With vision comes perspective, and over a distinguished career, IT and digital transformation leader Niraj Bhatt has held may titles, and earned three consecutive CIO 100 awards since 2023.

As a storied advisor for startups and Fortune 500 companies, helping them navigate the unpredictability and fluidity of AI, Bhatt knows how emerging tech is rapidly reshaping the way organizations build products and deliver value, and how challenges shift as companies move from experimentation to real-world deployment.

AI, of course means a lot of different things to different people, and also for frictionless startups and large enterprises. For the former, speed is a huge asset, allowing them to punch above their weight. But it also means they need lightning fast reactions when landscapes shift. “The same speed can also hurt them when larger AI companies release new offerings that disrupt what startups are building,” he says, referencing recent moves by Anthropic and Google.

On the enterprise side, the conversation is more about scale and risk. Many large organizations have moved past the POC stage and now wrestle with the realities of putting AI into production.

Cost for both is naturally a recurring theme as organizations scale up AI efforts, and true expenses become clear only after the initial excitement fades. “Every input and output token, and the model you’re selecting, add up,” he says. Some customers like Open AI, he adds, get throttled because their usage, volumes, and costs are growing so fast, making planning, observability, and monitoring critical for any team moving beyond experimentation.

So understanding the full software development lifecycle is also vital. Therefore, before committing to production, he helps clients see the big picture, and make sure they understand technical requirements as well as operational and financial implications. “The cost picture isn’t just about usage, but scale and the model choices teams make,” he says.

Bhatt also discusses effective approaches to AI and enterprise IT, technology leadership, and the evolving role of today’s CIOs. Watch the full video below for more insights, and be sure to subscribe to the monthly Center Stage newsletter by clicking here.

On AI hype: If you can’t explain something to someone who’s eight or 80, you don’t really understand it. It’s gone from LLMs, to RAG, to agentic AI, and now the essence is all about tokens. It’s predicting that next token and understanding that is key. So when LLMs came out, they were good at doing that on the data on which they were trained. When the enterprises looked at it, they wanted to make those LLMs work for their data. And the question became how to provide our data and context. It’s about building the right context for the LLM. Agentic AI is similar and that’s where the RAG evolution came in, in that I’ve got my data because every LLM has limitations in terms of how much context it can carry.

There are ranges of LLMs, where Google has the highest in regard to the context window size and what they support. Agentic AI is more action oriented, though. LLMs rely on the metadata you provide for the tools. Then they’re doing token prediction in that whatever I’m looking for, I should use a specific tool. Then it’s the infrastructure underlying which LLM it relies on to invoke the agent. So if you try to explain the microservices to a person, you’re going to struggle. But it’s very important to understand the evolution and that’s where you can cut through the hype. Understanding in this context is key.

On navigating challenges around talent: What I’m seeing on the IT side is there’s so much cognitive load, so how do we empower people to build solutions with the right mix of products and platforms? I think it’s about democratizing AI for the entire organization. Your talent strategy is everyone, all inclusive, starting from interns, the business and tech sides, CEO, everybody.Like your customer success or revenue officers, you need a talent strategy because in the end, IT alone isn’t going to be in a position to deliver for everyone in the organization.

AI has the potential to make everyone in the organization more productive. You have to plan that and facilitate broad innovation across the organization.That’s where the talent strategy, and working with HR and the people officer becomes very important providing those tools. One part of it is training, but how do I build an agent for a receptionist receiving calls, for instance?I’m not going to rely on vibe coding or things of that nature. But what are the tools? Where do I go, where do I host this? I think through that entire ecosystem beyond copilots. That’s where innovation can kick in, and that broader talent strategy is something I’m working with my customers on.

On collaboration: I heard a panel discussion recently, and a question was asked about what’s the number-one trait CIO needs to be successful at in the world of AI, and the answer was collaboration. You need to bring everybody together, move forward together, and make sure everybody’s on board. And in my mind, simplifying that is more like systems thinking when you operate, just bringing everybody along and ensuring they’re meeting outcomes.

But maybe what’s more important is managing expectations. Because if you’re a CIO, there’s a tremendous amount of pressure to deliver and have a rock solid AI strategy. So what I’m doing with my customers is get the board, CEO, and CFO into a room and help them understand what I’m talking about, the evolution, and what’s the art of possible. You don’t want to be a CIO who thinks I have a hammer and everything is a nail. Having buy in from the senior leaders is essential to know you’re headed in the right direction. You’re not reacting to pressure from top leadership, but driving and becoming the change agent for good for the company.

On navigating AI: It’s interesting times. I’m covering a spectrum of startups, non-technical and technical founders, and advising Fortune 500 companies. What I’m seeing is they love the velocity and momentum because that’s what they’ve always wanted, and AI is providing that. They’re able to bring their products to markets very quickly, so something that would’ve taken three years a couple of years ago is probably now taking them three months. There’s a lot of excitement there. But on the flip side, the same velocity is also hurting them. There are so many frontier AI companies getting disrupted. OpenAI, for instance, has offerings in sales and marketing, and Google has an interactive video model. So a lot of startups working in the marketing space are getting stuck. A lot of what I’m focused on is working with founders, helping them pivot in the gen AI space, ensuring their systems and products are built and structured in the right manner.

And on the enterprise space, what I’m seeing is the POC wave, and people have seen the value. There’s some excitement but now the struggle is getting them to production. That’s where you run into cost, latency, legal compliance, privacy issues, and customer concerns that if we get tickets to production, how’s it going to look and how are we going to scale. So engineering and product teams have to be ably supported by the enterprise architecture and R&D teams. I then help them get up to speed and build that internal platform product for the production workloads. It’s exciting times on both sides.

Medical technology companies have always been in the business of trust, and Abbott has been building it with AI for over 10 years. Long before gen AI entered the enterprise conversation, Abbott was using algorithmic AI to help diabetics manage their glucose, and imaging AI to guide surgeons in real time. Here, Sabina Ewing, Abbott’s CIO, explains how a principled approach to AI governance, deep cross-functional partnerships, and a commitment to demonstrating results from within IT have kept them ahead of the curve, and its mission intact.

How is Abbott using AI to achieve its mission and growth strategy?

As a medical technology company, Abbott’s mission is to help people live life to the fullest. For over a decade, we’ve been using AI to deliver on that mission, but whether it’s AI or any other technology, we’re intentional about how it ties to our mission.

Trust is earned in drops and lost in buckets. To ensure we maintain trust with our customers and employees, we’re guided by principles of fairness, safety, quality, and transparency. With these and our mission as our guide, we’re in command of the table we set for ourselves.

How have you been in the AI business for so long?

For decades, we’ve provided FreeStyle Libre, a glucose monitoring sensor built on algorithmic AI, that delivers continuous glucose readings to diabetics, and in some instances, connects to insulin pump applications.

In late 2025, we developed Libre Assist, which leverages generative AI to let FreeStyle Libre users take pictures of their food and receive guidance on the impact of that meal on their glucose levels, including when to eat what, because sequence affects how the body processes glucose.

In our medical devices business, Ultreon, launched in 2021, uses imaging AI to guide optimal stent placement during cardiovascular procedures, supporting the physician’s decision-making in real time.

So whether it’s algorithmic, generative, or agentic, we’re intentional about matching the capability to the specific therapeutic problem.

When technologies evolve, your mission doesn’t change. But how has the CIO evolved during this AI boom?

Today’s CIO must have the strengths of conviction, credibility, and communication. You need technical expertise and to surface data to have the right discussions. You also need to be brilliant on the fundamentals and clear about the strategy, and then execute against it. If I tell the business it can use AI to drive outcomes, then I need to demonstrate it in IT. This is why I’ve committed two commas of results in IT from new AI operational capabilities.

How can CIOs influence their company’s investment in AI?

Working with senior leaders in HR and finance ensures we’re educating the organization and securing necessary investment, and then maintaining financial discipline where investments occur. We hold to that discipline and we’re deliberate about how we deploy the resources of the organization to measurably have impact. We look for high-impact opportunities where new technology delivers results even as it evolves.

We established an executive steering committee on generative AI, and senior leaders are engaged in how we deploy capital. We’re not going out with a thousand flowers blooming.

We also have traditional financial measures we apply to AI investments. And we know you need to be able to identify quantifiable outcomes and then measure them. Those conversations happen in partnership with all senior leaders, especially those business leaders requesting specific capabilities.

As the CIO, you need to have strong relationships with all other parts of the company. I don’t need to be in the spotlight, but you need those relationships in order to lead and effect change. I led a program that helped educate our top leaders on AI foundations, and we’re all working together on the talent side, too. We’ve embedded AI into our talent processes, and we have a continuous cycle of enterprise education through the ranks, both in person and virtual, to ensure our people are ready to use the latest tools and technology. If you want to do something sustainable, you can’t do it by yourself.

What’s your message to your technology team?

What I tell our team is no one is better positioned to lead the organization through this transformative era than its own technical experts. That means we lean into our expertise and AI-first mindset.

Years ago, we crystallized our vision for Abbott IT by unleashing the power of technology and our people in service of Abbott’s purpose. That’s the constant reminder. Our role isn’t to deploy tech but to unlock what technology and people can do together, in service of the mission.

I have asked the team to be bold, bring their best, and pursue excellence. Our strategic pillars are modernization, and protecting Abbott in both enterprise and product cybersecurity, digitization, and advanced analytics. That’s always been part of our mandate.

But what does an AI-first mindset look like? I asked our executive assistants how they, as a community, think about using AI to radically expand what they can do with it as a companion to their work, but not a replacement for it. The models that exist today can’t be a great executive assistant. Models don’t have the judgment, institutional knowledge, or nuanced reasoning required to prioritize work and navigate unspoken rules. That expertise is irreplaceable. Our question is how to augment it.