AI in the C-suite What Executives Expect By 2031

Introduction

AI in the C-suite what executives expect by 2031 is no longer a speculative question, because the shift is already visible in how leadership teams are being rebuilt. In a 2026 study of 2,000 CEOs, IBM found that 76 percent of organizations now employ a Chief AI Officer, up from just 26 percent a year earlier. That jump signals that artificial intelligence has moved from a technology project into a permanent executive responsibility with its own budget, authority, and accountability. Chief executives are also betting on autonomy, since they expect AI to make nearly half of all operational decisions without human intervention by 2030. Those expectations collide with uneven reality, because most workforces still use AI rarely and many agentic projects will be abandoned before they pay back. This article maps the roles, decisions, risks, and operating models that executives expect to see by 2031, using survey data, analyst forecasts, and real company experience. Read it as a planning guide for the CEO, CFO, CHRO, CIO, and board director who must turn bold expectations into governed results.

Quick Answers on AI in the C-Suite and Executive Expectations

What does AI in the C-suite mean for executives by 2031?

AI in the C-suite what executives expect by 2031 means AI-owned operational decisions, dedicated AI leadership, redesigned executive roles, and board oversight. IBM’s CEO study expects AI to make 48 percent of operational decisions by 2030.

How many companies already have a Chief AI Officer?

IBM’s 2026 study of 2,000 CEOs and senior leaders found that 76 percent of organizations have a Chief AI Officer, up from 26 percent a year earlier, though other benchmarks report much lower rates.

What is the biggest risk to executive AI plans?

Gartner predicts over 40 percent of agentic AI projects will be canceled by the end of 2027 because of escalating costs, unclear business value, or inadequate risk controls.

Key Takeaways for Leaders Planning Toward 2031

• AI has become a standing executive responsibility, with 76 percent of organizations in IBM’s 2026 study reporting a Chief AI Officer.
• CEOs expect AI to make 48 percent of operational decisions by 2030, so leaders must decide which choices stay human.
• Finance, people, technology, and board roles are converging, and boards still lag badly on AI knowledge and oversight.
• Agentic ambition outruns delivery, so disciplined operating models, measurable returns, and honest limits will separate winners from cancelled projects.

What Is AI in the C-Suite and What Do Executives Expect?

AI in the C-suite what executives expect by 2031 describes leaders expecting artificial intelligence to own operational decisions, reshape reporting lines, and require board oversight. It treats AI as governed executive infrastructure, not isolated pilots.

The Executive Mandate Taking Shape Before 2031

Executive teams are being asked to carry a mandate that did not exist five years ago, which is to turn artificial intelligence into measurable enterprise performance. An IBM study reported by Retail Insider found that 79 percent of executives expect AI to drive significant revenue by 2030, versus 40 percent today. The same research shows that only 24 percent of those leaders have clarity on where that future revenue will actually come from. That gap between ambition and clarity defines the early part of the decade for every chief executive. Investment is rising to match the ambition, with executives projecting AI spending to grow roughly 150 percent through 2030. The mix of spending is also expected to shift, from 47 percent focused on efficiency today toward 62 percent dedicated to innovation by the end of the decade. Leaders therefore face a dual task of banking near-term savings while funding experiments that might open new revenue lines.

What makes this mandate unusual is that it belongs to the whole suite rather than to a single technology leader. Executives who want a deeper primer on the fundamentals can start with this overview of what the C-suite should know about AI before returning to the 2031 outlook here. In practice the mandate splits into four strands that this article follows in turn: decision rights, money, people, and governance. Decision rights ask which choices machines may make alone, money asks how returns are proven, people asks who must change, and governance asks who answers when things go wrong. Each strand has a different executive owner, yet all four must move together or the program stalls. Companies that treat the mandate as an IT upgrade tend to discover, usually in the second year, that the real work is organizational. The rest of this guide to AI in the C-suite what executives expect by 2031 lets leaders speak first, then tests their expectations against the evidence.

How the Chief AI Officer Role Is Rewriting Reporting Lines

Building on that mandate, the clearest structural change is the rapid appearance of a dedicated Chief AI Officer. IBM's 2026 CEO study reports that 76 percent of surveyed organizations now have one, up from 26 percent a year earlier. Such a rise in twelve months suggests boards and chief executives no longer see AI as a side responsibility of the CIO or CTO. The role typically carries cross-functional authority over model selection, use-case prioritization, vendor standards, and risk review. It also gives the company a single accountable name when regulators, customers, or directors ask who owns an AI decision. Where the role reports matters as much as the title, because a Chief AI Officer buried three levels down cannot overrule a business unit. The same IBM research links AI-first C-suite design to organizations that scaled 10 percent more AI initiatives across the enterprise.

Not every benchmark shows the same adoption curve, and executives should read the numbers with care. Randy Bean's 2026 benchmark of senior data and AI executives, summarized by Babl AI, found that 38.5 percent had added a Chief AI Officer. The same survey found that 90 percent had appointed a Chief Data Officer. The gap with IBM's figure likely reflects different samples, since one study polls CEOs across 33 geographies and the other polls data leaders at Fortune 1000 firms. A sensible planning assumption is that the Chief AI Officer is becoming standard in large enterprises but is still absent in a large share of the mid-market. Definitions also differ, because some firms give the title to a data leader while others create a new executive seat. Readers comparing studies should always check who answered and how the role was defined. A deeper profile of the seat is available in this analysis of the Chief AI Officer role, which covers mandate and skills.

Three reporting patterns are emerging, and each carries trade-offs that executive teams should weigh deliberately. In the first, the Chief AI Officer reports directly to the CEO, which maximizes authority but risks duplicating the CIO's infrastructure mandate. In the second, the role sits under the CIO or CTO, which keeps platforms coherent but can slow business-led use cases. In the third, a federated model places AI leads inside each business unit with a small central office setting standards. IBM reports that 79 percent of executives are decentralizing decision-making as AI expands, which favors the federated pattern for large, diversified firms. Whatever the pattern, the charter should name decision rights in writing, including who can approve a model for production and who can halt it. Without that clarity, the title becomes decoration and the real authority stays wherever the budget already lived.

What CEOs Expect AI to Decide on Their Behalf

Turning to the chief executive's own desk, the most striking expectation is how much judgment leaders are willing to delegate. IBM's 2026 survey found that 64 percent of CEOs are comfortable making major strategic decisions based on AI-generated input. The same study reports that CEOs expect AI to make 48 percent of operational decisions without human intervention by 2030, compared with 25 percent today. Those two figures describe different things, because strategic input still lands on a human desk while operational decisions increasingly will not. The distinction between advising and deciding is the most important design choice an executive team will make this decade. Advice can be wrong without catastrophe, since a person reviews it, whereas an autonomous decision acts before anyone looks. Leaders who blur the two tend to over-trust systems in areas such as pricing, routing, and approvals.

Practical executives are sorting decisions into tiers based on reversibility, financial exposure, and regulatory sensitivity. Low-risk, high-volume choices such as ticket routing, inventory reorders, and scheduling are the first candidates for full autonomy. Mid-tier choices such as credit limits, discounts, and hiring shortlists generally keep a human approval step or a sampled review. High-stakes choices such as layoffs, acquisitions, and safety-critical changes remain human decisions supported by AI analysis. This tiering gives a CEO a defensible answer when a director asks which decisions the machine can make alone. It also creates a measurable path, because the autonomous share of decisions can rise as evidence of accuracy accumulates. The tiers should be reviewed every quarter, since model quality and regulatory expectations both move quickly.

Expectations are also shaped by who actually uses the tools. IBM reports that only 25 percent of the workforce uses AI regularly in their jobs, even though 86 percent of CEOs believe employees have the skills to collaborate with AI. That perception gap means a CEO who expects half of operational decisions to be automated may be working from an optimistic picture of readiness. Eighty-three percent of surveyed CEOs also agree that AI success depends more on people's adoption than on technology. Executives who want a structured way to probe readiness can borrow these C-suite questions for elevating AI investments in their next leadership meeting. The questions force the team to name the decision, the data, the owner, and the measure of success before money is committed. Asking them early is cheaper than discovering the answers during a failed rollout.

Delegated decisions also raise accountability questions that no dashboard can answer. When an automated system denies a loan or rejects a candidate, the CEO remains answerable to customers, regulators, and the board. Executives exploring this territory should read the discussion of ethics in AI-driven business decisions before deciding which choices to automate. A useful rule is that any decision affecting a person's livelihood, health, or legal standing needs an explanation a human can give. Another is that every autonomous decision class should have a named owner, a log, and a kill switch. Chief executives who institute these rules early find that later regulatory demands become a documentation exercise instead of an emergency. The expectation for 2031 is therefore not blind automation but selective, auditable delegation.

CFOs, Budgets, and the Pressure to Prove Returns

Moving on to the finance chief, the central expectation is that AI spending must now justify itself in the income statement. According to McKinsey's 2025 State of AI survey, 88 percent of organizations use AI in a business function, yet only 39 percent report any EBIT impact. Among those that do see an effect, most attribute less than 5 percent of enterprise EBIT to AI. Only about 6 percent qualify as high performers, defined as attributing 5 percent or more of EBIT to AI and reporting significant value. For a CFO, the lesson is that widespread use and financial return are two different achievements. Executives expect AI investment to rise roughly 150 percent through 2030, which will draw sharper scrutiny from audit committees. Finance leaders are therefore building AI value ledgers that track savings, revenue lift, and avoided cost per use case.

The measurement challenge is real because productivity gains rarely appear as a clean line item. Time saved by an assistant may be reinvested in lower-value work, absorbed into slower meetings, or never converted into headcount or revenue. Randy Bean's benchmark reports that 97.3 percent of senior data and AI executives say their AI investments deliver measurable value. That figure sits awkwardly beside McKinsey's much lower EBIT numbers. The likely explanation is that respondents define value differently, with some counting hours saved and others counting profit. IBM adds a warning that 68 percent of executives worry their initiatives will fail because of insufficient business integration. A disciplined approach to measuring ROI on AI investments therefore starts by agreeing on a definition of value before the first dollar is spent. CFOs who impose that discipline early will be able to defend their budgets when the 2028 and 2029 planning cycles tighten.

CHROs, CIOs, and the Convergence of Talent and Technology

Beyond finance, the sharpest role changes fall on the people and technology leaders, whose territories are converging. IBM reports that 77 percent of CEOs see talent and technology leadership roles merging, and 59 percent expect the CHRO's influence to increase. The logic is straightforward: when AI agents perform tasks that humans once did, workforce planning becomes inseparable from technology planning. A CHRO who cannot discuss model capabilities cannot forecast which roles will shrink, and a CIO who ignores culture cannot get tools adopted. Eighty-five percent of CEOs say every functional leader must become a technology expert in their own domain. That expectation extends well beyond the CIO and puts new demands on the marketing chief, the supply chain chief, and the general counsel alike. Organizations that hire for these hybrid skills now will have an easier path to 2031.

The scale of the workforce shift is large enough to justify its own executive attention. IBM estimates that 29 percent of employees will need reskilling for different roles between 2026 and 2028, while 53 percent will need upskilling for their current roles. A separate IBM study found that 57 percent of executives expect current employee skills to become obsolete by 2030. The same research found that 67 percent say job roles are becoming shorter-lived. Taken together these figures describe a labor market in which roles change faster than traditional training cycles can follow. CHROs are responding by shifting from annual curricula to continuous, task-level learning embedded in daily tools. They are also redesigning career paths so employees can move laterally as tasks are automated, a major theme in the debate on AI and the future of work.

For the CIO and CTO the change is subtler but just as deep. Their classic mandate of keeping systems secure, available, and cost-efficient now sits beside a new duty to govern a growing fleet of models and agents. In that same IBM research, 72 percent of executives expect small language models to surpass large language models, and 82 percent anticipate multi-model capabilities by 2030. That outlook means technology chiefs will manage portfolios of specialized models rather than a single vendor relationship. Data foundations, identity controls, and observability tooling become strategic assets, because agents act with credentials just as employees do. The CIO also becomes the natural partner of the Chief AI Officer, supplying infrastructure while the CAIO sets use-case priorities. When the two roles collide, the CEO must arbitrate quickly, since ambiguity in this seam is where security gaps and duplicated spending hide.

Boards Learn to Govern What They Barely Know

Looking at the boardroom, the gap between executive ambition and director readiness is wide. As reported by Axios in April 2026, McKinsey research found that only 39 percent of Fortune 100 boards have any AI oversight. That oversight might be a committee, an expert director, or an ethics board. Two-thirds of surveyed directors say their boards have limited to no knowledge or experience with AI, and nearly a third say AI does not appear on their agendas. ISS data cited in the same report shows just 13 percent of S&P 500 companies have at least one director with AI-related expertise. Directors who cannot interrogate a model risk report are in no position to challenge management on the largest technology bet of the decade. The NACD figures are equally blunt, with only 17 percent of boards establishing AI education plans and 6 percent forming a dedicated committee.

Boards are beginning to close the gap through three practical moves. The first is education, with structured briefings and scenario sessions that let directors use the tools they are asked to oversee. The second is structure, by assigning AI risk to an existing audit or risk committee or by creating a technology committee with a clear charter. The third is outside expertise, and IBM's research expects 25 percent of enterprise boards to include AI advisors by 2030. The payoff may be material, since the Axios report cites an MIT study finding that boards with AI-literate directors outperformed peers by 10.9 percentage points in return on equity. That finding is correlational, so it should not be read as proof that literacy alone produces returns. Even so, it supports the intuition that informed oversight and good performance travel together. For governance design ideas, look at how future roles for AI ethics boards are being defined.

Agentic Systems Move From Pilots to Operating Models

Stepping back from roles to systems, the technology that will most change executive work is the autonomous agent. Gartner's 2025 forecast projects that 15 percent of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from essentially zero in 2024. It also expects 33 percent of enterprise software applications to include agentic capabilities by 2028, compared with less than 1 percent in 2024. For executives, agents are not another tool in the stack but a new kind of worker that acts, spends, and commits the company. McKinsey's survey adds that 62 percent of organizations are at least experimenting with agents and 23 percent are scaling an agentic system somewhere in the enterprise. Those numbers show an experimentation phase that is wide but shallow, with production-grade adoption still concentrated in a minority of firms.

The same Gartner research tempers the excitement with a sobering prediction. Over 40 percent of agentic AI projects will be canceled by the end of 2027 because of escalating costs, unclear business value, or inadequate risk controls. Gartner analyst Anushree Verma describes most current projects as early-stage experiments driven by hype and often misapplied, which blinds organizations to real deployment costs. The firm also warns of agent washing, noting that only about 130 of the thousands of vendors claiming agentic products offer genuine capabilities. Executives buying agents should therefore demand evidence of autonomy, such as multi-step task completion rates, rather than accept a rebranded chatbot or workflow script. A short pilot with a written success metric and a stop rule is the cheapest protection against joining the canceled 40 percent. Leaders who want a skeptical but constructive treatment can consult this guide to navigating the hype of agentic AI.

Agents change the executive question from what a tool can do to what the company will let it do. An agent with access to email, payments, and customer records can create value quickly, and it can also create liabilities at machine speed. That is why identity and permissions become board-level topics, since every agent needs a defined role, a credential, and a spending limit. Gartner's survey of 3,412 respondents in January 2025 found that 19 percent had made significant agentic AI investments and 42 percent had made conservative ones. A further 8 percent had made none, while 31 percent were taking a wait-and-see approach or were unsure. Those proportions describe a market where most leaders are still deciding how much authority to grant, which is the right moment to write the rules. Rules written before deployment are far cheaper than rules written after an incident.

By 2031 most executive teams expect to supervise hybrid workflows in which people direct agents and agents handle the routine steps. IBM's finding that CEOs expect 48 percent of operational decisions to be automated by 2030 fits this picture, though the pace will vary by industry. Regulated sectors such as banking and healthcare will keep tighter human review, while logistics and customer service will move faster. Procurement, legal, and security leaders will need new playbooks for contracting with agent vendors and for investigating agent errors. Executives will also need a vocabulary for agent performance, including task success rate, escalation rate, and cost per completed outcome. Teams that adopt these measures early will compare vendors on evidence rather than demonstrations. Over time the agent roster itself becomes an asset register that the COO and CIO review as they would any critical supplier.

Putting an Executive AI Operating Model Into Practice

Rounding out the structural picture, executives need an operating model that turns expectations into repeatable practice. IBM reports that organizations redesigning five core business areas are four times more likely to deliver on their objectives than those that layer AI onto existing processes. Redesign means changing the workflow, the roles, the controls, and the measures together, not merely inserting a model into a step. A workable model has five elements, starting with a strategy tied to business outcomes and a portfolio of prioritized use cases. The other three are shared data and platform services, a risk function, and a change program for people. The strategy element should be concrete enough to say no, which is where guidance on defining an AI strategy for businesses proves useful. Without an explicit list of use cases the company will not fund, every request looks urgent and the portfolio sprawls. Quarterly portfolio reviews then reallocate money from stalled efforts to those showing measured results.

Randy Bean's benchmark shows that scale is achievable, since 39.1 percent of respondents now operate AI at scale, up from 4.7 percent two years earlier. The same survey reports that 93.2 percent name culture and change management as the top barrier to adoption, ahead of any technical limit. This finding explains why successful programs spend as much effort on training, incentives, and communication as on engineering. Executives can accelerate adoption by publishing a small number of approved tools, naming local champions, and rewarding teams for retired manual steps. They should also measure usage depth, such as tasks completed with AI assistance, instead of counting licenses. When usage is shallow, the cause is usually unclear permission, weak trust in outputs, or no time to learn. Fixing those causes is a management task, not a technology purchase.

Governance must keep pace with deployment, and tooling is starting to help. Vendor platforms are beginning to offer agent registries and governance controls, a trend discussed in this look at Microsoft Agent 365 and enterprise agent governance. Whatever platform is chosen, the executive team should insist on a central registry of agents, owners, permissions, and data access. A monthly review should confirm that each agent still has a business purpose and a human owner. Incident drills, similar to those used in cybersecurity, help teams practice how to pause an agent and communicate with customers. Internal audit should receive read access to agent logs so that it can test controls independently. These habits cost little compared with the reputational damage of an unexplained automated error.

Where Executive AI Ambitions Fall Short and Create Risk

Despite the enthusiasm, programs for AI in the C-suite what executives expect by 2031 carry risks that deserve the same attention as the opportunities. The first is over-automation, in which a company removes human capacity faster than the technology can prove itself. Klarna illustrates the pattern, since it reversed its AI-heavy customer service model after its CEO admitted lower quality, as covered in Klarna's CEO replacing workers with AI. The second risk is concentration of failure, because one flawed model or prompt can repeat the same error across thousands of decisions in minutes. The third is workforce disruption, with 32 percent of respondents in McKinsey's survey expecting headcount reductions of 3 percent or more in the coming year. The fourth is legal exposure from biased, opaque, or unexplained automated decisions that regulators increasingly scrutinize.

Oversight is the common remedy, yet oversight itself is hard to design when systems act autonomously. A related discussion of autonomous AI agents challenging oversight frameworks explores why. Traditional approval chains assume a human at every step, an assumption that agents break. Executives should therefore shift from approving individual actions to approving boundaries, such as spend limits, data scopes, and prohibited actions. They should also test for failure deliberately, using red-team exercises that probe how an agent behaves when instructions conflict or data is wrong. Insurance and contracts need review because liability for an agent's mistake may fall unevenly between the company and its vendors. Finally, communication plans should assume that a visible failure will occur and prepare honest, fast explanations for customers and staff.

Ethics, Accountability, and the Human Judgment Question

Shifting to ethics, the executive question is less about abstract principles and more about who answers for outcomes. Randy Bean's benchmark reports that 79.4 percent of surveyed leaders view responsible AI as a top corporate priority and 88.7 percent have governance mechanisms and guardrails in place. Those figures sound reassuring, though they measure the presence of mechanisms rather than their effectiveness. A guardrail that nobody tests, or a policy that nobody enforces, offers comfort without protection. Real accountability means that a named executive can explain, in plain language, why a system made a consequential decision and what was done to correct it. Building that capability requires documentation, monitoring, and a culture where staff can raise concerns without penalty. A practical foundation can be found in this guide to responsible AI governance frameworks.

The regulatory landscape is hardening, and the overview of AI governance trends and regulations is a useful map for executives tracking obligations across jurisdictions. IBM reports that 83 percent of executives agree AI sovereignty is essential to business strategy, reflecting concern about where models run, whose data trains them, and which laws apply. Sovereignty pressures push firms toward regional deployments, local model options, and stricter data residency. Compliance leaders therefore join the executive AI team earlier, because retrofitting controls after launch is expensive. A pragmatic stance is to build to the strictest regime a company faces and relax controls only where law allows. Documentation produced for one regulator tends to be reusable for others, which lowers the marginal cost of expansion.

Human judgment remains the most valuable and the most fragile part of the system. When AI recommendations are usually right, reviewers drift into rubber-stamping, a pattern known as automation bias. Executives can counter it by sampling decisions for deep review, rotating reviewers, and rewarding people who catch errors. They should also protect the dignity of work by being transparent about which tasks are automated and why. Employees who understand the plan are more likely to share the process knowledge that makes automation succeed. In contrast, secrecy breeds resistance and quiet sabotage that no technical control can detect. Ethical leadership by 2031 will therefore be measured as much by how companies treat displaced workers as by how they treat their models.

Skills and Culture Beyond the Executive Floor

Next, the culture question determines whether any of the structural changes above actually take hold. Only 25 percent of the workforce uses AI regularly according to IBM, which means three quarters of employees are still outside the transformation. Executives can narrow this gap by making AI use a normal expectation, supported by time to learn, safe practice environments, and visible examples from leaders. Leaders themselves must model the behavior, because a CEO who never uses the tools sends a clear signal about priorities. Teams adopt what their managers visibly use and reward, so manager capability is the real bottleneck. Training programs should therefore start with middle managers, who translate executive intent into daily routines. A curriculum that covers prompting, verification, data handling, and when not to use AI gives them a shared language.

Skills planning should look three years ahead and be reviewed twice a year. The most valuable capabilities combine domain depth with the ability to supervise AI. Examples include an analyst who can audit a model's reasoning or a lawyer who can validate an automated contract review. Hiring profiles are already shifting toward adaptability, judgment, and data literacy over narrow tool skills that expire quickly. Succession planning matters as well, since the next generation of executives must be comfortable steering hybrid teams of people and agents. Leaders looking for a practical orientation can read this guide to AI agents in 2025 for leaders and adapt its checklists to their own functions. Pairing junior staff with experienced mentors preserves tacit knowledge that models cannot capture. This mix of technical fluency and human craft is what executives mean when they say they want an AI-ready organization.

The Future of the Executive Suite Through 2031

Looking ahead to 2031, the executive suite is likely to look familiar in its titles but different in how it works. IBM's research reports that 74 percent of executives believe AI will redefine leadership roles across their organizations. Those same executives expect 25 percent of enterprise boards to include AI advisors by 2030 and expect AI to make 48 percent of operational decisions. Gartner's 2028 milestone of 15 percent autonomous day-to-day work decisions sits well below that expectation, although the two figures define decisions differently. A realistic planning window runs from a cautious Gartner-style baseline to an aggressive IBM-style ceiling, and executives should stress-test strategy against both. Companies that prepare for the aggressive case but budget for the cautious case keep options open without overcommitting.

Competitive advantage is also expected to shift in the second half of the decade. According to the IBM research reported by Retail Insider, 64 percent of executives say innovation rather than resource optimization will drive competitive edge by 2030. Smaller, specialized models should lower costs and let companies keep sensitive data in-house, while multi-model strategies reduce dependence on any one vendor. Quantum-enabled AI is a more distant wager, since 59 percent of executives believe it will transform their industry yet only 27 percent plan to adopt it. Leaders should treat that gap as a signal to monitor rather than a mandate to invest immediately. Time horizons matter here, because capabilities that look speculative in 2026 can become standard procurement categories by 2031. A small, well-governed horizon-scanning team costs little and prevents strategic surprise.

The takeaway for boards and executives is clear even when the forecasts differ. AI in the C-suite what executives expect by 2031 comes down to five commitments. Leaders must name accountable AI leadership, tier decisions by risk, prove returns, educate the board, and invest in people. Executives who make those commitments now will shape the outcome rather than react to it. Those who wait for certainty will find that competitors, regulators, and employees have already set the terms. The data in this article show enthusiasm, uneven readiness, and real hazards at the same moment, which is exactly when disciplined leadership matters most. Revisit your assumptions every quarter, update the decision tiers as evidence accumulates, and keep humans accountable for outcomes that matter. That discipline, more than any single technology choice, will define successful executive teams at the end of the decade.

Key Insights From the Latest Executive Research

• IBM's 2026 CEO study found that 76 percent of organizations now have a Chief AI Officer, up from 26 percent, showing AI ownership is now a permanent executive seat.
• CEOs expect AI to make 48 percent of operational decisions without human intervention by 2030, versus 25 percent today, according to the same IBM research on executive roles.
• McKinsey's 2025 survey found that 88 percent of firms use AI but only 39 percent report any EBIT impact, so adoption and profit are different goals.
• Gartner predicts that over 40 percent of agentic AI projects will be canceled by the end of 2027, so executives should demand clear returns and risk controls before scaling.
• Only 39 percent of Fortune 100 boards have any AI oversight and 66 percent of directors report limited AI knowledge, according to Axios, which leaves governance dangerously thin.
• Randy Bean's benchmark, summarized by Babl AI, shows 39.1 percent of firms now operate AI at scale versus 4.7 percent two years earlier, evidence of rapid maturation.
• An IBM study found that 79 percent of executives expect significant AI revenue by 2030 while only 24 percent know where it will come from, exposing a clarity gap.

Taken together, these findings on AI in the C-suite what executives expect by 2031 describe a leadership class that has committed faster than it has built governing structures. Chief AI Officers, autonomous decisions, and agent deployments are arriving on schedule, yet financial returns, board literacy, and workforce readiness trail behind. The studies disagree on specifics such as how common the Chief AI Officer is, which should make every executive cautious about quoting a single number. What they agree on is direction, because every source expects more autonomy, more accountability, and more convergence among executive roles. The practical conclusion is to plan for the aggressive forecasts while budgeting against the cautious ones. Leaders who hold both views at once will be best placed to adapt as the evidence arrives.

Dimension	Where leaders stand in 2026	What executives expect by 2031	Main caution
Decision authority	CEOs say AI makes 25 percent of operational decisions without human intervention today	48 percent of operational decisions made by AI without human intervention by 2030	Strategic input is advice while operational action is autonomy, so the two must not be blurred
AI leadership role	76 percent of organizations report a Chief AI Officer in IBM data; 38.5 percent in Bean's benchmark	A standard seat in large enterprises with a written charter and decision rights	Studies differ on sample and on how the role is defined
Finance accountability	39 percent report any EBIT impact and about 6 percent are high performers	AI investment up roughly 150 percent through 2030, tracked in per-use-case value ledgers	Value is defined differently across surveys, which inflates self-reported returns
Workforce and skills	25 percent of the workforce uses AI regularly	29 percent need reskilling in 2026 to 2028, and 57 percent expect skills to be obsolete by 2030	86 percent of CEOs believe skills exist, which suggests leaders overestimate readiness
Board oversight	39 percent of Fortune 100 boards have AI oversight; 13 percent of S&P 500 have an AI-expert director	25 percent of enterprise boards include AI advisors by 2030	The link between AI-literate boards and returns is correlational
Agent deployment	62 percent are experimenting with agents and 23 percent are scaling one	15 percent of day-to-day work decisions made autonomously by 2028 in Gartner's forecast	Over 40 percent of agentic projects are expected to be canceled by the end of 2027
Governance and ethics	88.7 percent report guardrails and 79.4 percent call responsible AI a top priority	Named accountable executives, audit logs, and sovereignty-aware deployment	The presence of a mechanism is not proof that it works
Role convergence	77 percent see talent and technology leadership converging	Rising CHRO influence and every functional leader acting as a technology expert	Hybrid skills are scarce and slow to develop

AI in Practice: Three Executive Teams Rewiring Their Work

Moderna Merges People and Technology Under One Leader

Among the clearest examples of structural change is Moderna, which combined its HR and technology functions under a single Chief People and Digital Technology Officer, Tracey Franklin. The company grew from about 800 employees in 2019 to roughly 5,000 worldwide, and it built an environment where staff created more than 3,000 custom GPTs. One deployed tool, called Ask HR, routes employee questions about performance, careers, and benefits to specialized assistants, while analytics on recurring questions inform policy changes. The most visible result so far is adoption, an increase in employee-built tools that now numbers over 3,000 custom GPTs. Franklin has been candid about limits, describing the merger as still very much a work in progress and not a one-size-fits-all model. She also notes that success depends on strong data governance and change management, and that the company is still early with agentic AI. Unleash's interview with Franklin gives the fullest account of the reasoning and the caveats.

Klarna's AI Assistant and the Return of Human Support

Shifting to customer operations, Klarna rolled out an AI assistant in early 2024 that the company said did the work of 700 customer service agents. Within its first month the assistant handled 2.3 million conversations, covering about 75 percent of customer chats across more than 35 languages. Klarna also paused hiring for a year and shrank its headcount by 22 percent to roughly 3,500, mostly through attrition. By May 2025 CEO Sebastian Siemiatkowski said the cost focus had produced lower quality, and the company began recruiting human agents again. He argued that customers should always be able to reach a person, so Klarna targeted students and rural residents for remote support roles. The pivot is a reminder that a strong efficiency number can hide a quality problem until customers notice. Entrepreneur's report on the reversal details the timeline and the CEO's reasoning.

JPMorgan Chase Scales an Internal AI Assistant Bank-Wide

Beyond the technology firms, JPMorgan Chase offers a large regulated example of enterprise-wide rollout. The bank built an internal AI model trained on its proprietary data, and roughly 150,000 employees now use it weekly for research, report summarization, and contract scanning. In an October 2025 interview, CEO Jamie Dimon said the bank spends about $2 billion a year on AI and sees about $2 billion in annual benefit. One documented use, a cash flow intelligence tool, cut manual work by about 90 percent. Dimon called the savings just the tip of the iceberg, a figure that comes from the bank's own accounting rather than an outside review. He also acknowledged that there will probably be fewer jobs in certain functions, so retraining and redeployment are still required. Entrepreneur's coverage of the interview summarizes the figures and the caveats.

Lessons From Three Case Studies in Executive AI Leadership

Case Study: Shopify Makes AI Use a Baseline Expectation

Despite the polished AI rhetoric elsewhere, the March 20, 2025 memo from Shopify CEO Tobi Lütke may be the most direct executive statement of expectation yet. The challenge he described was stagnation, which he called slow-motion failure in a market where AI was changing what a small team could produce. He faced a company that had treated AI as optional, with earlier initiatives framed as suggestions rather than requirements. The solution he introduced made AI proficiency a baseline expectation for every employee, required prototyping to be dominated by AI exploration, and added AI-usage questions to performance and peer reviews. Teams must also demonstrate why they cannot accomplish a goal with AI before they request more headcount or resources. Lütke argued that some colleagues already achieved ten times what was once thought possible, although the memo offered no detailed measurement. He published the memo on X to control the narrative before it leaked.

The market reaction was muted but positive, with the stock recovering about 2 percent after a prior decline of roughly 25 percent. Revenue had grown 31 percent year over year in the fourth quarter of 2024. Those figures describe context rather than proof, because Shopify's growth predates the memo and cannot be attributed to it. A fair concern is that tying reviews and hiring to AI usage can push staff to perform AI use rather than achieve outcomes. The policy also shifts the burden of proof onto managers, which can slow legitimate hiring and strain teams during peak periods. For other executives the lesson is that a clear, written expectation changes behavior faster than optional programs, but it requires follow-through and careful metrics. BetaKit's coverage of the memo reproduces the key rules and quotes.

Case Study: Salesforce Cuts Support Headcount With AI Agents

Salesforce faced a familiar scaling problem in customer support, where a team of roughly 9,000 people answered a growing volume of requests. The company deployed its Agentforce platform in early 2025, letting AI agents handle a large share of inbound support work alongside human staff. CEO Marc Benioff said in an interview published on September 2, 2025 that he had reduced the support organization from 9,000 heads to about 5,000 because he needed fewer heads. That is a reduction of roughly 44 percent, and AI agents now manage about half of support calls according to the reporting. Benioff called the deployment the most exciting thing at Salesforce in the last nine months. The company had already announced about 1,000 job cuts in February 2025 to refocus hiring on AI.

The impact looks large, yet the picture is more complicated than a simple replacement story. A Salesforce spokesperson said the company had redeployed hundreds of employees into professional services, sales, and customer success, which softens the headline number without changing the direction. The cuts raise a fair concern about how quickly agent adoption can translate into job losses, even at a company that sells the technology. The approach also remains a trade-off, because support quality, escalation handling, and customer trust must hold up over time. Executives copying the model should measure resolution quality and customer satisfaction, not only headcount. KTVU's report on the interview provides the quotes and figures used here.

Case Study: NetDragon Websoft Appoints an AI as Chief Executive

NetDragon Websoft, a Hong Kong-based gaming company, faced the ordinary executive challenge of making faster, better-informed decisions across a complex business. In August 2022 it introduced Tang Yu, described as its first virtual CEO, to analyze high-level data, make leadership decisions, evaluate risks, and promote a productive work environment. Chairman Dejian Liu said AI is the future of corporate management and that the appointment showed the company's commitment to transforming operations. Tang Yu works around the clock without compensation, which the company framed as a route to a fair and efficient workplace. Reporting at the time noted that the company's shares rose about 10 percent over six months and outperformed the Hang Seng Index. The company's market value was around HK$9 billion, roughly $1.1 billion.

The case is instructive mainly for what it does not prove. A rising share price over six months cannot be attributed to a software executive, and the announcement offered no independent audit of which decisions Tang Yu actually owns. Corporate law in most jurisdictions still requires natural persons to bear fiduciary duties, so human directors and officers remain accountable. The experiment is better read as a signal of expectation, since it shows how far some boards will go in publicly embracing AI leadership. It also illustrates a communication risk, because a bold title can overstate what a system does and invite criticism. Executives planning for 2031 should treat the story as a prompt to define the limits of AI authority rather than as a template. 80 Level's report on the appointment summarizes the announcement and the early stock performance.

Frequently Asked Questions on AI in the C-Suite and Executive Expectations