By Vishal Chaddha, Executive Vice President – Wadhwani Accelerate Program, National Entrepreneurship Network
India has never lacked ideas designed for constraint. What it has often lacked is an affordable pathway to turn those ideas into reliable, scalable and globally competitive products. Artificial intelligence could begin to change that equation.
Constraint-driven problem-solving has long shaped India’s growth. Limited capital, uneven infrastructure and gaps in access have pushed Indian entrepreneurs to create solutions that are simple, affordable and ingenious. This instinct for frugal innovation is a genuine national strength. Yet many promising ideas remain local, informal or trapped at the prototype stage. The difficulty is rarely a lack of ambition or ingenuity. It is the cost of converting an inventive solution into a market-ready product that performs consistently, meets standards and can compete beyond its original market.
When it comes to growth-stage MSMEs, entrepreneurs often understand the problem intimately and have found a practical way to solve it. What they do not always have is affordable access to specialist expertise, structured experimentation, technology and markets. AI has the potential to narrow that gap.
Why good ideas struggle to scale
Moving from a clever solution to a dependable product requires much more than the original insight. It takes repeated design refinement, prototyping, user testing and engineering validation. Scaling then demands standardised manufacturing, consistent quality, reliable suppliers, certification and working capital. For a large company, many of these capabilities are available in-house. For a smaller enterprise, each one may require a different institution, consultant or vendor.
This fragmentation creates both cost and delay. A founder may need one partner for product design, another for testing, another for intellectual property, another for certification and yet another for market access. Simply identifying and coordinating the right expertise can consume scarce management time. As a result, a low-cost product that works well in one setting may never become sufficiently reliable, documented or standardised to enter larger domestic or international markets.
That is the central challenge for India’s frugal innovation ecosystem: not generating ideas but building an economical bridge between invention and scale.
AI can democratise expertise
AI’s most important contribution to frugal innovation may not be automation. It may be the democratisation of expertise.
Generative design tools can help entrepreneurs explore several product configurations before committing money to physical prototypes. Simulation and digital modelling can expose design weaknesses earlier. AI can support coding, technical documentation, market research and the analysis of user feedback. It can help a small team undertake parts of the research and iteration that previously required several specialists or external vendors.
The potential productivity gains are meaningful. Generative AI could improve productivity in software engineering by 20 to 45 per cent, particularly by reducing the time spent generating and refining code and developing initial system designs[1]. For a smaller business, the significance is not merely that existing work gets done faster. It is that work which was previously unaffordable may become possible.
Consider a small industrial equipment manufacturer in Rajkot seeking export. AI could help the team analyse field-failure reports, compare alternative component designs, organise technical documentation, identify relevant overseas standards and study potential customer segments. The company would still need engineers, physical testing, certification and investment in manufacturing. But it could approach each of those expensive steps with better information and fewer avoidable iterations.
The same logic extends beyond product design. AI-enabled tools can assist with quality inspection, demand forecasting, production planning and supply-chain optimisation. They can support localisation, customer research and the interpretation of market trends. Used responsibly, these capabilities can lower the cost of learning and shorten the distance between a locally successful product and one that is ready to scale.
Why India is well positioned
India enters this transition with important advantages. It has a vast base of entrepreneurs and MSMEs, deep digital adoption, expanding engineering capability and decades of experience in designing for price-sensitive markets. India ranked 38th in WIPO’s Global Innovation Index 2025, up from 66th in 2019, and continued to lead the lower-middle-income group. This progress reflects an innovation base that is becoming broader and more capable.
India’s generative AI ecosystem is also developing rapidly. More than 890 GenAI start-ups exist by the first half of 2025, a 3.7-fold increase in a year, with application-focused ventures making up the large majority of the ecosystem[3]. This matters for MSMEs because useful AI capabilities are increasingly likely to reach them through accessible products and services. Smaller firms need not build expensive AI infrastructure themselves to benefit from sophisticated tools.
India can therefore combine two complementary strengths: a deep instinct for solving problems under constraint and a growing ability to make advanced digital capabilities widely accessible. If brought together well, they can help Indian businesses create products that are not merely inexpensive, but reliable, adaptable and globally relevant.
The ecosystem must complete the bridge
AI, however, will not remove every constraint. It cannot substitute for physical testing, sound engineering, certification, patient capital or access to customers. Nor can smaller businesses adopt it effectively without trusted tools, usable data and people who understand both the technology and the business problem.
Government, industry, academia and philanthropy therefore have complementary roles to play. Shared testing and prototyping facilities can reduce the cost of experimentation. Academic and industry partnerships can make specialised technical knowledge easier to access. Investors and lenders can fund the transition from prototype to production. Technology providers can build tools around the workflows and languages of Indian MSMEs rather than expecting small firms to adapt to enterprise systems designed elsewhere.
Philanthropy can be particularly useful where the risk is too early for commercial capital. It can fund shared infrastructure, practical capability building and patient experimentation. It can also connect entrepreneurs from Tier 2 and Tier 3 cities with experts, institutions and markets that would otherwise remain difficult to reach. These innovators are often closest to the problems faced by large sections of the population; the missing ingredient is frequently not insight, but the exposure and support required to convert that insight into a scalable model.
The objective should not be to place AI beside the existing innovation ecosystem as another isolated initiative. It should be embedded within the full journey from problem identification and design to validation, financing, manufacturing and market access.
From frugal products to global advantage
India’s opportunity is larger than producing cheaper versions of products created elsewhere. Frugal innovation begins with a different understanding of the customer, the constraint and what is genuinely essential. AI can strengthen that instinct by giving smaller teams access to capabilities once concentrated in large companies.
The combination could be powerful: Indian ingenuity defining the problem, AI reducing the cost of expertise and experimentation, and a stronger ecosystem carrying the solution through to scale. If we get that combination right, the next generation of Indian frugal innovations will not remain clever local workarounds. They will travel further, scale faster and compete globally.