Studies show that AI can help India’s small businesses grow significantly. It can help MSMEs go green and keep them export-oriented. 

AI gives Indian MSMEs an opportunity to compete on quality, consistency, speed, traceability, and customisation, not simply cost

— Jaspreet Singh, Partner, Grant Thornton Bharat LLP

“AI-Force Multiplier: Leveraging AI for Manufacturing and MSMEs”, a recent report by EY, says AI can enable higher productivity without proportional increases in manpower, faster data-driven decision-making, and improved quality, consistency, and predictability for manufacturing MSMEs.

India has 7.94 crore MSMEs, of which 173,350 are involved in exports, according to official data. These stand to gain if they go green through AI adoption. 

The EY report “identifies energy optimisation and energy monitoring among the potential AI applications for manufacturers, allowing businesses to identify and address inefficiencies in their operations”.

But there is every indication that Indian MSMEs are not ready.

“AI gives Indian MSMEs an opportunity to compete on quality, consistency, speed, traceability and customisation, not simply cost,” Jaspreet Singh, Partner, Grant Thornton Bharat LLP, told The Secretariat. 

He, however, added, “Indian manufacturing MSMEs are interested in AI, but their readiness is uneven.”

High implementation costs, limited digital infrastructure, shortage of skilled talent, and fragmented data can prove to be big barriers for these small businesses, which form the backbone of Indian manufacturing.

India AI Mission is committed to ensuring AI has a meaningful impact on real sectors of the economy, especially manufacturing MSMEs

— S. Krishnan, Secretary, Ministry of Electronics and Information Technology

According to PwC India, “MSMEs in India are not yet fully AI-ready, with manufacturing adoption sitting at only around 15%.” 

How AI Can Help

A PwC report, Unlocking the AI Edge for MSMEs, estimates that AI could contribute US$ 83.2 billion-US$ 91.9 billion to MSME growth by 2035 if their share of Indian manufacturing remains at 35.4%. This potential rises to US$ 135.6 billion-US$ 149.9 billion if their contribution reaches 50%. 

“India AI Mission committed to ensure AI has a meaningful impact on real sectors of the economy, especially manufacturing MSMEs,” says Shri S. Krishnan, Secretary, Ministry of Electronics and Information Technology. 

China too has been pushing AI in manufacturing as it tries to stay competitive in global markets. This was in evidence at the 2026 World Manufacturing Convention in Hefei, Anhui province, this week. 

Today, with the AI layer, they can generate the planning within five minutes, with three or four alternatives

— Rajat Srivastav, founder and CEO of Digital Factory Operating System (Df-OS)

According to PwC, AI can help boost productivity through predictive maintenance, intelligent inventory management, demand forecasting and process automation, among others. EY also points to AI-powered quality inspection and intelligent production planning.

“For one of our leading fast moving consumer durables (FMCD) customers, the planning used to take around 14 days. Today, with the AI layer, they can generate the planning within five minutes, with three or four alternatives. Choosing from options is far easier than creating the options itself,” Rajat Srivastav, founder and CEO of Digital Factory Operating System (Df-OS).

Digital Factory Operating System (Df-OS) enables data to be processed and analysed in real time through digitisation of processes and by linking operational data to an AI layer. 

Hurdles To Adoption

But for many factories, AI adoption will be no cakewalk.

The first challenge is building the digital foundation needed for AI to work. A significant part of manufacturing operations still involves manual coordination between people, processes, materials, and machines, with operational data often spread across different systems.

“Pretty much 70% to 80% activities are governed through human processes, right? The process definitions are there, but to decide when and what process will happen is very much human oriented,” Srivastav told The Secretariat.

AI adoption among Indian manufacturing MSMEs is still at a relatively early stage and readiness varies

— Sunil Chordia, Chairman, Confederation of Indian Industry (CII) National MSME Council and CMD, Rajratan Global Wire Ltd

A survey by the Manufacturing Leadership Council, cited in the EY report, found that 65% of manufacturing leaders identified data issues, including access, format, integration, privacy and governance, as the main challenge for AI adoption in their companies.

Cost And Skills

For smaller manufacturers, however, the economics of adoption can be as important as the technology itself.

“AI adoption among Indian manufacturing MSMEs is still at a relatively early stage and readiness varies,” said Sunil Chordia, Chairman, Confederation of Indian Industry (CII) National MSME Council and CMD, Rajratan Global Wire Ltd.

Companies with simple digital systems, machines linked into the system, and organised data are more likely to get started with specific applications, says Chordia. Those with a disjointed or manual system may need to first digitise simple processes.

But digital readiness alone is not enough. 

The biggest barrier may be the cost and complexity of changing existing business processes, not AI cost itself

— Pei-Fu Hsieh, Co-Founder and CEO, AI Accountant

MSMEs also need people who understand both their manufacturing processes and the technology being introduced. A KPMG report cites a CII estimate that Indian MSMEs lose nearly 30% their productivity due to ad hoc processes, rework, and lack of SOPs.

Transitioning to new processes can also be more challenging than acquiring an AI tool for many companies.

“The biggest barrier may be the cost and complexity of changing existing business processes, not AI cost itself,” Pei-Fu Hsieh, Co-Founder and CEO, AI Accountant, told The Secretariat.

PwC also identifies subscription fees, hidden integration costs, and inappropriate use of technology as adoption concerns.

Crossing The Barrier

However, in the short term, the challenge is also about how AI fits into existing business processes rather than the creation of fully automated factories. 

Firms can first measure the time, errors or manpower savings from automating an initial repetitive process. This may also be an overall competitiveness issue for smaller manufacturers. 

Larger companies may have dedicated quality and process teams, while MSMEs may have less manpower for these tasks. However, these capabilities can be achieved with an AI-powered system without the need for a similar technology workforce.

“It is not the robotics that they have really advanced on. It is the software that they have really advanced,” Srivastav said.

EY also recommends a gradual approach to adoption, starting with pilots such as predictive maintenance, energy monitoring and quality inspection, before extending the technology across the value chain.

For Indian policymakers, it is not just about the quantity of MSMEs using AI, but about the outcomes. CII has also called for practical skilling, demonstration facilities, pilot projects, and less costly technology solutions to allow small businesses to test applications before scaling.