AI-Driven Business Model Innovation and TRIAD-AI in South Asian SMEs: Comparative Insights and Implications
Bibliographic record
Abstract
Artificial Intelligence (AI) is a transformational force reshaping business processes, financial decision-making, and enabling firms to create, deliver and capture value more effectively. While large corporations in South Asian countries, particularly Bangladesh, India, Pakistan and Sri Lanka have started leveraging AI to drive Business Model Innovation (BMI), Small and Medium Enterprises (SMEs) continue to face significant challenges. These include limited infrastructure, poor bandwidth penetration, unreliable electricity, weak institutional capacity and governance immaturity, along with ethics and compliance concerns. These challenges hinder SMEs from fully exploiting AI-driven BMI and reduce their financial resilience and competitiveness in increasingly digital and globalised markets. This paper examines how South Asian countries are adopting AI technologies in SMEs by comparing patterns and variations in adoption, capability, ethics, risks, compliance, and financial outcomes. The paper proposes a tailored, actionable framework, called TRIAD (Target, Restructure, Integrate, Accelerate, and Democratise)-AI, designed to address technical, organisational and institutional challenges that shape AI-driven BMI across South Asian SMEs and to meet regional and global SME needs. The framework integrates the best practices from global AI leaders such as China, Estonia and Singapore, emphasising responsible AI adoption through robust ethics and compliance standards, and risk management, and offering practical guidance for South Asian SMEs. By adopting this framework, South Asian countries can gain a competitive advantage, enhance operational efficiency, support GDP growth across the region and ensure adherence to all relevant international AI standards for responsible, sustainable, and financially sound innovation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".