For decades, CIOs have been challenging their organizations to shed one of the most persistent labels in our profession: order taker. Instead of waiting for the business to tell them what they want, technology leaders need to understand what the business is trying to accomplish, get invited into the first meeting, and earn the right to become a strategic partner. But in a fast-moving environment, is being a trusted advisor enough?

To reach the next level, top CIOs are increasingly transforming their IT organizations into what I call “innovative anticipators”: These IT orgs understand the business, customer, and possibilities so deeply that they can see around corners, anticipate what’s needed next, and create value before someone asks for it.

Janet Robertson, global CIO of RS Group, believes building that anticipation muscle isn’t simply about moving the IT organization to a higher level of maturity. Increasingly, it’s a business imperative.

For a recent episode of the Tech Whisperers podcast, Robertson and I explored the deeply human side of leadership: how trust goes both ways, how great leaders serve as a lighthouse when others are navigating through the fog, and how we prepare the next generation to lead in a world none of us has led in before. But hiding inside that conversation was another fascinating story to unpack.

RS Group is a global product and service solutions provider for industrial customers operating in 33 markets. Its journey has been remarkably similar to the one our IT profession has been making — from taking orders, to advising customers, to anticipating what they might need next. As Robertson evolves IT at RS into an innovative anticipator, the company as a whole is doing the same.

Two journeys, similar challenges. And as Robertson explains in the following conversation, strategic partnership may no longer be the destination for today’s CIO. It may be the starting point for what comes next.

Dan Roberts: You’ve said something that sounds simple but has enormous implications: There isn’t a technology strategy and a business strategy. There’s one business strategy. What changes in the way a technology organization operates when people genuinely believe that?

Janet Robertson: When the technology group is focused on an outcome, they’re looking at solving a business problem more than implementing a technical solution. That’s a nuance, but it’s really the most direct route to a solution, because they’re trying to solve a business problem and get a business outcome more than implementing a technology solution or a technology transformation. It’s a different mindset.

In practical terms, you end up with increased adoption because you’ve got buy-in from the business on the direct answer to the problem. You also end up with fewer bells and whistles — a solution that’s not gold-plated and that doesn’t have things you won’t need, which is more efficient, considering how fast things are moving today. Everything could be different.

In fact, we’re doing product development now as more of a continuous evolution using new, innovative tools that are available to us at each space. The days of planning a three- or four-year program are over. By the time you’re going live, the technology’s out of date, or the processes have changed, or the world’s changed around it. You’ve got to create solutions fast with the most direct available technology to support the business outcomes.

You can’t anticipate a business you only understand through dashboards. How do you get your technology leaders close enough to the work, the customers, and the people to see what they otherwise might miss?

I encourage my leaders to spend time in the everyday working life of the recipients of our solutions. Sit with some of our biggest customers and watch them place an order through our website. Sit with our middle- and back-office team and watch their process of paying an invoice, receiving an order, entering an order, processing an order. Go to the distribution center. Spend time with the people who are using your solution.

Our customer experience officer has been riding around with salespeople, experiencing what they are going through in a day-to-day environment. I’m going out to Milan, Italy, next week to open our new our automated warehouse there. I’m almost more excited about launching it and seeing what we’ve implemented in use than I was planning the program. I want to see the ship sail in clear waters.

When technology leaders spend the time doing this, they start to see the connection directly between what they’re putting out there and what actually has to take place. You get very different perspectives from different sides of the fence when you get hands-on. It’s less about KPIs and dashboards and the results out of the other end of it. It’s more about understanding who’s consuming what you’re producing and how you’re providing value to them. Because sometimes, their biggest pain point is something you put down as a priority 5, and it’s in the lowest level of the backlog, but would completely change their perspective, improve their experience, and drive value.

What have the best consumer experiences taught you about orchestrating a B2B customer experience that will continue to differentiate RS Group?

Our catalog is massive, and we’ve got so much to navigate. As a consumer, with the new developments you can go on to an Amazon experience and find anything you want using even cryptic keywords. Or you can go on to Google and find 20 different buying options. That is absolutely transformative for us, because a lot of our customers buy that way. They’re searching for something that’s maybe very specialized, so they expect the same kind of Amazon-type experience when they’re looking for a specialized manufacturing-line repair part.

For us in the technology space, it means we’ve got to make sure that our data and our foundations are prepared to enable those large language models for the AI and generative search for agentic purchasing. What’s a little bit different from the B2C space is that many of our customers are starting to implement agents to do a lot of their procurement. So we’ve got to be prepared on the receiving end of those agents to be able to place those digital orders. It’s not really an e-commerce, add-to-cart channel anymore. Now it’s a direct agentic experience in purchasing for our bigger customers that use agentic purchasing.

To lead the way on that is a huge differentiator, and we are aware of it, and we’re keeping an eye on it. We have to be able to move fast and pivot quickly. Who would have thought two years ago that we were going to be talking to agentic procurement?

For years, we’ve challenged IT organizations to do a better job demonstrating their value to the business and to become trusted advisors. How has your organization made that pivot, and what does the next evolution of that journey look like?

When I first joined RS, my department was seen as less of a technical advisor or an expert in new technologies and new ways of getting things done. We were seen more as order takers and a cost center, basically a necessary evil. The pivot we’ve gone through has happened by listening and engaging with the business to understand and help them articulate what problems they’re truly trying to solve. I say, “Please tell me the what, and let me and my experts provide you the how.”

It’s taken some time, honestly. That evolution from an order taker to a trusted advisor is a journey, because you’ve got to prove that you know what you’re talking about, that you’re up on the latest technology, you understand the latest tools, and you are thinking outside of the box — truly providing the vision and the art of the possible to a problem they don’t know how to articulate. The biggest driver for me is connecting my leaders and my team to the business in general. They’re out looking for tools to solve those business problems.

Now we’re starting to partner with our business partners to show them the art of the possible with some of the new trends that are coming, like agentic procurement and large language model search. SEO is gone; now it’s GEO, generative engine optimization. So instead of “Tell me your business problem,” it’s moving to “Let me tell you how to solve a problem you might not know you have yet. This is a way to anticipate what is probably coming down the pipeline, and it’s going to be a problem soon.”

Because we’re leading the way in talking to our business about where the future’s going in technology and customer search and customer analytics and data science, we’re being brought in to discuss these developing trends.

That level of anticipation requires intelligence, or what you’ve described as “intelligent automation.” What’s involved in building that kind of anticipation engine?

Just like you can’t expect a 300-year-old house to be a smart house overnight, you can’t turn a 100-year-old business into an efficient, highly data-connected enterprise overnight. We know that we’ve got some legacy plumbing that we need to repair, and we are well into that journey now. We know that the data we have is incredibly valuable, but we need to understand how to use it, how to interrogate it, and how to get the right answers out of it. Without a structured format, and without all the speed and the pipes and efficient use, we can’t take advantage of that.

Bad data and a bad foundation only accentuate bad decision-making from the data. If you put a lot of intelligence over the top of an unstructured or bad format and foundation, you get really bad data to make decisions from, and it just makes it that much worse. So sometimes you have to build a little bit of a foundation before you’re able to just run with it.

For years, the goal for technology organizations was to become the trusted advisor who gets invited to the first meeting, not just be handed requirements after the important decisions have been made. Robertson’s experience suggests that today’s CIOs need to challenge their organizations to go further, from “Tell me what you’re trying to accomplish” to “Let me show you what’s coming.” That requires getting close enough to your business colleagues and customers to see what they may not see yet. It’s the difference between being invited to the first meeting and being the reason the meeting gets called.

For more from Janet Robertson’s leadership playbook, tune in to the Tech Whisperers.

See also:

• IT reinvention requires mastering the art of translation
• What separates true enterprise leaders from strong tech execs
• The 7 new Cs of IT leadership for the AI era

In Foundry’s 2026 State of the CIO survey, 83% of CIOs say their role is becoming more of a hybrid to lead both business and tech initiatives, rather than simply manage IT. As the two become more inseparable, the CIOs who see collaboration as a competitive advantage are putting daylight between them and those who see it as a cursory soft skill. And C-suites are responding as they rely more on IT to drive growth and accelerate efficiencies. So the CIOs who deliver the most value will bring collaboration skills to drive change and technical expertise.

Silos between IT and the business are falling

As the World Economic Forum notes, not every tech initiative requires deep collaboration. But as businesses move beyond routine IT investments into cross-functional, innovation-focused transformations, collaboration becomes essential.

For example, how can IT decisions help an organization open new markets faster, or emerging technologies reduce risk in areas like data privacy. It’s no surprise, then, that Deloitte found that 65% of CIOs reported directly to CEOs in 2025, up from 41% in 2015.

Because tech is now part of almost every business decision, it takes everyone’s expertise and insights — from the CIO to the CFO — to see the full picture of how an initiative will affect the company. Shared knowledge leads to better-informed decisions that give tech its biggest impact.

When silos persist, CIOs struggle to get buy-in for their initiatives. Protiviti’s Global Transformation survey found that while 61% of CIOs report being confident in their tech transformation outcomes, only 34% of CEOs and boards feel the same — and the gap is even wider for AI implementations.

That gap is, at its core, a collaboration problem. Without effective collaboration and communication, others can’t see the value of transformation efforts. CIOs who excel in these areas can more easily demonstrate value, win buy-in and keep their organizations at the forefront of technology.

Delivering speed and innovation

Collaboration goes well beyond getting buy-in. While it may be thought to slow IT down, it actually brings in the insights that make change more effective and faster.

As I’ve highlighted in the past, when organizations move away from rigid data ownership, cross-functional teams can share what drives further innovation.

The same holds across the C-suite. Perspectives from outside IT show CIOs how a tech implementation works for the company as a whole, and where new opportunities lie. That lets CIOs test, refine, and reshape solutions faster.

Most importantly, when teams share a common purpose and goals, everyone stays aligned on the best overall outcome for the business. Iteration happens much quicker because everyone is focused on the big picture rather than individual agendas. With increasingly rapid tech cycles, that speed is a competitive advantage in its own right.

Building your collaborative abilities

For CIOs used to the old way of doing things, shifting to a collaborative mindset can be a challenge. But like any capability, it can be developed and improved by applying some fundamental principles.

Earn trust through follow-through. Trust isn’t built at a kickoff meeting or with a mission statement. It’s built when people see you do what you said you would, again and again. For CIOs, that means delivering on commitments or flagging early when you can’t. Clear and consistent communication is one of the most reliable ways to build trust across an organization.

Make transparency the default, especially when news is bad. Transparency means more than status reports. It means being candid about costs, risks, trade-offs, and setbacks before they surprise anyone. When leaders sidestep difficult discussions, trust erodes and goals drift. And in technology programs, small unspoken issues have a way of becoming expensive ones.

Seek cultural fit in how you work, not who you are. Cultural fit isn’t about sameness or shared backgrounds. It’s about compatibility in management philosophy, values, decision-making, and communication. A CIO who values speed and experimentation, and a CFO who prioritizes predictability and control will clash unless both recognize the difference and agree on how to bridge it. The same is true when it comes to finding the best technology providers.

Focus on the what, not the how. Few things undermine collaboration faster than micromanagement. When leaders dictate how a colleague or partner should work, they signal distrust, stifle creativity and innovation, and tie up their own time policing activities instead of driving results. The better approach is to agree on clearly defined, measurable outcomes, and then step back. Letting go of the how isn’t giving up control but building enough trust that everyone gets there together.

This requires adjusting your workflows and skills, but the payoff is valuable insight from your collaborators and wins that lift the entire organization.

The new life of the CIO

The days when the CIO could simply be the tech expert behind the scenes are long gone. With AI and other technologies ingrained in every aspect of the business, the CIO must lead well beyond IT.

By making collaboration a priority, you can gain critical insights across teams to guide your tech strategy, and deliver the greatest value to your organization.

The next generation of founders aren’t pinned to one mindset, one technology, and for that matter, one coast. TechCrunch’s Founder Summit event in Boston’s SoWa Power Station on November 4 gives the East Coast its due and is focused on equipping the next generation of disruptive ideas with the tools and connections they need to succeed. 

For investors, this is an incredible opportunity to get in early with the minds who will power the next era of innovation. 

For founders, you’ll be learning directly from peers who have been in your position in close-knit, hands-on workshops that will give your peers envy. 

And for IT leaders, you’ll glean insights into the operations and innovations of potential startup partners and learn how to capitalize on the market for your team’s own innovations as the call for revenue-generating CIOs gets louder. 

That all comes along with a 50% discount if you follow this link, or you use the code CIO50 with any of your tickets. 

And now there are more ways to save. Current ticket offers include buy one, get one free, up to $100 off individual tickets, 50% off a second ticket, and up to 30% off group orders. Plus, CIO readers can use code CIO50 for an additional 50% discount on eligible purchases, subject to the applicable offer terms.

What’s on the Founder Summit agenda?  

Founder Summit is a mix of sessions (NOT lectures), small breakout panels, networking opportunities, and industry events happening across Boston throughout the week. 

For just a glimpse of the interactive sessions packed into the one-day agenda:

• The Boston Founder Playbook: HqO co-founder and CEO Chase Garbarino on building a category-defining company outside Silicon Valley, having raised $200 million and scaled to 30+ countries.    
• The New Rules of Raising Capital: Brian Devaney, partner at Underscore, breaking down what investors look for now and where founders lose leverage without realizing it.  
• Built for AI From Day One: Hear what it takes to build an AI-native organization — from team structure to operations — from 7AI CEO and co-founder Lior Div.  
• Your Company Is Who You Hire: Learn how leading startups identify and sign the right talent for growth when every hire in a tight market matters from NEA partner Melissa Taunton.
• Building 10x Teams: Cogent Security co-founder Vineet Edupuganti sheds light on how to build a high-performing team with the ambition to build something of their own. 
• Plus, sessions on determining product market fit, what constitutes a market-creating technology solution, and GraphRAG in action — as well as the opportunity to meet founders looking to partner with enterprise customers before raising Series A capital.

The full agenda is available here. And you can explore all the side events during Founder Summit week here, including interactive product demos from Boston’s leading physical AI startup scene.  If you want all of that, and much, much more as detailed in this lengthy guide from TechCrunch, you need to act swiftly. Your best bet to save early, find emerging talent before competitors do, empower your own founding journey, and show what the Northeast is capable of, is right here with code CIO50 to save 50%. 

The next generation of founders aren’t pinned to one mindset, one technology, and for that matter, one coast. TechCrunch’s Founder Summit event in Boston’s SoWa Power Station on November 4 gives the East Coast its due and is focused on equipping the next generation of disruptive ideas with the tools and connections they need to succeed. 

For investors, this is an incredible opportunity to get in early with the minds who will power the next era of innovation. 

For founders, you’ll be learning directly from peers who have been in your position in close-knit, hands-on workshops that will give your peers envy. 

And for IT leaders, you’ll glean insights into the operations and innovations of potential startup partners and learn how to capitalize on the market for your team’s own innovations as the call for revenue-generating CIOs gets louder. 

That all comes along with a 25% discount if you follow this link, or you use the code CIO25 with any of your tickets. 

And now there are more ways to save. Current ticket offers include buy one, get one free, up to $100 off individual tickets, 50% off a second ticket, and up to 30% off group orders. Plus, CIO readers can use code CIO25 for an additional 25% discount on eligible purchases, subject to the applicable offer terms.

What’s on the Founder Summit agenda?  

Founder Summit is a mix of sessions (NOT lectures), small breakout panels, networking opportunities, and industry events happening across Boston throughout the week. 

For just a glimpse of the interactive sessions packed into the one-day agenda:

• The Boston Founder Playbook: HqO co-founder and CEO Chase Garbarino on building a category-defining company outside Silicon Valley, having raised $200 million and scaled to 30+ countries.    
• The New Rules of Raising Capital: Brian Devaney, partner at Underscore, breaking down what investors look for now and where founders lose leverage without realizing it.  
• Built for AI From Day One: Hear what it takes to build an AI-native organization — from team structure to operations — from 7AI CEO and co-founder Lior Div.  
• Your Company Is Who You Hire: Learn how leading startups identify and sign the right talent for growth when every hire in a tight market matters from NEA partner Melissa Taunton.
• Building 10x Teams: Cogent Security co-founder Vineet Edupuganti sheds light on how to build a high-performing team with the ambition to build something of their own. 
• Plus, sessions on determining product market fit, what constitutes a market-creating technology solution, and GraphRAG in action — as well as the opportunity to meet founders looking to partner with enterprise customers before raising Series A capital.

The full agenda is available here. And you can explore all the side events during Founder Summit week here, including interactive product demos from Boston’s leading physical AI startup scene.  If you want all of that, and much, much more as detailed in this lengthy guide from TechCrunch, you need to act swiftly. Your best bet to save early, find emerging talent before competitors do, empower your own founding journey, and show what the Northeast is capable of, is right here with code CIO25 to save 25%. 

AI is accelerating the pace of change, shortening the shelf life of expertise, reshaping operating models, and compressing the time IT leaders have to learn, decide, and act. As a result, IT leaders are increasingly forced to make consequential decisions faster, often with less information, greater ambiguity, and higher stakes.

Formore than four decades, I’ve worked with hundreds of CIOs, building trusted relationships with many of the world’s most accomplished technology executives. Through my Tech Whisperers podcast, CIO Whisperers interviews, the CIO100 Leadership Masterclass, and other conversations, these leaders have candidly shared what they’re wrestling with, where they’re struggling, and what they’re doing differently as the world around them changes.

For years, I’ve shared my “7 Cs of Great Leaders,” which are rooted in the brilliance of these practitioners. Customer Centricity, Courage, Change, Cultivate, Communication, Collaboration, and Culture — every one of these facets of leadership matters to this day. But the conditions under which we’re being asked to lead are changing. And through my conversations of late, new signals have been growing louder, and a new set of 7 Cs is emerging to help guide leaders through the challenges they face today.

AI transformation requires developing new mindsets and muscles, in ourselves, our teams, and ultimately our organizations. Those muscles — Coherence, Communication, Currency, Confidence, Conviction, Courage, and Change — are being demonstrated by leading technology executives as they accelerate business impact in environment marked by Compression.

BNY CIO Leigh-Ann Russell has made Coherence a defining leadership discipline for this era. Johnson & Johnson CIO Jim Swanson’s emphasis on continuous learning and remaining future ready captures the essence of Currency. Liberty Mutual CIO Monica Caldas is demonstrating what it means to build Confidence in people so they’re equipped to adopt new ways of working. Cox Automotive Chief Product and Technology Officer Marianne Johnson’s willingness to move where there is no established playbook embodies Courage. And GSK CTDO Shobie Ramakrishnan’s emphasis on assertive, high-velocity decision-making captures the reality of Compression surrounding all of them.

These capabilities build on, reinforce, and compound one another. It starts with making sense of a world that increasingly doesn’t and then giving people something coherent to move toward.

Coherence: Making the pieces make sense

Confused minds don’t act. And in today’s environment, overwhelmed minds don’t transform. Before we accelerate, everyone must know where we’re going and how the pieces connect. Otherwise, speed creates chaos faster.

The easiest way to understand coherence is to consider its opposite. Between AI initiatives, transformation programs, business priorities, technology investments, talent strategies, and new operating models, very few organizations today suffer from a shortage of activity. Instead, they suffer from incoherence: too many disconnected activities. Everyone can move quickly, work hard, do good work, but still be moving in different directions.

Coherence answers a deceptively simple question: How does everything fit together? It turns disconnected activity into aligned movement, creating the foundation for everything that follows.

Great leaders create coherence by connecting strategy to execution, ambition to capability, and today’s decisions to tomorrow’s destination. They help people understand what they’re doing and why, as well as how it connects to the collective mission and how their work helps get us there.

In a pressure-cooker environment where workloads are growing, roles are changing, and people are wondering what AI means for their futures, that connection between work and impact may be the most important thing a leader can provide. People can handle demanding work when they know why it matters and can see they are making a difference. Nothing drains energy faster than working incredibly hard without knowing whether any of it matters.

Coherence energizes people by creating focus, prioritization, and meaning. Great leaders connect the dots so their people can see the impact they’re having, whether that’s showing how their work changed an outcome, helped a customer or colleague, improved a decision, or created value for the business.

Communication: Creating shared understanding and movement

The best leaders consistently differentiate themselves through their ability to communicate — not simply to convey information, but to take people on a journey. They help people see the next hill, understand why it’s worth climbing, and believe they can get there together, even when the path is uncertain and the risks are real.

Master communicators start with their audience, recognizing that different stakeholders have different motivations, concerns, and hot buttons. They ask: What matters to these people? What do they need to understand, believe, or feel before they’re ready to move?

Many leaders, however, start instead with what they want to say. They organize the facts, build the deck, and deliver the message. Great communicators reverse that lens, starting not with “What do I want to tell them?” but “What do they need to hear from me?”

Master communicators also don’t stop at the “what.” The what may be a new AI capability, transformation program, operating model, or organizational change. It’s also rarely what causes people to move. Great communicators translate the what into the “so what”: Why does this matter? What opportunity does it create? How will it make the business, team, or my work better? Because different audiences care about different outcomes, the so what must be translated through their lens, not ours.

Then the best communicators go one step further, providing the “what’s next.” They help people see where we’re going, what will be expected of them, and what they can expect from their leaders. In an environment filled with uncertainty, people need leaders who can help them anticipate — not predict — the future and give them enough confidence to take the next step.

RS Group Global CIO Janet Robertson offers this simple question for any communicator: “What’s it like to be on the receiving end of you?”

Rather than how polished your presentation is, the real test is what the person on the receiving end heard, understood, and felt, and whether they’ve been given reason to be excited about the path forward.

Currency: Keep earning your relevance

Experience without continuous learning can become an anchor. J&J’s Jim Swanson has talked about the extraordinarily short half-life of an IT professional’s skillset and the need to remain continually future ready. That’s what is meant by currency.

The CIOs I see thriving invest more time in learning. They read, study, take classes, experiment, put hands on keyboards, spend time understanding how AI-native companies operate. They observe what’s changing outside their industries, spend time with people who know things they don’t, and get close enough to emerging technologies to distinguish reality from hype. The higher they rise, the more intentional they become about remaining a “leader as learner.” They know that relevance must be continually earned.

But currency isn’t just learning more about technology. Great CIOs get closer to the business and the work itself. They spend time in the field, sit with customers and frontline employees, observe workflows, ask questions, and see firsthand where friction, work-arounds, opportunities, and unmet needs exist. That proximity creates context you can’t get from a PowerPoint or conference room.

One of the reasons Afshean Talasaz, my Practitioners for Practitioners colleague, was so effective as a data scientist and as a CIO was that he went into the field. He put on the steel-toed boots and hard hat, rode in trucks, talked with the people doing the work, and watched them do it. He experienced the realities technology was supposed to improve, and that gave him something enormously valuable: context. He wasn’t learning about the business; he was learning the business.

Context matters even more in the AI era. To redesign workflows, automate processes, and fundamentally change how work gets done, we need to understand the work and the people doing the work before we try to reinvent it. You can’t transform what you understand only from a distance.

Currency is a function of curiosity plus proximity. Keep experimenting, questioning, observing, and learning while staying close to the technology, business, customer, and people doing the work.

Confidence: Building the muscles to move despite uncertainty

Currency produces earned confidence. Not swagger, but what comes from experience, experimentation, and enough repetitions to trust yourself when the answer isn’t obvious.

Resistance to change is certainly an issue, but one of the greatest obstacles to transformation is fear of incompetence. Accomplished people are accustomed to being good at what they do. AI suddenly puts us into beginner mode. Asking someone to change how they work without helping them develop confidence in their ability to succeed is a recipe for hesitation, avoidance, and stalled adoption.

The best CxOs I know building confidence, not just in themselves but in others as well. They give their teams the tools, context, permission, and opportunities to develop new muscles, and then room to use them as they move into unfamiliar territory.

Confidence can’t be mandated or taught in a classroom. It has to be developed through real-world “reps” — experimenting, learning, making mistakes, and trying again. Every successful rep creates more competence, and with it more confidence.

This is something Liberty Mutual’s Caldas reinforces in how she thinks about leadership and transformation. Great leaders don’t eliminate uncertainty for their people, she says; they equip them to operate successfully within it. They create environments where people can experiment safely, ask questions without embarrassment, challenge old assumptions, and stretch beyond their comfort zones. In the process, they give their people the confidence that, together, they can figure this out.

Conviction: Shortening the distance from signal to decision

Confidence gives us the conviction to make critical judgment calls, because seeing signals isn’t enough. In a quickly moving environment, leaders must determine which signals matter, which problems are worth solving, which opportunities deserve action. This is a vital source of competitive advantage, one Deluxe CDTO Yogs Jayaprakasam calls “speed to conviction.”

As AI makes technical capability more accessible, knowing how to build something becomes less differentiating. The premium shifts to knowing what is worth building and how quickly you can reach a well-founded decision to act. Jayaprakasam notes that startups don’t necessarily have better technology or smarter people; they’re often able to reach conviction and act while larger organizations are still studying the question. Every unnecessary layer between a signal and a decision adds latency. That’s a leadership challenge, not a technology problem.

This isn’t just “move fast and break things” while sacrificing judgment. Great leaders know which risks must be understood, whose perspective needs to be in the room, and when they have enough information to move. They resist impulsiveness on one side and analysis paralysis on the other.

In a fast-paced, uncertain world, waiting for 100% confidence can become a costly decision. The advantage going forward belongs not to the leader who knows everything but the one who knows when they know enough to move.

Leadership today demands making consequential decisions with incomplete information, stopping work that no longer matters, confronting legacy assumptions and deeply embedded ways of operating, and asking people, including ourselves, to leave comfortable territory. With conviction, leaders become willing to venture into territory where there may not yet be a playbook. And that’s when courage comes into play.

Courage: Choosing to take the shot

Courage isn’t the absence of fear; it’s what we choose to do in fear’s presence. That’s particularly relevant right now, because the fears are real — fear of getting it wrong, making a career-limiting decision, not knowing as much as we think we should. Fear can be paralyzing, and in an era of unprecedented uncertainty, there are plenty of legitimate reasons to hesitate.

Cardinal Health CIO Michelle Greene reminds us, “The land of comfort has no growth.” For leaders and the organizations we’re asking to transform, growth only happens once we venture beyond what we already know and feels safe.

But we can’t ask our organizations to experiment, reinvent work, and embrace AI while creating an environment where people are afraid to mess up. If we want more shots made, we have to create permission for more shots to be taken. That means creating intelligent guardrails, making thoughtful bets, learning quickly from the misses, and getting back in the game.

Cox Auto’s Johnson talks about leading when there is no map or established playbook. Great leaders can’t promise people certainty but they can provide a North Star, the right fundamentals, and the courage to take the next step. Sometimes that means taking the shot themselves. Increasingly, it means creating an environment where thousands of other people believe they have permission to take theirs.

Change: Turning movement into outcomes

This is where the preceding Cs lead us, because AI transformation ultimately comes down to the organization’s ability to change how work gets done. Leaders must be able to take people on that journey, while every individual has to strengthen their capacity to adapt, learn new skills, redefine what they contribute, and continually reinvent themselves.

The importance of IQ (intelligence quotient) and EQ (emotional quotient) remain essential. But legendary CIO Rob Carter makes the case that what will increasingly differentiate leaders and organizations going forward is AQ (adaptive quotient), the ability to adapt as the world around us changes.

Skills, jobs, operating models, and entire industries are being reinvented at extraordinary speed. As a result, adaptability is a prerequisite for relevance as a leader. AI’s value is unlocked when people adopt it, work changes, and decisions improve. Our ability to adapt determines how quickly and successfully we can make that happen.

Change creates new information, capabilities, and complexity, which means we have to find coherence all over again.

There’s one more C that belongs in this conversation, and it describes the environment the other seven operate in: compression. Technology cycles, strategy cycles, transformation cycles, and decision cycles are all compressing. The 7 Cs are how great leaders respond to this environment.

The compounding leadership flywheel

I see these new 7 Cs as a flywheel: Coherence creates clarity. Clarity enables communication. Communication creates the shared context for currency. Currency builds confidence. Confidence fuels courage. Courage enables change. Change creates new learning that requires a new level of coherence, and the flywheel turns again.

With every turn, those capabilities strengthen. The more we learn, the more confidently we can move. The more confidently we move, the more willing we become to act courageously. The more courageously we act, the more change we create. Every cycle produces learning that allows us to move through the next one faster and stronger. That’s compounding leadership.

My original 7 Cs described enduring attributes of great leaders, and I still believe deeply in every one of them. The new 7 Cs are an evolution of these fundamentals. They describe the mindsets and muscles great leaders need to continually strengthen when disruption isn’t an event but the reality we’re leading in.

We know the world around us will continue to change. The question is: Are our leadership muscles compounding fast enough to change with it?

We recently wrote about the emerging Internet of Agents: A world in which AI agents no longer operate only inside the boundaries of one company or one software platform, but interact with agents run by suppliers, customers, partners and other technology providers.

The basic premise was that this world needs infrastructure. Agents need to identify each other, establish trust, discover capabilities, communicate securely and operate across organizational boundaries. Without that foundation, the agentic enterprise becomes another collection of proprietary silos. But there is a second problem hiding immediately behind the first one: Connecting agents does not mean those agents can actually work together.

Humans know this intuitively. Give 10 people Slack, email and Zoom and you have given them the ability to communicate. You have not created a high-performing team. They may interpret the same goal differently, operate from different information, optimize for competing objectives or simply talk past one another. AI agents have the same problem, potentially at enormous scale.

That is the next frontier being explored by Outshift, Cisco’s emerging-technology incubation group, which has been one of the groups helping build the infrastructure behind the Internet of Agents. Outshift calls this next layer the Internet of Cognition: an architecture intended to move multi-agent systems from exchanging information to forming shared intent, context and coordinated action. And while the term itself is new, the problem behind it is becoming increasingly real.

Communication is not the same as coordination

Much of today’s discussion about multi-agent AI assumes that once specialized agents can talk to each other, collective intelligence will naturally emerge. The early research suggests otherwise.

A 2025 NAACL study created a benchmark specifically to test how LLM agents coordinate. The researchers found that agents performed reasonably well when decisions depended mainly on information in their environment, but struggled when successful coordination required understanding another agent’s beliefs and intentions, with substantial room for improvement in joint planning.

More recent research makes the problem even starker. SILO-BENCH, presented at ACL 2026, tested distributed coordination across multi-agent LLM systems. Researchers identified what they called a “Communication-Reasoning Gap”: agents communicated actively but often failed to turn that interaction into effective collective computation. On the most complex tasks, performance deteriorated dramatically as the number of agents increased.

In other words, more agents exchanging more messages does not necessarily produce more intelligence. That distinction is at the heart of what Outshift is trying to solve. As one Outshift leader put it, “message passing is not collaboration.” Agents may be technically capable of exchanging data while still lacking a shared understanding of what that data means, what they are collectively trying to accomplish or how to resolve competing interpretations.

Consider something as simple as the phrase “Priority 1.” To one organization’s IT agent, that might mean an incident requiring resolution within an hour. To an agent operating in a healthcare environment, it could imply an immediate life-or-death situation. Both agents received the same words. They did not receive the same meaning. As agents increasingly operate across systems, functions and eventually enterprises, those semantic differences become operational problems.

From an Internet of Agents to an Internet of Cognition

The Internet of Agents work has largely focused on the infrastructure required to make agents interoperable: identity, discovery, messaging, access and observability. Outshift and partners have been developing much of that work through AGNTCY, an open-source effort designed to provide common infrastructure for multi-agent systems. Its goal is to allow agents built on different frameworks and platforms to discover one another, communicate and operate without requiring a custom integration every time two systems meet.

That is a major step. But think of it as the networking layer. Once the agents are connected, the Internet of Cognition asks another set of questions: Do they understand the objective the same way? Do they have enough shared context? Can they recognize that they disagree? Can they negotiate? Can one agent challenge another? Can knowledge discovered by one become usable by the others? And when the system reaches a decision, can a human understand how it got there?

Outshift’s proposed architecture focuses on three areas: protocols that establish shared intent and coordination; a cognition fabric that supports shared, policy-governed context and memory; and cognition engines that can help agents negotiate, coordinate or enforce guardrails.

That may sound futuristic. But Outshift says it is already seeing the coordination failure in its own multi-agent experimentation. Papi Menon, Chief Product Officer and VP of Product Management for Outshift, told us that the team is considerably further into autonomous multi-agent workflows than many enterprises and has begun encountering problems that others have not yet reached. “Agents don’t align and they kind of tend to diverge,” Menon told us.

Outshift has been experimenting with an open-source project called Mycelium, designed to give agents mechanisms for coordination and alignment. According to Menon, in Outshift’s testing, agents without those mechanisms reached alignment in only roughly 36% of cases. With Mycelium introduced to the same agents, he said, alignment rose to approximately 93%. These are Outshift-reported results rather than an independent benchmark, but the magnitude illustrates the problem the team believes enterprises will soon encounter.

“The truth is, when we talk about multi-agent workflows, we are further along on that journey than most people are,” Menon said. Problems such as agents failing to align, coordinate their actions or establish common semantic grounding can sound theoretical, he acknowledged, because many companies are not yet operating autonomous multi-agent systems at that level. That will change quickly if the agent population grows the way vendors expect.

We are already seeing the first layer work

This is not entirely theoretical. Menon described work between Outshift and ServiceNow in which agents operating on separate platforms were able to participate in the same end-to-end enterprise IT workflow. A ServiceNow agent could identify and work a ticket, hand a networking problem to a Cisco agent capable of diagnosing and proposing changes, receive the result back for validation, and then continue the workflow through implementation and documentation. What previously could have required substantial custom integration between the platforms was connected in a matter of days, according to Menon.

The broader market is moving in this direction as well. ServiceNow now supports external AI agents through the open Agent2Agent protocol, allowing agents operating on different platforms to participate in common workflows. That is the Internet of Agents coming to life.

But imagine what happens when the workflow contains not two relatively bounded agents, but 20 autonomous agents representing security, finance, procurement, HR, suppliers and customers, each with different data, goals, permissions and interpretations. At that point, connectivity becomes the easy problem.

Don’t forget the humans

There is another reason the cognition layer matters. The future enterprise is unlikely to consist exclusively of agents talking to agents. It will be a hybrid intelligence system of humans and agents reasoning and acting together.

That raises a problem our own research into human-agent collaboration has repeatedly surfaced: humans cannot exercise judgment over systems they cannot understand. Outshift has been thinking about this as well. Its Human-Agent-Experience, or HAX, initiative focuses on how agent reasoning, evidence and actions should be surfaced to people in understandable and consistent ways. The framework emphasizes clarity, user control, recoverability and predictable interaction patterns.

Menon described the challenge this way: even as agents become increasingly autonomous, their activity ultimately has to be “explainable, rationalizable and justifiable to a human.” Human feedback then needs a path back into the multi-agent system. That piece may ultimately be as important as agent-to-agent coordination itself. Collective intelligence is not just about making agents smarter together. It is about creating systems in which intelligence can move among agents and humans without losing context, accountability or judgment along the way.

The CIO question is changing

For CIOs, this suggests that counting how many agents an enterprise has deployed will soon become an increasingly meaningless measure of AI maturity. A company could have 500 highly capable agents and still have very little collective intelligence.

The more important questions may be different:

• Do our agents share an understanding of the outcome, or merely exchange instructions?
• What happens when two agents disagree or optimize for competing objectives?
• Can knowledge generated by one agent become trusted context for another without destroying provenance, permissions or accountability?
• Can humans see enough of the system’s reasoning to challenge it when necessary?

Those questions matter before enterprises reach massive agent scale, not after.

Outshift’s Internet of Cognition is still an emerging architectural thesis, not an established industry standard. Menon himself acknowledged that it sits roughly where the Internet of Agents did a couple of years ago: early enough that many organizations have not experienced the underlying problem and skepticism is understandable.

But that may be precisely why CIOs should pay attention. Technology architectures are easiest to shape before thousands of systems depend on them. The original internet connected computers. The emerging Internet of Agents is beginning to connect autonomous AI.

The next question is whether connecting intelligence can produce something greater than a network full of individually smart machines. As Outshift argued in presenting its work on the Internet of Cognition, this generation may ultimately be less about connecting machines than connecting intelligence. This would provide something much more valuable: agents and humans that can actually think together.

AI forces organizations to make consequential decisions during uncertainty. Roche chief digital and technology officer Wafaa Mamilli says this is the new leadership imperative. She believes success depends not on certainty but building the right foundations: trusted data, governance that enables speed, upskilling the employee base, and focusing on outcomes. At Roche, that approach drives applications ranging from kidney disease prediction and digital pathology, to new ways of discovering drugs and coaching people leaders. The challenge today, Mamilli says, is building organizations that can learn, adapt, and deliver value while the future unfolds, rather than predicting what’s next.

How is Roche using AI to drive growth and impact?

Our overarching mission is to prevent and cure diseases, and improve patient outcomes while reducing structural healthcare system costs. Data and AI are foundational enablers of this strategy. The high-quality data we’ve been assembling throughout our more than 125 years are part of this foundation.

We’re using data and AI to quickly make sense of a massive amount of information —  including genomic sequences, medical images, clinical records, and complex biology — and turn it into real medical breakthroughs at every stage of the patient journey.

We understand AI is a human and change management challenge, not a technical one. That’s why we prepared a holistic approach, which includes what we call everyday AI, to upskill our employees and embed AI in everyday work; reshaping with AI, which is reimagining our end-to-end processes; and our big ideas, which are large transformational investments.

Can you detail some real examples that bring this strategy to life?

We’re building AI tools across the entire value chain. Our enterprise platform, Galileo, includes internal generative AI tools, search engines, and compliance-safe sandbox environments.

We also have custom AI-powered coaching tools to help managers prepare for staff conversations. In our diagnostics business, we implemented an AI algorithm called Kidney Klinrisk to help physicians predict the risk of chronic kidney disease progression before irreversible damage.

Plus, we use AI image analysis in our digital pathology to assist pathologists in identifying cancer cells faster, and in R&D, we have AI-powered simulations to model clinical trial scenarios.

In manufacturing, we’ve deployed digital twins of our production lines, reducing development cycles by two to four times, and cutting technology transfer times by 50%. In drug discovery, we allow scientists to model over 100 experiments, compared to five in a physical plant.

But none of these examples imply that we’ve figured it out. We need to be confident and humble at the same time, but we’re still working through this just like anyone else.

How do you balance AI innovation and risk?

We develop life-saving medicines and vital diagnostics that directly impact human health, so we’ve always balanced speed and safety with the right governance framework. To position governance as a velocity enabler, we established a dedicated, horizontal framework focused on AI risk and ethics, and an AI strategy and governance function to ensure we leverage models responsibly.

But as important, I personally engage in understanding what’s at stake, because everything is changing overnight. That attention to detail is critical, because our job is to align, comply, and govern while allowing speed. We act with speed, assess, experiment, and then say yes or no to scaling the innovation. That’s how we lead this from the executive team down.

What changes have you made to your IT organization for this new wave of AI innovation?

I renamed my organization Roche Digital Technology, RDT, to position us as powering the business with digital tech and AI in a federated model. I created dedicated AI and data teams, which report to two new roles in RDT, chief AI officer and chief data officer. I intentionally made these two different roles because we need our data structured and ready for what we want to do with AI. There’s no AI without data, and we have foundational work to do there.

Think of the AI team as what the market calls forward deployed engineers — they work with all the other teams to re-engineer processes and deploy AI, while feeding the AI platform and tools teams so the platforms evolve to match the enterprise and marketplace. Enterprise architecture and portfolio governance are capabilities teams that support the full organization.

Describe the profile of the CAIO, as you’ve defined it on your team.

The AI officer is business outcomes driven and passionate, as well as tech savvy with high learning agility. This leader needs an entrepreneurial, risk-taking mindset, an ability to deliver, and deep process knowledge.

Remember business process re-engineering? There are some similarities here. We need people who can think through the workflow, not just what’s implemented in the systems but the actual workflow, what people do offline between systems, and think work and workflow, which we’ve never fully figured out. We need people who can step back, look end-to-end, and, with the knowledge of AI, reimagine what a process can look like now. So business process savviness is critical.

What advice do you have for tomorrow’s CIOs?

You have to be both a business and a technology leader. I call that a business minded technologist since business fluency is table stakes. You need to understand strategy, customers, and the economics of how the enterprise creates value. But particularly, in this moment, deep technology acumen matters enormously. The CIO has to understand not only what technology can do today, but what’s becoming possible tomorrow, and translate that into choices for the enterprise.

This is why I believe this is the most exciting time to be CIO. Technology and now AI is no longer enabling the business. It’s reshaping how companies operate, how decisions get made, how people work and increasingly how value is created.

So my advice for tomorrow’s CIOs is don’t be passive or apologetic about being a technologist. If there isn’t a chair for you at a table, bring your own by bringing an enterprise perspective and insights others can’t.

Few roles have the same vantage point across the organization. Use it. Connect the dots across functions. Understand the technology deeply enough to challenge assumptions. Bring confidence and humility, have the courage to make choices, and recognize that the hardest part of transformation is rarely the technology itself but bringing people with you.

AI is known to be confidently wrong, and now it’s influencing humans to be that way, too.

In a new study, researchers tested AI’s influence on humans reviewing innovation proposals, and found that AI recommender tools were persuasive enough to convince the evaluators to reject decisions made by independent human experts, thus causing them to pass on promising innovations. Similarly, they went along with AI approval of ideas that the human experts found sub-par.

Interestingly, reviewers were also more inclined to defer to an incorrect AI decision when the model explained itself. Narrative explanations degraded human judgment, rather than enhancing it. People did better when they weren’t given a reason for the AI’s decision.

“Our findings reveal that LLM explanations do not necessarily improve decision-making,” the researchers, associated with Harvard Business School, MIT, and the University of Washington explained in their findings. “Effective human-AI collaboration requires designs that preserve rather than supplant independent human judgment.”

AI rationale can undermine human judgment

Every enterprise screens proposed projects before pursuing them, but there is always uncertainty, and the risk of trade-offs like false positives (going forward with projects that ultimately fail) or false negatives (rejecting ideas that might have succeeded). For an example of the former, the researchers point to Google Glass or Amazon’s Fire Phone; for the latter, Xerox terminating early Ethernet and PostScript projects.

Because they have limited time and only basic information to go on, decision-makers are increasingly turning to LLMs that use predictive algorithms to generate recommendations and rationales based on context.

The researchers set out to explore AI’s role in what they called “early-stage innovation screening.” They judged how human evaluators were influenced by LLM recommendations, both with and without explanations from the model on how and why it reached its decision.

Their experiment asked 228 experienced evaluators to assess nearly 50 submissions to an MIT challenge. They tested three different scenarios: human-only proposals with no AI assistance; LLM evaluations with a written rationale for the decision; and black-box AI pass-fail recommendations with no accompanying explanation.

Evaluators’ decisions were then compared to those made by four human experts. Those decisions were considered the ‘correct’ baseline. They were judged on whether they outright complied with the LLM’s recommendations, overrode them, or productively overrode them, meaning they independently verified persuasive model outputs before making a decision.

Their decisions were classified as correct (agreeing with human experts’ positive/negative decisions), false positive (supporting submissions that experts would reject), and false negative (rejecting submissions experts would move forward with).

Overall, the evaluators accepted LLM recommendations 67% of the time. They agreed with both black-box and narrative LLM decisions roughly 75% of the time, but only agreed with human decisions 54% of the time.

Seemingly counterintuitively, black-box recommendations improved the quality of decisions (aligning them with human experts) but recommendations with narratives did not. When given an LLM recommendation to reject a submission and an accompanying reason why, evaluators disproportionately agreed, which reduced false positives, but “substantially” increased false negatives.

The researchers posit that this is because narrative explanations “suppress” productive overrides; LLMs provide a convincing argument that is easy to accept, essentially discouraging independent human verification. This contradicts a common assumption that LLM explanations augment human decision-making.

The researchers pointed out that people are cognitively predisposed to weigh negative information more heavily than positive information; the phenomenon is known as ‘negativity bias.’

“Rejection is an active, eliminative decision that feels more consequential and accountable than preserving optionality,” they wrote. It also maintains the status quo, avoids risk and bias, and requires no resource commitment.

LLM explanations provide “ready-made justifications” for going along with rejection decisions without independently verifying them; humans effectively offload their thinking to AI, researchers explained. Evaluators often rely on surface cues such as fluency, coherence, and seeming credibility. LLMs are particularly well-suited to exploit this because they are linguistically fluent and expert-like, creating an “illusion of explanatory depth.”

Thus, “individuals tend to overestimate their understanding of a decision despite limited insight into its reasoning,” the researchers wrote.

Finding a balance in recommendation systems

The researchers pointed out that their findings have “clear implications” for enterprises designing AI-assisted evaluation systems.

Enterprises should be cautious with LLM explanations in high-stakes decision-making, they advised. AI recommendations should not be taken at face value; they should always be tested before any associated deployment. This helps improve accuracy and encourages human reviewers to detect errors and learn how models operate, or potentially can even increase human-AI agreement.

In decision contexts such as quality control, compliance screening, or fraud detection, LLM explanations could support conservative human decision-making, the researchers noted. On the other hand, in tasks like early-stage screening, LLM narratives could undermine performance by “discouraging independent judgment and suppressing productive human override.” In this context, simpler or more opaque recommendations may preserve human discretion and verification.

Future design of explanation systems should factor in the nature of the task and the potential cost of errors made by AI, the researchers advised. Enterprises could experiment with models that support contrasting narratives (reasons to reject an idea alongside reasons to accept it) or uncertainty disclosures based on a fixed threshold, rather than on purely binary decisions. Systems could also be structured to invite human disagreement.

The researchers also noted that there is opportunity to test whether narrative explanations have different impacts at later stages of decision-making, when evaluators have fewer options, more information, and increased incentive to verify outputs and think the problem through.

Ultimately, the researchers emphasized, “organizations should treat AI explanations not as universally beneficial transparency tools, but as behavioral interventions whose effects depend on how evaluators process information under uncertainty.”

Each year the CIO 100 Awards showcase outstanding IT initiatives, and every year they illustrate the power and potential of technology to transform how people work, how organizations perform, and the value they offer to customers.

The 2026 cohort of winners is no different. Each one demonstrates how IT executives and their teams successfully move from ideation to deployment to scaling a solution for the future, overcoming challenges and driving adoption along the way to ensure their organization gets a return on its investment.

[ Interested in meeting and learning from all CIO 100 winners? Join us next week at CIO 100 Awards & Conference in Frisco, TX. Limited seats remain! Register here ]

The winning initiatives come from a range of industries and utilize a host of technologies to achieve their goals, as is the case annually. A growing proportion of these stand-out projects leverage artificial intelligence, raising the bar on the art of the possible for all IT departments.

The following 10 award-winning projects serve as representatives for the outstanding work done by all the 2026 honorees.

ABB democratizes AI agent creation and deployment

Organization: ABB

Project: ABBY — AI Agentic Platform for Workforce Transformation

IT leader: Vikke Kandell, CIO

IT leaders at ABB, a manufacturer, had some big hurdles to clear when it came to building an AI strategy.

They had to overcome employee fears that AI would take away jobs, the potentially high cost of AI vendor licenses, and pressure from investors, customers, and executives to advance the use of AI in the enterprise.

“We looked at this and asked, ‘How do we address all this?’ and build something that the company is proud of,” says Babu Kuttala, vice president of data analytics and AI.

The answer is ABBY, an AI agentic platform that enables employees to create and deploy specialized AI agents for specific business tasks.

To build ABBY, Kuttala and his team used best-of-breed LLMs (about 25 in total). They built a centralized orchestration layer using generative AI that integrates internal knowledge bases with external ecosystems, creating a unified platform where agents can access enterprise data, understand required actions, and execute tasks across multiple systems. And they created preconfigured skills so that employees could build agents tailored to their workflows without having to code.

ABBY was rolled out in 2025 to 100 users but is now used by 63,000 (more than 75% of the company’s workforce, Kuttala notes) with an average of 10,000-plus workers using it daily. IT continues to add LLMs and capabilities to expand use of ABBY even further, Kuttala says.

Belcorp modernizes manufacturing with Smart Factory

Organization: Belcorp

Project: QPlant — Smart Factory

IT leader: Venkat Gopalan, Chief Digital, Data, and Technology Officer

Legacy processes were limiting Belcorp’s ability to scale and compete. Its manufacturing relied on ERP-driven processes with limited shop-floor automation and weak connectivity across production, packaging, quality, and maintenance. The company depended heavily on manual records and post-process reconciliation, resulting in fragmented data, limited real-time insight, inefficiencies, and higher risks for errors.

Smart Factory changed all that. The IT initiative reimagined how manufacturing teams work “by creating a connected, data-driven environment where production, quality, maintenance, and operations are aligned around real-time information and standardized execution,” says Venkat Gopalan, chief digital, data, and technology officer.

At Smart Factory’s core is a manufacturing execution system that orchestrates production workflows, quality processes, and operational execution, he explains. IoT-enabled equipment integration and a centralized SCADA platform provide real-time visibility into shop-floor operations, while electronic batch records digitize production execution, strengthen traceability, and reinforce compliance by design.

Integrating those operational technologies with the company’s enterprise platforms was another critical component of success, Gopalan says, creating a trusted flow of real-time data across manufacturing, quality, maintenance, and business systems. “This connected architecture transformed isolated data into actionable insights, enabling faster decision-making, greater operational visibility, and continuous improvement across the manufacturing lifecycle,” he adds.

The initiative generated more than $1 million in financial benefits in its first year alone.

“Most importantly, Smart Factory established the digital foundation for the future of manufacturing at Belcorp,” Gopalan says. “With real-time operational data and connected systems now in place, we’re well positioned to accelerate advanced analytics, AI-driven optimization, predictive maintenance, and other Industry 4.0 capabilities that will continue delivering value for years to come.”

Cohesity replatforms post-acquisition for commercial growth

Organization: Cohesity

Project: Lead to Cash Replatforming Program (Veritas Integration)

IT leader: Brian Spanswick, CIO

Cohesity set an ambitious objective: Complete an enterprise-scale lead-to-cash replatform in under six months.

That’s a tight timeline for any replatforming initiative, but Cohesity’s project had another layer of complexity. It followed Cohesity’s December 2024 acquisition of Veritas, a company twice its size in revenue, leaving Cohesity to integrate the majority of a global enterprise revenue engine into its own operating model without disrupting customers, partners, or sellers.

“We had to bring the two companies together, merge the workforces together, and create an overall harmonized organization and operating infrastructure platform,” says Eric Brown, who as CFO and COO led the project.

The program migrated heavily customized CRM, CPQ, PRM, ERP, and subscription platforms (some of which were “very brittle, very bespoke,” Brown says) to a unified SaaS CRM, CPQ, and ERP environment with uninterrupted selling, billing, and partner operations.

This was no lift-and shift, Brown stresses. “It was a business process optimization project as well. We want to run very efficiently, so we questioned everything and used the migration process to simplify and streamline the business in every possible respect.”

The initiative enabled continuity for 13,000-plus customers, protected revenue during integration, and established a scalable commercial foundation for future growth.

Brown cites several factors that contributed to success. First, leadership was upfront about what it would take to meet the deadline, a process that involved carefully prioritizing the capabilities that would appear in the first iteration. Leadership also streamlined decision-making, establishing office hours that “ran with military precision” to handle issues. And the company selected a specialized partner, requiring its top talent be assigned to Cohesity.

Dairyland Power goes agentic to protect field crews

Organization: Dairyland Power Cooperative

Project: ODIN — Organizational Effectiveness Agentic AI

IT leader: Nate Melby, VP and CIO

Dairyland Power Cooperative had amassed a large collection of field observations, incident reports, near-misses, safety rules, and work methods that could yield insights into processes and practices that could help protect its workers.

But the insights were essentially out of reach, trapped in siloes.

Dairyland’s organizational effectiveness team turned to CIO Nate Melby for help unlocking those insights. Melby then turned to agentic AI, recognizing that the technology could address the team’s need to make better use of its data.

“This was about finding insights on how to work more safely,” Melby says. “It’s about preventing incidents.”

The collaboration between the two teams created ODIN, the first agentic AI implementation of its kind in the electric utility industry.

Focused on worker safety, ODIN autonomously connects the collective safety knowledge of the organization and delivers actionable insights directly to field crews at the moment work is planned.

ODIN was developed through a hybrid approach that combined an agentic AI platform and Dairyland’s internal private generative AI platform called VoltWrite. ODIN leverages LLMs, retrieval-augmented generation, and a coordinated swarm of autonomous agents.

Agents work together to analyze internal safety data, performance history, work practices, and safety rules and then synthesize the information into clear guidance on the safest way to perform specific tasks.

ODIN has produced results, including a reduction in OSHA recordable injuries and improvements in the quality and consistency of pre-job safety briefings.

ODIN was deployed in early 2025 for use by Dairyland’s workers in transmission construction and electrical maintenance, which are the highest-risk work areas. Dairyland is looking to expand ODIN’s use to other teams.

Dow’s digital sustainability ledger drives low-carbon sales

Organization: Dow

Project: Carbon Footprint Ledger

IT leader: Deb Bauler, Chief Information and Digital Officer

Executives at Dow consider the Carbon Footprint Ledger (CFL) as more than a technology or innovative carbon accounting methodology. According to Senior Global IT Director Jeremy Preston, CFL is “a digital business capability that enables Dow to translate sustainability investments into customer value.”

CFL transformed how Dow uses greenhouse gas emissions data. It combines a methodology aligned to international standards with an enterprise-scale digital platform. It also integrates manufacturing, supply chain, commercial, and sustainability data to generate product carbon footprints under enterprise-level governance and management at scale.

In doing so, Preston says it creates “a trusted, traceable link between low-carbon processes and raw materials implemented across its manufacturing network and the lower-carbon products customers seek.”

The technology team worked closely with sustainability and business teams, collaboratively developing the capabilities needed to reconstruct product genealogy, maintain end-to-end data lineage, track low-carbon attributes across interconnected manufacturing processes, and generate product carbon footprints that can support customer offerings and commercial transactions.

CFL was built on Dow’s Integrated Data Hub and in partnership with Boston Consulting Group and Databricks.

The core CFL platform is fully deployed and supports commercial transactions today.

Preston says CFL “enables Dow to turn sustainability investments into customer value, commercial differentiation, and new growth opportunities.” Dow reports that it has driven hundreds of millions of dollars in low-carbon product sales in 2025 and 2026.

The company is now expanding its use. “We are extending adoption across additional products, manufacturing networks, business segments, and customer use cases while continuing to enhance automation, analytics, and integration with commercial processes,” Preston says.

J&J transforms quality management with AI

Organization: Johnson & Johnson

Project: Q&C Strategy

IT leader: Michael Comprelli, Vice President, Head of Technology, Technical Operations, and Risk; Joel O’Connor, Head of Technology, Medtech Quality, and Compliance

Johnson & Johnson is using AI to transform quality management through its Q&C Strategy.

QuIn is an AI-powered digital assistant that fuses human expertise with machine learning, automation, and data-driven insights to boost efficiency, reliability, and worker impact. By embedding gen AI into core quality management systems processes, QuIn proactively gathers actionable insights, increases operational efficiency, and allows teams to focus on high-value, patient-centric work.

Cora is an innovative generative AI platform that provides regulatory intelligence monitoring, impact analysis, and augmented content revision. Cora assists with document analysis, compliance comparison, stakeholder analysis, policy/standard creation, procedural/document updates, and document comparison. Cora is purpose-built for regulated environments, validating outputs against source material and offering a user experience that instills trust in the outcome.

QuIn and Cora, which automate time-intensive tasks and democratize information access, are on track to deliver significant value, with J&J reporting more than $62 million in documented true cost savings by 2028 from QuIn alone. Cora delivered $2 million in cost efficiency in 2025 and will deliver a documented cost savings of $25 million by 2028.

“Our teams proved responsible AI can be applied meaningfully in a highly regulated environment without compromising the rigor, accountability, or human judgment that quality requires,” says Michael Comprelli, vice president, head of technology, technical operations, and risk.

He continues, saying that J&J “moved these ideas beyond experimentation and into products that employees use in their daily work. We did that by bringing together Quality expertise, product management, data engineering, architecture, cybersecurity, user-experience design and AI engineering around a common purpose.”

JLL brings intelligent automation to business services

Organization: JLL

Project: Business Service Digitization

IT leader: Pinak Dash, Global Head of JLL Business Services and Legal Technologies

JLL launched its digitization initiative to drive process redesign as well as systematic AI and RPA deployment across JLL Business Services (JBS).

The initiative was designed to address inefficiencies that hampered scalability and competitive positioning. It was also designed to eliminate manual processes that consumed thousands of hours across finance, HR, legal, procurement, marketing, research, IT, and lease administration.

Pinak Dash, global head of JBS and legal technologies, says the digitization initiative had a dual-strategy combining traditional digitization with generative AI innovation to hundreds of processes.

JLL lists three innovations critical to the program’s success.

First is a hybrid platform that integrates RPA with JLL’s proprietary AI platform called Falcon, which created intelligent automation that adapts and learns. It enables real-time process automation, intelligent document processing with automated extraction/validation, and smart decision-making for continuously optimizing workflows.

The second innovation is its use of ProHance for real-time process monitoring and enabling of data-driven optimization. Sensors capture granular productivity metrics, identify bottlenecks, and provide actionable insights for continuous improvement across automated and manual processes.

Third is its custom AI assistants and transaction agents. Falcon-powered assistants provide intelligent knowledge search while specialized agents execute complex transactions across enterprise SaaS platforms. These handle multisystem workflows, reducing human touchpoints while maintaining accuracy and compliance.

Dash says the initiative has delivered quantifiable benefits through improved efficiency, accuracy, and quality of services provided to clients.

“The initiative delivers on our business goals, makes us more efficient, provides customers better service, and it opens up the capabilities and bandwidth of our people to do what they like to do and to find innovative ways to serve our business,” he adds.

Nationwide partnership platform delivers efficiencies, business growth

Organization: Nationwide

Project: Enterprise Digital Platform (EDP)

IT leader: Michael Carrel, EVP and CTO

Nationwide’s new Enterprise Digital Platform (EDP) gives the company “a scalable way to connect with external partners quickly, securely, and consistently across all areas of our business,” says company EVP and CTO Michael Carrel.

He explains that “instead of treating every integration as a custom effort, EDP creates a common front door for digital products, documentation, onboarding and governance.”

That innovation has produced better experiences for the company’s partners. It saves time for Nationwide teams, partners, and customers. And it supports faster launch times for new products and enables growth across the business.

“EDP changed the model from fragmented, point-to-point integrations into an enterprise platform built around reusable digital products. That shift lets us support a range of integration options in one governed environment, meet partners at different stages of technical maturity, and add new capabilities over time without redesigning every relationship from scratch,” Carrel explains.

EDP uses cloud-native microservices, role-based access control, and advanced analytics. Nationwide IT created modular microservices to make EDP more scalable, resilient, and adaptable. And IT decoupled it from infrastructure-specific dependencies so that it would be a platform-agnostic developer portal. That, Carrel says, reduced operational constraints across environments.

Additionally, IT shifted from a user-specific model to role-based access, which improved security, simplified administration, and better served the needs of different audiences.

Meanwhile, robust analytics delivers visibility into platform usage and performance, which Carrel says helps ensure Nationwide continuously evolves the platform based on measurable outcomes.

The core platform is fully deployed, with Nationwide planning to expand it.

“Our Enterprise Digital Platform is more than a piece of technology,” Carrel notes, “it represents a strategic enabler to support growth objectives across Nationwide’s businesses.”

PITT Ohio fast-tracks shipment requests with AI assist

Organization: PITT Ohio

Project: No Touch Email (N@TE AI)

IT leader: Scott Sullivan, President and CEO (formerly CIO)

As PITT Ohio started its AI journey in 2024, the mandate was clear: Use the technology to solve “real problems,” says Ryan Carner, director of enterprise IT solutions.

“We wanted to hit the ground running and find a problem that was solvable,” Carner says, noting that the company also wanted to use the experience to build in-house AI skills. “The idea was to find a business case for AI that would be our first but not the only one.”

PITT Ohio leaders decided to tackle what Carner describes as a “mundane but very important task for how our business operates”: handling emails to the customer service team.

The need was significant. Customer service representatives were manually processing hundreds of pickup request emails daily, each requiring five to 15 minutes to interpret and re-enter shipment details into the company’s transportation management system (TMS). The emails were complicated, containing a lot of information submitted in nonstandardized ways and varying formats. This repetitive task consumed valuable time, introduced errors, and delayed customer response.

N@TE uses generative AI and natural language processing to transform unstructured email content into structured pickup orders automatically and in real-time. N@TE scans incoming emails, extracts key shipment data, and creates orders directly in the TMS via API integration. It operates seamlessly within existing workflows, requiring no change in customer behavior or retraining of staff.

PITT Ohio deployed N@TE in 2025, and the company also secured a patent for the product that year. N@TE has produced a 30-60X increase in processing speed, 99% accuracy in extracting and populating order data, and a 70% reduction in handling costs per pickup order.

SMU builds AI adoption through grassroots ambassador program

Organization: Southern Methodist University

Project: Scaling AI Without Scaling AI: Organizational AI Scaling Through Willingness

IT leader: Jason Warner, Associate CIO

Like executives in most organizations, leaders at Southern Methodist University encountered mixed attitudes about AI. Some workers had little interest in using the tech, others were afraid it would take jobs, still others were curious about what it could do.

Associate CIO Jason Warner and other leaders decided to leverage that last group, believing the best way to get SMU faculty and staff to embrace AI was to use enthusiasts to help smooth the way.

So, instead of treating AI as a conventional technology rollout, Warner and his colleagues built opt-in communities of practice known as the AI Coalition of the Willing and Operation Copilot.

The goal, Warner says, was to build institutional capability, reduce risk, and generate momentum.

“We knew the fastest way to scale AI was to scale the willingness of people to use the technology, and not talking to people about cost savings and the like,” Warner says, adding that willing users as great ambassadors and evangelists who showcase in formal and informal ways the technology’s potential for hesitant or skeptical colleagues.

Participating faculty members have access to a licensed ChatGPT account as long as they use it. Staff members have access to Copilot accounts after taking a self-paced training course and likewise must use it to keep that access.

Warner says these willing workers are demonstrating the benefits of AI (significant time reclamation, reduced cognitive load, improved quality of outputs, expanded professional capacity).

SMU is now moving to a single solution and scaling AI, confident that its use will deliver returns following in the footsteps of the early adopters.

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Too often, brands treat compliance as a downstream exercise. Teams build products, launch new capabilities and then tack on controls afterward.

The pace of technology evolution and adoption has never been faster, and regulatory bodies are doing their best to keep up. For brands, that means they’re standing on shifting ground. They need  to modernize legacy infrastructure, adopt AI responsibly, deliver better customer experiences, maintain trust and navigate increasingly complex regulatory requirements – all at once.

I’ve witnessed this shift firsthand in payments. Fraudsters adapt faster than regulatory cycles, and customer expectations continue to rise regardless of where legislation stands. In one of the most highly regulated sectors, waiting for new mandates to arrive is a losing strategy.

The brands that lead have embraced regulatory readiness as an advantage to better inform technology architecture, operating models and partner strategy.

If I had one piece of advice for CIOs, it would be to treat compliance as part of the blueprint instead of the punch list at the end of a build. With a controls-by-design approach, a collaborative culture, and the right partnerships, any brand can embrace change with confidence and resilience.

3 ways regulatory readiness is a competitive advantage

1. Build a solid foundation

One of the most impactful strategies I’ve seen is the shift from compliance-after-the-fact to controls-by-design.

Forward-thinking financial institutions increasingly treat regulatory frameworks like DORA and the EU AI Act as design principles rather than external requirements. Instead of asking how to retrofit compliance into modern systems, they are asking how thoughtful governance can shape modernization from day one.

For example, the EU AI Act mandates transparency for high-risk AI systems like automated credit scoring. Instead of burying disclosures in the fine print, a smart bank builds an interactive feature directly into its digital banking app, which allows customers to simulate how adjustments will improve their approval odds. By doing so, they transform a regulatory obligation into innovation that builds trust.

After all, when an AI-driven decision fails, customers do not blame the algorithm. They blame the brand. The controls-by-design approach helps ensure those risks are anticipated and managed before they reach the customer.

This feels particularly urgent in the payments industry, where FedNow and stablecoins allow funds to move instantly – and irrevocably. As settlement windows shrink from days to seconds, brands need to embed capabilities like behavioral monitoring, AI-driven fraud detection, account verification and orchestration functionality directly into the transaction architecture itself – as part of the initial design – to identify and mitigate fraudulent activity as it evolves. Regulation, like Nacha’s new rules around ACH fraud, reinforces that direction, but for trust-focused brands, the work begins long before the rules change.

Each of these examples points to the same trend. Brands that embrace a controls-by-design philosophy are constructing technology architectures that are ready to adapt long before the inspectors arrive on site.

2. Align your crew

Technology architecture is only half of the story. The other half is how well your crew works together to bring that architecture to life.

For years, compliance lived in its own lane. Governance acted like a checkpoint. When technology evolved in predictable cycles, that made sense. But today, the brands making the greatest progress build shared accountability into their operating models so they can adapt to regulation in a more coordinated, consistent way.

After all, a construction project is only successful when electricians, plumbers, framers and masons coordinate every step and trust the work happening around them.

The same is true in the enterprise. Instead of focusing on separate priorities, product, engineering, operations, risk and compliance must align around shared outcomes, with greater transparency into how decisions are made, ongoing oversight and continuous feedback loops between teams. As a result, regulatory readiness becomes part of how the business works every day, change becomes easier and the broader benefits across the organization become clear.

In many organizations, I’ve observed how harmony between teams not only increases compliance but also fosters greater customer-centric innovation. When teams operate from a shared, real-time view of the customer, every interaction becomes more connected. Customers experience one brand, not a collection of disconnected teams.

That spirit of collaboration becomes even more important as AI moves deeper into customer-facing and operational workflows. AI innovation has outpaced AI regulation, which makes it even more important for brands to take the initiative to ensure proper controls are in place.

We are already seeing this play out with SR 26-2, the Federal Reserve’s latest guidance on AI for banks. While it establishes important expectations around model risk management, it leaves room for institutions to determine how agentic AI and generative AI should be governed. Instead of treating this as carte blanche, banking leaders should see this as an opportunity to build trust. By leading the way with governed, responsible GenAI and agentic AI operating models, banks can win customers’ trust long before regulation requires it.

No single department should shoulder that responsibility alone. Product teams understand how AI shapes the customer experience. Engineering teams understand how models are built, deployed and monitored. Risk and compliance teams understand governance expectations, while operations teams see how those decisions play out every day. Effective AI governance and innovation emerge when those perspectives come together around a shared view of accountability.

3. Expand your toolkit

Innovation in today’s regulatory environment requires more tools than you may have in your own toolkit.

Technology is more complex, fraud threats evolve faster and AI capabilities require significant investment and ongoing tuning. At the same time, brands have to stay ahead of customer expectations, market dynamics and evolving risk requirements.

It just doesn’t make sense to build every capability yourself when trust, resilience, compliance and speed-to-value are such integral parts of the equation. 

Throughout my career, I’ve seen success with a build-buy-partner approach that brings together the right tools for the right project.

This is particularly important in highly regulated environments, where implementation risk can be as significant as technical risk. That’s where proven results – especially through partnership – might take precedence over experimentation.

I went through this consideration just recently. CSG Forte partnered with IBM to launch PaymentsProtection.ai.

We set out to provide customers with AI-powered fraud detection and financial risk management without spending years recreating capabilities that already existed. By partnering with IBM, we were able to access additional specialty tools: AI capabilities, real-time monitoring, financial risk management expertise and external validation in one of the most sensitive areas of payments. The collaboration reduced fraud losses by 50-70%, lowered false positives and offered customers a smoother, safer experience.

In a market that never stands still, the right tools give brands the freedom to build with greater precision, adaptability and purpose.

Raise the standard

Successful brands are changing how they think about regulation. Instead of looking at it as a burden or a constraint on innovation, they are treating it like a key factor in architectural decisions, crew alignment and partner strategy.

That approach increasingly separates the brands raising the standard from those struggling to keep up. It changes the role regulation plays within the business. It infuses trust, governance and adaptability into a brand’s foundation.

Those capabilities make it easier to scale new builds, navigate future change and innovate with confidence as markets, customer expectations and regulatory requirements continue to charge ahead.

The brands shaping the future won’t be scrambling to reinforce the structure after the cracks appear. They’ll be the ones that construct resilience from the very beginning.

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