Abhijit Dubey, the President, Chief Executive Officer, and Chief AI Officer of NTT DATA, says the age of treating artificial intelligence as an experiment is over. In a new interview set to air at 2 p.m. on December 16, 2025, he argues that AI now sits at the heart of corporate strategy and leadership. Speaking on the ET AI Podcast, he links AI’s rise to shifts in pay, new debates on social policy, and the hidden networks that keep digital life running.
Dubey’s message is clear. He believes executives must steer AI directly rather than delegate it to side teams. His remarks arrive as large companies race to adopt automation, machine learning, and generative tools across their operations.
AI Moves to the Center of Strategy
Dubey frames AI as a board-level issue. He says the winners will be the firms that align core goals, data, and operating models with machine intelligence.
“AI is not a side project but the core strategy of modern companies,” Dubey says.
That shift is already visible across industries. Banks are using AI to flag fraud and tailor offers. Manufacturers are applying predictive models to reduce downtime. Retailers are adjusting prices and inventory with demand signals. Many of these uses were once pilots; now they are spreading into daily workflows.
Analysts have tracked heavy investment since 2023 as cloud credits, dedicated chips, and software budgets surged. Boards now ask how AI affects growth, cost, and risk. Dubey’s view fits that trend: align AI with the product, the customer, and the P&L.
The Infrastructure Behind Everyday AI
Dubey also points to the unseen systems that make digital services possible. He notes that most internet traffic runs through layers few consumers ever see, from subsea cables to data centers and network routing.
He references “how internet traffic flows through unseen systems,” highlighting the scale and fragility of the stack that AI depends on.
This matters because AI workloads are hungry. Training large models requires massive compute, cooling, and power. Inference at scale demands low-latency networks. Operators are rethinking site design, energy sourcing, and hardware lifecycles to keep up with demand. That infrastructure lens is central for a firm like NTT DATA, which sits at the crossroads of services, networks, and enterprise software.
Shifts in Work and Pay
One of Dubey’s strongest claims addresses the labor market. He argues that AI will change what people are paid to do. Tasks once billed by the hour may be automated or compressed. New roles will appear around data quality, model oversight, and product integration.
Dubey says AI will “change what humans get paid to do” and could “revive the debate on Universal Basic Income.”
Economists have warned of uneven impacts. Routine work faces pressure, while creative, analytical, and hands-on jobs may adapt. Some studies have found productivity gains when workers use AI copilots, but the gains vary by task and skill level. Labor groups urge safeguards, citing risks of bias, surveillance, and deskilling.
UBI sits inside that debate. Supporters see it as a buffer if automation cuts low-wage hours. Critics say better training, wage supports, and job matching would be more targeted. Dubey does not endorse a policy, but he signals that the question is back on the table as adoption widens.
Why Leaders Want Direct Ownership
Dubey urges executives to take personal ownership of AI programs. He frames the issue as one of accountability and speed. When AI touches pricing, customer support, hiring, or safety, leaders cannot outsource the judgment calls.
Direct ownership also helps firms manage risks. Model errors, security gaps, and data misuse can bring legal and brand damage. Clear governance, audits, and incident response are now part of the job. Companies that centralize responsibilities tend to move faster from pilots to production while keeping guardrails in place.
Signals for the Year Ahead
Several indicators will shape the next phase of adoption:
- Costs per model token and per inference, which affect rollout economics.
- Data quality pipelines, which determine output accuracy and trust.
- Energy and water use at data centers, a growing policy focus.
- Workforce outcomes, including wage patterns and reskilling results.
- Regulatory clarity on transparency, liability, and safety checks.
Case studies will matter as well. Firms that tie AI to revenue, not just cost cuts, will set the pace. Partnerships across cloud, chipmakers, and integrators will define who scales and who stalls.
What Dubey’s Message Means for Companies
For many leaders, the takeaway is practical. Map the core processes, set measurable targets, and pick the right stack. Build a small, skilled team that works with security, legal, and operations. Focus on data readiness and outcomes, not hype.
For workers, the shift calls for new skills. Prompting, data literacy, and oversight of AI outputs can add value. For policymakers, monitoring labor effects and infrastructure strain will remain urgent.
Dubey’s call is blunt: treat AI as the centerpiece, not a skunkworks. His interview lands as spending grows and pressure rises to show real results. The next test will be execution—shipping products that are safer, faster, and measurably better. Watch for clearer governance, steadier unit costs, and early proof that AI can lift both productivity and wages without widening gaps.
