BOSTON — Business leaders are looking beyond a single-minded focus on artificial intelligence and the returns generated by individual agents as they confront the broader challenge of continuously reinventing their organizations around what emerging technologies make possible.

This shifting mindset emerged during CIO 100 Leadership Live Boston, where discussions began with the human dimensions of transformation and quickly moved into the dollars and cents of technology investments. The day ultimately revealed a deeper conversation about how enterprises must continually rethink processes, operating models, technology architectures and investment priorities as capabilities evolve.

Panelists, and an extremely active group of audience participants, presented perspectives increasingly focused on what businesses should become.

It represents a major evolution from the broad calls for “tokenmaxxing” that dominated the AI narrative as we closed out 2025. The idea of setting ambitious transformation goals followed by a disciplined assessment of emerging technological capabilities has fundamentally changed the context of AI agents, automation and emerging autonomous systems. In the process, agentic initiatives, large language models (LLMs), and enterprise and small language models (SLMs) are now seen as the means to a more sustainable end by becoming components of broader enterprise reinvention strategies.

Discussions on return on investment similarly moved beyond measuring the productivity generated by individual tools toward determining whether technology investments systematically and comprehensively improve revenue, customer experience, operating efficiency, resilience or other strategic business outcomes.

Transformation starts with people

The strategic shape of day’s discussions began taking form during the opening session, “Real Leaders, Real Challenges: Championing Tech-Enabled Business Transformation,” featuring Mona Bates, senior vice president and chief information and digital officer at BAE Systems; Sal Companieh, chief information and digital officer at Cushman & Wakefield; and Paul Hlivko, CIO at Optum Financial. Dan Roberts, host of the Tech Whisperers Podcast, moderated the session.

Hlivko described AI as the early stage of a potentially long technology cycle and suggested that leaders should focus on increasing the speed at which their organizations learn rather than simply counting the number of applications moved into production. He compared emerging AI investments with venture capital portfolios, where leaders make multiple bets knowing some will fail while others could generate disproportionate returns.

The discussion nevertheless focused heavily on people. Bates described the CIO as occupying an increasingly important position at the intersection of business and technology. Companieh emphasized frequent communication and creating environments in which employees feel comfortable acknowledging uncertainty. In this context, Hlivko summarized today’s leadership imperative around providing their teams with clarity, culture and conviction in a climate of uncertainty.

Bates added adaptability to the leadership equation, arguing for what she described as AQ alongside traditional measures of IQ and emotional intelligence.

Effective transformation, the panel agreed, will depend first on creating organizations capable of absorbing change.

Complexity becomes a business problem

In a fireside chat titled “The Foundation for AI: Building an Enterprise Ready to Scale,” Jim Chilton, executive vice president of enterprise ventures at Southern New Hampshire University and author of “Enterprise Coherence in the Age of AI,” explored how accumulated technology and legacy process complexity can inhibit the accomplishment of important business transformation objectives.

Chilton argued that complexity rarely results from a single bad decision. It accumulates as organizations add applications, vendors and processes over time to address legitimate requirements as they emerged. AI, he said, requires leaders to expose those layers because deploying agents across fragmented systems requires organizations to understand how information and work actually move through the enterprise.

This, in turn, requires an intimate understanding of all the processes that underpin critical operations. Chilton distinguished between standardized activities that can be automated and differentiating capabilities that may be better augmented by AI. The decision centers on where automation or augmentation contributes to the way an organization creates value.

In “Where AI Delivers: Strategy, Architecture and the Path to the Intelligent Enterprise,” PwC’s Stephen Coyle and Robbie Voigtmann joined Boston Scientific’s Shoubhik Sinha in extending the question of “core value” into enterprise architecture.

Their discussion focused on moving beyond disconnected AI tools and proofs of concept toward reusable capabilities that support production deployments across the enterprise. That requires business, technology and risk leaders to identify use cases according to value, feasibility, risk and their potential to be reused elsewhere.

Companieh illustrated how that reuse plays out in practice when she returned for the afternoon session on “Build, Buy, or Partner: A CIO’s Framework for Scaling AI Across the Enterprise.” She walked through the framework Cushman & Wakefield uses to decide whether to buy a point tool, build in-house or partner for a given AI investment.

She described how a single use case in lease extraction became the seed of an enterprise-wide data foundation. Companieh credited clear roles and shared ownership between Cushman & Wakefield and its technology partner, Unframe, with converting a fast-moving product into measurable business impact.

The push toward reuse, she explained, also exposes how executives can look at increasingly sophisticated AI demonstrations and conclude that their organizations are ready to scale, while technology teams see fragmented data, redundant applications, governance gaps and architectural dependencies that must be addressed.

Transformation confronts the dollars and cents

Those architectural questions led into the session on “Return on Transformation: Time, Talent and Tradeoffs,” where the conversation explicitly addressed the new economics of strategic planning and execution. The discussion featured Lesley Dickson of VantagePoint Strategic Partners, MGX Beverage Group CIO Scott Gardner and Afshean Talasaz, former senior vice president of strategic projects and innovation and chief technology and data officer at Colonial Pipeline.

The panel challenged the idea that organizations should evaluate transformation primarily by measuring individual AI initiatives. The more consequential question, they all agreed, is whether the enterprise itself is becoming more competitive, productive, responsive and/or resilient.

As a result, true transformation begins with defining strategic business outcomes before committing resources to technology, and then assessing how existing tools, available resources and emerging innovations can optimize the chances of success. This last part, they emphasized, is critical, given that the vast majority of large scale transformation initiatives (including mergers and acquisitions) fail to meet their stated objectives.

The panelists warned against automating legacy processes simply because AI makes automation possible. Poor data, unnecessary work and outdated processes do not necessarily improve when machines execute them faster. Organizations, they suggested, should simplify and redesign operations before deciding what to automate.

The same discipline applies to investment. Leaders, they advised, should establish ambitious enterprise goals while making smaller, testable bets underneath them. Successful initiatives can receive additional resources, while those failing to produce results can be terminated.

That approach also requires deciding what not to transform. Some processes warrant fundamental reinvention. Others may require incremental improvement. Many others will likely derive little strategic value from the application of AI in any form.

The result is a broader definition of return. Productivity remains important, but ROI can also include contribution to revenue growth, risk reduction, operating leverage and improved customer experience. In short, CEOs and boards should establish realistic expectations about the economic path from experimentation to enterprise maturity.

Failure becomes something to contain

The afternoon session “Running Enterprise Technology When Failure Isn’t an Option” brought to the stage Paul Beswick, senior vice president and chief information and operations officer at Marsh McLennan, and Sri Sriraman, chief technology officer at Mass General Brigham. The session was moderated by Joan Goodchild, a contributor to CIO and CSO.

The panel kicked off by noting the inherent flaw in the session title. With breaches and incidents occurring every day across a threat landscape that continues to worsen, the leadership team would be well advised to focus on preventing individual failures from becoming catastrophic.

As autonomous systems enter production, it is a task easier said than done. Uncoordinated pilots and vendor agents can create fragmented workflows and dependencies that are difficult to observe or control. Enterprise deployment therefore requires governance, observability and mechanisms that allow organizations to intervene when autonomous systems behave unexpectedly.

Resilience and trusted data become equally important, according to the panelists. High-stakes systems require reliable infrastructure and information foundations calibrated to the risk and value of the functions, assets and technologies they support. The amount of autonomy granted to AI should reflect the consequences should things go wrong at different points on that spectrum.

CIOs begin thinking like investors

The session, “Think Like a VC: Investment Shifts Towards Focused AI Applications,” returned to an idea introduced during the opening session.

Rudina Seseri, founder and managing partner of Glasswing Ventures, and Chris Gardner, general partner at Underscore VC, explored how venture investment practices could apply to enterprise technology decisions.

Seseri described an investment environment focused on identifying sustainable opportunities in AI-native companies serving enterprise, science and cybersecurity markets.

Both advised CIOs to replace static notions of future-proofing by embracing the concept of “continuous portfolio management.” Gardner observed that technology leaders increasingly need to identify market signals amid AI hype, evaluate the adaptability of vendor management teams and distinguish defensible enterprise capabilities from impressive demonstrations. Investments can then be governed through milestones that determine whether organizations scale, maintain or exit them.

In the process, enterprises will need to become more comfortable making calculated technology bets while becoming less tolerant of uncontrolled risk inside production environments.

Reinvention becomes continuous

The closing session, “What’s Next for the CIO: Preparing for the Next 12-24 Months,” extended that argument into the future of work.

Boston Dynamics CIO Chad Wright said organizations should begin preparing for three categories of coworkers: humans, virtual agents and physical robots. Boston Dynamics already has its Spot (quadriped) robots working across hundreds of organizations, while humanoid (bi-ped) robots introduce additional questions involving infrastructure, physical safety, accountability and workforce acceptance.

Terry Carpenter, CIO of MIT Lincoln Laboratory, stated that, as agents assume tasks traditionally performed by entry-level employees, organizations will need to seriously consider how younger professionals are trained to develop the judgment, delegation skills and institutional knowledge to move into middle management roles, and ultimately, senior leadership positions.

Jacqui Nevils, Global CIO, Fresenius Medical Care, and a veteran Air Force officer, emphasized that there is a growing case for new and significant investments to be made in onboarding talent early and training them right away for major responsibilities. If the next generation of employees are to supervise multiple agents (along with human subordinates) while remaining accountable for their output, organizations will need to teach skills that were previously acquired through years of progressively more responsible work.

The discussions ultimately brought the program back to where it started.

While the agentic economy is introducing another generation of enterprise technology, it is also accelerating the rate at which organizations must reconsider how work gets done, where capital gets allocated, who does what, and which risks are acceptable as new capabilities are deployed to provide competitive differentiation.

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.