Social enterprises reporting greater AI capability also report stronger transformation and higher alignment with Sustainable Development Goals, according to a mixed-methods survey of 132 organisations; however, the evidence is cross-sectional and based on a convenience sample, limiting causal interpretation.

Main Finding

The study finds that Artificial Intelligence (AI) capability positively and significantly enhances transformative social entrepreneurship (β = 0.356, p < .001), which in turn significantly increases measurable Sustainable Development Goal (SDG) achievement (β = 0.389, p < .001). AI capability also shows a direct positive effect on SDG achievement (unstandardized B = 0.295, p = .000). Model fit statistics indicate an acceptable structural model (χ2/df = 1.88, GFI = 0.84, RMR = 0.041).

Key Points

• AI is conceptualized as a dynamic organizational capability that can increase agility, innovation capacity, and measurable social impact in social enterprises.
• Survey means: modest AI awareness (Mean = 2.94), high willingness to adopt AI (3.78), high organizational agility (3.85), strong transformative orientation (4.02), and moderate–high SDG alignment/impact (3.91).
• Main barriers to AI adoption: high implementation costs, limited digital infrastructure, scarcity of technical skills, ethical/data-security concerns, unclear regulations, and potential bias in data or technology acceptance.
• Research gap addressed: limited prior empirical work linking AI capability, transformative social entrepreneurship, and SDG outcomes—especially for social (noncommercial) enterprises.
• Sample demography (quantitative): N = 132 social entrepreneurs (72% male); sectors include education & skills (30%), healthcare (22%), rural development (18%), women empowerment (15%), renewable energy (15%).
• Qualitative component: n = 10 AI-enabled social enterprises interviewed; thematic analysis used to surface benefits, barriers, and readiness.

Data & Methods

• Design: Mixed methods (quantitative survey + qualitative semi-structured interviews).
• Quantitative:
  • Sampling: Convenience sampling; 160 surveys distributed, 132 valid responses (Krejcie–Morgan used to target n ≥ 120).
  • Instruments: Multi-item scales measuring AI capability, organizational agility, transformative innovation, SDG alignment/impact, and adoption barriers. Reliability/validity checked (Cronbach’s α, Composite Reliability, AVE; discriminant validity via √AVE).
  • Analysis: Exploratory Factor Analysis (SPSS), Confirmatory Factor Analysis, and Structural Equation Modeling (SEM). Measurement model fit acceptable (CMIN/DF < 3; GFI > .80; RMR < .10).
• Qualitative:
  • Participants: 10 organizations with ≥1 year of AI use (predictive analytics, ML, dashboards).
  • Method: Semi-structured interviews, audio-recorded, transcribed, analyzed in NVivo; thematic analysis on awareness, benefits, barriers, and scalability/readiness.
• Analytical framework: Structural relationships among AI capability, organizational agility, transformative social entrepreneurship, and SDG achievement.

Implications for AI Economics

• Efficiency and Social ROI: Empirical evidence that AI capability increases transformative outcomes and SDG achievement implies higher social returns on investment where AI is appropriately deployed—supporting arguments for impact investors and public funders to finance AI adoption in social enterprises.
• Scaling and Market Structure: AI can lower marginal costs of serving beneficiaries (automation, predictive targeting), enabling scaling. However, the digital divide may lead to concentration of capabilities in better-funded organizations, risking increased inequality among social providers and potential market power asymmetries.
• Labor and Skill Dynamics: Adoption requires digital skills that are currently scarce in many social enterprises, implying demand for training, human capital investment, and potential shifts in labor composition (more data/tech roles, fewer routine admin roles).
• Public Goods, Externalities, and Policy Role: Data and AI models used for social impact have public-good characteristics and positive externalities (improved targeting, aggregated insights). Market failures (underinvestment in shared datasets, trust/ethics frameworks) justify public support—grants, subsidized platforms, open-data initiatives, and regulation to ensure fairness/transparency.
• Cost-Benefit and Financing Needs: High upfront costs are a major adoption barrier. Blended finance, concessional capital, and outcome-based funding (e.g., social impact bonds tied to AI-enabled outcomes) could unlock adoption and align incentives.
• Measurement and Impact Attribution: AI tools (dashboards, predictive metrics) improve real-time monitoring and impact attribution, strengthening evidence for pay-for-success contracts and lowering monitoring costs for funders.
• Risks and Governance: Ethical/data-security concerns and algorithmic bias can create negative welfare consequences. Economic policy should focus on standards, interoperable data governance, and capacity-building to mitigate harms and enable equitable diffusion.
• Research & Evaluation Needs: Further cost-effectiveness and causal impact studies (ideally randomized/quasi-experimental) are required to quantify economic returns of AI interventions across SDG domains and to guide prioritization of public subsidies or market interventions.

Limitations to note for economic interpretation: convenience sampling, modest sample size, self-reported measures, cross-sectional design, and limited geographic/generalizability. These constrain external validity and causal claims; policymakers and economists should treat findings as indicative but supportive of targeted pilots and rigorous impact evaluations.