17 Feb 2026 · 30m · Matthew Demello, Nick Gertsch, Nishtha Jain · 19 quoted facts
Nishtha Jain argues that most enterprise AI efforts stall because of people and business-problem issues, not weak technology. Unrealistic expectations, tools bought without a clear use case, and employee and executive fear of the unknown all get in the way. She says leaders should build around real customer problems, put users at the center of design, and measure success more broadly than headcount savings.
The central tension is between pressure for fast, visible savings and the slower work of building lasting capability. Jain's answer is to experiment quickly, replace long roadmaps with monthly ones, and count employee experience and future-facing value as returns. She compares today's AI hype to the dot-com and metaverse cycles and says firms with solid data, security and governance foundations can adapt when the hype fades.
Jain says companies buy models, platforms and copilots but skip the real business problem that creates return. Until AI solves a defined problem, she calls it an expensive science experiment.
Jain proposes three measures: financial return, return on employee experience (less burnout, more focus on high-value work), and return on future (new products and agility). Finance teams that only ask for headcount back miss value that does not show up as cost cuts.
Employees worry about replacement, leaders about picking the wrong thing, and legal, IT and finance about risk and cost. Jain says the fix is simple, relatable storytelling and designing systems that fit how people already work.
Because technology and markets move so fast, Jain says leaders are shifting from one-, two- and five-year plans to monthly roadmaps and small bets. This lets companies change direction before conditions shift.
Jain points to the dot-com bust, Blockbuster's decline against Netflix, and the metaverse wave as cases where expectations outran reality. Investing in data, security, integration and governance lets a company adapt when the next wave arrives.
Nishtha Jain cites a report saying 90 to 95 percent of generative AI pilots fail to reach production. She blames unrealistic expectations, a lack of clear business use cases, and fear among employees, leaders, legal teams, IT and finance.
ROI is the usual financial return, including savings, fewer errors and new revenue. ROE is return on employee experience, such as less burnout, and ROF is return on future, meaning investment in new products, agility and competitive advantage.
Jain says finance teams often ask whether AI gives headcount or cost back, but forward-thinking leaders use a broader view of people, performance and innovation. A project can still deliver value if it improves employee and customer experience or builds the future.
Jain recommends building flexibility rather than predicting the future: short planning cycles, small incremental bets and monthly roadmaps instead of multi-year ones. She also says decisions should follow customer problems, not technology trends.
Jain says expectations often outpace reality, as with the dot-com bust and the metaverse push around 2020, and that companies that ignored the internet suffered, as in the Blockbuster and Netflix story. Her lesson is to separate what is real from what is not and build strong data, security and governance foundations.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
Nishtha Jain said a report on the state of AI in 2025 found that 90 to 95 percent of generative AI pilots fail to move into full production.
“I understand that the report says that ninety to 95% of gen AI pilots fail to transition to full production.”
Nick Gertsch recalled a large MIT survey that claimed most generative AI projects stall before scaling.
“I think there was a big MIT one at some point this year claiming that the vast majority of gen AI projects stall before scaling.”
Nishtha Jain said executives expect almost ten times productivity from AI by the third quarter, and teams want everything automated immediately.
“Executives want almost ten x productivity. Um, say by end of quarter three or every quarter, teams want auto everything now”
Nishtha Jain said employees fear AI will replace them while leaders fear implementing the wrong thing.
“the employees are worrying, will AI replace me? Leaders are worrying, what if we implement the wrong thing?”
Nick Gertsch said that when OpenAI released its version 5.0 model, users clamored to have the previous version back, which he used as an example of change made without the right information.
“when they brought out 5.0, um, and everyone was clamoring to have 4.5 or whatever it was back.”
Nishtha Jain said finance teams typically evaluate AI initiatives by asking whether they return headcount or save cost.
“The way finance asks about AI initiatives is, uh, you know, will you give me the headcount back?”
Nishtha Jain described ROI as financial return, including savings from faster processes, fewer errors and new revenue streams.
“ROI here is, you know, what it sounds, which is return on your investment or return on your finances.”
Nishtha Jain defined ROE as return on employee experience, which she said is hard to quantify.
“ROE, so that is return on employee experience, because that's, you know, something you cannot put a number to”
Nishtha Jain said research shows companies investing in employee experience retain employees better and improve customer satisfaction.
“research is showing that, you know, companies in investing in employee experience can retain employee better, and, you know, also promote customer satisfaction.”
Nishtha Jain defined ROF as return on future, treating AI as a strategic enabler of innovation and competitive advantage.
“ROF, which is the return on future. So, you know, AI is a strategic enabler, um, for bold innovation and creating competitive advantage.”
Nishtha Jain said companies have used machine learning and other traditional AI approaches for over a decade.
“AI has been there for a really long time, and over a decade, companies have been using machine learning, um, and, you know, more traditional AI approaches”
Nishtha Jain said organizations are moving from one-, two- and five-year roadmaps to monthly roadmaps.
“instead of the traditional roadmapping that used to be for a year, for two years, for five years, um, you know, organizations are now mo moving to monthly roadmaps”
Nishtha Jain said enterprise decision-making should be anchored in customer problems rather than technology trends.
“it needs to be anchored in customer problems and not the technology trends”
Nishtha Jain described a virtual reality and metaverse hype wave around 2020, when much activity moved online.
“then there was a VR burst, I would say when, um, it, it was 2020, when everything was going virtual”
Nishtha Jain said many companies chose not to move from paper-based to digital operations during the internet era.
“even with internet, you know, there were a lot of companies that decided not to move from paper-based to digital.”
Nishtha Jain cited the Blockbuster and Netflix story as a famous example of how technology change reshaped a market.
“the the famous story we hear is the blockbuster Netflix story, um, that, you know, how, how that all changed.”
Nishtha Jain is an AI innovation and strategy leader in the biopharma industry at Takeda Pharmaceuticals.
“Today's guest is Nishtha Jain, AI Innovation and Strategy Leader in the biopharma industry at Takeda Pharmaceuticals.”
The show stated that Nishtha Jain's views in the episode do not reflect those of Takeda Pharmaceuticals or its leadership.
“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.”
Host Matthew Demello summarized that leaders should measure AI value through employee experience, process quality and organizational learning speed, not only cost and headcount.
“Second, ROI needs to be measured beyond cost and headcount. Leaders should evaluate value through employee experience, process quality, and organizational learning speed”
Jain opens with the first of three barriers: leaders and vendors expect near-magic results on short timelines. This sets up the episode's theme that belief and time matter more than the technology itself.
“the first one is expectations are unrealistic. So people are expecting AI to be magic.”
She argues that buying platforms without a defined problem wastes money. Value comes only from solving real business problems.
“until AI solves a real business problem, it's nothing but an expensive science experiment.”
Jain draws on design thinking to say fear is overcome when users help build systems that fit how they work, with feedback loops built in.
“users need to be partners in creation”
She introduces a wider ROI frame, starting with how AI affects burnout, collaboration and retention, which headcount math ignores.
“ROE, so that is return on employee experience”
Jain explains the third measure, value that pays off later through new products and business models, and why investment should not be judged on this fiscal year alone.
“ROF, which is the return on future”
Generative AI lets teams test and show value quickly, which can justify longer-term investment.
“it's to experiment fast, fail fast, and learn fast.”
Jain gives three ways to handle uncertainty: adaptable planning, customer-driven decisions and a culture with psychological safety and AI literacy.
“the organizations that learn fastest will win the future”
Comparing AI to the dot-com bust and the metaverse push, she says strong foundations and governance let firms outlast hype.
“expectations always, uh, often, you know, they outpace reality”
Welcome everyone to the AI in Business Podcast. I'm Matthew Demello, Editorial Director here at Emerj AI Research. Today's guest is Nishtha Jain, AI Innovation and Strategy Leader in the biopharma industry at Takeda Pharmaceuticals. Nishtha joins Emerj 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 and 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.
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That's Emerj dot 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 dot com slash expert one. Again, that's E M E R J dot com slash expert one. Without further ado, here's our conversation with Nishtha. Nishtha, welcome to the program. It's so good to have you back on the show.
Thank you, Nick. It is so wonderful to be back.
Thank you, Nick. It is so wonderful to be back.
Really, really exciting. Um, always good to have a friend back on the show. Um, 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, um, across industries, um, there are s 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 gen AI projects stall before scaling. And and even when a pilot is successful, they often face challenges in workforce readiness, in organizational processes, um, and even defining ROI beyond kind of those pure financial metrics. So, in the face of these high expectations, um, alongside a kind of growing skepticism. Many companies, um, and indeed many of our enterprise listeners are asking what it really takes to make adoption work.
And, uh, I don't think we can think of anyone better than yourself to 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?
Thank you for referencing the State of AI 2025 report. I, um, I understand that the report says that ninety to 95% of gen AI pilots fail to transition to full production. Um, and you know, let's be real. So I think so, my two senses, and these are the three big challenges that I see and that might be leading to, um, this report. And then this report is also very nuanced, so I would not just read it like that.
Um, so I think the first one is expectations are unrealistic. So people are expecting AI to be magic. Executives want almost ten x productivity. Um, 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. Uh, and that times takes that belief will take time and clarity.
So I think that's definitely true. Um, that that's one of the challenge, the unrealistic expectations. Um, now, um, 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, uh, enterprises are buying models, platforms, you know, co-pilots, uh, but they are 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. Um, so that would, that's, you know, number two, and then I think number three is, um, you know, people itself. So AI isn't about technology, but it's fundamentally about people. It's, um, the employees are worrying, will AI replace me? Leaders are worrying, what if we implement the wrong thing? Legal teams are, and, you know, they are they, they have fear that, you know, this is posing a risk.
IDPs are worried about integrating it in their current systems. Finance is fearing the cost of it, and so so everyone fears uncertainty, and this is all leading to a lot of fear. Um, so here, you know, AI isn't the problem. It's, uh, it's basically the story problem. So business isn't clearly telling the right story about the future. Um, and I personally think for human centered AI, uh, it's crucial, uh, to connect the visionary strategies, um, you know, into into that real, um, uh, world success.
Like, for that to happen, the right story needs to be told, and, um, and organizations need to approach to close this gap, which is focus on your people, because, you know, um, simplify AI for your people, make it relatable and relevant and real for them.
Yeah, absolutely. Um, I know you're, uh, a fan of the kind of system of design thinking, um, and really kind of thinking through the problem end to end. Um, you know, when one kind of looks at the vast, uh, layout of an organization, do you find that the resistance, um, and perhaps a bit of the fear is kind of coming more from those cultural factors that you've just listed, or maybe more from kind of structural or operational constraints that they're literally butting up against?
What is your perspective on that?
Yeah, so I so, you know, from the design thinking angle, so users need to be partners in creation, and they they cannot be just the end user. So a lot of these decisions, um, you know, are are being made without keeping users, uh, in mind. So I think the the best way is the only way the fear can be overcome by trust is when the systems are designed to fit, um, how people naturally work, rather than forcing, you know, new ways of working on them.
And I think embedding feedback loops, so so keeping designing technology with human in the center, but embedding those feedback loops where, uh, every interaction make AI feel familiar and helpful for them. Because, you know, um, in the future, winning won't be about having the best algorithms, but it would be about ha having people in front and center of AI.
Hm, hm, yeah, um, really, really fascinating. And I think, given the um, as you've just mentioned now, um, people rarely being at the center of of this experience. And, you know, I think you you said directly that, you know, you can't change systems and expect everyone to kind of follow along. I mean, I I think that, um, even from within the world of of the um, AI vendors like OpenAI, they saw that perfectly when they switched to, I think it was when they brought out 5.0, um, and everyone was clamoring to have 4.5 or whatever it was back.
Um, just a perfect, uh, example of of change for, I guess, the sake of change, but without the right information is only gonna, um, I guess, result in disarray. Um, so, with that in mind, um, how do you think we should start shifting? If we start thinking about the outcomes and shifting to the thought, the ever-present need for ROI from these investments, how should leaders start thinking about that in this time? And and age of deployment, where things are a little bit, um, edgy, and there are fears and and changes in place that are causing a little bit of chaos.
Um, yeah, to restate the question, how should in that environment, how should leaders think about R ROI from their investments?
Yeah, so, um, I, I certainly think, um, that, you know, we need to shift the thinking of ROI, because the question shouldn't be, ha, you know, uh, the right question actually should be, how do we become a company that's learning faster? Um, you know, as this as the world is changing, and, um, and I think it's it's AI is not replacing the organizations, but the organizations that fail to adapt AI will be replaced. So, well, finance, you know, um, and I, I go through this a lot.
The way finance asks about AI initiatives is, uh, you know, will you give me the headcount back? Is is it saving me the cost? And it's clear we need to think of a new way about ROI. Um, traditional focus has been always on the head count, you know, um, uh, because what they're saying is if, you know, the headcount reduction is not giving the value, is it the right use case for AI? Uh, but the forward thinking leaders are now measuring AI, uh, with a broader lens that balances people, performance, and innovation.
And I want to highlight, you know, three ways, um, we could, we could shift, um, uh, of thinking of ROI, which is ROI, ROE, and RF. So ROI here is, you know, what it sounds, which is return on your investment or return on your finances. Um, and it's, you know, beyond cost cutting. So what what we need to see is that how AI is driving real savings by accelerating processes, reducing error, and unlocking maybe new revenue streams.
So it goes beyond the head count. Um, and so I think that's ROI, which is, you know, very clear, and a lot of traditional organizations are following that now. ROE, so that is return on employee experience, because that's, you know, something you cannot put a number to, uh, you know, as we make AI, um, you know, as as AI is making our our work more and more meaningful, it it reduces burnout. It's empowering people to focus on high value tasks because a lot of admin work, um, you know, is being done by AI.
It's helping them, you know, improve collaboration, and also research is showing that, you know, companies in investing in employee experience can retain employee better, and, you know, also promote customer satisfaction. Uh, you know, on the other side, so so think so. I think that's the other metrics to think about that, as long as it's improving the employee experience and the customer experience, it's actually delivering value. So, you know, it's a little bit different thinking from traditional.
And now, yeah, and now the last one, which is ROF, which is the return on future. So, you know, AI is a strategic enabler, um, for bold innovation and creating competitive advantage. So if if organizations start to see differently, where what they are investing today is not only for today, uh, but it's actually creating new products, you know, enhancing agility and future proofing the organization. So that's the return for the future. Um, so if, um, so that's the third way of thinking about the ROI, is, you know, as long as it is investing in the future.
And maybe you are not reaping the return today, but it's going to be through either creation of that new product or the work stream or the new business model. Um, you know, the that should be considered, um, as as, um, ROI, because I feel like with expanding this ROI conversation to inco- encompassing all these dimensions, organizations can build will for stronger business cases, um, and, you know, gain a better stakeholder buy-in and sustain to realize AI's full potential.
yeah, I, I. I really love this idea of of redefining ROI and kind of placing it in these more non-financial realms. Um, I think that that's really powerful and and should be something that that starts to infiltrate many businesses and may have to. Um, given, given, I guess, again, the uncertainty that we're kind of facing today, it's really, really fascinating and pretty intriguing way of of trying to frame returns. Just to kind of stick on the the returns side of thing, um, a little longer.
How would you advise leaders to kind of balance those those long-term capabilities and building those long-term capabilities? While they're facing this kind of constant pressure of short-term, quick wins now, now, now, um, you know, how does one face that and and go forth in a manner that is not gonna shoot you in the foot further down the line?
Yeah, yeah, so I think, and that's where, uh, the thinking around that AI is force multiplier for today's productivity, but it's a foundation for tomorrow's transformation. So I think certainly that's one of the views, but I think it's to experiment fast, fail fast, and learn fast. So it comes back to all the innovation principles, um, that, you know, whenever, uh, there are, um, and and I think gen AI sort of has unlocked that. So AI has been there for a really long time, and over a decade, companies have been using machine learning, um, and, you know, more traditional AI approaches to try to solve our problems.
Um, and I think with gen AI, though, there are there is possibility to focus on the right problems, prioritize them, and then, you know, uh, experiment fast and fail fast, and show the value through that experimentation quickly. Because if you are showing the value, then, you know, that could be put into, uh, investing in the future. So I think, I think it's certainly that that shift in in thinking of that AI as more of foundation for tomorrow and not just short term.
Um, that, you know, we wanted this year, this fiscal year, now, um, it should be part beyond that.
Yeah, absolutely sure, sure. Um, how would you? Or actually, how should, in your perspective, and and given the experience that you've had, you've been embedded in this industry for a fair amount of time. I'm sure you, you've seen a lot. How would you s, or how should enterprises approach uncertainty in technology and markets? You know, I, I'm sure you've seen many waves come and go, excitement bubble up and die down. Um, yeah, from from your experience, um, how does an enterprise go about, um, approaching uncertainty in the both the tech and and the and the markets?
Yeah, so I, um, I will probably share three examples here too. So I think number one is flexibility is the new competitive advantage. Um, so I think what organizations need to do is build adaptability and not prediction, uh, because, you know, there is so much uncertainty now in how traditionally organizations used to plan, um, and now, instead of trying to predict the future, leading enterprises are building capabilities that adapt to whatever the future is. Uh, so, which is very, very short planning cycles, you know, very agile organizational structures, uh, to fit those cycles, rapid experimentation, which I, um, talked about, and, you know, very small incremental bets.
Because, um, flexibility, um, is is really, you know, the new competitive advantage for organizations. Um, I've noticed leaders moving away from rigid, long-term strategies, um, and, you know, instead of the traditional roadmapping that used to be for a year, for two years, for five years, um, you know, organizations are now mo moving to monthly roadmaps, uh, which is, you know, how can, which allows, you know, companies to quickly pivot before the condition changes? Because, uh, you can build as many roadmaps as you want, but to keep up the, uh, pace, um, you know, flexibility is is, um, super, super crucial.
The other thing is, um, that the second thing that I want to highlight is that, you know, and the decision making, which is it needs to be anchored in customer problems and not the technology trends. Uh, because, you know, as markets are shifting, our customers are shifting, and most successful enterprises, uh, you know, they are always obsessed with the real customer problem, um, and then technology is sort of serving that mission. So, you know, whi, which comes back to what we discussed earlier is start with the real use cases, avoiding shiny objects, you know, not just bringing in multiple AI platforms and validating fast with customer feedback.
So if, um, you know, and and letting the business value drive AI investment. So I think as long as the business value continues to keep showing up, then I think making decisions on investment based on that is is is better approach. Um, so, you know, I I think that's, um, the second takeaway I would say is that, you know, clarity comes from customer voice and not technology roadmaps. So, you know, sticking to that principle, um, and um, thirdly, which I think is the most crucial one, um, is is creating a culture, because, as you know, AI is, um, and technology adoption is about people, um, so culture that learns faster as the markets are shifting, um, because uncertain envir environments
punish slow learners. You know, they are never happy with the change, um, but enterprises, so so enterprises need to build cultures where teams can experiment, they can collaborate, share insights, um, and, you know, adopt new tools, especially all the AI tools without any fear. So certain things, you know, is psychological safety. How can that be created? How can there be upskilling and AI literacy programs that organizations could focus on? Data-driven decision making, which, again, has been part of organizations for a really long time, but how can there be transparent storytelling, uh, for leadership there?
Um, because I think the organizations that learn fastest will win the future. Um, and enterprises that foster a culture that values learning, iteration, and innovation over perfection will adapt swiftly to this market changes and emerging technologies.
Yeah, that last line, innovation over perfection, I think, is just such a beautiful summary of, I guess, the mindset that we need to have, you know? Um, super, super fascinating. Touching on a thread that we just started on, um, your experience in the field, obviously, you've, as I mentioned, you've been there for a while, you've seen a lot, you've been through all of these hypes. We've seen pandemics, we've seen economic downturns, um, we've seen warfare, um, affecting global supply chains and on and on.
Um, but then, more specifically, we've seen a whole lot of technology changes over the last ten, fifteen years, and of course, we're in one of the biggest right now. But what can companies learn from some of those past technology hype cycles? Because, again, there's, there's this idea floated in front of the world or the public at large. Um, here's a new technology, and here are all of the potential ways it can change everyone's lives. Um, and the hype skyrockets, and usually, or often, the case is that we are left somewhat disappointed.
Um, and and in and amongst all of that, companies have kind of shifted the way they do things or reorganized or reengineered, um, because they believed in the hype cycle. So, from your perspective, having seen certain things, what can companies learn from these past mistakes? Um, moving forward,
Yeah, so, um, I would, you know, like to maybe discuss the dot com burst. So when internet was new, um, and you know, I grew up in ninety, so, you know, I basically experienced both the worlds, uh, pre-internet and post-internet. Yeah,
same, same. I remember those days too.
yeah, so, you know, we, so I think, I mean, it's it's easy to see that contrast. So I think when that the dot com burst was one, then there was a VR burst, I would say when, um, it, it was 2020, when everything was going virtual and there was a lot of and and people really started thinking that, you know, in future, we'll be metaverse. Because if this persists, how are people gonna meet otherwise? Um, so I think, I think, going through those cycles, um, certainly, I think expectations always, uh, often, you know, they outpace reality.
So I and I, I think I when I when we started discussing this, um, I talked about expectations. So, so, you know, just like, um, just like internet, I think AI is here now, and now we are seeing it, and it's just a different way of of living life. Um, uh, so, so, you know, when we, when we talk about these, I think, um, it's, it's. It's very, very important to understand what's real and what's not.
And and with all the, you know, uh, all the discussions that we had, I think the companies, the business that are not gonna keep up, or they are just gonna treat it more as, oh, you know, I will, I'll get to it. They are not gonna be able to survive. So I think even with internet, you know, there were a lot of companies that decided not to move from paper-based to digital. Um, and you know, again, uh, the the famous story we hear is the blockbuster Netflix story, um, that, you know, how, how that all changed.
So I think certainly that's, um, very, very, uh, critical for companies to actually, you know, start adapting to it. Um, and then, um, I think there's certain other things that I feel like one is resilient foundation that, you know, um, companies need to think of, uh, their data system security, um, integration more deeply, rather than just getting whatever is there and just starting to work on that. So, so going back, uh, to more of of that, uh, establishing a stronger foundation, um, then, um, also, you know, governance and and learning as to how they can balance enterprise, um, innovation with the governance.
So because, um, prioritizing compliance and realistic ROI and continuous learning is is where, you know, um, you can pivot faster than the hype fades. Because, um, I mean, even even with AI, we are seeing different kind of technology hypes, um, that are coming and going. Um, so it's. It's definitely, I think, for organizations to think of all, all the foundational aspects of how they could sustain. Um, even if it's like, maybe it's like, uh, chatgpt wave right now, and then we may see something different in AI.
Um, if if all of that the groundwork is laid fully, then I think it's easier to adapt to any technology or hype.
Yeah, really, really great advice, Nishtha. Um, yeah, I think there's so much, uh, that we've discussed here for our audience to to really think about and for enterprise leaders to take back to their teams. So I'd just like to thank you for being on the show once again, and I really look forward to our next discussion.
Awesome, thank you, Nick, thank you for having me. It was such a wonderful, um, time spent.
Wrapping up today's episode, I think there were at least three critical takeaways for biopharma, healthcare, and regulated industry technology and transformation leaders from our conversation today with Nishtha Jain, AI innovation and strategy leader in the biopharma industry at Takeda Pharmaceuticals. First, successful AI adoption starts with business problems, not models. Enterprise leaders need to anchor investment decisions in concrete, high-impact use cases that solve real operational or clinical workflow challenges, rather than treating AI as a standalone technology initiative.
Second, ROI needs to be measured beyond cost and headcount. Leaders should evaluate value through employee experience, process quality, and organizational learning speed, recognizing that sustainable performance gains often come from reducing friction, rework, and decision latency. Finally, long-term advantage comes from building flexible, human-centered systems. Enterprises that prioritize adaptability, continuous feedback loops, and a culture of safe experimentation are better positioned to scale AI responsibly as regulatory, market, and technology conditions evolve. Interested in putting your AI product in front of household names in the Fortune 500?
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Also, don't forget to follow us on X, formerly known as Twitter, at Emerj, and that's spelled again, E M E R J, as well as our LinkedIn page. I'm your host, at least for today, Matthew Demello, Editorial Director here at Emerj AI Research, on behalf of Daniel Fagella, our CEO and Head of Research, as well as the rest of the team here at Emerj. Thanks so much for joining us today, and we'll catch you next time on the AI in Business Podcast.