Transforming Small Businesses 2025

2 Foundational challenge for AI – data and digital readiness Data for AI is a major challenge for SMEs Before focusing on AI applications for SMEs, it is important to come to grips with the role that data plays vis-à-vis AI. Data is AI’s lifeblood, fuelling every prediction, decision and outcome. Just as humans learn from experience, AI learns from data. Consultations conducted with stakeholders in the SME ecosystem confirmed that the successful adoption of AI will hinge on data. At the same time, stakeholders quickly pointed to the major problems SMEs face in collecting and sharing data effectively. These include: Data infrastructure challenges: Most SMEs face financial barriers to adopting advanced technologies such as industrial internet of things (IIoT) sensors and cloud platforms. Real-time data systems require significant upfront investment in hardware, integration and maintenance, which many SMEs cannot afford. Transitioning legacy systems to the cloud presents challenges such as technological complexity and data continuity. Workforce capability: The lack of technical expertise is a major hurdle. SME employees often lack the skills to operate IIoT systems, analyse data or handle cybersecurity risks. Their resistance to adopting new technologies and inadequate access to training programmes will slow progress, making workforce readiness a major gap in establishing robust data systems.Cybersecurity and privacy concerns: Data breaches and hacking risks deter many SMEs from sharing data, especially if they have not invested in robust cybersecurity frameworks. Privacy concerns, such as the misuse of business data, further exacerbate SMEs’ distrust of digital systems. Without safeguards, these issues undermine confidence and slow the adoption of real-time data-based solutions. Regulatory barriers: Fragmented regulations and the fear of increased tax burdens discourage SMEs from embracing data-sharing. Interoperability challenges make the process more complex, highlighting the need for standardized frameworks. Over-regulation further deters adoption, making data-sharing efforts cumbersome. Thus, the transformation of SMEs through AI will require a change in how businesses view and use data assets. Currently, most rely on analogue devices such as ledgers, physical logs and, at most, computerized spreadsheets to manage their operations. They capture financial data in Tally or Zoho while operational records such as inventory levels and production schedules are maintained in spreadsheets. These manual methods introduce errors, inefficiencies and delays in decision-making. They also create bottlenecks, because this kind of data capture, interpretation and analysis is constrained by experience and availability. AI algorithms can reduce SMEs’ dependence on humans by automating data processing, identifying patterns and providing data- driven insights. The transformation of SMEs through AI will require a change in how businesses view and use data assets Visuals from a workshop with MSME promoters in Goa Transforming Small Businesses: An AI Playbook for India’s SMEs 20