Krishna Says AI Is the Business Model. He's Right.
Image: IBM CEO Arvind Krishna, Depositphotos
Source: Rajeev Ronanki
IBM CEO Arvind Krishna recently made a point in the Wall Street Journal that is worth taking seriously.
“AI is not helping your business. It is your business model.”
He’s right, because most conversations about AI are still happening at one level below what he’s describing. We talk about copilots, pilots, productivity gains, and automation. Krishna is talking about something much bigger. AI isn’t becoming another capability that companies can simply add to the business. It’s becoming part of how the business operates.
The IBM CEO Study, released earlier this month and based on responses from 2,000 CEOs across 30 industries, gives that argument real weight. My first reaction was that Krishna is directionally right.
My second reaction was that “AI operating model” is about to become one of those phrases everyone agrees is important, then spends years defining instead of building. Companies will create playbooks, governance structures, and executive roles around it, but none of those things matter unless they change how decisions move through the company.
The numbers in the study reinforce that point. Seventy-six percent of organizations now have a Chief AI Officer, up from 26% just a year ago. That doesn’t necessarily mean companies have figured out what that role should do. In many cases, it feels more like a signal that AI matters than evidence that something fundamental has changed. At the same time, 79% of executives say they’re decentralizing decision making as AI becomes more involved, and 64% say they’re comfortable making major strategic decisions based on AI-generated input. By 2030, CEOs expect AI to make nearly half of all operational decisions without human intervention. Today, that number is about 25%.
Then there’s another statistic that stood out to me. Only 25% of employees regularly use AI, while 86% of CEOs believe their workforce already has the skills to do so. I don’t think that’s mainly a training problem. It’s a coordination problem. Companies are producing more intelligence than they know how to use. New models are deployed, new leadership roles are created, but the way decisions move through the company hasn’t changed enough to consistently turn those outputs into action.
That’s why I keep coming back to the same idea. Every operating model, whether it was industrial, digital, or now AI, is really about deciding where attention goes. We usually explain operating models in terms of people, capital, or technology, but underneath all of that is something simpler. What gets noticed? What gets escalated? Which signals reach someone who can make a decision before they lose value? Those questions shape how a company actually works.
It also explains why so many AI initiatives stall. In many companies, the models aren’t the limiting factor anymore. They’re already producing useful outputs every day. What breaks down is everything that happens afterward. A recommendation gets surfaced, a risk gets flagged, or an anomaly gets detected, but nothing happens. Not because the model failed, but because the flow of decisions was never redesigned for the speed and volume AI now creates.
Adding a Chief AI Officer doesn’t solve that problem. Neither does moving boxes around on an org chart. Even the goal of automating 48% of operational decisions by 2030 assumes something many companies haven’t built yet. It assumes there’s already a reliable way to decide which signals deserve attention, where they should go, and who should act on them.
The IBM study also includes an observation from Gary Cohn that decision cycles will continue to get shorter while traditional boundaries between teams begin to disappear. I think he’s right, but it raises another question. As decisions happen faster, what determines which signals actually move through a company? What makes sure the right information reaches the right person before the opportunity to act disappears? That isn’t primarily a technology question. It’s a question of design.
Without an answer to that question, faster decision cycles simply mean the same overwhelmed people receive more information in less time. Speed doesn’t solve the coordination problem. It makes it more visible. The challenge isn’t getting AI to produce more answers. It’s making sure people can consistently act on the answers that matter.
That’s where Systems of Attention comes in. It sits between AI outputs and the decisions people make. It isn’t another dashboard or another stream of alerts. It’s a way of deciding what needs action now, what should be monitored, what belongs with someone else, and what doesn’t require a person at all. Instead of asking people to pay attention to everything, it helps them focus on the few things that matter.
Most companies are still trying to use AI with operating models that were built for a different world. They assumed less information, slower decision cycles, and enough time for people to connect the dots themselves. That isn’t the world we’re in anymore. The challenge isn’t that people have become slower. It’s that the amount of information arriving every day has grown faster than the way companies make decisions.
This is where I think many companies will spend the next few years. They’ll keep refining definitions, adding new leadership roles, and introducing new governance models, while the harder work of redesigning how decisions move through the company happens much more slowly. AI operating models will only matter when they change how work actually gets done.
The difference between the 86% of CEOs who believe employees have AI skills and the 25% who use AI consistently isn’t mainly about skills. It’s about how work is organized. AI still feels optional because most companies haven’t changed the way decisions are made. Technology is moving quickly. The organization around it is not.
Krishna is right that AI becomes part of the business itself. But that only works if the business knows what to do with what the model produces. That’s where many companies are still running into the same problem. The challenge isn’t generating more intelligence. It’s making sure the right information reaches the right people while there’s still time to do something with it.
Organizations have always had to decide which information deserves attention and which doesn’t. AI didn’t create that challenge. It simply increased speed and volume until the old ways of managing information stopped working.
The companies that pull ahead over the next several years won’t necessarily be the ones with the best models. They’ll be the ones that redesign how decisions move, how attention is directed, and how AI outputs become action. That’s the operating model conversation worth having.
Rajeev Ronanki
Rajeev Ronanki writes about AI adoption, organizational design, and how companies turn AI outputs into decisions.
Rajeev continues to reimagine the future of healthcare by harnessing the power of AI and data to provide consumers with predictive, proactive, and personalized insights at the intersection of healthcare supply and demand. His experience spans over 25 years of innovation-driven industry and social change across healthcare and technology, and he regularly speaks on topics related to navigating the future of healthcare, harnessing data-driven insights, and delivering personalized experiences. In November 2021, Rajeev released “You and AI: A Citizen’s Guide to AI, Blockchain, and Puzzling Together the Future of Healthcare,” which has become an Amazon Best Seller.
When he served as the President of Carelon Digital Platforms, Rajeev led efforts to transform Elevance Health into a digital platform for health and wellbeing. He and his leadership team collaborated with internal and external partners to expand virtual care, create a longitudinal patient record to improve care and reduce overall administrative burden, deploy AI to increase auto-adjudication of claims and expedite manual claims review processes, modernize the provider data lifecycle into a single source of truth, and pilot innovative solutions to transform the way consumers interact within the healthcare ecosystem.
Before Elevance Health, Rajeev was a partner at Deloitte Consulting, LLP, where he established and led Deloitte’s life sciences and healthcare advanced analytics, artificial intelligence, and innovation practices. Additionally, he was instrumental in shaping Deloitte’s blockchain and cryptocurrency solutions and authored pieces on various exponential technology topics such as artificial intelligence, blockchain, and precision medicine.
Rajeev obtained a bachelor’s degree in mechanical engineering from Osmania University in India and a master’s degree in computer science from the University of Pennsylvania.