AI adoption can lift MSME productivity: For a small business, productivity depends on how well it manages orders, raw materials, inventory and payments. Artificial intelligence (AI) can help with each of these tasks, but the gains depend on the quality of the information available and the ability of the business to act on what the technology recommends.
This is an important distinction for India’s micro, small and medium enterprises (MSMEs), which account for a substantial share of economic output and exports. A joint Confederation of Indian Industry (CII) and Indian School of Business (ISB) study, MSMEs in India: Challenges, Opportunities and the Road Ahead, found that 65.8% of 2,919 surveyed enterprises used AI tools. Yet only 21% reported using enterprise resource planning (ERP) systems or customised business software.
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The survey covered 36 states and Union Territories and 40 industry sectors. Since participation was voluntary and the sample was not probability-weighted, its findings cannot be treated as representative of all Indian MSMEs. Even so, the contrast points to a question worth examining: how much can AI improve productivity when businesses have not integrated the information needed to manage their operations?
AI adoption: From individual tasks to business decisions
The study found that respondents used AI for business communication (34.9%), content creation (22.3%) and training (18.9%). These applications can save time and help smaller firms perform tasks that might otherwise require specialist staff. Their effect on productivity, however, depends on whether the time saved translates into better business performance.
A garment manufacturer, for instance, could use past orders and sales records to estimate demand and plan fabric purchases. A food-processing unit could examine production data to identify recurring wastage or anticipate equipment maintenance. A wholesaler could compare sales with inventory to identify slow-moving goods before they tie up working capital.
Such decisions require reliable business data. A chatbot cannot produce a useful demand forecast from incomplete sales records or recommend sensible purchasing levels without information about inventory and orders. Data on procurement, accounts and customer payments also need to be accessible in a form that allows the business to analyse them together.
This is where integrated business software becomes useful. It connects information that might otherwise remain scattered across spreadsheets, invoices and handwritten registers. AI can then help owners interpret that information and identify patterns that are difficult to spot manually.
MSMEs do not have to begin with expensive customised systems. Digital bookkeeping, invoicing and inventory management can establish the basic records needed for more sophisticated applications. Adoption should follow a clearly identified business need. Buying software without improving the underlying processes is unlikely to deliver much benefit.
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Delayed payments can defeat productivity gains
Even when AI identifies a way to reduce costs or improve production, a business may not have the cash to implement the recommendation. A manufacturer that wins a large order must purchase raw materials, pay workers and meet electricity bills before receiving payment from the buyer. Better production planning can help control expenses, but it cannot finance the order.
The Economic Survey 2025-26 estimated that approximately ₹8.1 lakh crore was locked in delayed payments to MSMEs. These unpaid dues restrict the working capital available for restocking, equipment purchases and technology adoption. A business may have a viable order book and profitable opportunities yet struggle to meet its immediate obligations because customers have not settled their invoices.
The policy response must therefore extend beyond subsidies for digital tools and training in AI. Payment timelines need stronger enforcement, while the Trade Receivables Discounting System (TReDS) and other invoice-financing mechanisms need wider use. Lenders should make greater use of verified receivables and business cash flows when assessing credit applications, alongside conventional collateral.
AI can assist by forecasting cash requirements, identifying overdue invoices and helping owners plan expenditure against expected receipts. Such tools can improve financial management, but they cannot resolve a dispute with a buyer or compel a late payer to settle an invoice. Access to working capital remains essential if firms are to translate better information into higher output.
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Government support must reach smaller firms
Access to public assistance presents another difficulty. The CII-ISB study found that 74.9% of surveyed MSMEs knew of at least one government scheme, while 42.6% reported using one. Firms with recognised certifications, external credit ratings and higher turnover were more likely to access support. Smaller enterprises can struggle with eligibility requirements, documentation and follow-ups across departments.
A unified digital platform could help businesses identify relevant schemes, check eligibility, submit applications and track their progress through a single interface. But digitising an application process will have limited value if the eligibility conditions remain difficult to satisfy or approvals are delayed. The design of the service must account for the administrative capacity of the smallest firms.
The World Bank-supported Raising and Accelerating MSME Performance (RAMP) programme recognises the importance of better scheme delivery and coordination between the Centre and states, alongside improvements in finance and market access. These institutional reforms are relevant to technology adoption because businesses need workable routes to credit and support before they can invest in productivity-enhancing equipment and software.
The CII-ISB report proposes five national missions covering liquidity and payment discipline, integrated digital services, technology adoption, skills development, and market access, quality and green competitiveness. Their performance should be judged by the outcomes they deliver. Shorter payment cycles, lower production costs, fewer defects, quicker scheme approvals and increased export participation would provide stronger evidence of progress than registrations or training numbers alone.
India had reported 9.77 crore registrations across the Udyam and Udyam Assist platforms as of October 6, 2026, according to the figure cited in the draft. That administrative reach offers a potential base for extending support to businesses, but registration by itself says little about productivity or the ability to expand.
The test for India’s MSME technology policy is whether smaller firms can use digital tools to make better commercial decisions and then finance the changes those decisions require. AI can contribute to that process. Its economic value will depend on the business systems, working capital and public institutions that allow enterprises to turn its recommendations into results.