Building Value Through Real-World AI and Smart Technology Adoption

Q: Hi, Alicia, welcome to the Big Unlock podcast. It’s great to have you here. This podcast is now in season six, Alicia, and you’re looking towards an exciting conversation here. I am Rohit Mahajan. I’m the Managing Partner and CEO at BigRio and Damo Consulting, and would love an introduction from your side. 

Alicia: Hi, Rohit. Uh, lovely to be here, and thank you so much for the invitation. Well, thank you. Yes, I’m Alicia Abella, and I am the AI Product Lead with Novo Nordisk. I joined Novo Nordisk in November of 2024. I will say that I am new to pharma and was very intrigued by the opportunities that pharma has for AI. My whole background since graduate school has been in AI in some form or fashion, so we could talk a little bit about that and where I am. And I’m from Morristown, New Jersey.

Q: Absolutely. That is awesome that you’re new to pharma and you’re kind of looking at it from perhaps multiple different experiences that you already have. So please tell us, Alicia, how did you get started? What drew you to AI and now to pharma, and what are some of the experiences that you’ve had before you got here? 

Alicia: Yes, happy to do that. I would say that my involvement with AI started rather serendipitously in graduate school. I was a PhD student at Columbia University and I was looking for an advisor—specifically a tenured advisor—because I knew as a student that if you signed up with a tenured advisor, you wouldn’t run the risk of them not getting tenure and then having to move to another university to follow your professor.

So the professor I worked with on my thesis was in the area of computer vision and image processing. I also had a co-thesis advisor who was in natural language processing, so I had a very early experience in that area that now is so popular with large language models and AI. This was a period where the technology was using different techniques, algorithms, and approaches than we do today. But still, there was that sentiment that artificial intelligence could be used to do many things and assist people in discovery.

The actual work I did for my PhD thesis was in the life sciences and healthcare industry. Ironically, even though I’m new to pharma now, I feel like I’ve come full circle. My thesis involved developing a software system that could do image processing on radiographs of human kidneys and the urinary tract system—automatically detecting calcific densities and kidney stones—and then automatically generating radiology reports.

Part of my thesis was to see how well a machine could do that compared to a radiologist, and it turned out it did pretty well. And this was in the mid-nineties.

That experience propelled me into AI, and especially the work I did on my thesis in natural language processing led to an opportunity at AT&T Bell Labs. I got a job in their research organization specifically doing speech and natural language processing research. What I think most people don’t realize is just how long research in this field has been going on. We often think that large language models and ChatGPT just burst onto the scene, but in fact, the foundational work has been going on for decades.

When I joined Bell Labs in the mid-nineties, they had already been doing speech and signal processing research for nearly half a century—40 to 50 years. That’s a humbling experience because you realize just how difficult these problems are.

So that was a challenge, and I spent a lot of my career—25 years—at Bell Labs doing many different things. But the part that still resonates with me is the work I did initially on spoken dialog systems for customer care. 

So that’s a little bit about my initial beginnings with AI. And then yes, after a long career at AT&T, I took on a role working at Google. Because of my long career at AT&T, Google—at the time, around COVID in 2020—was standing up an entire organization devoted to different industry verticals. They were hiring a market lead, a managing director for the telecom, media, and entertainment industry vertical. I was recruited for that role and joined Google in August of 2020, right in the middle of the pandemic.

It was interesting because Google Cloud at that time was growing very rapidly. They really wanted to go after some of our strategic customers in the telecom space. Having spent so much time at AT&T, I could talk the talk. I’d walked in the shoes of our customers. My role was really to talk to our most strategic senior executives and C-suite executives across all of the Americas in that industry to try to understand: What are their pain points? What problems are they trying to solve? And how could Google and Google Cloud’s products and services help them solve those problems?

In my last year at Google, before joining Novo Nordisk, I was the Global Practice Director for AI and Machine Learning. I was essentially the bridge between our go-to-market organization and our product and engineering teams that were developing the AI products that are now very much in use—Gemini, Vertex AI, and others. I was ensuring those products and features were really meeting our customers’ needs globally and across all industries.

So I went from telecom to representing all industries and from the Americas to a global role. I spent about a year in that role until I got a call from Novo Nordisk. As I mentioned earlier, I was very intrigued by the opportunity to take AI and apply it to an industry that I think has tremendous potential—to apply AI across all of its business functions.

We often hear about AI being used for drug discovery, and pharma companies are very much involved in using AI to help accelerate drug discovery. But there’s also an opportunity across commercialization functions as well, which is where my current focus is at Novo Nordisk—to bring AI to the commercialization space.

We’re trying to move from traditional commercialization techniques to thinking about how we can use AI to accelerate the work that needs to get done—whether it’s marketing campaigns, legal issues, HR—you name it. The entire organization involved in that go-to-market aspect of taking a drug, once it’s been discovered and approved, to market.

How can we use AI to accelerate that process and make it better? Make it more personalized? How do we find the right patients? There are so many applications in the commercialization space, and I think we’re only scratching the surface.

Q: Absolutely. Very exciting, Alicia. Thank you for sharing that.
As you go about this role, we talked earlier about AI adoption and change management—how do you get people to embrace it? What are you seeing in the business enterprise, and what are some of the things you’re doing to make this happen?

Alicia: That’s a great question, Rohit. When I first joined Novo, I spent the first few months—maybe a good solid three months—just going around and talking to various leaders across different business functions to understand what they were doing, what their current sentiment was, and what their understanding of AI looked like. I needed to understand where Novo Nordisk was in that journey.

It was varied. There were folks who were very excited about the prospect of AI, and others who were afraid of it—or still are—partly due to a lack of understanding and education, and partly because of the compliance and regulatory risks they know or have heard about. We’ll probably get into that later in the podcast.

I also created a survey at the time, which I sent to the marketing teams to assess their general understanding of AI and create a baseline for myself. Through that process—conversations and survey results—I realized there was a need to demystify AI for many people. I also needed to develop very strong relationships with our legal and compliance departments, to involve them very early in any AI solutions we were thinking of developing. That way, everyone would feel safe and confident that what we were building wouldn’t create risk for the company.

So I came up with and launched, just two weeks ago, an AI Ambassador Program. I wanted to find and engage the people who were excited about AI, wanted to learn more, and wanted to be part of a community that could share that knowledge with their peers.

It was important to me that these ambassadors represented all job functions within the enterprise because they know their day-to-day work better than I do. They know where AI could be applied. They could become a kind of flywheel for me within their own organizations.

I put out a request to the organization for volunteers, and the response was overwhelming. I surpassed my expectations in terms of the number of ambassadors I hoped to recruit, and now I have a big cohort.

Now the real work begins—how do we equip the ambassadors with the knowledge and education they need? My goal is to awaken their curiosity about AI and inform them in a way that is relevant to their context. I want to give them a broad understanding of AI, what’s out there, what’s coming, and what’s on the horizon. Get them excited. Get them thinking about how to apply AI to their day-to-day work so they can bring that knowledge back to their peers.

I think it’s really important that this happens peer-to-peer. It’s not coming from a top-down directive that says, “You must do this training.” That kind of approach doesn’t work—especially if it’s not connected to their day-to-day work. That contextual understanding is critical, and that’s what the ambassadors can bring. I can help supplement it by bringing in the outside-in perspective of what’s going on in the AI landscape today.

We also have internal tools—Novo Nordisk ChatGPT, for instance—that employees can use to query and ask questions. It’s all within compliance and legal, so it’s a safe place for them to experiment. We also have Microsoft Co-Pilot, and there’s a lot of training available there too. But my focus is to expand their thinking with a broader understanding of AI.

We launched the AI Ambassador Program last week. We meet monthly to discuss different AI topics, and I know the next topic we’ll cover will likely be use cases that are relevant to the organization. We already have a lot of AI use cases across the company—including globally—so knowledge sharing is a big part of this program too.

Q: That’s wonderful. It’s a very unique approach to increase adoption, Alicia. You just mentioned use cases—what are some of the ones you’re already seeing, or that you’ve seen other pharma companies pursue? 

Alicia: Sure. Since my focus is on commercialization, I’ll highlight use cases in that area. One of them is what I’d call using AI for knowledge search. One of the first things I noticed was how much data our marketing and insights teams have access to—whether it’s reports they generate themselves or third-party vendor research to understand how our products are doing in the market and what our competitors are doing.

There are so many disparate data sources and documents. It’s hard to find what you need. So, we’re working on using generative AI to provide a conversational interface where market researchers can ask questions, and the solution can sift through thousands of pages to return useful responses—with attribution, so they can validate the answers by going back to the original documents.

That’s one use case—market research and competitive intelligence using knowledge search.

Another is content generation. We’re using generative AI to come up with variations on ad messaging and new campaign ideas. We’re also exploring image generation and short video clips to help marketers communicate their ideas to ad agencies faster. Using GenAI in this way can help accelerate time-to-market for campaigns.

Ultimately, the hope is that we could use these technologies ourselves to create content, instead of outsourcing it.

Then, more traditional AI techniques are being used to analyze large datasets to understand healthcare providers—their habits, the types of patients they’re seeing, and who they’re diagnosing with conditions our products support. These insights help us better position our messaging and outreach to HCPs.

It’s a powerful use case that helps us reach more healthcare providers, who can then reach more patients. That’s how we ultimately expand access to our therapies.

Q: Great use cases, Alicia. Early on when we were talking about a product mindset and you know, a very disciplined way of approaching the implementations or the use cases itself. So, could you tell us a little bit about what does that mean?

Alicia: Yeah. So, when I was first recruited to join Novo Nordisk, I thought the part that intrigued me, in addition to it being a new industry for me and a great learning opportunity, was this idea of applying the product mindset to developing AI solutions for the pharma commercialization team. Because I’ve seen it too often in my history of being in technology and development, and being around technologists, that they get very excited about an idea—an idea for a product—and they go off and build it without actually taking into consideration: should we be building it?

Just because you can build it doesn’t mean you should build it, right? There’s a lot of work that goes on in evaluating and determining whether an idea should actually be productized.

So my role was to bring my experience—all the way back from my AT&T days and Google days—around a product management lifecycle mindset. Think about product management as: design, develop, test, monitor, iterate. Design, develop, test, monitor, iterate—how to do that, and how to put that kind of rigor into decision-making about what AI products and solutions we should be developing.

That’s what I’ve brought to the exercise of us picking what AI products to focus on. Because there’s limited resources, so we have to figure out and prioritize.

Step number one is: let’s understand the user need. Let’s understand and talk to those end users—those key stakeholders and sponsors—and understand the problem they’re trying to solve. Figure out if indeed you need AI to solve it. Because in some cases, you may not. So that’s part of the process.

If indeed AI is a good tool for it, then what’s the business value? Can we create a business case for it? What are the implications of building this solution in terms of compliance, risk assessment, technical feasibility? All the things you have to consider to generate and create a new product.

Bringing that process here helps us focus on developing AI products that we know will create the biggest impact and have the most value across the largest set of stakeholders.

And so that’s kind of what I’ve been bringing to Novo Nordisk and to our AI product experience—because I think that’s maybe something that any company needs. And yes, pharma is still relatively new to AI, especially in commercialization. And I think a little bit of governance, without it being too heavy-handed, can help drive the innovation and drive it in the right way for the right problems.

Q: Absolutely. And in pharma, it’s highly regulated, as we all know, Alicia, and there’s a lot of compliance. How do you tackle that aspect of it in the journey? Could you tell us how you’re approaching that as well? 

Alicia: I’m glad you asked that question because it’s one of the things I mentioned earlier about my listening tour when I first joined Novo. When I was talking to different folks, I made sure that legal and compliance were on that tour. I wanted to understand Novo Nordisk’s guiding principles and how they were thinking about AI.

We have AI principles and guidelines that we follow today. And what I’m currently involved in is working very closely with our data ethics, compliance, and legal teams—very early on.

I told them, “You’re going to be my BFF,” right? You’re going to be my best friends. Because it’s important to make sure that what we’re developing is done in a compliant manner.

What I can bring to that process and that team is an understanding of where to put those guardrails so we don’t stifle innovation. We still need them, but we do it in such a way that we manage risk while still being able to innovate.

Having a strong relationship with that team—where we both understand what we’re each trying to achieve—is important. I bring them in very early, even when we’re still just thinking about an idea for an AI solution. I say, “Look, this is what we’re thinking—are there any big red flags I should be considering?” So we can address those right at the beginning, before too much effort and resources have been devoted to developing a solution that they might later come in and say, “Oh, you can’t do that.” We definitely don’t want to do that.

That’s an important component, especially in a highly regulated industry. And I think there’s still a lot of room for innovation, even within those boundaries.

Q: Great. So, as we come towards the end of the podcast, Alicia, I’d like to ask—what are your thoughts or ideas about future trends? What do you see from your perspective? 

Alicia: I wish I had a crystal ball! But if I look into it, one area that I think will be very interesting for AI in the future is making sure that we’re marrying the AI fundamental technologies with the user experience.

Part of that is driven by my entire career—I’ve always been focused on that user-centric view. To drive adoption of any product, you have to make it usable. You have to make the experience something that people will want to use—something intuitive, easy to use—that will drive adoption.

It obviously has to solve a problem. Assuming it is solving a problem, it should do it in a way that makes it easy to interface with. I think part of what made ChatGPT so prolific in terms of adoption was how simple that interface was. It’s just a window that says, “Ask me a question.” You just type it in the way humans are used to asking questions.

So I think as we build wrappers and layers on top of these fundamental large language models, it’ll be important to ensure that simplicity of user experience remains.

That will be a trend going forward. I think one of the big tech giants creating large language models will now focus heavily on user experience.

Maybe I’m just channelling my experience when the iPhone first came out. Steve Jobs had that focus and fascination with experience. It was all about the experience—being very user-centric. I think we can’t lose sight of that.

Another trend I think we’ll see is large language models evolving beyond just text, image, and video—to start bringing in more contextual knowledge. The kind that humans bring. That’s the missing link right now.

It’ll be interesting to see what the future holds on that front. I’m a big Star Trek fan—especially The Next Generation. There’s an important character named Data. He’s a machine, an AI. All he wants to do is be human.

All of his attempts at being human—wanting to be an artist, a writer, a pet owner—everything is about the machine wanting to be more human.

I think we still have that desire—to see how we can make these machines behave more like humans, without taking us out of the loop. Of course, there’s that fear too, but that’s a whole other podcast episode, Rohit.

But there’s a lot to be excited about in the future, and I feel fortunate and privileged to be part of this experience and to see where it all unfolds.

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Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity.