Indian micro, small and medium enterprises (MSMEs) are increasingly turning to artificial intelligence (AI), but translating experimentation into measurable productivity gains remains a challenge, industry experts said.
A Confederation of Indian Industry (CII)–Indian School of Business (ISB) study found that 65.8% of surveyed MSMEs used AI tools, while only 21% used enterprise resource planning (ERP) or customised business software to manage operations. AI was primarily used for business communication (34.9%), content creation (22.3%) and training (18.9%), highlighting the need to examine how deeply the technology is integrated into business processes.
According to Akshay Garkel, Partner and Leader – Cyber at Grant Thornton Bharat, AI can help MSMEs manage higher volumes of orders, customer queries and invoices without proportionately increasing headcount. Applications such as defect detection, invoice reconciliation, demand forecasting and GST compliance can reduce manual errors and improve turnaround times.
However, affordability, fragmented data, limited skills and uncertainty over returns continue to constrain wider adoption. Businesses must also establish safeguards around data privacy, security and inaccurate AI outputs.
Manu Gupta, Managing Partner, and Kanika Bali, Tax Partner at Optimyze Finance LLP, said AI adoption was largely being driven by individual employees rather than integrated into core operating systems. They said using AI to draft an overdue payment reminder was different from deploying an agent to monitor sales ledgers, issue approved reminders and escalate unpaid accounts.
The gap between adoption and integration is also reflected in Dun & Bradstreet India's 2026 MSME study. Of the 210 MSME leaders surveyed across 12 industries, 76% reported using AI in some form, but only 5% had made it central to their operations. Further, 84% primarily relied on general-purpose AI assistants rather than AI integrated into business systems.
The financial benefits require closer scrutiny. Gupta and Bali said time saved through AI does not automatically translate into lower costs or higher profits. Returns materialise when businesses can serve more customers, reduce errors, shorten delivery cycles or increase revenue without proportionately expanding their workforce.
Rananjay Singh, COO of Enso Group, said businesses should measure AI against operational and financial outcomes rather than employee usage. He cited the need for better data integration, skilled employees and clearer business cases to scale AI agents.
For India's smaller businesses, the challenge is no longer simply accessing AI tools. It is building the operational readiness and accountability needed to turn adoption into sustainable productivity gains.