Enterprise AI Adoption at a Moment of Maximum Skepticism - with Nishtha Jain

**Matthew Demello** (0:17)
Welcome, everyone, to The AI in Business Podcast. I'm Matthew Demello, editorial director here at Emerge AI Research. Today's guest is Nishtha Jain, AI innovation and strategy leader in the biopharma industry at Takeda Pharmaceuticals. Nishtha joins Emerge Client Narrative and content strategy lead Nick Gertsch on today's program to explore why most enterprise AI pilots fail to scale, how unrealistic expectations in misaligned use cases undermine ROI and what it takes to design human-centered AI systems that fit how people actually work inside regulated organizations. Their conversation also examines practical shifts in how leaders measure value, moving beyond headcount reduction to returns on employee experience and long-term compatibility building, along with concrete approaches for faster experimentation, customer-driven use case prioritization, and building flexible operating models that adapt as technology and markets change. Just a quick note for our audience that the views expressed on today's program by Nishtha do not reflect that of Takeda Pharmaceuticals or its leadership. Today's conversation with Nishtha Jain is about AI skepticism. And if you're leading enterprise AI initiatives, you probably share some of that skepticism. The challenge isn't excitement, it's execution. Data readiness, integration complexity, security concerns, proving ROI, it all has to align. Our sponsor, SHI, has been a major global IT solutions provider for over 35 years, helping 17,000-plus organizations navigate exactly these challenges. SHI guides enterprise leaders through their proven framework. First, imagine the right AI strategy for your business context, experiment in their AI and cyber labs to validate solutions with your actual data and workloads, typically in two weeks, and adopt solutions that deliver measurable outcomes in production. If you're looking for guidance on moving from AI ambitions to real results, check out shi.com. But first, are you driving AI transformation at your organization, or maybe you're guiding critical decisions on AI investments, strategy or deployment? If so, the AI in Business Podcast wants to hear from you. Each year, Emerj AI Research features hundreds of executive thought leaders, everyone from the CIO of Goldman Sachs to the head of AI at Raytheon, and AI pioneers like Yoshua Bengio. With nearly a million annual listeners, AI in Business is the go-to destination for enterprise leaders navigating real-world AI adoption. You don't need to be an engineer or a technical expert to be on the show. If you're involved in AI implementation decision-making or strategy within your company, this is your opportunity to share your insights with a global audience of your peers. If you believe you can help other leaders move the needle on AI ROI, visit emerj.com and fill out our Thought Leader Submission form. That's emerj.com and click on Be an Expert. You can also click the link in the description of today's show on your preferred podcast platform. That's emerj.com/expert1. Again that's emerj.com/expert1.
Without further ado, here's our conversation with Nishtha.

**Nick Gertsch** (3:37)
Nishtha, welcome to the program. It's so good to have you back on the show.

**Nishtha Jain** (3:41)
Thank you, Nick. It is so wonderful to be back.

**Nick Gertsch** (3:44)
Really, really exciting. Always good to have a friend back on the show. So, since you were last here, I think that we've all watched with awe, with fear, with hope, maybe a bit of trepidation, the acceleration of investment in AI. But, and there is a big but, across industries, there are, and there might even be some stirrings in the interviews that we're having, that leaders are grappling with this paradox. And the paradox is, implementations are failing to deliver the value promised. We're seeing kind of more and more surveys releasing. I think there was a big MIT one at some point this year, claiming that the vast majority of Jain AI projects stall before scaling. And even when a pilot is successful, they often face challenges in workforce readiness, in organizational processes, and even defining ROI beyond those pure financial metrics. So in the face of these high expectations, alongside a growing skepticism, many companies and indeed many of our enterprise listeners are asking what it really takes to make adoption work. And I don't think we can think of anyone better than yourself to provide something of an answer to this. So from your perspective, Nishtha, what are the main challenges that enterprises are facing with AI adoption right now?

**Nishtha Jain** (5:23)
Thank you for referencing the State of AI 2025 report.
I understand that the report says that 90% to 95% of gen AI pilots fail to transition to full production. And let's be real. So I think my two senses, and these are the three big challenges that I see and that might be leading to this report. And then this report is also very nuanced, so I would not just read it like that. So I think the first one is expectations are unrealistic. So people are expecting AI to be magic. Executives want almost 10x productivity, say by end of quarter three or every quarter. Teams want auto everything now. And vendors, so many vendors in the space, are promising miracles. So, I mean, technology is nothing, but what matters is that people believe in it deeply to change. And that belief will take time and clarity, so I think that's definitely true. That's one of the challenge, the unrealistic expectations. Now, I think the second challenge that I see is right business problems. So, I think there's too much technology, but very little understanding. So, enterprises are buying models, platforms, you know, copilots, but they're forgetting the one actual thing that truly unlocks the ROI. And which is the use cases. So, it's not the models, not the features, not the experiments, because, you know, where the value lies is the use cases. So, until AI solves a real business problem, it's nothing but an expensive science experiment. So, that's, you know, number two. And then I think number three is, you know, people itself. So, AI isn't about technology, but it's fundamentally about people. It's the employees are worrying, will AI replace me? Leaders are worrying, what if we implement the wrong thing? Legal teams are, you know, they have fear that, you know, this is posing a risk. IDEs are worried about integrating it in their current systems. Finance is fearing the cost of it. And so everyone fears uncertainty, and this is all leading to a lot of fear. So here, you know, AI isn't the problem. It's basically the story problem. So business isn't clearly telling the right story about the future. And I personally think for human-centered AI, it's crucial to connect the visionary strategies, you know, into that real world success. Like, for that to happen, the right story needs to be told.