AI Readiness in India's Corporate Sector: A Reality Check

EY India's 2025 report on GenAI productivity — "The AIdea of India" — is one of the most important business documents published in recent years. Its central finding: GenAI could drive a 35–37% productivity improvement in India's retail sector by 2030, and transform 38 million jobs across the economy. The numbers are compelling. The boardroom enthusiasm they have generated is real. And the gap between that enthusiasm and the organisational readiness to capture the opportunity is, in our experience, vast.

The same EY report contains a data point that receives far less attention than the productivity headlines: only 15% of surveyed Indian enterprises have actually implemented GenAI in production. A full 36% have not yet begun any experimentation. And of those who have moved to production, only 8% can fully measure and allocate their AI-related costs.

This is not a technology problem. It is a people and capability problem — and it is the lens through which Nexora approaches the AI conversation with our clients.

The Large Enterprise vs. MSME Divide

The EY data reflects the experience of large enterprises — companies with dedicated technology teams, access to global best practices, and the budget to run proofs of concept across multiple use cases simultaneously. For this cohort, the question is largely one of execution: how do we move from pilots to production, and how do we build the workforce capability to extract value from the tools we are deploying?

For India's 63 million MSMEs — which employ roughly 110 million people and contribute approximately 30% of GDP — the picture is entirely different. The AI conversation in MSME India is not about choosing between GPT-4 and Claude. It is about whether the business has a CRM at all. Whether invoicing is digital. Whether the owner's children, who are running WhatsApp marketing campaigns, know what a prompt is.

Source: EY India, "The AIdea of India: 2025"

The risk is that India ends up with a two-speed AI economy: large enterprises and startups capturing the productivity gains, while MSMEs — which are the backbone of employment and regional economic development — fall further behind. This is not inevitable. But it requires a very different kind of capability intervention than the one the corporate training industry is currently offering.

The Three Capability Gaps We See Most Often

Working with organisations across Banking, FMCG, Manufacturing, Retail, and Education, we consistently encounter three capability gaps that technical AI implementation on its own cannot solve:

• Prompt literacy across functions. The productivity of a GenAI tool is almost entirely a function of how well the user can communicate with it. A skilled prompt writer can generate a month's worth of marketing content in an afternoon. A poor one will generate unusable output that takes longer to edit than to write from scratch. This is a skill — a learnable, teachable, measurable skill — and most organisations are not training it systematically.
• AI ethics and judgment. As GenAI tools are used for customer communication, HR decisions, credit assessment, and content creation, the question of what constitutes appropriate use cannot be left to the individual employee's discretion. Organisations need policies, training, and governance frameworks. Most have none of the above.
• Change leadership for AI transitions. The greatest implementation failures we have seen are not technical. They are cultural. Teams that feel threatened by AI tools sabotage adoption — not through deliberate resistance but through the simple mechanism of not using the tools, not reporting problems, and not experimenting. Building a culture of constructive engagement with AI change requires leadership capability that most managers have not yet developed.

What EY's Data Means in Practice

The 35–37% productivity improvement projected for retail specifically — and the broader 5.44% GDP lift across sectors — assumes that the workforce has the capability to work effectively alongside AI tools. The report itself acknowledges this: "Large-scale upskilling initiatives, supported by public-private partnerships and AI-focused training programs, are crucial to bridging the skill gap."

In practice, what this means for an organisation planning its AI strategy: the investment in technology and the investment in people capability need to move in parallel, not sequentially. The most common failure mode is organisations that invest heavily in technology, deploy tools without adequate training, see poor adoption, and conclude that the tools do not work — when the actual problem is that the people were never equipped to use them.

Three Practical Recommendations for Leadership Teams

• Audit capability before technology. Before selecting any AI tool or platform, map the current digital and AI literacy of the workforce. This assessment will tell you where the highest-leverage training investments are and help you sequence adoption in a way that builds momentum rather than frustration.
• Build prompt literacy into your L&D calendar. Prompt engineering is not a specialist skill for IT teams. It is a general business skill for every function — marketing, finance, HR, sales, operations. Every employee who will use a GenAI tool needs to be trained in how to use it effectively. This is not a half-day workshop. It is an ongoing learning programme.
• Designate AI champions, not just AI owners. The CTO or IT head cannot drive AI adoption alone. You need credible, enthusiastic practitioners in every business function who can model effective AI use, answer peer questions, and surface implementation challenges before they become adoption failures. These people need time, recognition, and visible leadership support.

The AI productivity revolution is coming to India. Whether your organisation captures its share of it — or watches others do so — will depend less on which tools you purchase and more on how seriously you invest in the human capability to use them well. The technology is not the constraint. The people are. And in India, that is actually good news, because building people capability is something we know how to do.