{
  "id": 13869910,
  "title": "Beyond automation: How AI is reshaping the SME landscape in Southeast Asia",
  "url": "https://urgent.news/2026/10/12/beyond-automation-how-ai-is-reshaping-the-sme-landscape-in-southeast",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-12T04:00:42.000Z",
  "source": {
    "name": "e27",
    "slug": "e27",
    "url": "https://e27.co/beyond-automation-how-ai-is-reshaping-the-sme-landscape-in-southeast-asia-20261010/"
  },
  "original_language": "en",
  "account": null,
  "summary": "Artificial intelligence is rapidly transitioning from an experimental technology to an operational tool for small and medium-sized enterprises (SMEs) across Southeast Asia. However, the critical question is not whether SMEs will adopt AI, but how they can leverage it to create measurable business value without adding excessive complexity, cost, or risk. This is particularly relevant in the region's diverse SME landscape, which includes highly digitized economies like Singapore alongside markets where many smaller businesses are still establishing basic digital infrastructure. According to the Asian Development Bank’s Asia Small and Medium-Sized Enterprise Monitor 2025, SMEs constitute 99.8% of enterprises and 67.6% of employment across its 26 developing member economies in Southeast Asia. Consequently, the economic significance of AI hinges on whether smaller businesses can convert these increasingly accessible AI tools into productivity gains. For many SMEs, the immediate value of AI lies in improving existing processes rather than completely transforming their businesses. Customer service, finance and accounting, marketing, inventory management, demand forecasting, and internal knowledge management are among the functions where AI can streamline repetitive tasks or accelerate decision-making. Singapore serves as a notable example where AI adoption among SMEs increased from 4.2% in 2023 to 14.5% in 2024. SMEs utilizing AI-enabled solutions supported by the Productivity Solutions Grant reported average cost savings of 52% in 2024. While the specific percentages are not as crucial, this example illustrates that AI's commercial viability for smaller companies can emerge from relatively narrow applications. Companies do not need to develop proprietary AI systems; they can utilize existing models to automate repetitive workflows, analyze information, assist employees, or enhance customer interactions. This approach reduces the technological barrier but does not eliminate the organizational one. The real constraint is digital readiness, as AI systems rely on the quality of processes and data. A fragmented digital infrastructure with inconsistent records and manual workflows may hinder the effective utilization of sophisticated AI tools. Recent research published by ADB highlights that selected digital applications and digital business model digitization were associated with greater experimentation and changes in business models among 681 entrepreneurs across Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. This suggests an important sequencing issue: AI is often perceived as the next step after digitalization, but for many SMEs, basic digital infrastructure remains a prerequisite for meaningful AI adoption.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}