NTT DATA targets 70% AI-led productivity gains; Indian firms enthusiastic but risk “overconfidence”

Artificial intelligence (AI) is rapidly reshaping global productivity, and the acceleration is far steeper than many expected. In a conversation with Jan Wuppermann, Senior Executive, Head – Service, Assurance and Data & AI at NTT DATA APAC, the company revealed that AI is already delivering extraordinary gains—50% productivity improvement in 2025, with expectations of reaching 70% within the next two years. These figures, Wuppermann clarified, reflect “productivity gains, not only cost savings,” driven by AI’s deep integration into software development and engineering workflows.

Speaking to CNBC-TV18, Wuppermann said NTT’s biggest efficiencies are coming from AI-generated code and automation throughout the software development life cycle. He emphasised that these improvements apply specifically to engineering and development functions, where AI is now embedded across every stage. The company’s AI-led momentum is also contributing to its ambitious target of generating $2 billion in revenue from Agentic AI, a goal he said they are “on track” to meet, with exponential growth already visible.

Despite such dramatic productivity gains, Wuppermann dismissed the notion that AI reduces the need for engineers. Instead, he argued the opposite: the opportunity curve is expanding faster than headcount. “If anything, we probably need more engineers,” he said, adding that AI is enabling teams to deliver more, not making them redundant. All engineers, he stated, are being redeployed into high-value projects where AI boosts their output rather than replaces them.

The conversation also touched on NTT’s global AI report, which surveyed 2,500 companies across 34 countries and 15 industries. Wuppermann confirmed that Indian enterprises were part of the research and said the sentiment toward AI in India is not just strong, but unusually energetic. “A huge enthusiasm,” he said, noting that Indian companies often show even more excitement than global counterparts. But with this enthusiasm comes a challenge: a degree of “overconfidence” and confusion about where to begin in a fast-moving landscape.

When asked what he meant by overconfidence, Wuppermann explained that many firms underestimate the complexity of implementing AI at scale. The assumption that AI adoption is easy, he warned, is misleading. Mature organisations are focusing on the basics—clean, well-governed data, clear business strategies anchored in AI, robust stakeholder alignment, and value-linked use cases. Others, however, are rushing in without foundational readiness and risk becoming “victim of the FOMO.”

From explosive productivity gains to soaring demand for AI-skilled engineers and a global race to operational readiness, Wuppermann’s insights underscore a pivotal shift: AI is no longer experimental. It is now a large-scale productivity engine, and the enterprises that build strong foundations—not just excitement—will be the ones to harness its full potential.

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