis, 2026 (synthesizing McKinsey 2025, Salesforce ASEAN 2025, Source of Asia 2025, and industry-specific surveys)

Financial Services: The Adoption Leader

Financial services leads AI adoption among Southeast Asian SMBs for structural reasons. The industry is data-rich by nature, faces intense competitive pressure from digital-native neobanks and fintechs, and operates under regulatory requirements that create both mandates and incentives for AI adoption.

Singapore's financial sector exemplifies the potential: 64% of the sector has adopted AI, with DBS Bank deploying AI in 85% of customer service cases and reducing response times by 67% (Source of Asia, 2025). While DBS is a large enterprise, its AI ecosystem creates demand for AI-capable SMB vendors, technology partners, and service providers across the value chain.

Across the region, the global AI adoption rate in financial services stands at 71% (McKinsey, 2025), but Southeast Asian SMBs in the sector lag this figure at approximately 38%. The gap reflects the predominance of smaller banks, insurance brokers, and financial advisors who face the same data security and compliance requirements as larger peers but with fewer resources.

Key use cases for SMB financial services firms: Anti-money laundering screening, automated customer onboarding (KYC/KYB), credit risk assessment using alternative data, chatbot-driven customer support, and regulatory reporting automation.

Technology and IT Services: Adoption from Within

Technology and IT services firms have the highest SMB AI adoption rate at 45%, which is expected given that these firms possess the technical capability to evaluate, deploy, and maintain AI systems. The sector serves as both an adopter and an enabler — IT service SMBs that implement AI internally often then offer AI implementation services to clients in other industries.

The rise of generative AI has been particularly impactful here. Code generation tools (GitHub Copilot, Cursor, and similar), automated testing frameworks, and AI-driven project management have become standard in many SEA tech SMBs. The Menlo Ventures State of Generative AI 2025 report found that software development is the leading enterprise use case for generative AI, and this pattern holds for SMBs across the region.

Retail and E-Commerce: Accelerating Through Platform AI

At 28% adoption, retail and e-commerce SMBs occupy the middle tier. The significant driver here is platform-embedded AI — Shopee, Lazada, Tokopedia, and other marketplaces provide AI-powered features (recommendation engines, pricing tools, demand analytics) that their merchant SMBs adopt indirectly. Many retail SMBs are "using AI" without having made a deliberate adoption decision, because the platforms they depend on have embedded it.

Beyond platform AI, independent retail SMBs are adopting AI for demand forecasting, inventory optimization, customer segmentation, and personalized marketing. The Google/Temasek/Bain e-Conomy SEA 2025 report highlights AI as a driver of the region's next phase of e-commerce growth, with recommendation engines and dynamic pricing cited as key value creators.

Manufacturing: The Data Readiness Bottleneck

Manufacturing SMBs register the second-lowest AI adoption rate at 18%. The barriers are primarily structural: most manufacturing SMBs in Southeast Asia operate with limited digital infrastructure, paper-based processes, and equipment that lacks IoT connectivity. Without digitized data, AI has nothing to work with.

Where adoption exists, it concentrates in predictive maintenance (reducing equipment downtime), quality control (computer vision for defect detection), and supply chain optimization (demand forecasting, logistics routing). IDC projects that manufacturing will be one of the fastest-growing AI adoption sectors in Asia-Pacific over 2025-2028, but the starting base among SMBs is low.

Healthcare: Regulatory Caution Meets Growing Demand

Healthcare SMBs (clinics, small hospital groups, pharmacy chains, telemedicine startups) show the lowest AI adoption at 15%. Regulatory caution is the primary factor — healthcare AI applications involve patient data, clinical decision-making, and liability concerns that create higher adoption thresholds. Singapore's AI in Healthcare initiative, which funded 75 AI health startups since 2021, demonstrates the potential, but most of these are larger or venture-backed entities rather than typical SMBs.

Use cases with the most traction among healthcare SMBs include administrative automation (scheduling, billing, claims processing), patient triage chatbots, and diagnostic decision support tools that assist rather than replace clinical judgment.

Professional Services: The Generative AI Sweet Spot

Professional services firms (consulting, legal, accounting, marketing agencies) at 22% adoption are positioned for rapid acceleration. Generative AI tools align naturally with the text-heavy, analysis-intensive work of these firms. Document drafting, proposal generation, research synthesis, contract review, and client reporting are all amenable to current generative AI capabilities.

The barrier is less technical than cultural: many professional services firms sell expertise and judgment, creating anxiety that visible AI use may undermine client perceptions of value. Firms overcoming this resistance are finding that AI amplifies rather than replaces professional judgment, enabling faster delivery of higher-quality work.

Barrier Analysis

The barriers to AI adoption among Southeast Asian SMBs are remarkably consistent across markets, though their relative severity varies by country. This analysis ranks the top barriers by frequency of citation across our source data, with specific regional context for each.

Barrier Rankings

Source: Pertama Partners Analysis, 2026 (synthesizing Deloitte 2026, McKinsey 2025, Cisco 2025, Salesforce 2025, and regional surveys)

1. Talent and Skills Gap (55% of firms)

The talent shortage is the most cited barrier across every market and every industry segment. It manifests at multiple levels: shortage of data scientists and ML engineers who can build custom AI solutions; shortage of "AI translators" who can bridge technical and business requirements; and shortage of general AI literacy among the broader workforce.

The scale of the gap is significant. Deloitte's 2026 survey found that 94% of leaders report AI skills gaps of 40-60% relative to needs. In Vietnam, 55% of firms report talent gaps as their strongest barrier despite the country's large IT graduate population, because many graduates lack deep AI expertise (UNDP, 2025). In Hong Kong, the HKPC identified talent shortage as the single biggest challenge, exacerbated by competition from large financial institutions that can offer significantly higher compensation.

For SMBs specifically, the talent challenge is compounded by inability to compete with large enterprises on compensation. A data scientist in Singapore commands SGD 100,000-180,000+ annually; in Vietnam, USD 25,000-50,000. These figures, while lower than Silicon Valley, represent significant overhead for businesses with 10-100 employees.

Mitigation approaches emerging in the region: Malaysia's HRD Corp-funded training model, Singapore's SkillsFuture enterprise credits, the rise of no-code/low-code AI platforms reducing the technical skill threshold, and the increasing availability of "AI-as-a-Service" offerings from regional and global providers.

2. Cost and Budget Constraints (40% of firms)

Cost operates at three levels for SMBs: the direct cost of AI tools and platforms, the cost of implementation and integration, and the ongoing operational cost of maintaining AI systems. For many Southeast Asian SMBs operating on thin margins, even USD 50-500 per month for AI SaaS tools represents a non-trivial expense that requires justification.

The World Bank's MSME Finance Gap Report (March 2025) estimates a USD 5.7 trillion finance gap across 119 emerging markets, with 40% of formal MSMEs being credit-constrained. This broader financing challenge directly limits AI investment capacity.

However, the cost barrier may be diminishing faster than firms realize. The dramatic decline in AI model costs (GPT-4 class capabilities now available at a fraction of 2023 pricing), the proliferation of freemium AI tools, and government subsidy programmes (Singapore's PSG, Malaysia's HRD Corp) are all reducing the effective cost of entry. The Pertama Partners analysis suggests that cost may be more of a perceived barrier than an actual one for many use cases, with firms overestimating required investment because they associate "AI" with expensive custom development rather than accessible SaaS tools.

3. Integration Complexity (38% of firms)

The Deloitte 2026 report found that nearly 60% of AI leaders cite integration with legacy systems as a primary challenge in adopting AI. For SMBs, this manifests as the difficulty of connecting AI tools to existing accounting software, CRM systems, inventory management platforms, and communication tools that may not have been designed with AI integration in mind.

In Southeast Asia, many SMBs operate on fragmented technology stacks — a mix of spreadsheets, WhatsApp communication, basic accounting software, and platform-specific tools (Shopee Seller Center, Grab Merchant, etc.). Introducing AI into this environment requires either significant workflow redesign or the use of AI tools that can operate independently of existing systems.

4. Data Readiness (35% of firms)

AI systems require data to function — and the quality, volume, and accessibility of that data determines the quality of AI outputs. Many Southeast Asian SMBs face a foundational data readiness challenge: their business data is scattered across spreadsheets, paper records, messaging apps, and siloed software platforms.

Indonesia's IBM study (2025) found that infrastructure challenges (cited by 84% of respondents) remain the top barrier, with inadequate data availability and poor data quality representing foundational obstacles. The OECD's 2025 report on AI adoption by SMEs highlighted that robust data governance and management practices must become a strategic priority before AI deployment can succeed.

5. Regulatory Uncertainty (30% of firms)

The regulatory landscape for AI in Southeast Asia is evolving rapidly but unevenly. Vietnam's landmark AI law (effective March 2026) provides one of the most comprehensive frameworks in the region. Singapore's approach emphasizes sector-specific guidance and voluntary frameworks. Other markets are at various stages of developing AI governance rules.

For SMBs, regulatory uncertainty creates hesitation: firms are reluctant to invest in AI systems that may need to be redesigned or abandoned if new regulations are introduced. Data privacy regulations (PDPA in Singapore, PDPA in Thailand, PDP Law in Indonesia) add compliance complexity, particularly for AI applications that process personal data.

6. Cultural Resistance (25% of firms)

Cultural barriers operate at both organizational and societal levels. Within organizations, employees may fear AI-driven displacement — a concern particularly acute in markets like the Philippines, where the BPO sector employs millions in roles potentially amenable to automation. Management skepticism about AI's relevance to "our type of business" remains common among SMB leaders who view AI as enterprise-grade technology.

At the societal level, Southeast Asia's cultural emphasis on personal relationships and face-to-face interaction can create friction with AI-mediated processes. Customer-facing AI applications (chatbots, automated responses) may be perceived as impersonal in cultures that value human connection.

7. ROI Uncertainty (22% of firms)

The Deloitte 2026 report revealed a significant ROI gap: while nearly three-quarters of organizations want AI to drive revenue growth, only about one in five report that it has actually done so. For SMBs with limited budgets and low tolerance for failed experiments, this uncertainty creates a rational hesitation to invest.

The challenge is partly measurement-related — many SMBs lack the analytics capability to measure AI's impact accurately — and partly a reflection of the early stage of adoption, where most deployments have not yet had time to demonstrate sustained returns.

Success Pattern Analysis

What Top-Performing SMBs Do Differently

While the regional average paints a picture of early-stage experimentation, a segment of Southeast Asian SMBs has achieved meaningful results from AI adoption. Analysis of the available data reveals consistent patterns among these top performers — patterns that are replicable regardless of market or industry.

Pattern 1: Problem-First, Not Technology-First

The most successful SMB AI adopters begin with a clearly defined business problem, not with a desire to "use AI." Research consistently identifies this as the most critical differentiator. As the OECD's 2025 SME AI adoption report stated, "The process must begin with business pain points, not technology solutions."

In practice, this means top performers have identified specific, measurable operational challenges — a customer response time that is too slow, an inventory forecasting error rate that is too high, a manual process that consumes too many staff hours — and then evaluated whether AI can address that specific problem. This contrasts with the more common pattern of acquiring AI tools and then searching for applications.

Regional example: Vietnamese e-commerce SMBs that implemented AI-powered demand forecasting to solve a specific inventory overstock problem achieved measurable ROI within 3-6 months, while peers who adopted broad "AI transformation" programs without specific targets remained in perpetual pilot mode.

Pattern 2: The Pilot-to-Platform Strategy

Top-performing SMBs follow a disciplined progression from an initial quick win to a broader AI capability platform. The initial pilot is deliberately scoped to be achievable (typically 4-8 weeks, single use case), measurable (clear before/after metrics), and visible (stakeholders can see the impact). The ROI from this pilot is then reinvested in building a unified data infrastructure that makes every subsequent AI deployment faster, cheaper, and more powerful.

This "Pilot-to-Platform" approach was identified across multiple research sources as the strongest predictor of successful AI scaling. It directly addresses the integration complexity barrier by building connective infrastructure incrementally rather than requiring a "big bang" transformation.

Pattern 3: Digital Maturity as a Prerequisite

The data consistently shows that pre-existing digital maturity is the strongest predictor of AI adoption success. SMEs with high digital capability scores show up to a 52% higher likelihood of successful AI adoption (OECD, 2025). This finding has practical implications: SMBs that have not yet digitized core processes — moving from paper to digital records, implementing basic cloud software, establishing data collection practices — should prioritize these foundations before investing in AI.

In Southeast Asia, this finding maps directly to the adoption gradient. Singapore's SMBs, operating in the region's most digitized economy (digital economy at 18.6% of GDP), are better positioned for AI adoption than Indonesian MSMEs, 37% of which are not yet using digital tools for daily operations.

Pattern 4: Workforce Investment, Not Just Tool Investment

Growing SMBs that succeed with AI invest in people alongside technology. Research shows that 83% of growing SMBs are experimenting with AI, and 78% plan to increase investments in the coming year (OECD, 2025). But the distinguishing factor is how these investments are allocated.

Top performers create supportive environments that empower employees to view AI as a collaborative tool rather than a replacement. They invest in comprehensive training, maintain flexible adaptation strategies, and cultivate a progressive organizational culture around AI use. Singapore's finding that 85% of AI-using workers report increased efficiency validates this approach — when workers are supported in using AI, the tools deliver value.

Malaysia's HRD Corp-funded training model demonstrates this pattern at a national scale: by subsidizing AI workforce development, the government removes one barrier to the workforce investment that predicts adoption success.

Pattern 5: External Partnerships for Capability Gaps

SMBs that achieve AI implementation without in-house technical teams do so through strategic partnerships — with AI consultants, managed service providers, industry-specific AI vendors, or platform ecosystem partners. This pattern is particularly relevant in Southeast Asia, where the talent shortage makes in-house AI teams impractical for most SMBs.

The most effective partnerships involve vendors or consultants who understand both the AI technology and the specific industry context. Generic "AI solutions" that are not tailored to the SMB's operational reality tend to fail during integration. The emergence of regional AI consulting firms (operating across multiple Southeast Asian markets) and government-backed AI solution marketplaces (Singapore's GenAI Navigator, Malaysia's MDEC AI Skills Training) are creating more accessible partnership channels.

Pattern 6: Governance Before Scale

The Deloitte 2026 report found that only 21% of organizations have established governance models for AI agents, even as 73% cite data privacy and security as their top AI risk. Top-performing SMBs address governance early — establishing policies around data handling, AI decision-making boundaries, human oversight requirements, and output quality standards — before scaling AI across the organization.

This counter-intuitive pattern (governance as an enabler rather than a brake) reflects the reality that ungoverned AI deployments create risks that eventually force rollback or restriction. SMBs that establish clear but pragmatic governance frameworks can scale with confidence, while those that skip governance in pursuit of speed often find themselves retreating later.

Predictions and Outlook for 2027

Prediction 1: The Regional Index Score Will Rise to 38-42 by End of 2027

Current momentum — particularly the 33.6% CAGR in Asia-Pacific AI spending (IDC, 2026), declining tool costs, expanding government programmes, and the maturation of generative AI applications — supports a 7-11 point increase in the regional composite score within 18 months. The acceleration will be driven primarily by movement from experimentation to implementation, as the large pool of SMBs currently running pilots begin converting them to production systems.

Prediction 2: Vietnam Will Overtake Malaysia in the Index by Q4 2027

Vietnam's 39% year-on-year AI adoption growth rate, combined with the regulatory clarity provided by its new AI law (effective March 2026), positions it for the steepest adoption curve in the region. If current velocity is maintained, Vietnam's Index score should reach 42-46 by end of 2027, surpassing Malaysia's expected 38-40.

Prediction 3: Agentic AI Will Be the Defining Technology for SMBs in 2027

Salesforce data shows AI agent creation and deployment surged 119% in H1 2025, with employee-agent interactions growing 65% per month. IDC's 2026 predictions for Asia-Pacific specifically highlight agentic AI as a transformative force. For SMBs, AI agents that can autonomously execute multi-step tasks — customer service, order processing, procurement, scheduling — represent the most accessible path to AI-driven productivity gains. By end of 2027, agentic AI will be the primary AI deployment type for SMBs across the region.

Prediction 4: Indonesia Will Become the Largest AI SMB Market by Revenue

Indonesia's 65+ million MSMEs represent the region's largest addressable market. While the country's per-SMB AI spending will remain below Singapore's or Malaysia's, the sheer volume of potential adopters — combined with rapidly improving digital infrastructure and the government's Making Indonesia 4.0 strategy — will drive aggregate market size past Singapore by 2027. AI application revenue growth of 127% (H1 2024 to H1 2025) signals the beginning of this scaling trajectory.

Prediction 5: The SMB-Enterprise AI Adoption Gap Will Narrow by 30%

Globally, large enterprises adopt AI at approximately 2x the rate of SMBs (McKinsey, 2025). Three forces will compress this gap in Southeast Asia by 2027: (1) the democratization of AI through low-code/no-code platforms and embedded AI features in standard business software; (2) declining costs that remove the budget barrier for smaller firms; and (3) government programmes specifically targeting SMBs (Singapore's GenAI Navigator, Malaysia's HRD Corp AI training, Indonesia's digital MSME initiatives). The 2x gap should narrow to approximately 1.4x by end of 2027.

Prediction 6: Regulation Will Accelerate Rather Than Inhibit Adoption

Vietnam's comprehensive AI law (March 2026), Singapore's evolving AI governance framework, and the anticipated introduction of AI-specific regulations in Thailand, Malaysia, and Indonesia will create clearer operating parameters for SMBs. While some firms cite regulatory uncertainty as a barrier, the introduction of clear rules actually removes ambiguity and enables confident investment. Markets with clearer regulatory frameworks (Singapore, Vietnam) consistently show higher adoption rates than those without.

Prediction 7: The "Training-to-Transformation" Gap Will Emerge as the Critical Challenge

The largest near-term risk to regional AI adoption is the gap between AI training (which has scaled rapidly through HRD Corp, SkillsFuture, and similar programmes) and actual operational transformation. By 2027, millions of Southeast Asian workers will have received some form of AI training, but the translation of that training into organizational capability will require deliberate implementation support, change management, and leadership commitment. Regions that invest only in training without accompanying implementation support will see diminishing returns.

Implications for Business Leaders

For SMB CEOs and Founders

Start now, start small, start with a problem. The data is unambiguous: SMBs that have adopted AI are 1.8 times more likely to experience growth, and 91% report revenue improvement. Waiting for AI to "mature" or for "the right time" means falling further behind competitors who are already building AI capabilities.

The most effective starting point is not a technology evaluation — it is a business problem audit. Identify the 2-3 operational processes that consume the most time, generate the most errors, or create the most customer friction. Evaluate whether AI tools (many of which are available at less than USD 100/month) can address those specific problems. Run a 6-8 week pilot with clear success metrics. Measure. Decide.

Budget allocation guidance: Based on regional data, SMBs achieving positive AI ROI typically allocate 3-5% of revenue to technology investment (inclusive of AI), with 40% going to tools, 35% to training, and 25% to external implementation support. For a USD 1 million revenue SMB, this represents USD 30,000-50,000 annually — a figure comparable to one junior employee's salary.

For CTOs and IT Leaders

Build the data foundation first. The single most common failure pattern in SMB AI adoption is deploying AI tools on top of inadequate data infrastructure. Before selecting AI solutions, ensure: (1) core business data is digitized and accessible; (2) data quality practices are in place (deduplication, validation, regular cleaning); (3) basic data governance policies exist (who can access what, how long data is retained, how it is protected). This foundation work may take 3-6 months but dramatically increases the probability of successful AI deployment.

Evaluate the build-vs-buy-vs-embed spectrum. The three pathways to AI capability for SMBs are: (1) building custom models (high cost, high specificity, requires data science talent); (2) buying AI SaaS solutions (moderate cost, faster deployment, requires integration); (3) using AI embedded in existing platforms (lowest cost, easiest adoption, limited customization). Most SMBs should start with path 3, graduate to path 2 as needs become clearer, and consider path 1 only for truly differentiating capabilities.

For Operations Leaders

Map the automation opportunity. Operations leaders should conduct a systematic audit of repetitive, rules-based processes that consume staff time. Common high-ROI targets across Southeast Asian SMBs include: customer inquiry handling (chatbots and auto-response), document processing (invoice handling, contract review), scheduling and resource allocation, quality inspection, and inventory management.

Quantify the status quo cost. Before evaluating AI solutions, calculate the current cost of manual processes — staff hours, error rates, customer wait times, opportunity costs. This baseline makes ROI measurement possible and creates a compelling internal business case for AI investment.

For Government and Policy Leaders

The training-to-transformation gap is the next frontier. Government programmes across the region have successfully addressed the awareness and initial training challenges. The next phase of support must focus on implementation — helping SMBs convert training into operational deployment. This means: providing subsidized implementation consulting (not just training), creating industry-specific AI solution marketplaces, establishing shared data infrastructure for sector-level AI applications, and funding pilot programmes with measurement requirements.

Data infrastructure is a public good. Just as governments invest in physical infrastructure (roads, ports) to enable economic activity, investment in data infrastructure — data standards, open data initiatives, data sharing frameworks, privacy-preserving data collaboration platforms — will determine whether SMBs have the raw material needed for AI adoption.

Methodology Appendix

Detailed Scoring Methodology

Index Construction

The SEA SMB AI Adoption Index is constructed through a five-stage process:

Stage 1: Source Data Collection Data is collected from 17 primary sources (listed in the Data Sources section). Sources are categorized as:

• Tier 1 (highest reliability): McKinsey, IDC, Stanford HAI, Deloitte, OECD, government statistics (IMDA, HRD Corp, etc.)
• Tier 2: Cisco, Oxford Insights, Salesforce, IBM, industry-specific surveys
• Tier 3: Regional media reports, academic studies, analyst estimates

Stage 2: Data Normalization

Raw data from different sources uses different scales, samples, and definitions. Normalization converts all metrics to a 0-100 scale:

• Percentage adoption rates are used directly (e.g., 14.5% SME AI adoption = 14.5 on the Implementation dimension)
• Government readiness scores (Oxford Insights, Cisco) are mapped to 0-100 and applied to Culture and Awareness dimensions
• Spending growth rates are converted to relative scores benchmarked against the global median

Stage 3: SMB Adjustment

Most enterprise AI surveys include or are dominated by large organizations. Where data covers all enterprise sizes, an SMB adjustment factor is applied based on the McKinsey 2025 finding that large enterprises adopt AI at approximately 2x the rate of smaller firms. This adjustment is calibrated by market using available SMB-specific data (e.g., Singapore's IMDA data provides direct SMB adoption rates, reducing the need for adjustment).

Stage 4: Dimension Scoring

Each of the six dimensions receives a score based on triangulated data:

• Awareness (10% weight): Government AI readiness indices, survey data on AI familiarity, training participation rates, consumer AI awareness metrics
• Experimentation (15% weight): Generative AI usage rates, pilot project prevalence, AI tool trial rates, startup ecosystem activity
• Implementation (25% weight): Reported AI deployment rates among SMBs, PSG/grant usage data, AI solution market revenue, enterprise AI spending
• Integration (25% weight): Proportion of firms reporting AI embedded in core workflows, multi-function AI deployment, AI governance maturity, data infrastructure readiness
• Optimization (15% weight): AI ROI measurement capability, continuous improvement practices, advanced AI adoption (agentic AI, custom models), scaling metrics
• Culture (10% weight): Workforce AI literacy rates, leadership commitment indicators, change management maturity, organizational readiness assessments

Stage 5: Composite Score Calculation

The final Index score for each market is the weighted average of the six dimension scores:

Index Score = (Awareness x 0.10) + (Experimentation x 0.15) + (Implementation x 0.25) + (Integration x 0.25) + (Optimization x 0.15) + (Culture x 0.10)

Country Score Derivations

Singapore (52/100)

• Awareness: 78 — Driven by IMDA data showing 95% SME digitalization, 75% worker AI usage, and comprehensive government communication
• Experimentation: 65 — 14.5% SME AI adoption (2024) represents significant experimentation activity beyond basic awareness
• Implementation: 48 — Adjusted from 62.5% enterprise rate using 2x SMB discount, calibrated against 14.5% SME-specific data and 52% PSG cost savings
• Integration: 38 — Only 13% of organizations fully prepared for AI (EY Singapore); limited multi-function deployment among SMBs
• Optimization: 32 — Some SMBs measuring ROI through PSG framework; agentic AI adoption nascent
• Culture: 55 — Strong government support, 85% of AI-using workers report efficiency gains, but talent shortage persists

Vietnam (34/100)

• Awareness: 55 — 73% of companies using AI "in some form," but varying depth; strong among tech-adjacent firms
• Experimentation: 48 — 39% YoY growth; 47,000 new enterprises adopting AI in a single year
• Implementation: 30 — 18% of all enterprises with AI deployment; ranked 6th in WIN World AI Index
• Integration: 18 — Concentration in IT (31%) and finance (22%); limited breadth of integration across business functions
• Optimization: 10 — Early stage; limited ROI measurement infrastructure among SMBs
• Culture: 40 — Highest AI trust in SEA; new AI law signals regulatory commitment; talent gap remains 55%

Malaysia (33/100)

• Awareness: 58 — HRD Corp's 70,000+ training programmes and 1 million-person NTW target create broad awareness
• Experimentation: 45 — High training participation translating to experimentation with AI tools
• Implementation: 28 — Gap between training and deployment; limited production AI among SMBs
• Integration: 18 — Early-stage integration; most adoption remains single-function
• Optimization: 12 — Very limited ROI measurement and optimization among SMBs
• Culture: 42 — Strong training culture through HRD Corp; SME GDP contribution of 40% creates business motivation

Thailand (30/100)

• Awareness: 55 — 80% consumer AI usage; 48.3% AI excitement among population
• Experimentation: 40 — 39% AI adoption rate among e-commerce merchants (Salesforce); 21% Cisco readiness
• Implementation: 24 — Gap between consumer familiarity and business deployment; 17.1% organizational adoption share
• Integration: 16 — Limited integration depth; most MSME adoption remains basic digital tools
• Optimization: 10 — Nascent optimization practices among SMBs
• Culture: 35 — Consumer enthusiasm contrasts with organizational caution; skills gaps prominent

Hong Kong (29/100)

• Awareness: 70 — Near-90% awareness/adoption at surface level; sophisticated financial center
• Experimentation: 42 — Widespread tool experimentation; only 32% with AI roadmaps
• Implementation: 18 — Only 2% pacesetters (Cisco); massive gap between awareness and deployment
• Integration: 12 — Limited AI embedding in core workflows; SME sentiment index at cautious 43.9
• Optimization: 8 — Very few organizations measuring or optimizing AI performance
• Culture: 28 — Talent shortage is primary barrier; financial sector competition for AI talent

Indonesia (27/100)

• Awareness: 50 — Growing awareness but 37% of MSMEs not yet using basic digital tools
• Experimentation: 38 — 127% AI application revenue growth; strong experimentation momentum
• Implementation: 20 — Only 26% organizational AI implementation (IBM); 63% MSME digital tool usage is mostly basic
• Integration: 12 — Infrastructure barriers (84%); limited AI integration beyond experimentation
• Optimization: 8 — Early stage; most AI use is trial-based
• Culture: 30 — 93% confidence in AI ability (sentiment) but 45% digital talent gap (reality)

Philippines (23/100)

• Awareness: 38 — Only 1 in 5 firms aware of AI technologies (PIDS); despite high individual usage
• Experimentation: 30 — 46% personal generative AI usage; 86% professionals using AI in some form individually
• Implementation: 15 — Only 14.9% firm-level AI adoption; 3% cross-industry enterprise adoption
• Integration: 10 — Minimal AI integration in organizational processes outside BPO sector
• Optimization: 6 — Very early stage
• Culture: 32 — High individual AI comfort but low organizational awareness creates cultural disconnect

Data Source References

1. McKinsey & Company. "The State of AI: How Organizations Are Rewiring to Capture Value." Global AI Survey, 2025.
2. IDC. "Asia/Pacific AI Spending Guide." FutureScape 2026 Predictions, 2026.
3. Stanford University Human-Centered AI Institute. "Artificial Intelligence Index Report 2025." Stanford HAI, April 2025.
4. Google, Temasek, Bain & Company. "e-Conomy SEA 2025." 10th Edition, 2025.
5. Cisco Systems. "AI Readiness Index 2025: Realizing the Value of AI." October 2025.
6. Oxford Insights. "Government AI Readiness Index 2025." December 2025.
7. Deloitte. "The State of AI in the Enterprise, 2026." January 2026.
8. Salesforce. "2025 SMB Trends: Why ASEAN Businesses Are Investing in AI and Automation." 2025.
9. Salesforce. "2026 Predictions: Six Trends Shaping the Future of ASEAN Businesses." December 2025.
10. OECD. "AI Adoption by Small and Medium-Sized Enterprises." December 2025.
11. Asian Development Bank. "Asia Small and Medium-Sized Enterprise Monitor 2025." 2025.
12. Infocomm Media Development Authority (IMDA). "Singapore Digital Economy Report 2025." 2025.
13. Malaysia HRD Corp. "National Training Week 2025/2026 Data." 2025.
14. Malaysia Digital Economy Corporation (MDEC). "AI Skills Training Programme Data." 2025.
15. IBM. "IBM Study: Indonesia Businesses Primed for AI." June 2025.
16. Hong Kong Productivity Council. "AI Readiness in Workplace Survey 2025." 2025.
17. UNDP. "Vietnam AI Landscape Report 2025." April 2025.
18. Philippine Institute for Development Studies (PIDS). "State of AI in the Philippines in 2025." September 2025.
19. SCBX. "Thai Consumer AI Adoption Report 2026." January 2026.
20. World Bank / IFC. "MSME Finance Gap Report." March 2025.
21. EY Singapore. "Helping SMEs Take the Next Leap Towards AI." 2025.
22. ASEAN Secretariat. "SME Policy Index: ASEAN 2024." September 2024.
23. IMARC Group. "Vietnam Enterprise Artificial Intelligence Market Report 2034." 2025.
24. Thryv, Inc. "AI Adoption Among Small Businesses Surges 41% in 2025." September 2025.
25. Menlo Ventures. "2025: The State of Generative AI in the Enterprise." 2025.

This research was produced by Pertama Partners, an AI consulting firm helping SMBs across Southeast Asia and Hong Kong adopt artificial intelligence through training, implementation, and custom engineering. For more information, visit pertamapartners.com.

Published: February 2026 Data current as of: Q1 2026

The SEA SMB AI Adoption Index is proprietary methodology of Pertama Partners. Data synthesis and composite scoring represent the analytical judgment of the research team based on publicly available sources. Individual data points are attributed to their respective sources throughout the report. This report does not constitute investment advice.