At CIO100 in Frisco, Texas, roughly 100 rising technology leaders sat down for our “Next CIO” session. The group was asked to reflect on a single question: Are you ready to take on the role of CIO? Using the CIO Readiness Framework that we have developed and refined over years of advisory work, we asked each person in the room to score themselves across the five dimensions of the framework. The results point to a gap that should worry any organization building its next generation of technology leaders.

The CIO Readiness Framework

The CIO Readiness Framework organizes the CIO job into five dimensions. We asked each rising leader to score themselves on the same 1-to-5 scale, from “Emerging” to “CIO-Ready.” The five dimensions of the framework are:

• Enterprise leadership: the ability to lead beyond your own function, anticipate where the business is headed and mobilize people through change.
• Business value and financial acumen: understanding how the enterprise makes money well enough to connect technology decisions to growth, margin and risk.
• Influence, narrative and enterprise selling: building belief and support before a decision is ever formally proposed, not just presenting sound logic once it is. 
• Relationships, talent and operating leverage: building trusted executive relationships, developing successors and creating an organization that delivers beyond your own personal reach.
• Technology stewardship and digital judgment: the technical fluency and architectural judgment needed to make durable enterprise technology decisions.

Where the room stands

Across the five dimensions, the average self-assessment landed at 3.4 out of 5, squarely in ‘Proficient’ territory. Consider who was in the room: people already selected by their own organizations as ready to be developed for the next level. Even so, not one of the five dimensions averaged ‘Advanced’ or higher across the entire group. Technology Stewardship and Digital Judgment (the ability to make sound decisions on platforms, architecture and risk) came in as the most mature dimension in the room. At the bottom sat two dimensions in a near tie: Influence, Narrative and Enterprise Selling; and Relationships, Talent and Operating Leverage.

Much more interesting, however, is the spread between the highest- and lowest-rated dimensions. On these bottom two dimensions, ~65% of attendees rated themselves Proficient or below. Compare that to Technology Stewardship, where the number was only 36%. Put plainly, the people in that room are confident in their technical judgment. They are far less confident in the parts of the job that have nothing to do with technology at all.

Why the human dimensions lag, and what to do about it

This tracks with what we hear constantly in our advisory work. Most people who reach the doorstep of the CIO role got there by being excellent at the technical and operational core of IT. Few of them spent their first fifteen years being evaluated on stakeholder mapping, coalition-building, or developing a successor. Those muscles simply were not required until now.

The good news is that these are learnable skills. We recommend a simple approach to close these capability gaps: for the dimensions where you rated yourself lowest, identify a goal that targets your weaknesses, then attach a tactic (a concrete action or behavior) that moves you toward achieving your goal. Lastly, give the whole thing a timeframe. Six months is often a good starting point, as it is long enough to make real progress and short enough that you’ll actually check.

In this activity, the goal represents the destination – for example, to develop a brand of “enterprise leader,” rather than just “strong IT operator.” The tactic is how you get there, something specific enough that you’ll know in six months whether you did it or not. “Get better at influence” is a goal with no tactic attached, which is exactly why it rarely changes anything. “Hold pre-alignment conversations with three sponsors before my next major proposal” is a tactic, and it’s either done or it isn’t.

Here’s what that pairing looks like applied to the two lowest-scoring dimensions from the CIO100 room:

• For Influence, narrative and enterprise selling, a reasonable goal is building support for ideas before they ever reach a formal decision point. Tactics in service of that goal include identifying the informal decision-makers behind a priority and earning their support early, or taking on an external opportunity (e.g., industry panels, published point of views) to build credibility beyond the building.
• For Relationships, talent and operating leverage, a reasonable goal is creating executive capacity instead of personally absorbing more of the work. Tactics in service of that goal may include adding standing one-on-ones with two peers on the executive team, and delegating two recurring items off your own plate with clear decision rights attached.

The takeaway for CIOs building their bench

If you’re a sitting CIO developing your own successors, this data serves as a useful gut check. The people you’re grooming may already operate at an advanced level technically while carrying real gaps in the skills that determine whether they succeed once they have the title. Executive presence, coalition-building and delegation take years to build, so the earlier you start, the better.

The future leaders we worked with at CIO100 had no shortage of ability. What most of them lacked were the specific, practiced habits that turn a strong technology leader into an enterprise one, and the self-assessment data shows they already know it. Acknowledging that gap is the first step toward closing it.

When Sastry Durvasula joined TIAA in early 2022, he saw an organization fighting against outdated legacy technologies and in need of a major IT refresh.

Since then, the financial services organization has completed two phases of a comprehensive transformation initiative called Technology Ecosystem Transformation, or TETRIS, leading to a huge reduction in tech debt and a major expansion of functionality for customers.

The ongoing project, anchored in cloud and AI technologies, started in 2023 with phase one that modernized the core technology stack with 10 new enterprise platforms. Phase two, launched in late 2024, went further by enabling 87 use cases across all major lines of the business.

The project, for example, allowed TIAA to launch its MyChoice Multi-Year Guaranteed Annuity product, and helped create the TIAA Gateway portal, an API-based suite that integrates with partners in retirement and wealth planning using industry standards.

TIAA Gateway took home a CIO 100 Award in 2025, and phase two received a CIO 100 Award in 2026.

Durvasula, TIAA’s chief operating officer, pitched the multimillion-dollar TETRIS project to the board as a three-pronged strategy, with empowering business growth, fueling innovation, and transforming the IT core as its key goals.

Not only did TETRIS need to modernize the company’s IT systems, decommission legacy processes, and automate other processes, but Durvasula pitched it as the way to expand the reach of TIAA’s products and move the company into the future.

“As you expect in a company of our size, we have problems of yesterday, today, and tomorrow being solved at the same time,” he says.

Focus on business use cases

As TETRIS moved into phase two, project leaders shifted their goals from pure technology modernization to business outcome-driven prioritization. So once phase one delivered needed IT platforms like a data cloud and design studio, TIAA pivoted toward enabling business use cases.

This business-first approach ensured continuing executive support and clear ROI at every key milestone, TIAA says.

In 2022, just before the project launched, more than 80% of TIAA’s IT workloads resided in fragmented, end-of-life platforms, which created operational risk, compromised security and resilience, and constrained its ability to innovate. Through TETRIS phase two, however, the organization has cut that tech debt nearly in half.

And consolidating 17 design systems also led to digital products looking and behaving differently, depending on the team that designed them, and accelerated product launches by 35%, enabled multi-lingual capabilities, and increased accessibility to more than 185,000 customers who don’t speak English.

In addition, TETRIS allowed TIAA to combine multiple middleware systems and data lakes, Durvasula says, and the organization moved mainframe applications and data center infrastructure to the cloud.

A giant leap forward

TETRIS has been a huge project, with the company saying it empowered TIAA to have one of the largest leapfrog moments in company history in its submission for the 2026 CIO 100 Award.

Despite the reported failure rates of large transformation projects — some estimates suggest up to 95% fail to meet their goals — TETRIS was essential to keep TIAA competitive and move it forward in the market, Durvasula says.

A big part of the project has been workflow modernization, he says, because TIAA were using some technologies and workflows that were decades old.

“There’s your classical platform and application rationalization, and then there’s your end-of-support, end-of-life stuff that should’ve been remediated long ago,” he says. “Some of the processes we have, because we’re such a large, old company, were designed when the internet just came along.”

Stick to the metrics

Two keys to pulling off such a large project are establishing metrics for success and transparency with leadership, Durvasula says. Project leaders set milestones to indicate when things went well, and they planned for bumps in the road so the TIAA board knew when setbacks happened.

“Not everything is as pretty as it sounds in an awards application, but the success measures we established with our board were based on both phases,” he says. “For the first one, we said we’d deliver enterprise-grade platforms and accomplish migration objectives, but not tied to any specific business objectives.”

Phase two metrics focused more on business objectives, and the project team kept the TIAA board updated as TETRIS moved forward. Setting realistic goals was important, he says, with the team determined not to overpromise results.

“Large programs have a range of objectives, and if you publish the outcomes you’re looking for, people start looking for them, especially stakeholders, the C-suite, and board,” he says. “You have to be honest about which metrics or KPIs you can deliver in the first and second year, and when you’ll start seeing real business scale and impact, which definitely won’t be that soon in a large program like this.”

Goals also need to be flexible, Durvasula says, so transparency with leadership sometimes means telling them the project needs to reset. “If something doesn’t go well, what’s the level of fungibility you have?” he says. “We pick this tool, but what if it doesn’t work? You need to have a plan B.”

So TIAA’s IT team is heavily focused on flexible systems, and what was contemporary three years ago is probably legacy now, especially thanks to AI.

The power of change management

Another big lesson from a project of this size is the need to focus on change management. Retiring old IT systems requires the organization to bring employees along on the journey and convince them the changes are for the better.

TIAA established a multi-disciplinary team to implement a change management program focusing on breaking down silos and setting common adoption goals across the organization and its lines of business. Stakeholder forms and a huge focus on continuous collaboration helped employees understand the need for the changes.

“It’s a big organizational change,” Durvasula says. “If you’re working on a legacy system, and you think at some point it’s going to be modernized, then you become a legacy talent, and won’t have a job.” But the right change management program can convince these employees they can upskill and bring value to the new systems.

“You can bring your functional knowledge of the business and learn new technical skills,” he says. “It’s a massive culture- and people-change initiative as much as tech initiative.”

TIAA’s change management efforts were also made easier because TETRIS happened at the same time as the recent AI boom and involved AI elements. So it wasn’t hard to convince employees they needed to improve their AI skills.

“Because of AI, everybody woke up to this new reality,” he says. “We rode that wave when transformation drove from a cultural and organizational change management point.”

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.

One of the biggest challenges companies like Aetna face every year is an annual HEDIS review of its records to identify gaps in care. For large national payors, the scale of the challenge is immense. So Aetna has deployed a gen AI-driven document intelligence platform that has reduced the need for manual review by 65%.

“We have a large group of amazing trained medical coders who do this every day,” says Nathan Frank, chief digital and technology officer at Aetna. “This is about making it easier for them by speeding up the process. Something that might have taken weeks or months we can now do in days.”

The Healthcare Effectiveness Data and Information Set (HEDIS) is a range of performance measures for the managed care industry. Developed and maintained by the nonprofit National Committee for Quality Assurance (NCQA), the first version of HEDIS was released in 1991.

Under the HEDIS measures, large managed care providers like Aetna review more than 10 million medical records annually to identify gaps in care. These gaps are missed or overdue preventative care or chronic disease management tests including missed cancer screenings, blood sugar tests for diabetics, eye exams, and immunizations. Closing these gaps improves patient outcomes, and health plans are measured in how well they perform. But processing medical records is no easy task.

“We’re talking about medical charts that have white space filled with handwritten notes,” Frank explains. It’s not just structured data, it’s lots of physical clinical documentation.”

Adding up the numbers

Frank says industry benchmarks for large providers indicate an annual review process that requires about 50,000 work weeks, equivalent to nearly 1,000 dedicated full-time employees. It would take a team of 50 reviewers more than 20 years to complete a single annual review using fully manual processes.

Enter AI Medical Chart Review, a platform developed by Aetna that leverages cloud services and gen AI to automatically extract clinically relevant data from records, and prioritize records based on the likelihood of measure closure and evidence strength.

“Large language models and gen AI give us the ability to train a model to decipher the charts, identify the high value codes, and build correlations,” Frank says.

In the space of about six months, Frank’s team ideated the platform, and designed and trained a PoC that was able to process millions of records in just two weeks. As a result, AI Medical Chart Review has earned Aetna a CIO 100 Award in IT innovation.

“Now we’ve gone through 14 million documents,” Franks says. “We’re seeing a reduction of manual effort, which is now being transitioned into other areas like quality control and making sure the automated chart review is working as expected.”

Behind the curtain

Using gen AI, the platform automatically ingests and analyzes unstructured medical records and clinical documents. And as part of that process, it identifies and extracts clinically relevant information for specific HEDIS measures like diagnosis codes, medication records, lab results, and visit documentation. With this data, the platform generates a prioritized set of records based on the likelihood of measure closure and strength of clinical evidence, which is then passed to human employees for review and validation.

Frank says the platform has increased gap closure rates (leading to improved Star Ratings and higher reimbursement), streamlined workflows, and enabled teams once dedicated to manual record review to shift focus to higher-value activities.

Frank says much of the speed and success in building the platform comes down to a shift in the way it approached the design and build process. Rather than exhaustively writing specifications and requirements, Aetna created a team that included engineers and subject matter experts who worked together to build out capabilities iteratively.

“It allowed us to move much faster, and having a business subject matter expert sitting in the same virtual or physical room with us got us a much better outcome,” Frank says. “The product model, our cloud compute model, and our AI governance model allow for quick reviews to make sure we’re using AI responsibly with the right guardrails. It’s increased the speed to get from product launch to go-live.”

He adds that small teams that don’t have to deal with a lot of bureaucracy are key to moving quickly.

“You need to design with security, compliance, and a responsible use of AI as core principles from day one,” he says. “Everything we do from a new build standpoint starts with thinking about how we make it cloud native, how we build with the right elasticity and speed, and how we optimize the cost.”

The most important element of all, he says, is a good relationship with your subject matter experts.

“You can have a great product manager and engineer, but you really need that business subject matter expert who’s excited about it, and who has a passion for transforming the process,” Frank says. “Once you put those three together, you’ll see amazing things like this happen all the time.”

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.