On the ground, AI is already delivering. Engineers are shipping code faster, customer service teams are resolving tickets in half the time, and operations teams are automating workflows that used to require approval from three departments. Workers equipped with AI tools are operating at speeds and scales that would have seemed impossible two years ago.
At the top, it’s a different story. The same C-suites championing AI transformation in earnings calls are the ones slowing it down in practice. Sequential sign-offs. Functional silos. Decisions that get reopened after they’ve been settled. Leadership teams designed for a slower, more predictable era are now the primary constraint on the transformation they claim to be leading.
Here’s the uncomfortable truth: AI can’t fix a broken C-suite running on an antiquated operating system.
Why Most C-Suites Can’t Get Out of Their Own Way
In the Conference Board’s 2026 annual leadership survey, CEOs ranked investing in AI and building AI expertise as their top priorities. Boards are demanding efficiency gains. Investors reward headcount reductions tied to automation. But while the pressure to move increases, most leadership teams are stuck answering the wrong questions.
Some are treating AI as a functional project — a tech deployment led by a transformation office. Others frame it as a change management exercise run by the Chief People Officer, narrowing the conversation to talent acquisition and reskilling.
Each of these approaches produces real gains. Pilots can generate efficiency and can even be built out at scale. Transformation offices can attract great talent and provide governance and oversight. Change management discussions surface important workforce questions.
But none of this addresses the real bottleneck: the C-suite itself. A transformation office cannot, on its own, change how the executive team makes decisions, shares information, resolves trade-offs, or holds itself accountable. And transformation won’t succeed if it starts from a single operational function under one executive.
Boardroom conversations sound like transformation. Execution looks like incremental optimization: doing the same things faster, with fewer people, at marginally lower cost. That’s what gets measured — and that’s what analysts reward in earnings calls.
The actual bottleneck isn’t the technology. It’s the leadership team that refuses to operate any differently than it did five years ago.
The Dysfunction Most Leaders Won’t Name
“Every member of the C-suite now needs to be able to think and operate at the enterprise level, not just as the leader of a BU or function. But most leadership teams were not built or trained that way. If the operating model at the top does not evolve to support faster, more integrated enterprise decision-making, the market, the technology, or the organization will eventually force the issue.”
— Carolyn Dewar, co-author, A CEO for All Seasons: Mastering the Cycles of Leadership
The traditional C-suite model was designed for a different era. Reporting structures were vertical. Each executive owned a lane. Contribution to enterprise decisions was filtered through a functional lens. Executive meetings were primarily information exchanges: here’s what’s happening in my domain. The purpose was coordination and consensus.
This worked well when change moved slowly enough to stay within functional boundaries. AI doesn’t do that. AI-led change flows horizontally, touching every function simultaneously. A decision about how AI will process supplier invoices is also a decision about finance controls, legal liability, HR roles, and operational continuity. When that decision requires Sign-off A, then Sign-off B, then Sign-off C in sequence, decision velocity dies before the meeting has even started.
And there’s a deeper problem: most leaders know this and aren’t changing their behavior anyway. Leaders have committed to AI-driven transformation timelines and efficiency targets in shareholder conversations. Many haven’t thought through what honoring those commitments requires of them personally — what it demands of how they make decisions, spend their own time, share information, and hold each other accountable. The assumption, often implicit, is that it will happen downstream. That the organization will figure it out.
That won’t happen. Without a new leadership model at the top, the organization will not figure it out.
Three Failure Modes — And What Actually Works
Moving from a siloed leadership model to an integrated one requires more than better collaboration. What’s needed is a new architecture — both behavioral and structural — built for horizontal, high-velocity decision-making. Here are the three failure modes holding most C-suites back, and what works instead.
Failure Mode 1: Optimizing for Functions, Not the Enterprise
When AI transformation touches the entire enterprise, the decisions driving it can’t be left to any single function to lead. Yet that’s exactly what happens when leaders default to protecting their own domains.
Consider a CTO who wants to fundamentally reimagine software engineering: not just adding AI tools to the existing workflow but redesigning the roles and the work itself. Everything from how engineers are hired and assessed, to how they collaborate, to what ‘output’ even means when AI is generating a significant share of the code. That redesign requires HR to co-design the workforce model, rethink career progression frameworks, and adjust how performance is measured. It also requires Finance to model the cost implications of a fundamentally different talent profile.
Or take a ubiquitous workflow like Quote-to-Cash. This process takes a customer from initial proposal through contracting, fulfillment, invoicing, and revenue recognition. In most organizations, this passes through Commercial, Legal, Finance, and Operations in sequence, each adding a layer of review, adjustment, and approval. AI can compress that sequence dramatically: intelligent routing, automated generation of standard contract terms, real-time risk flagging.
But fully transforming how we work requires every one of those functions to redesign simultaneously — not in isolated sequence, but together, with a shared view of the trade-offs.
Change of this scale requires the leadership team to align on what they’re willing to let go of in the current structure to make room for the new one. If any of those functions design in isolation, the result is a patchwork, not a transformation.
What works instead: Enterprise judgment.
One approach: a ‘Rotation of Perspectives’ model where leaders are explicitly tasked with stress-testing their peers’ cross-functional proposals from the vantage point of the whole enterprise, not from their own functional seat. Over time, this could build the muscle memory needed to make trade-offs that benefit the company over any individual department’s interests.
Failure Mode 2: Leading on Curated Data Instead of Reality
At scale, the information that reaches the C-suite is heavily curated. By the time a signal travels from the people doing the work to the people leading the organization, it’s been filtered, summarized, and optimized at every layer in between. Leaders hear about the AI pilot that’s working. They hear less about the one that quietly failed. They get headline metrics from the leader responsible for rolling out the change, but they may not hear about a team member who invented a workaround that improved on it.
The result: leadership makes decisions based on managed narratives, not ground truth.
Some of the most effective leaders address this by going directly to the source. Not waiting for the quarterly business review, but spending time — deliberately, structurally — on the front lines of the processes where AI is being applied. The goal is for leadership to have first-hand knowledge of what is possible, to fuel good decision making grounded in real facts.
What works instead: Unfiltered visibility.
One option: C-suite leaders engaging in rotating deep-dives with team-level AI task forces. These resemble reverse-mentoring programs that pair executives with early-career employees at the frontier of AI adoption.
The hyperscaler answer to addressing the visibility challenge has been to reposition the CEO with 30-60 direct reports. This challenges our traditional view of the CEO’s role, turning it into the single, central point of integration — an approach only viable in a small handful of the most powerful tech companies.
One critical prerequisite: none of these approaches generate useful intelligence if employees believe their candor will be used against them. If the ground-level read is that AI adoption is primarily about role elimination, the feedback mechanism will produce carefully managed responses. Trust in the organization’s intent has to come first.
Failure Mode 3: Consensus as a Shield, Not a Strategy
This is the hardest failure mode. It’s behavioral rather than structural, and its absence is the single most common reason well-designed AI transformations are stalling at the execution layer.
In a high-velocity environment, trust is an operational requirement. Where it’s thin, second-guessing becomes the norm. Back-channeling replaces direct escalation. Consensus replaces ownership. Settled decisions get reopened. Each of these behaviors adds friction, slows decision cycles, and signals to the broader organization that it isn’t safe to move decisively.
Before designing structural solutions, it’s worth asking a more precise diagnostic question: when trust breaks down in a C-suite, what exactly is the breakdown?
“When trust breaks down between senior leaders, it is worth asking whether the issue is competence or intent. Are you worried that a peer cannot deliver because they lack the capability, capacity, or team around them? Or are you worried that they will not optimize for the enterprise because their incentives or priorities point somewhere else? Those are very different problems, and distinguishing between them is often the first step to unlocking the team.”
— Carolyn Dewar, co-author, A CEO for All Seasons: Mastering the Cycles of Leadership
What works instead: Radical trust — built on explicit decision rights.
The structural answer to both failure modes is explicit decision rights. Rather than seeking consensus-based alignment from every functional leader on every initiative, shift to a single named decision-maker for each domain. Peers are consulted, and their input is genuinely incorporated, but they don’t hold veto rights. One person owns the final call.
This removes what I call the ‘alignment tax’: the time, energy, and goodwill consumed by relitigating decisions that were effectively done but never formally closed. It also creates genuine accountability. When one person owns the call, there’s no ambiguity about where the decision lives.
Leadership teams also need protected time for this. One of the underappreciated costs of the current environment is that C-suites are already at capacity. Creating deliberate space for decisions that span functional lines is a requirement — not a luxury.
The Human Cost — And the Honest Question Leaders Should Ask
The senior leaders under pressure to lead through this transformation are the same leaders who navigated a global pandemic, managed through years of supply chain disruption, absorbed (and continue to absorb) geopolitical shocks, led their organizations through social reckonings, and, in most cases, multiple restructurings. For many, this is the fifth or sixth major transformation challenge in as many years, with little time to recover between them.
We’re already seeing the weight of this. Many senior leaders are quietly asking themselves whether they have the energy to lead through this next chapter. Some of the most celebrated executives in the world have chosen this moment to step away. That kind of honest self-assessment, done before committing to lead the next chapter, is necessary.
The most effective C-suites will be the ones where leaders honestly audit their own capacity: their energy, their appetite for personal growth. The kind of personal commitment it will take to genuinely operate differently needs to match the magnitude of the ask they are making of their employees. If a leader is driving transformation by mandate while privately protecting their own functional territory, the organization will sense it.
The Real Question
The C-suite of the next decade won’t be defined by the technology it buys or the efficiency gains it reports. It will be defined by whether leadership teams can actually operate at the speed their organizations require — or whether they become the constraint that holds everything back.
Enterprise judgment, unfiltered visibility, and radical trust are interdependent. Without enterprise judgment, decisions optimize for functions rather than the whole. Without unfiltered visibility, those decisions get made on assumptions and curated data. Without radical trust, neither judgment nor visibility gets acted on at the speed the environment demands.
Start with the hard question that most C-suites are avoiding: What exactly are we designing for? Take an honest audit of your current operating system. Map where decisions live and how long they take. Look at where your information comes from and ask whether it represents reality or a managed version of it. Examine your own behavior — not just your direct reports’ — and your own energy level to take this on. Ask yourself whether the leadership team you currently have is designed to work together at the speed and scale that AI transformation demands.
The leadership team needs to upgrade because virtually nothing in most executives’ careers has prepared them for this moment. The companies that will lead in the AI era won’t be the ones with the best technology or the biggest budgets. They’ll be the ones whose leadership teams adapted to the speed of AI — or were replaced by leaders who could.
The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.
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