SME AI adoption only pays off when firms build supporting capabilities: surveys show skills, data readiness, managerial backing and ecosystem supports drive value, while cost, trust and regulatory gaps block uptake; a capability-gated pathway is proposed to translate evidence into policy and firm action in Bangladesh.

Main Finding

AI adoption by SMEs in emerging economies delivers business value only when complementary assets—human capital, data routines, leadership commitment and institutional supports—are deliberately assembled. Adoption is uneven: drivers are often organizational and ecosystem-based, while barriers cluster around trust, cost and regulatory gaps. The paper proposes a capability‑gated pathway (diagnose → pilot → integrate → govern → scale) with context-specific supports (e.g., Bangla‑ready tools, shared training, affordable finance, vendor assurance, proportionate governance) to translate international evidence into an actionable framework for Bangladesh.

Key Points

• Scope: Systematic literature review (PRISMA 2020) of AI adoption by SMEs, with an eye to translating evidence for Bangladesh.
• Search & inclusion: 16 OpenAlex search routes → 800 records; 320 duplicates removed → 480 screened; 171 database + 14 supplementary assessed; 40 primary empirical studies included; 8 reviews/conceptual sources retained for triangulation.
• Theoretical framing: Technology–Organization–Environment (TOE) framework, interpreted via resource‑based and dynamic‑capability perspectives.
• Most frequent adoption drivers (number of studies): human capital / employee skills (21), top‑management support (12), ecosystem support (11), relative advantage (9), digital/data readiness (9).
• Main barriers (number of studies): privacy, security, ethics and distrust (7); high cost and limited finance (6); institutional/regulatory gaps (5); implementation complexity (4).
• Reported business outcomes (number of studies): workforce learning (14), decision quality and agility (8), operational productivity (8), competitive advantage (8), revenue performance (7), sustainability (6).
• Methodological limitations: 19 studies used SEM or PLS‑SEM; most evidence is cross‑sectional; only 2 primary studies directly examine Bangladesh—limiting causal and contextual claims.

Data & Methods

• Protocol: PRISMA 2020 principles followed for systematic review.
• Search strategy: 16 structured search routes in OpenAlex database; supplementary searches added 14 reports.
• Screening/results: 800 initial records → 320 duplicates removed → 480 unique screened → 309 excluded at relevance pre‑screen → 171 database reports + 14 supplementary assessed → 40 empirical studies included.
• Coding & analysis: Studies coded using TOE framework (technology, organization, environment); interpreted through resource‑based view and dynamic capabilities.
• Study designs sampled: predominantly cross‑sectional surveys and structural equation modelling approaches (SEM/PLS‑SEM common); few longitudinal or experimental designs; geographic skew with limited Bangladesh-specific primary research.

Implications for AI Economics

• Complementary assets matter: AI is not a plug‑and‑play productivity booster for SMEs. Economic gains arise when firms combine technology with skills, data infrastructure, managerial capabilities and enabling institutions—consistent with complementarity theory in industrial organization and firm-level production economics.
• Policy levers for emerging economies (and Bangladesh in particular):
  • Skills & training: subsidize shared training programs, create Bangla‑language learning resources and encourage vendor-provided upskilling.
  • Finance: design affordable, targeted financing instruments (e.g., blended finance, leasing, pay‑for‑success) to lower upfront cost barriers.
  • Data & digital readiness: invest in basic digital infrastructure and support routines for data collection, cleaning and governance at SME scale.
  • Trust & assurance: implement vendor assurance mechanisms, certification, and proportionate regulation that address privacy/security/ethics without stifling adoption.
  • Ecosystem support: foster incubators, shared service providers and platforms that reduce scale disadvantages for SMEs.
• Firm strategy: SMEs should follow a capability‑gated adoption pathway: diagnose needs and data readiness, pilot small use cases, integrate successful pilots into routines, establish governance/ethics controls, and then scale incrementally—aligning investments with capacity-building.
• Research priorities for AI economics:
  • More causal and longitudinal studies to estimate returns to AI investments and identify complementarities (skills, data, management).
  • Field experiments and quasi‑experimental evaluations of financing, training, or governance interventions.
  • Contextual studies for Bangladesh and similar economies to capture institutional and linguistic frictions (e.g., Bangla‑language tools).
  • Heterogeneity analysis (firm size, sector, digital maturity) to guide targeted policy and firm-level strategies.
• Conclusion for practitioners and policymakers: Promote bundled interventions (technology + skills + finance + institutions). Expect AI adoption to create sustainable business value only when these complementary factors are assembled and governed proportionately.