People, process, data, and governance shape meaningful enterprise AI.
Our AI journey has progressed well beyond isolated experimentation and is now on the path to enterprise-wide adoption. Real AI adoption depends on getting three things right—people, process, and technology. Our strategic focus is on the value we extract from digital investments—whether to improve efficiency and profits, grow revenue, or drive societal impact such as sustainability, worker safety, or better outcomes for farmers. In a manufacturing and supply chain– heavy setup like ours, everything begins with data.
“Think impact, start small, scale responsibly—that’s the AI playbook we follow.” ~ Gaurav Kataria Vice President – Digital (Manufacturing) & CDIO, PSPD, ITC
On the technology front, investing in foundational data infrastructure is a prerequisite. Without clean, accessible, and well-governed data, no AI layer can deliver value. We have traditionally stored data on-premises. Only the datasets needed for MLOps or training are moved to the cloud. Embedding AI with Purpose: A Playbook for Scalable, Responsible Impact People, process, data, and governance shape meaningful enterprise AI. “Think impact, start small, scale responsibly—that’s the AI playbook we follow.” This hybrid model gives us the best of both worlds—security and cost efficiency—while preserving OT-IT segregation, governance, and layered security. Process alignment is equally critical.
Every AI project is jointly owned by a business and a tech leader to ensure accountability and problem-solution alignment. Often, AI insights require process changes—and that only works if business stakeholders are aligned from day one. Then comes the people aspect. We’ve made training and change management a priority—from the boardroom to the shop floor—to enable innovation and build trust in the system. One key lesson is never to underestimate the cost of scale.
We’ve seen great pilots fail because scalability wasn’t factored in early enough. Now, every proof of concept is evaluated for both business impact and scalability from the beginning. Operational challenges such as model drift, hallucinations, and black-box logic are real. So, every project begins with a clear objective, owner, and success metric. Societal impact is deeply woven into our AI strategy. Whether it’s improving worker safety, reducing water consumption in manufacturing, or optimizing energy in our mills, we are using AI as a lever for sustainability and shared value. Our journey is about more than technology. It’s about embedding AI into how we work—responsibly, at scale, and with lasting impact.