For MSMEs, the challenge extends beyond purchasing an AI tool. The real investment lies in the infrastructure, data and technical capabilities required to put it to effective use.
The potential is considerable. A 2025 report by the World Economic Forum and Boston Consulting Group estimates that AI could unlock over $500 billion in economic value for Indian MSMEs. Achieving this, however, will require solutions that reflect the financial and operational realities of smaller enterprises.
When businesses consider adopting AI, the first expense they often look at is the software or subscription. However, that is only one part of the investment. AI applications also require computing capacity, storage, reliable connectivity and access to good-quality data. There may be additional costs for integrating the tool with existing business systems, training employees, maintaining security and managing the solution over time.
For instance, a manufacturing MSME looking to use AI for predictive maintenance may need to collect machine data through sensors, store it, and process it to identify patterns that indicate equipment failure. A logistics business may want to use AI to improve route planning, but this requires access to accurate operational and location data. In both cases, buying an AI application is not enough. The supporting infrastructure and the quality of the data determine whether it can deliver useful results.
This is also why smaller businesses should avoid investing in infrastructure they may not fully use. Building dedicated computing capacity for occasional or limited AI workloads can make the economics difficult. A cloud-based model, where businesses access computing and storage as required, can offer a more practical starting point.
Cloud can lower the entry barrier
Cloud infrastructure allows MSMEs to use computing resources without having to purchase and maintain all the underlying hardware themselves. They can begin with the capacity required for a specific application and increase it as their needs grow. This reduces the need for large upfront investments and gives businesses more flexibility.
According to Rahul, “Affordability is not just about the initial cost. Businesses also need to understand what they will pay as usage increases. Computing, storage, data transfers, backups, and additional services can all contribute to the final bill. Unpredictable charges can make it difficult for an MSME to plan its technology spending.”
He further adds, “At BharathCloud, we believe cloud infrastructure should be aligned with the actual requirements of a business. This means selecting the right computing and storage capacity, monitoring usage, and avoiding resources that remain underutilised. Predictable pricing and clear visibility into infrastructure costs are particularly important for smaller businesses operating with limited technology budgets.”
Start with a business problem, not the technology
Another issue is the tendency to adopt AI because it is gaining attention, rather than because a business has identified a specific need. MSMEs do not have to begin with complex AI models or large-scale automation projects. They can start with clearly defined tasks where better data processing or automation can save time and reduce operational effort.
Businesses should also check whether their existing data is organised and usable. AI tools cannot deliver reliable results if information is scattered across spreadsheets, disconnected applications, or inconsistent records. Data preparation and integration may therefore need to happen before investing in more advanced applications.
Making AI adoption sustainable
Access to infrastructure is only one part of the challenge. MSMEs also need people who can use these tools, understand their limitations and manage the data being shared with them. The World Economic Forum’s playbook identifies gaps in awareness, data readiness, skills, and access to suitable solutions as important barriers to wider adoption.
Cloud providers, technology companies, industry associations and government initiatives can help address these gaps through shared computing resources, practical training and solutions designed around sector-specific needs. The IndiaAI Mission, with an approved outlay of ₹10,371.92 crore, also includes a compute capacity pillar aimed at widening access to AI infrastructure.
The AI revolution should not require every business to make the same level of investment. A small manufacturer and a large enterprise will have different workloads, budgets, and infrastructure requirements. What matters is giving each business access to the resources it actually needs, with the flexibility to expand when there is a clear business case.
For India’s MSMEs, making AI affordable is ultimately about making the infrastructure behind it accessible. Ultimately, the businesses that gain the most from AI will not necessarily be those with the largest technology budgets, but those that can put it to work efficiently and at a cost they can sustain.
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