Unlocking AI: What Indian Businesses Must Embrace for Greater Impact

For years, the conversation around artificial intelligence in India has followed a predictable script: talent shortages, tool adoption, and the race to “do something with AI.” Yet despite the noise, many enterprises remain stuck at pilots, proofs of concept, and isolated wins. The uncomfortable truth is that India’s AI gap has far less to do with ambition or intelligence and far more to do with how organizations are designed to make decisions.

“The biggest misconception is that AI readiness is primarily about hiring talent or deploying AI tools,” says Raghvendra Singh, CTO of Cashify. “In reality, readiness hinges on decision-grade data, modular and scalable architecture, and clear ownership of AI outcomes and guardrails.”

That distinction between experimentation and readiness is where many Indian enterprises falter. AI, unlike traditional IT transformations, exposes organizational weaknesses brutally fast. Models don’t just automate tasks; they challenge how data flows, who owns decisions, and whether leadership is prepared to trust algorithmic recommendations at scale.

The illusion of talent shortages

India produces some of the world’s best engineers and data scientists, yet AI initiatives frequently stall. According to Singh, the problem is not the lack of people who can use AI tools, but the scarcity of professionals who can operate AI systems end to end.

“We have many people who can prompt models or train algorithms,” he notes, “but very few who understand data quality, product design, and risk management together.”

This view is echoed on the enterprise side. Mahesh Toshniwal, Group Head of IT Operations at Jindal & Steel argues that the obsession with hiring ML engineers often masks deeper capability gaps. “Talent is not just data scientists,” he says. “There’s an underfocus on domain experts who can frame the right questions, product owners who can translate AI outputs into actions, and frontline teams who actually trust and use AI recommendations.”

Without these roles working in tandem, AI remains trapped in innovation labs technically impressive but operationally irrelevant.

Data: The uncomfortable reality check

If talent is misunderstood, data maturity is even more overestimated. Many leaders confidently claim they have “years of historical data,” but as Mahesh bluntly puts it, “ ‘We have data’ does not mean ‘we have usable data.’”

“In many organizations,” he explains, “data is inconsistent, definitions vary by department, and critical information still lives in Excel sheets, WhatsApp threads, or PDFs. Leaders overestimate data maturity and underestimate the effort required to make data AI-grade.”

This fragmentation becomes fatal at scale. AI systems depend on clean, well-governed data that flows seamlessly across silos. No model, no matter how sophisticated, can compensate for broken data foundations.

Why AI pilots don’t scale

Most enterprises don’t struggle to start AI initiatives; they struggle to sustain them. Sriram Arthanari, Vice President – IT at Kaleesuwari Refinery, describes this as underestimating the “grind” of AI.

“AI pilots are easy,” he says. “Production-grade AI that sustains is hard. The real effort lies in data engineering, legacy system integration, continuous model monitoring, security hardening, and most importantly change management on the business side.”

In fact, Arthanari believes AI rarely fails because the models don’t work. “It fails because workflows, incentives, and decision rights don’t change. Until AI is embedded into core business processes and jointly owned by IT and business teams, scale will remain elusive.”

This explains why many promising initiatives quietly fade after initial enthusiasm. AI challenges not just systems, but power structures.

Governance: Still reactive, still late

As AI systems influence more decisions, governance becomes non-negotiable. Yet most Indian organizations remain reactive.

AI governance is still something that kicks in after problems appear, bias, misuse, or risk aren’t embedded into everyday product and engineering workflows.Raghvendra Singh, CTO of Cashify

Regulatory uncertainty and data privacy concerns further dampen leadership confidence. Arthanari points out that conservative capital allocation, combined with unclear regulations around generative AI, makes leaders cautious. “What they need is not blind optimism,” he says, “but structured confidence, clear governance, strong data controls, and a portfolio-based approach where high-impact use cases fund broader experimentation.”

The operating model blind spot

Perhaps the most critical insight emerging from these leaders is that AI readiness is fundamentally an operating model problem, not a technology one.

AI depends on clean data, clear ownership of decisions, and processes that can actually absorb model outputsMahesh Toshniwal, Group Head of IT Operations at Jindal & Steel

“In many Indian organizations, data is fragmented by business units, vendors, or legacy ERP setups. No model can compensate for that,” he says.

There’s also a persistent myth that AI will automatically drive productivity. In reality, AI systems require incentive alignment, human-in-the-loop design, and deliberate change management especially in people-heavy Indian processes. Ignore that, and the result is silent non-adoption.

What needs to change now

Looking ahead, the path forward is less glamorous but far more impactful. Leaders must invest in strong data foundations, move from isolated pilots to standardized AI platforms, and anchor every initiative to measurable business outcomes.

“AI teams shouldn’t be judged on models built,” Arthanari emphasizes, “but on value delivered.”

Ultimately, India’s AI gap is not a supply-side problem. It is a demand and design failure. Until enterprises clearly define which decisions they want AI to change, what success looks like, and who owns the outcome, no amount of certifications or tools will bridge the gap.

AI is ready. The question is whether Indian organizations are ready to change themselves.