Shaping & Harnessing AI for Real Business Impact
Amit Ss Jain
Agentic AI Product Lead
Accenture
Shaping & Harnessing AI for Real Business Impact
Amit Ss Jain
Agentic AI Product Lead
Accenture
As per the recent MIT research, most AI initiatives fall short of real business outcomes. Companies are pouring resources into AI, but the real work lies in understanding your customers, their pain points, and identifying the right problems that may or may not require AI. For sixteen years, Amit Ss Jain has taken on that challenge, focusing on customer experience and working backwards, utilizing cutting-edge AI tech for outcome-focused solutions, not chasing the hype. He says, “AI matters when insights become decisions—and decisions become outcomes.” From his days as a software developer to pioneering agentic AI products at Accenture, he has built and scaled products across functions and industries. He earned his engineering degree from NSUT and completed his management education at the Indian School of Business (ranked #5 worldwide, according to LinkedIn’s latest rankings), winning the Young Leader award at ISB and multiple merit scholarships. His idea is simple and effective – “Build where it matters, scale only what serves outcomes.” Now pursuing a PhD in Generative & Agentic AI at IIM Sambalpur, Amit is focusing on developing frameworks that help organisations adopt AI profitably. He also serves as a guest faculty and a mentor for students across IIMs, ISB, etc. In an exclusive interview with TradeFlock, he shares these insights and his vision for the future of AI.
What drew you from data product management to Agentic AI?
Honestly, I believe Generative AI, Agentic AI, and all these latest AI technologies are deeply grounded in data products’ management. Without a strong focus on how you manage your data products and infrastructure, GenAI/ Agentic AI fails to deliver, and that is what we are seeing. Agentic AI uses large/small language models to understand data, reason, and trigger goal-oriented actions. Over the last year, large language models (such as OpenAI o4, Gemini 2.5 pro, and DeepSeek-R1) have reached a level of maturity in reasoning, opening the doors for semi-autonomous decision-making. However, I still believe there is a lot to be done before we attain fully autonomous decisionmaking in complex environments. Currently, we are orchestrating learning in LLMs through context engineering; however, if we can enable these models to learn on the fly as we interact with them, that would be a significant turning point.
Which of your current projects excites you the most?
My PhD work at IIM Sambalpur excites me because it brings together research and practice. I am studying how humans will work with AI-Agents to realize true business value. The magic does not lie in using the biggest model, but in creating strong data foundations, clean data pipelines, and a delightful customer experience that solves their real pain points. Interestingly, as per the latest research by NVIDIA, small language models may actually be the future of Agentic AI. And I am extremely excited about bringing all this research-based learning to build Agentic AI products in my current organization. As one of my professors often says, That mindset is guiding me to develop a framework that businesses worldwide can adopt to leverage these technologies sustainably.
“Research is not about answers, it is about finding the right problem.”
What inspires your creativity, and how do you recharge?
What inspires me the most is building products that solve problems that matter. And for that, I continue to learn, unlearn, and reinvent myself. This intrinsic curiosity and hunger to learn fuels both my research and product intuition. Outside work, I read about the latest AI advancements daily, read at least one research paper a week, and attend weekend AI workshops.
I love spending time with my kid and family, meditating, walking a bit, and staying grounded in the values I grew up with. And that is what charges me for the next day.
"This intrinsic curiosity and hunger to learn fuels both my research and product intuition."
Can you share one setback that shaped you and one milestone that surprised you?
In 2019, post my MBA at ISB, I joined a startup in the office commute space. Things were looking up until the pandemic hit. Overnight, revenue dropped to zero, and I had to look at alternative options. It was mentally draining, but it taught me to stay in the present, unlearn quickly, and adapt fast. I feel that we understand the value of learning from these experiences only in hindsight. And such was my MBA year at the Indian School of Business. I had initially evaluated it in terms of ROI, but the real value I got from it was a truly transformative experience. The friendships, the learning from the diverse, incredible talent around me, and the standing ovation I received for leading the career council all reshaped me. It reminded me that real growth cannot be measured in numbers, and that year opened my mind to the dynamic environment around us; now, nothing surprises me.
What are the biggest gaps in India’s AI journey, and how do you see them closing?
India has exceptional talent, but has often been reactive rather than proactive. China foresaw the AI wave back in 2010, invested in research and infrastructure, and hence now leads the research output on LLMs and Agentic AI. For India, the solution lies in investing in robust research programs, building supporting infrastructure, and fostering a mindset that fosters innovation. Professionals, students, and researchers need to contribute by being lean, fast, and experimental. The coming decades will favour those who are research-driven and customer-focused, and I believe this is where India should focus its efforts.
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