Why 168 S&P 1500 companies changed CEOs in 2025, what the new leadership profile looks like, and how enterprises should redesign for an AI-first operating model.
April 30 2026 | Chris Ruch, CEO
The biggest leadership story in business right now is that AI is changing what the CEO job requires — to the extent that long-standing CEOs are stepping aside. And if the CEO’s job is changing, so is everyone else’s.
On December 10, 2025, Coca-Cola announced that Henrique Braun would succeed James Quincey as CEO effective March 31, 2026. A month later, Coca-Cola created a Chief Digital Officer role to unify digital, data, and operational excellence and accelerate technology adoption across the enterprise. Walmart made a parallel move on November 14, 2025, naming John Furner to succeed Doug McMillon effective February 1, 2026, and by January 2026 Walmart said its leadership changes were meant to fuel innovation and drive a new era of retail. This week, Apple announced Tim Cook’s retirement.
This is not a story about CEOs walking away. It is a story about boards recognizing that the next wave of growth requires a different kind of leadership. In James Quincey’s own words, his board decided it was time to “put someone else on the field for the next wave of growth.” Walmart’s framing points the same direction: John Furner’s leadership profile is explicitly tied to building a new era of retail fueled by innovation and AI across the enterprise.
Boardrooms are responding to that shift more broadly. Of the S&P 1500 companies, 168 appointed new CEOs in 2025 — the highest number since 2010, according to Spencer Stuart. Increasingly, boards have less patience for leaders who are slow to transform their organizations for an AI-centric future.
Why AI Changes the CEO Job
The reason is simple. AI is not another technology upgrade. It is driving change throughout the business — enhancing how every worker does their job, automating business processes, becoming embedded in products, and ultimately reshaping the business model itself.
That is why the last pre-AI wave of digital transformation is no longer enough. Large scale agile transformations, the product operating model, and digital modernization helped companies optimize in the pre-AI world: better software delivery, stronger customer focus, more iterative execution. But consumer expectations are shifting toward experiences that are contextual, conversational, personalized, and predictive. Walmart has publicly said its OpenAI partnership is meant to create AI-first shopping experiences where customers can shop by chatting. Coca-Cola committed $1.1 billion to Microsoft Cloud and generative AI as part of its ongoing technology transformation.
The next wave is not digital transformation with a few AI pilots bolted on. It is an operating model reset across business operations, product development, and corporate IT.
What an AI Operating Model Reset Actually Requires
Agile Rising’s AI implementation roadmap makes that point explicit. AI has to be embedded across nine essential elements of how the enterprise works:
- Enterprise strategy
- Decision rights and governance
- AI-native workflows
- Value streams and operational domains
- Team Topologies
- Data and platform foundations
- Product development
- IT as a strategic enabler
- Talent and change leadership
No single one of those is optional. The organizations that win with AI will be the ones that redesign all of them to work together – not the one with the largest pilot budget.
Why Some CEOs Are Choosing to Step Aside Now
This is why a new leadership profile is emerging at the top. The next phase is not about squeezing more value out of the old operating model. It is about leading a multi-year reinvention of the enterprise.
As Quincey explained his retirement:
“As you go through the business cycle, there are these three-plus year cycles…There are waves of the organization momentum. We were coming up to another one. My job is to think about who’s the best team to put on the field to get the next wave done. And I concluded that it was time to put someone else on the field. Need someone with the energy to pursue a completely new transformation, which is not a one-year or two-year problem. It is a multi-year problem.”
The need for new skills, focus, and energy starts at the CEO level, but it flows through the entire organization. Today it’s the CEO role that needs to change. Tomorrow, it’s yours.
The Double Transformation Burden
For companies that have already completed a successful digital, agile, or product operating model transformation, AI is a significant undertaking and an urgent catalyst for reinvention.
For companies that never finished that work — or struggled to capture the benefits of earlier transformations — the challenge is bigger. They now face a double transformation burden: modernizing the organizational foundations and leaping into AI at the same time.
Either way, the work is the same in its shape. It means:
- Rethinking strategy so AI creates differentiated, not generic, value.
- Redesigning workflows so people and AI work together — instead of AI being a side tool attached to old processes.
- Changing team structures, decision rights, and platform capabilities.
- Building governance strong enough to scale AI safely without grinding the organization to a crawl.
- Responding to customers whose expectations are being reshaped in real time by AI-native experiences.
How Agile Rising Helps Leadership Teams Lead the Reset
Agile Rising is built for exactly this moment. The next wave of AI transformation is not a platform selection exercise. It is a blueprint for how the enterprise should operate in an AI-first world – connecting executive ambition to enterprise execution across business operations, product development, and IT.
Strategy and ambition. We help leadership teams produce a genuine AI ambition statement — not an AI wish list. A statement specific enough that a competitor reading it would learn something they could not have guessed. That means clarifying where AI creates differentiated value, which domains should become AI-native, what outcomes matter, and where human judgment must remain central.
Team design for AI-first execution. AI either bottlenecks when all expertise is centralized or fragments when every team improvises alone. The Team Topologies–based approach at the heart of our model resolves that tension through four deliberately designed team patterns: stream-aligned teams close to value, enabling teams that spread AI literacy and workflow redesign practice, platform teams that provide reusable AI capabilities and controls, and complicated-subsystem teams that own deep technical complexity behind clean interfaces.
Workflow redesign and change management. The real value of AI comes from redesigning workflows, not adding AI tools to old ones. We help leaders make the workflow pattern choices — human-in-the-loop, copilot, agent with escalation, or full automation — that fit the risk and outcome of each piece of work. And we help them build the adoption loops that make the change durable rather than performative.
Governance, platforms, and agent-ready infrastructure. Scaling AI safely requires governance built in from the start: risk tiers, human oversight rules, evaluation, observability, security, and clear escalation paths. It also requires platform capability — model access, retrieval, evaluation, governance tooling — built as shared services that teams consume through clean interfaces rather than rebuild locally. This is the backbone that lets organizations move beyond demo-stage copilots into agentic workflows that actually run the business.
An implementation roadmap, not just a framework. Our six-stage roadmap takes organizations from establishing the enterprise frame, through mapping domains and designing team interactions, through focused pilots and pattern standardization, into scaled operation. Each stage produces a concrete artifact — an AI ambition statement, an opportunity backlog, team charters, pilot reviews, a pattern library, an enterprise outcomes dashboard — that carries learning forward and keeps the transformation honest.
For companies already behind on digital or agile transformation, those same six stages address both jobs at once. The organizational foundations — clear ownership, flow of work, platform discipline, data and knowledge readiness, adoption capability — are the same foundations AI needs to scale.
The Real Leadership Test
The CEOs stepping aside today are not admitting failure. They are doing what strong leaders are supposed to do: recognizing that the next chapter requires a different operating cadence, a different leadership profile, and a different enterprise design.
The companies that win in the AI era will not be the ones with the most pilots, the loudest AI messaging, or the biggest experimentation budget. They will be the ones that redesign strategy, teams, governance, workflows, and platforms so that AI creates trusted, repeatable business value.
That is the transformation that Agile Rising is built to help you lead.
Ready to design your AI operating model?
Book a 30-minute working session with an Agile Rising partner and leave with:
- A first-pass view of where your organization sits on the six-stage roadmap
- The two or three highest-leverage moves for the next 90 days
- A candid read on whether you’re facing a single or a double transformation
Frequently asked questions
What is an AI operating model reset?
An AI operating model reset is the simultaneous redesign of strategy, team structures, decision rights, workflows, governance, data platforms, and talent practices so that AI is embedded in how the enterprise creates value — not bolted onto existing processes as a set of pilots.
Why did so many CEOs step down in 2025?
168 S&P 1500 companies appointed new CEOs in 2025, the highest number since 2010. Boards are increasingly concluding that the AI era requires a different leadership profile — one suited to multi-year enterprise reinvention rather than incremental optimization of the pre-AI operating model.
What is the “double transformation burden”?
It is the challenge facing companies that never completed their digital, agile, or product operating model transformation. They now have to modernize their organizational foundations and adopt AI as an operating model — at the same time.