What Would CEOs Let AI Decide Tomorrow?
Today, the question isn’t whether AI can make executive decisions. It’s about which decisions CEOs should always make themselves.
News
- AI Vendor Lock-In Emerges as the Next Enterprise Risk in the UAE: IBM Study
- Meta Sued for Allegedly Using AI to Lay Off Employees on Leave
- Hundreds of AI Experts Urge Governments to Prepare for AI-Driven Economic Disruption
- Saudi Arabia Adds Agentic AI to Its National Data Platform
- SoftBank CEO Dismisses AI Bubble Fears, Predicts $5T Annual Investment
- Nadella Warns of AI’s Reverse Information Paradox
[Image: Chetan Jha/MITSMR Middle East]
Key Takeaways
01
As AI automates analysis and coordination, CEOs are spending less time gathering information and more time exercising judgment on which decisions should remain human.
02
Research suggests that while AI improves decision speed, leaders face growing pressure to validate AI-generated insights and avoid overreliance on confident but flawed outputs.
03
Successful adoption requires CEOs to rethink workflows, decision rights, incentives, and accountability.
04
AI can accelerate execution, but defining strategy, navigating uncertainty, building trust, and communicating purpose remain fundamentally human responsibilities.
Earlier this year, reports indicated that Meta CEO Mark Zuckerberg was developing AI assistants both to support his own work and to enable employees to interact with an AI version of him. Whether symbolic or practical, the idea reflects a broader question many corporate leaders are beginning to consider: Can an AI executive extend the reach of leadership?
Technology leaders have been increasingly candid about AI’s implications for executive work. Google’s Sundar Pichai has suggested that many CEO responsibilities could eventually be automated. OpenAI’s Sam Altman has said AI may one day do his job better than he can. Klarna CEO Sebastian Siemiatkowski has argued that AI can perform every role within the company—including his own.
The question, however, is not whether AI will replace CEOs. It is whether it will redefine what leadership looks like. As organizations embed AI into strategy, operations, and decision-making, the role of the chief executive is shifting from being the organization’s primary decision-maker to becoming the architect of decision-making.
AI Changes What CEOs Optimize
For decades, CEOs have operated under a familiar constraint: time. Gathering information, aligning teams, and weighing strategic options consumed much of an executive’s attention. AI is beginning to remove many of those bottlenecks.
“AI is changing the texture of the job more than the fundamental nature of it,” says Janine Seebeck, CEO of BeyondTrust. “I’m still making the same kinds of decisions—on people, strategy, customers, and culture—but the inputs are better and faster. As a result, I’m spending less time waiting for information and more time stress-testing my own thinking.”
That distinction is significant. AI is not eliminating executive judgment; it is compressing the time required to reach it. The technology is allowing leaders to spend less effort collecting information and more effort evaluating competing scenarios, testing assumptions, and considering second-order consequences.
“I’m still making the same kinds of decisions—on people, strategy, customers, and culture—but the inputs are better and faster. As a result, I’m spending less time waiting for information and more time stress-testing my own thinking.”
— Janine Seebeck, CEO of BeyondTrust
For Benny Czarny, CEO of OPSWAT, AI has fundamentally expanded the CEO’s responsibility. Rather than delegating AI transformation to technical teams, he argues that executives must understand the technology well enough to shape its deployment.
“The CEO role is no longer only about setting the vision,” he says. “You need to get into the details—understand the tools, the risks, the costs, the privacy implications, and the security trade-offs. AI transformation cannot be managed only through inspiration.”
The CEO is no longer simply responsible for approving technology investments. Increasingly, they must redesign operating models around AI, deciding which decisions remain human, which become automated, and where oversight is essential.
Jessica Apotheker, Managing Director and Senior Partner at Boston Consulting Group, says that many companies remain trapped by incremental thinking. The choice facing leaders, she adds, is whether AI becomes another efficiency initiative or the foundation for reinventing the business itself.
This is the challenge many organizations are facing today: a gap between using AI to improve existing workflows and redesigning how work gets done entirely.
A Need For Better Judgment
AI promises faster analysis, more comprehensive insights, and better forecasts. Yet those same capabilities introduce a new leadership challenge: distinguishing confidence from correctness.
The pressure on CEOs is no longer limited to delivering AI strategies—it increasingly extends to their own tenure. Dataiku’s 2026 CEO Confessions Study, which surveyed business leaders across major global markets, found that nearly four out of five CEOs believe their own position could be at risk if their organizations fail to generate measurable business value from AI by the end of 2026.
More strikingly, more than half expect successful AI execution to become one of the primary criteria boards use when selecting future chief executives. The findings suggest that AI has crossed an important threshold. It is no longer viewed as another enterprise technology initiative; it has become a proxy for executive competence. Boards are beginning to judge CEOs not by whether they adopt AI, but by whether they can translate AI investments into measurable outcomes.
Leadership expectations are changing because AI has become an enterprise capability rather than a technology initiative. Boards increasingly expect CEOs not only to champion AI but to demonstrate its return.
At the same time, confidence in AI often exceeds organizational readiness.
The optimism surrounding AI also masks a growing execution gap. Cisco’s global CEO research found that while leaders overwhelmingly believe AI will reshape their organizations, many acknowledge that their companies lack the infrastructure, talent, and governance needed to scale it effectively.
Infrastructure modernization emerged as one of executives’ highest priorities, reflecting a broader realization that AI transformation depends as much on organizational capability as it does on model performance.
“One of the biggest issues right now is that AI confidence is being mistaken for accuracy.”
— Benny Czarny, CEO of OPSWAT
Cisco’s companion AI Readiness Index reinforces this disconnect, finding that relatively few organizations believe their networks and systems are fully prepared to support enterprise-scale AI workloads. The implication is straightforward: many organizations are attempting to build AI-enabled businesses on foundations designed for an earlier generation of technology. That mismatch creates operational risk long before it creates competitive advantage.
The technology may be advancing fast, but many organizations don’t have the basics in place to take full advantage of it.
But the bigger challenge is judgment.
“One of the biggest issues right now is that AI confidence is being mistaken for accuracy,” says Czarny. “AI systems can produce answers that sound polished, logical, and complete, but that does not mean the answer is correct.”
That risk becomes particularly acute in executive decision-making, where AI-generated analyses often arrive fully formatted, well-written, and seemingly authoritative. When underlying data is incomplete, biased, outdated, or poorly governed, executives may find themselves making high-stakes decisions based on convincing—but flawed—recommendations.
Evidence suggests that AI can also subtly alter managerial behavior. Research by Emma Wiles of Boston University and collaborators from Boston Consulting Group examined how managers evaluated work produced by AI “employees.”
They found that managers reviewed documents less carefully when they believed the work had been generated by AI rather than a human colleague. Rather than increasing vigilance, AI appeared to reduce it. The researchers argue that anthropomorphizing AI systems can create ambiguity around accountability, encouraging managers to assume that responsibility for errors lies elsewhere. As AI becomes embedded in knowledge work, the greater risk may not be overreliance on algorithms alone, but the gradual erosion of managerial oversight.
The implication extends beyond technology. As AI assumes more cognitive work, leaders must become more disciplined—not less—in validating assumptions and questioning outputs.
“I want executives to use AI aggressively,” says Czarny. “But I do not want blind dependency.”
Organization as a Design Practice
AI is poised to reshape leadership, not by replacing CEOs, but by changing how organizations work together.
Increasingly, AI systems are making recommendations in areas such as pricing, hiring, forecasting, customer engagement, and capital allocation. Agentic AI extends this further by executing tasks with limited human intervention.
As decision-making becomes more decentralized, the role of the CEO shifts from making every critical decision to designing the systems that guide decision-making. This transformation requires as much authority to govern as it does the right technology.
“The hard-won parts of leadership—credibility, trust, and the sense that people know where you stand—aren’t things you can automate,” says Seebeck. “If anything, AI raises the bar for authentic communication.”
She says AI should remove friction from leadership, not replace it. Drafting communications, summarizing reports, or preparing briefings might get much faster. But the judgment, accountability, and values behind those messages must remain human. “The substance—the point of view, the acknowledgment of difficult realities, and the human connection—still have to come from you.”
Czarny frames the challenge differently. Within every organization, he says, there are “AI saints” and “AI vampires.” The former experiment responsibly, improves productivity, and respects governance. The latter pursue rapid deployment without understanding security risks, costs, or operational consequences. Even well-intentioned experimentation can expose organizations to significant vulnerabilities if governance fails to keep pace with adoption.
The task for CEOs, therefore, is not simply to accelerate AI adoption but to ensure that the organization develops the institutional discipline to use it responsibly.
That may require rethinking executive structures altogether. As AI becomes embedded across business functions, organizations are beginning to establish dedicated leadership roles responsible for enterprise-wide AI strategy, governance, and workforce transformation. These leaders, often titled Chief AI Officer, are taking on the role of orchestrators of organizational change rather than just managing technology.
More broadly, CEOs must redesign incentives, decision rights, and workflows so that human expertise and AI capabilities complement rather than compete with one another.
Broaden The Horizon
The conversation around AI often centers on automation and cost reduction. That framing is too narrow.
“For me, AI is not only about increasing the bottom line,” says Czarny. “The bigger opportunity is asking how AI helps us accelerate our roadmap, improve execution, reduce friction, and prepare the organization for the next level.”
Ultimately, AI will not determine which organizations outperform. Leaders of organizations that create lasting advantage will be those who redesign decision-making, invest in governance alongside technology, and cultivate employee habits that enable judgment in an environment where information is abundant but certainty remains scarce.
“The tool doesn’t determine the outcome. The intention behind it does. And our job, as leaders, is to stay relentlessly curious—not just well-briefed.”
— Janine Seebeck, CEO of BeyondTrust
AI can accelerate tasks at an unprecedented scale. But it cannot determine what an organization should value, which risks are worth taking, or what future is worth building. It can be a force, but not the movement itself. Those remain fundamentally human decisions.
Ironically, as AI gets better, good judgment from leaders—not just their authority—may become the rarest and most valuable thing in business.
“The tool doesn’t determine the outcome,” Seebeck says. “The intention behind it does. And our job, as leaders, is to stay relentlessly curious—not just well-briefed.”
What CEOs Should Do |
| Redesign decision-making | Decide which decisions should stay human and which AI can automate. |
| Prioritize judgment | Validate AI outputs instead of relying on confident-looking answers. |
| Build AI Governance | Embed accountability, security, and oversight into AI deployment. |
| Rethink the operating model | Redesign workflows and incentives around human-AI collaboration. |
| Lead transformation, not adoption | Focus on creating new value with AI—not just improving efficiency. |
RESEARCH CONTEXT
This article is based on interviews with:
- Janine Seebeck, CEO, BeyondTrust
- Benny Czarny, Founder and CEO, OPSWAT
- Jessica Apotheker, Managing Director & Senior Partner, Boston Consulting Group (BCG)
Additional reporting draws on Dataiku’s 2026 CEO Confessions Study, which found that nearly four out of five CEOs believe their jobs could be at risk if they fail to deliver measurable AI outcomes. It also references Cisco’s Global CEO Study and AI Readiness Index, which reveal a growing gap between executive optimism about AI and organizations’ readiness in infrastructure, talent, and governance. Additionally, research by Boston University and Boston Consulting Group found that managers scrutinize AI-generated work less rigorously than human-produced work, highlighting the risk of overreliance on AI and the growing importance of human oversight.
