Artificial intelligence is not another cycle of change. It is a structural shift in how organizations think, decide, and compete.
Every era has underestimated the leaders who moved early. From industrialization to digitization, the winners were not those who waited for certainty, but those who recognized inflection points and acted with intent. AI is such a moment—one that rewards decisiveness and penalizes hesitation. (View Highlight)
The CEO’s role has always been to lead through disruption. What AI changes is the velocity and consequence of leadership. Enterprises that succeed will operate AI-first—not as a layer of technology, but as a new operating model. Decision cycles will compress. Boundaries between functions will dissolve. Advantage will accrue to those who can learn, adapt, and execute faster than their competitors.
AI’s first dividend is productivity—freeing time, talent, and capital once consumed by friction. But productivity alone does not create advantage.
The real differentiator is how leaders redeploy that capacity. Growth will favor CEOs who reinvest aggressively, reimagine roles and workflows, and channel AI-driven insight toward new products, new markets, and new sources of value. (View Highlight)
This is where leadership becomes catalytic. CEOs are no longer just stewards of performance; they are architects of intelligence. As AI expands what organizations can see and know, leaders must decide what matters, align the enterprise around it, and move with speed and coherence. Strategy becomes continuous. Execution becomes inseparable from insight.
This is not reinvention for its own sake. It is evolution with purpose. AI does not replace sound leadership—it raises the standard. The shift is from managing activity to engineering outcomes; from protecting legacy advantage to building the next one. Organizations that embrace this mindset early will not just adopt AI—they will compound its impact over time.
History rewards leaders who recognize when the environment has changed and respond with focus, discipline, and intent. AI is not a distant promise.
It is here, it is moving fast, and it is redefining what effective leadership looks like. The future will belong to those prepared to lead at its speed. (View Highlight)
The C-suite could focus less on AI-powered productivity gains and more on business model transformation, led by executives who operate as a united front, not in siloed functions.
That’s why 2026 is the year CEOs must rewire the C-suite—redesigning how decisions are made, how authority is distributed, and how AI reshapes influence—while preserving the decisiveness and clarity enterprises need to move fast. Getting there takes proactive leadership. CEOs will need to work with their C-suite leaders to build execution mechanisms, incentives, and operating models all focused on driving these outcomes. (View Highlight)
That’s the clear message of new proprietary data gathered by the IBM Institute for Business Value (IBM IBV). Our research shows that CEOs who have the greatest success with AI are actively rethinking cross-functional collaboration and embedding AI across end-to-end workflows. They’re building organizations designed to thrive in uncertainty, where productive debate sharpens strategy and smart risk-taking is rewarded.
“Trying to take AI tools and squeeze them into the existing organization is extremely likely to be the wrong approach.” (View Highlight)
The reality of enterprise AI lags CEO expectations Percentages show that CEOs were overly optimistic about the pace of AI adoption in 2024, when nearly half expected advanced AI (defined as generative AI) to primarily drive growth by 2026. In 2026, only 10% say advanced AI (defined as agentic AI) is primarily driving growth. (View Highlight)
Still, CEOs anticipate rapid progress Percentages show that CEOs are even more optimistic about the results agentic AI will deliver in 2030, with nearly three-quarters expecting it will primarily drive growth. (View Highlight)
AI-first transformation starts at the top. CEOs know what to do. Consistency is what sets the best CEOs apart.
The CEOs who are rewiring the C-suite share key characteristics. They’ve created environments where work flows organically across functions by identifying and directing workflows that need to be transformed. They are actively rethinking who should have authority over what area of the business—then giving those leaders, especially their COOs and business line leaders, the power to change how work is done.
They’re also prioritizing AI-first operations by appointing a Chief AI Officer (CAIO) with real authority to orchestrate transformation enterprise wide.
And they’re creating new roles at every level to take advantage of AI. These leaders have already scaled 10% more AI initiatives enterprise wide than all other organizations—and they’re quickly building the foundation needed to extend their lead. (View Highlight)
The CEO still sets strategy, drives focus, and makes the final call. But the pace and stakes of that evolution are prompting CEOs to intentionally redesign the organization, with the goal of increasing speed, responsiveness, and collabora (View Highlight)
ation. When AI-first operations are the target, the lines between business units blur.
As strategic objectives become increasingly interdependent, C-suite roles are evolving. In fact, the CEOs we surveyed say they expect the influence of every member of the C-suite to increase between today and 2030. In functions such as marketing and HR where AI transformation started first and has impacted many teams and tools already, the expected increase is lower than other tech-centric areas, where AI integration may still be ramping up. (View Highlight)
In this context, the role of the CAIO has become critical. In 2026, 76% of organizations have someone in this position, up from just 26% in 2025.3 And 100% of these CEOs expect the influence of CAIOs to increase by 2030.
When they have real authority, CAIOs can enable calculated risk-taking across the organization. These leaders typically have a strong background in both data and business strategy, according to our 2025 CAIO Study, which means they can be the voice that sets clear AI transformation targets and provides guidelines that let teams accelerate without spinning out of control.4 The Chief Human Resource Officer (CHRO) role will also become more important, with 59% of CEOs saying the CHRO’s influence will increase over the next few years. This reflects that, in an AI-first enterprise, people must be managed in a more integrated way. Instead of limiting people management to the realm of HR, it becomes part of virtually every technological, operational, and financial initiative.
As AI continues to play a larger role in managing employees, breaking down the walls between IT and HR will become mission critical. Already, 77% of CEOs say talent and technology leadership roles are converging. Across the board, CEOs expect their C-suite leaders to reinvent themselves as cross-enterprise orchestrators rather than functional specialists. (View Highlight)
The C-suite of 2030 will need to be upskilled to become more AI-native, more technology-centric, more operationally integrated, and more inclined to work across ecosystems. That’s why 85% of CEOs say all functional leaders must become technology experts in their domain. The real difference won’t be the titles on the org chart but how those leaders work together—and how willing they are to challenge one another to evolve. Speed is the result of productive friction, not the absence of it.
In the rewired C-suite, every leader is expected to own outcomes, not just manage tasks. They’re accountable for identifying opportunities, making bets, and driving change—whether or not it falls neatly within their functional domain. The CEO’s role is to make that accountability explicit: who owns what outcome, who can decide without consensus, and what risks each leader is expected to take.
The rewired C-suite distributes decision-making authority so that leaders closest to the problem can act, within clear guardrails but without waiting for permission. This means designing decision architectures where authority is explicit, accountability is clear, and AI provides real-time intelligence. (View Highlight)
Redesign decision rights before touching the org chart. Identify the handful of enterprise decisions that slow everything else down— AI deployment, pricing moves, capital shifts, partner selection—and assign a single owner, explicit authority, and clear escalation rules.
79% of CEOs are decentralizing decision-making, but you first must help leaders understand who decides what so they can accelerate without waiting for consensus. (View Highlight)
Make every leader accountable for enterprise outcomes. Tie a meaningful share of C-suite compensation (at least 30%) to shared outcomes—growth, margin, customer trust—not just functional metrics. Break down silo walls that hinder progress, especially between HR and IT. Incentivize joint ownership of workforce redesign, reskilling, and AI deployment to make sure people and technology decisions are made together, measured together, and delivered together. (View Highlight)
Give AI leadership authority—within limits. If you have a CAIO, clarify their mandate. If you don’t, appoint one now. Give them authority over AI priorities, standards, and funding gates—but not ownership of business results. Their job is to accelerate decisions, scale what works, and stop what doesn’t. Line leaders remain accountable for outcomes. That separation is what enables speed without chaos. (View Highlight)
Reward leaders who make it possible to securely connect AI across the ecosystem. Ask executives to reevaluate partners based on how easily third-party AI agents can discover, integrate with, and transact through your offerings. Identify shared growth plays, joint use cases, and reciprocal pipeline creation and prioritize the partners best equipped to scale with you. (View Highlight)
“AI adoption doesn’t progress in a straight line. It often feels slow at first. Then, suddenly, the impact accelerates rapidly.” Pavitra Shankar Managing Director, Brigade Group, India (View Highlight)
When you have to move fast, there’s no time to wait for perfect information or rely on precedent. You have to think differently, experiment rapidly, and iterate based on what you learn.
Our research reveals organizations that move fastest aren’t just executing better—they’re scaling faster. The most future-focused CEOs have scaled 23% more AI initiatives enterprise wide.5 They’re not just prepared for an AI-first future. They’re running AI-first enterprises today.
So, what does it mean to be future-focused? Our analysis shows that it starts with boosting productivity, then reinvesting those gains to fuel innovation with the support of strong governance. This financial flywheel then transforms the business model and operating model alike.
But that isn’t enough on its own. The CEOs who are farthest ahead are also accelerating execution by rapidly deploying AI and embedding it in end-to-end workflows, then using it to make tactical and operational decisions, backed by human judgment. (View Highlight)
Today, CEOs in our survey say 25% of operational decisions are made by AI without human intervention. These are often in areas where consistency and guardrails can be codified, such as pricing updates, inventory allocation, shipment rerouting, and automated incident remediation. But by 2030, CEOs expect the share of operational decisions made by AI to nearly double to 48% (see Figure 2). Humans won’t disappear from the loop. Instead, their role will shift from making each decision to designing the decision logic, setting guardrails, and stepping in only when exceptions carry material, ethical, or strategic consequences. (View Highlight)
In this next wave, AI will increasingly take on operational decisions that demand speed, scale, and continuous optimization—decisions where human intervention simply cannot keep pace. These include real‑time demand sensing and inventory optimization across global networks, dynamic workforce scheduling, and automated rerouting when disruptions occur. In fact, a majority of executives are already planning or executing AI‑led autonomy in areas like demand forecasting (65%) and inventory optimization (61%), signaling where decision authority is moving first.6 Figure 3 CEOs expect AI to make nearly half of operational decisions by 2030.
Percentage of decisions made by AI without human interve (View Highlight)
The confidence CEOs have in AI agents is growing, with 64% of CEOs saying they’re comfortable making major strategic decisions based on AI-generated input. This reflects a significant mindset shift from 2025, when 62% of CEOs said generative AI was still too risky for them to pursue for their core business functions.7 Of course, there are plenty of areas where AI agents shouldn’t be making decisions, such as regulatory filings, material disclosures, and sensitive legal judgments. The goal isn’t to replace executive judgment—it’s to scale it, with humans orchestrating and auditing AI that operates inside clear guardrails. (View Highlight)
Focus on decision points—not use cases. Identify a small set of repeatable, high-volume decisions where consistency matters more than judgment—pricing updates, inventory allocation, routing, incident remediation—and deploy AI agents there first. Shift humans from approving every decision to handling exceptions. Measure success by decision quality and velocity. (View Highlight)
– Lead in a learning environment. Review AI-led decisions based on what they revealed, not whether the outcome was flawless. Set clear exit criteria upfront—time-to-value, decision quality, and sunset conditions— and normalize stopping low-value automation early so capital and talent can move to what scales. 83% of CEOs say AI success depends more on people’s adoption than technology, so creating an environment where people are excited to think creatively and iterate is essential to spur behavior change. (View Highlight)
– Lock in reinvestment before the gains arrive. Before the next budget cycle closes, agree on a fixed reinvestment rate for AI-driven productivity gains (typically 60% to 80%) and commit to funding faster cycles of experimentation and scale. Stop incremental efficiency plays that don’t unlock new capabilities.
– Expand AI autonomy deliberately. Increase AI decision authority only where guardrails are explicit, auditability is proven, and accountability sits with a named business leader. Let AI execute where consistency can be codified, let humans orchestrate and audit, and grow confidence through evidence. (View Highlight)
Nearly 80% of executives expect AI to drive significant revenue by 2030—but only 24% know where it’s going to come from. In this environment, competitive advantage will belong to those with the clearest vision.9 Our analysis shows the CEOs who have defined a tailored AI vision are more optimistic about product and service innovation. CEOs who systematically incorporate proprietary data and IP into custom AI models and agents expect 13% more of their 2030 revenue to come from products and services not offered today.
So what differentiates a successful AI vision? In The enterprise in 2030, we found it’s not a single type of model that does the most to drive performance.
It’s the right mix. The organizations that most successfully scale AI across workflows use smaller, tailored models or a combination of custom and foundation models. They expect 24% greater productivity gains, 55% higher operating profit margin improvements, and twice the reduction in process cycle times by 2030 compared to those relying predominantly on large pre-trained models.10 (View Highlight)
Our 2026 CEO data confirms that trend. While 39% of CEOs say their organization primarily uses pre-trained foundation models today, by 2030 that is expected to drop to just 13%. Half of CEOs say they are shifting to a hybrid strategy that combines custom models, foundation models, and smaller specialized models based on specific business requirements (see Figure 3). CEOs also want the ability to change AI models as conditions shift. 83% of all CEOs—and 97% of AI-first CEOs—say developing and maintaining AI sovereignty is essential to their business strategy. (View Highlight)
This means defining a multi-model strategy that combines pre-trained large language models (LLMs) for reasoning with task-specific small language models (SLMs) and ultra-specialized language models (ULMs) for speed and precision—all fine-tuned on proprietary datasets. It means creating AI agents that embody the organization’s culture, values, and competitive edge, delivering outcomes no competitor can reproduce.
This last point is worth emphasizing, because differentiation doesn’t scale unless people buy in. The CEO must give the organization a clear vision to follow—one that explains why customization matters, where it delivers the most value, and how it separates the organization from competitors. Without that clarity, teams default to generic solutions because they’re easier, faster, and don’t require debate. (View Highlight)
“We see AI fluency as a core capability across the company. Our focus is on practical application— helping teams use AI in their day-to-day work across stores, distribution centers, and offices.” Patrice Louvet President and CEO, Ralph Lauren Corporation, US (View Highlight)
– Draw a hard line between commodity AI and competitive AI. 63% of CEOs agree that their competitive advantage in 2030 will come primarily from the sophistication of their AI models. To protect that edge, answer three questions with your CIO/CTO and business leaders: What makes us impossible to replicate? What proprietary data, IP, or business logic do we own? Where can ultra-specialized AI outperform generic models? Use the answers to classify AI investments into what you buy versus what you must tailor or build—and make that distinction the filter for every funding decision. (View Highlight)
Tailor your AI portfolio. Define where foundation models deliver general reasoning, where smaller task-specific models drive speed and efficiency, and where ultra-specialized models create differentiation. Avoid defaulting to the largest model simply because it’s available. Fit matters as much as scale. Stay on top of the latest AI advancements so you can make informed moves when the landscape shifts. Require your C-suite to do the same. (View Highlight)
Make your agents unmistakably yours. Require AI agents to reflect how your organization competes—your decision rules, risk tolerances, brand values and culture, and operating philosophy. Do this by codifying what the business will and won’t optimize for, defining escalation rules for judgment calls, and embedding approved decision criteria into agent workflows.
Require senior leaders to sign off on these principles and test agents against real scenarios to help ensure they behave the way the organization would. (View Highlight)
Assign business ownership for differentiated AI. Name a business leader for every competitive AI investment and hold them accountable for adoption, outcomes, and value realization. Make ownership explicit by tying the initiative to a revenue, margin, or growth metric and reviewing progress in the same forum as other strategic bets. If usage stalls or value doesn’t materialize, require the owner to course-correct or stop the investment. (View Highlight)
To avoid stagnation in a rapidly evolving industry, Unipol Assicurazioni S.p.A., a leading insurance provider in Italy, needed a more customized approach to AI. With a focus on enterprise-wide adoption, the company developed next automation monitoring insurance (NAMI), an AI-powered automation platform featuring generative AI use cases tailored to Unipol’s operational needs.
The platform uses a multi-model approach to match models to the right task. Natural language interfaces are integrated with tools for instant insights and streamlined workflow. And the company’s operations have seen an immediate impact. (View Highlight)
In two months, NAMI analyzed over 800 system events, autonomously resolving many and escalating only 538. Previously, Unipol’s control room monitored just 26% of events in near real time. Since June 2025, NAMI has taken over 100%, freeing technical teams to focus on higher-value, strategic initiatives. Event response times dropped from 20 minutes to just 90 seconds, accelerating resolution and reducing disruption.
Operational efficiency has significantly improved, with time for accounting and claims processes reduced from 21 to 18 hours, while incident handling time has dropped by 90%, enabling technical teams to analyze data more quickly. By integrating more than a dozen monitoring systems into a unified data lake, NAMI fostered real-time monitoring, predictive analytics and automated reporting, enhancing decision-making. Looking ahead, Unipol plans to expand NAMI with full event automation, AI-driven process transformation, and hybrid cloud support. (View Highlight)
Today, AI augments people. By 2030, people will augment AI. The biggest shift won’t be structural— it will be cultural.
The leaders who see this shift coming know that technology alone won’t deliver AI-first results. The real unlock comes from reimagining how humans and machines collaborate to achieve outcomes neither could deliver alone.
Many organizations are still focused on using AI to support traditional ways of working, but our analysis shows that those who are proactively rethinking how people and technology collaborate are already seeing better results. Of the CEOs we surveyed, those that are actively redesigning how cross-functional teams work together are more than twice as likely to have delivered on their business objectives, meaning they’ve realized the full benefits outlined in their business cases.
When AI handles specialized tasks with speed and precision, the human advantage shifts to those who can see patterns across functions, ask questions AI doesn’t know to ask, and integrate machine intelligence with strategic context. Tactically, that means breaking down traditional boundaries between business and technology—boundaries that 77% of CEOs say have become obsolete. (View Highlight)
Comprehensive redesign delivers more business value CEOs who have redesigned five key areas of the business are 4x more likely to have delivered on their business objectives Comprehensive redesign delivers more business value Organizations that have redesigned five key areas of the business are 4x more likely to be highly effective in realizing the full benefi (View Highlight)
“The introduction of AI is more transformative than the introduction of the internet was at the time—not because of the technology itself, but because of its impact on how people work, decide, and collaborate.” Jan Polkerman CEO, Dutch Tax and Customs Authority IT, Netherlands (View Highlight)
CEOs should focus on the interlocks that connect functions—the handoffs, decision rights, and collaborative flows. According to our research, when functions evolve independently, performance improves incrementally.
When they’re redesigned as an integrated system, performance improvement compounds.
Overall, CEOs say that, in the past year, 19% of their workforce has been reskilled to perform a different role and 41% have been reskilled to perform their current role more effectively. Between 2026 and 2028, CEOs say 29% of employees will require reskilling for a different role and 53% will need reskilling to perform their current role more effectively.
Employees appear ready for this shift. Across all generational groups, at least twice as many employees would embrace rather than resist greater use of AI by their employers. 61% say AI makes their job less mundane and more strategic—and 48% even say they would be comfortable being managed by an AI agent.12 Still, CEOs say only 25% of the workforce is using AI regularly as part of their job—despite the fact that 86% of CEOs say employees have the skills to collaborate with AI. The gap between capability and deployment is more an organizational design problem than a skills problem. (View Highlight)
– Redesign workflows before redesigning jobs. 87% of CEOs are actively embedding AI across workflows. But be sure to redesign the workflow first—then update roles to match. Start by mapping where AI executes, where humans provide judgment and integration, and where escalation occurs. Don’t fund reskilling unless there’s a redesigned way of working, backed by an AI-first culture, ready to absorb it. (View Highlight)
Take an end-to-end approach to human-AI collaboration. Map how work moves from signal to action: where AI executes, where humans integrate context and judgment, and where escalation occurs. Make handoffs, accountability, and collaboration rules explicit so teams know how to work with AI, not around it. Define no-go zones for regulatory, trust, or reputational risk. Pressure-test workflows against real scenarios to remove friction before it shows up in production. (View Highlight)
Reskill for orchestration, not replacement. Prioritize skills such as systems thinking, interpreting AI outputs, challenging recommendations, and managing exceptions. Redefine performance evaluations and manager training to reward people who blend human and machine intelligence into better outcomes—not just faster ones. (View Highlight)
Turn AI usage into a key operating metric. Measure where AI is available, where it’s actually used, and where work reverts to manual defaults.
When CEOs estimate that only 25% of the workforce is using AI regularly in their job, they need their leaders to explain gaps, remove friction in workflows, and be jointly accountable for closing them. If AI isn’t being used, treat it as an operating failure—not a skills issue. (View Highlight)
Becoming AI-first isn’t the final destination. It’s the foundation for what comes next.
While quantum advantage—the point when quantum computers can solve problems faster than classical computers alone—hasn’t arrived yet, leading CEOs have begun preparing for that eventuality, which is approaching faster than many leaders expect (see Figure 5). They recognize that AI-first capability and flexibility set the foundation for quantum-fueled results— and that adapting operations, infrastructure, and partnerships as conditions change is a prerequisite for preparing for a quantum future.
The earliest evidence of advantage may come in specific areas, such as materials development (where quantum can model complex molecular interactions) and pharmaceuticals (where it can accelerate drug discovery).
Impact will follow in areas such as supply chain and logistics, where quantum’s optimization capabilities could redefine how goods move globally. Ultimately, the window between “quantum is emerging” and “quantum disrupts our industry” will be narrow. (View Highlight)
Despite the imminent arrival of quantum advantage, fewer than half (46%) of CEOs in our 2026 survey say they have a team in place for identifying specific quantum use cases and the associated business value. That gap provides an opening for CEOs who are already exploring use cases, developing applications, and positioning their organizations to capture value once quantum advantage arrives.
One key preparation tactic is reliance on ecosystems. No enterprise will be able to tap into the value of quantum on its own. The computational infrastructure and expertise are too specialized and expensive to access without the right partners. 82% of AI-first CEOs are already actively engaging partners in one or more quantum ecosystems to access complementary strengths, reduce risk, and accelerate learning, compared to 50% of all CEOs.
These aren’t partnerships of convenience. They’re strategic alliances built on openness, interoperability, and trust. The same principles that enable AI-first transformation at scale become the foundation for quantum-era advantage.
AI-driven systems don’t just improve efficiency. They create the strategic confidence and operational flexibility needed to unlock new possibilities. The CEO pursuing strategic optionality needn’t worry about predicting the future. By building open business models and forging ecosystems that multiply capabilities, they are preparing the organization to thrive in multiple possible futures. By investing in a forward-looking technology foundation, they’re poised to capture opportunities as they appear and avoid disruptions that threaten to stall progress. (View Highlight)
– Explore quantum now. Don’t wait for certainty. Establish a small, cross-functional exploration team with a six-month mandate to identify plausible quantum use cases, simulate value, and engage ecosystem partners. Measure success by learning speed and strategic insight, not near-term ROI. (View Highlight)
Raise quantum literacy across the C-suite. Require every C-suite leader to develop working literacy in how quantum could affect their function and how AI, classical computing, and emerging technologies intersect. Make this practical: expect each leader to identify one plausible use case in their domain, articulate the value at stake, and explain what would need to change operationally if quantum capabilities mature. Review insights collectively to build shared strategic confidence.
– Invest in adaptable foundations that preserve choice. Focus near-term technology decisions on flexibility. Direct your CIO to prioritize hybrid infrastructure, portable data architectures, and AI systems that can experiment with emerging compute models without major rework.
Avoid premature specialization that locks the enterprise into a single possible future.
– Build strength in numbers. Prioritize partners and consortia that keep strategic paths open, with a focus on shared standards and platforms and access to scarce expertise without long-term lock-in. Use ecosystems to test use cases, share infrastructure costs, and learn faster than you could alone. Treat ecosystems as strategic infrastructure—multiplying options today so the organization can move fast when the window opens. (View Highlight)
