Why India’s MSMEs struggle to turn skills into productivity: KPMG answers

India’s micro, small and medium enterprises (MSMEs) employ tens of millions and contribute substantially to economic output. Yet a persistent paradox remains: the sector generates jobs at scale but struggles to translate labour into productivity.

The reasons, according to Sunit Sinha, Partner and Head, Human Capital Advisory Solutions at KPMG in India, go far beyond training gaps. In an interview discussing the findings of a recent KPMG–CII study on the future of MSME talent, Sinha says that skills alone cannot solve the productivity challenge unless they are embedded into disciplined systems of work, technology adoption, and operational scale.

The findings reflect a structural tension at the heart of India’s growth model: a large and vibrant enterprise base operating with limited formal capability infrastructure.

The scale–productivity paradox

India’s MSMEs form the backbone of the economy. The sector employs around 32.84 crore people and contributes roughly 30.1 per cent of the country’s GDP, according to the KPMG–CII report Talent Imperatives for MSMEs: Building a Future-Ready Workforce for India’s Growth Engine.

Yet productivity tells a different story. Each MSME worker generates only about 14 per cent of the productivity of workers in large enterprises, highlighting a major structural gap in economic efficiency.

For Sinha, this disparity is often misunderstood as purely a skills problem.

“While MSME workers deliver only about 14% of the productivity of large-enterprise workers—is not purely a skills deficit but a structural one. While weak formal skilling, low digital fluency, and limited managerial capability materially constrain output, a large share of the gap stems from small scale, low automation, informal operations, ad-hoc processes, and restricted access to capital.”

In other words, the productivity gap reflects the way work is organised, not just the capabilities of workers performing it.

He adds that training programmes alone will not resolve the problem if operational systems remain fragmented.

“Skills alone cannot close the gap unless they are embedded into standardised workflows, quality systems, and digitally enabled processes that allow MSMEs to convert human effort into repeatable, scalable productivity.”

A workforce with limited formal training

The structural nature of the productivity problem is reinforced by the sector’s skilling profile.

Only about 10 per cent of the MSME workforce has formal vocational training, compared with roughly 50–60 per cent in OECD economies, according to the KPMG–CII study.

The mismatch between industry needs and workforce preparation has become more pronounced as manufacturing, logistics and services adopt digital tools and data-driven workflows.

However, Sinha says that large-scale classroom training programmes are unlikely to close the gap fast enough.

“The traditional classroom-led skilling cannot scale fast enough and instead emphasis should be on work-embedded pathways such as Apprenticeships 2.0, Recognition of Prior Learning (RPL), NSQF-aligned micro-credentials, and on-the-job digital learning delivered through platforms like Skill India Digital Hub and PM Vishwakarma.”

Such approaches, he says, align better with the realities of small enterprises that cannot afford to pull workers away from daily operations.

“These approaches reduce time-to-productivity, validate existing shop-floor skills, and allow MSMEs to train workers without disrupting operations, making them far more realistic for scaling formal skilling across a largely informal workforce.”

AI readiness depends on basic discipline

The conversation around MSME capability is also increasingly tied to artificial intelligence.

Many policymakers and industry leaders see AI as a potential equaliser for smaller enterprises. But Sinha warns against assuming technology can leapfrog structural constraints.

“AI readiness is feasible for MSMEs only under specific conditions, not as a blanket leapfrog.”

For businesses with weak digital foundations, introducing AI prematurely can worsen operational inefficiencies.

“AI should be layered onto stable, standardized workflows with basic digital hygiene—such as SOPs, quality controls, and data discipline—already in place. For digitally immature MSMEs, premature AI adoption can amplify inefficiency and risk.”

Instead, he suggests that targeted deployment in routine operational areas can yield tangible benefits.

“However, when introduced incrementally into well-defined use cases like quality checks, invoicing, or customer support, AI becomes an equalizer rather than a threat.”

The broader implication is that digital transformation in MSMEs must begin with process discipline rather than technology procurement.

From visibility to productivity

The KPMG–CII report outlines a three-stage transformation model for MSME capability building, moving from skills visibility to productivity gains and eventually to long-term resilience.

The most difficult shift, Sinha says, occurs in the middle stage.

“The most difficult transition is from Phase I (skills visibility) to Phase II (skills-led productivity), where MSMEs must convert mapped skills into measurable operational gains.”

That step often exposes weaknesses in management practices.

“Execution discipline is the key barrier—specifically the absence of SOPs, weak supervisory capability, fragmented accountability, and lack of skills analytics linking roles to outcomes.”

Many firms recognise their capability gaps but struggle to operationalise improvements.

“Many MSMEs can see their skill gaps but struggle to institutionalise process discipline and role-embedded learning that reduces defects, cycle time, and rework.”

Why clusters may matter more than individual firms

Another challenge is scale. Individual MSMEs often lack the financial or institutional capacity to invest heavily in workforce development.

Sinha believes that cluster-based training ecosystems could help bridge that gap, particularly in semi-urban and rural industrial regions.

“Cluster-based skill ecosystems work by pooling training infrastructure, shared labs, master trainers, and digital learning resources across geographically concentrated MSMEs that individually lack scale.”

Such models are already embedded in several government initiatives.

“Shared skilling models—supported by cluster programs like MSE-CDP and ZED—outperform firm-level efforts by lowering unit training costs, improving access to advanced skills, and accelerating digital and green readiness.”

In practice, these clusters function as shared learning platforms.

“In semi-urban and rural hubs, clusters effectively function as ‘learning campuses’ that deliver scale without requiring each MSME to invest independently.”

Policy and industry must align incentives

Closing the productivity gap will require coordinated action across policy, industry bodies and larger corporations that anchor supply chains.

Sinha says that government incentives must evolve beyond traditional subsidies.

“Policy should shift incentives from pure capital subsidies toward skill creation, formalisation, and cluster-level learning infrastructure, using schemes like NAPS, CGTMSE, ZED, and PM Vishwakarma as strategic levers rather than compliance tools.”

Industry associations, he adds, have a role in standardising credentials and convening partnerships.

“Industry bodies should convene clusters, standardise credentials, and curate shared training and technology partnerships.”

Large companies can also influence capability development through supply-chain relationships.

“Private enterprises, especially anchor firms, must embed skills into daily work, co-invest in apprenticeships, and integrate MSMEs into digital and green value chains to make capability development economically viable.”

The real test will come by 2030

Looking ahead, Sinha believes the next decade will fundamentally reshape the MSME landscape as digitalisation, sustainability regulations and global supply-chain standards intensify.

By 2030, success will depend less on enterprise size and more on capability readiness.

“By 2030, the thriving MSMEs will not be defined by size but by capability readiness—those that built early skills visibility, converted skills into productivity, and then augmented stable processes with AI and sustainability practices.”

Firms that fail to make that transition may find themselves increasingly marginalised.

“Successful MSMEs will treat green compliance as a license to operate and AI as a productivity multiplier, while laggards that remain informal, ad-hoc, and reactive will struggle with rising digital, ESG, and global supply-chain demands.”

The challenge, ultimately, is not simply to train workers but to redesign how work happens.

For India’s MSMEs, the next phase of growth will depend on whether the sector can transform skills into systems—and labour into productivity.