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The CEO Imperative Series
The findings from the latest EY-Parthenon CEO Outlook survey provide evidence of an emerging productivity upswing. Almost all CEOs report productivity gains – as defined by more output per employee – over the past 12 months, with nearly half reinvesting to support future growth, innovation and transformation. Yet those gains do not always translate into enterprise performance. Nearly a quarter of CEOs say productivity gains are offset by the need to manage operational complexity, regulatory obligations, risk management requirements and increasing work demands, driven by the changes that are boosting productivity itself. The productivity dividend is emerging, but organizations still face challenges converting efficiency gains into growth, profitability and long-term value creation.
CEOs are navigating a more complex backdrop of slower growth, geopolitical disruption and technology change. Yet many increasingly view inaction as the greater risk. Rather than waiting for certainty, they are investing while building flexibility into how and when capital is deployed.
This creates a defining leadership challenge: how to convert newly created capacity into durable competitive advantage. The survey points to three critical choices. First, whether AI-driven productivity gains are translated into operating model improvements. Second, how work, skills and talent are redesigned around that new capacity. Third, how portfolio choices, including partnerships, acquisitions and divestments are used to build capabilities and redeploy capital.
In this edition:
CEOs are operating in an environment in which traditional economic signals are becoming less reliable guides for decision-making. But they are simultaneously under pressure to deliver growth despite external headwinds. And one avenue to do this is by unlocking productivity within their firms.
CEOs also need to make difficult choices around investment, transformation and how to position for the best possible future for their company in a very difficult and volatile environment. Growth is slowing and geopolitical risks rising (via EY.com US), yet equity markets remain elevated, credit is available and capital continues to flow into AI.
Our survey shows that the most common indicator of productivity-driving business value that CEOs consider is their ability to generate more output from the same talent and capital base. The emerging model is more about creating capacity that can support growth without a proportionate increase in scarce inputs.
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Read moreAI and technology will be a key part of this productivity story. But it will not be AI in isolation that produces the boost that CEOs want.
The real AI dividend is not simply lower cost but greater and better output. The next productivity divide will be between organizations that reinvest the capacity created and those that allow it to disappear into complexity and friction. AI-enabled tools and process redesign form a clear top tier of productivity drivers. The new technologies produce the strongest results when embedded in reimagined ways of working, not bolted onto the same processes. AI returns depend as much on the frictions leaders redesign and remove as on the new capabilities they create.
We require every project to have a named business sponsor, rather than being owned by the technology function, with a formal review after 90 days and a genuine option to stop. Two of our first five projects were discontinued. That discipline is what makes the returns from the remaining projects credible, and it is the step many firms skip.
The productivity question is becoming less about whether improvement is possible than whether it can be measured with confidence.
As is often the case during technological revolutions, firm-level benefits can appear before they are large or widespread enough to move national statistics, and the initial gains may be offset elsewhere by implementation costs, duplicated work or new demands.
That leakage is the central management challenge. Nearly half of CEOs say productivity gains are reinvested in growth, innovation or transformation, but close to a quarter say the gains are absorbed by complexity, regulation, risk or new work demands.
Most companies can identify a task that is faster. Fewer can show how the saved capacity changes the economics of the enterprise. Productivity becomes value only when management makes a second decision about whether they should raise output, shorten cycle time, improve quality, strengthen resilience or redirect capacity to higher-value work.
Legacy technology is the most visible barrier, but legacy management can be just as important. Outdated operating models preserve handoffs and controls designed for a slower, more fragmented information environment. They can also prevent gains from being measured at the level that matters. An AI program may meet its technical target while failing to improve unit cost, customer outcomes or cash conversion because the performance system stops at adoption.
What we need is a funded, multi-year modernization plan, prioritized around the processes that can deliver the greatest value. At the same time, it’s critical that the funding is protected during a market downturn. Modernization is often the first budget to be cut when conditions weaken, and we end up paying more later to recover the lost ground. Committing to it properly is a board decision, not an IT decision.
CEO action agenda:
The economics of AI in business is becoming an enterprise performance discipline, rather than simply a technology budget question. What are the costs and returns on that investment?
CEOs are pointing to a greater understanding of both, even if it is not fully clear for the majority.
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Read moreThe productivity dividend will remain an ambition unless CEOs can show that AI creates more value than it consumes. Only 16% of CEOs have detailed, real-time visibility into AI costs and high confidence in assessing return on investment. A further 56% have reliable visibility into most costs and reasonable confidence in returns, while a significant number of CEOs still face important gaps in their understanding of the costs and returns from AI.
AI can create capacity, but only CEOs can decide how to redeploy it. Without changes to the operating model, the benefits are likely to be lost to complexity and wasteful friction. As Gregory Daco, EY-Parthenon Chief Economist, notes, “The pace of adoption will depend not only on what the technology can do, but whether the value created by each token exceeds its cost.”
That discipline requires leaders to capture the full cost of AI. Model fees, cloud infrastructure and data are only the starting point. Integration, cybersecurity, governance, process change and training can materially alter the investment case, as can running new and legacy processes in parallel. Returns should be measured in output, unit cost, cycle time, quality, revenue or capital efficiency. Otherwise, pilots may meet their technical targets while weakening the economics as they scale.
The workforce implications are part of the same calculation. Four in five CEOs believe that, over the next three years, AI will have a greater effect on roles, skills and how work is organized than on workforce size.
As AI technology advances, the range of duties it is trusted to take on independently will expand. As a result, roles will increasingly be defined not by the tasks associated with them, but by the knowledge requirements and constraints of their associated contexts. In some contexts, it will be possible for agents to accomplish tasks and move across contexts easily, allowing for the unbundling of tasks, reorganization of workflows and consolidation of roles. In other contexts, the consolidation of roles may be less likely, but the balance of tasks associated with those roles may shift. Because contextual demands vary across an organization, these changes will not occur in a fixed sequence. Instead, role redesign, workflow changes, job creation and elimination may happen concurrently and at different rates within the same organization. Ultimately, however, advances in AI technology point toward a reality where organizations can sustain growth with fewer employees than comparable growth required in the past.
The crunch question is whether the way organizations get work done can adapt at the rate of technological development to realize this future. Almost half of CEOs (47%) say their organization is not developing skills quickly enough to keep pace with AI innovation, while 72% believe skills shortages will become a greater barrier to growth than access to capital within three years. Reskilling already ranks among the leading contributors to productivity gains, cited by 33% of CEOs. Yet only 15% say productivity gains typically fund reskilling and capability building. A key risk is that talent investment falls behind technology investment.
People need to understand specifically what automation will mean for them, and we have not always been good enough at explaining that. When you announce automation to a workforce in this industry, people do not hear ‘productivity.’ Instead, they hear ‘job losses.’ We need to be clear much earlier about which roles will change and how, and pair those changes with a genuine training pathway and a job at the end of it.
The skills agenda extends beyond hiring more AI specialists. Companies need domain experts who can redesign work, managers who can judge where human oversight matters and employees able to use AI critically. This view is reinforced by the OECD’s 2026 Skills in the AI Age¹, which says that AI’s effect will depend on a mix of foundational, digital and complementary skills, and by IMF research on new skills² showing that workers need routes to acquire new capabilities while firms need the organizational capacity to absorb and deploy them.
The strongest response is to manage AI as a reinvestment cycle. Establish a common value baseline, track financial and workforce outcomes together, decide where released capacity will go and remove the processes that would otherwise consume it. Where critical capabilities cannot be built quickly enough, CEOs may need partnerships, acquisitions or other portfolio choices.
CEO action agenda:
CEOs are moving beyond the traditional choice between organic growth and M&A. The portfolio now includes a wider range of routes to capability and a more active use of divestments to release capital, capacity and management attention.
CEOs remain ambitious about growth, but are increasingly selective about how they pursue it. They combine product, service and business model innovation with alliances, M&A and joint ventures, using multiple routes to move quickly while economics and technology continue to evolve.
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Read moreThe key question is no longer simply whether to build or buy, but which capability route best balances speed, control, cost and flexibility. Companies can build internally, acquire control, take a minority position, license intellectual property, form an alliance, create a joint venture or divest an activity that no longer earns its place in the portfolio. The strategic advantage comes from choosing the mechanism that fits the strategy. Ownership may be essential where intellectual property or integration creates enduring advantage. Partnership may be better where speed, optionality or shared investment matter more.
This more modular approach also changes how CEOs should think about the existing business. More than two-thirds (69%) of respondents say their growth strategy is more focused on strengthening the core than on disruption and transformation. That can be a disciplined choice, particularly in a volatile environment, but it can also disguise a lack of conviction in the company’s ability to respond in such a fast-changing environment. The test is whether investment in the core strengthens future competitiveness or merely extends the life of existing business models.
We are investing in higher-value manufacturing rather than remaining primarily a castings supplier, and we are developing products for applications beyond the automotive sector. At the same time, we are being cautious about adding capacity for products where long-term demand is declining, regardless of how strong utilization may look today.
The proportion of CEOs planning acquisitions over the next 12 months has fallen to 51%, down from a record 62% in May. Still, after the record second quarter in global M&A, with US$1.64t of deals announced, it was expected that 3Q26 may not hit such heights. Deal activity in both July and August has been closer to US$400b, down from roughly US$600b in preceding months, but remains well above historical averages. Importantly, acquisitions are broad-based by both geography and sector, albeit with the US and technology well out in front.
CEOs are conscious of the heightened macroeconomic volatility in today’s market, with 44% citing it as the main headwind they face over the next 12 months. It is likely to be a combination of accelerating inflation and subsequent policy to raise interest rates that will hold back dealmaking in the near-to-mid-term.
Divestments look set to drive deal market headlines in the coming months, with more than half of CEOs (51%) intending to divest a part of their business in the next 12 months, up from 42% in May.
Selling, spinning or carving out a business can release capital, reduce complexity and give an asset a more suitable owner. But familiar tensions remain for those looking to divest. Sellers are trying to balance speed and cost with value maximization while also preparing for diligence and communicating the asset’s growth potential. Those objectives are difficult to reconcile if preparation begins only after a decision to sell.
AI and advanced analytics can improve the divestiture process most where judgment begins, not just when financial analysis concludes. Nearly half of CEOs (45%) identify earlier identification and quantification of value as the leading opportunity, and more than a third (41%) want faster decision-making and deal execution. Better evidence can sharpen the value story, improve buyer targeting and expose separation issues before they narrow the range of options if not identified early enough. The broader lesson for corporate deal teams and their advisors is that the highest value may come from improving the quality and timing of decisions, rather than only accelerating document review.
CEO action agenda:
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Read moreThe same considerations that determine how AI-generated capacity becomes enterprise value also apply to transactions. CEOs need to understand how AI can accelerate deal processes and improve the quality of capital allocation decisions.
AI in business is increasingly being applied across the deal lifecycle, but fragmented experimentation will not deliver sustained value. The next challenge is to embed it in repeatable, governed workflows tied to deal outcomes. The AI measurement gap is particularly consequential in transactions. Most CEOs report reasonable visibility into costs and returns, but few have comprehensive, real-time insight. That may sustain experimentation but not fully enable strategic decisions on what to scale, what to stop or where to commit further capital.
Corporate transaction teams show a similar maturity curve. AI is being used, but it is not yet consistently embedded. Pilots dominate, while relatively few organizations describe AI as central to transaction strategy. This is a common inflection point: experimentation has proven that technology can help, but scaling requires common data, clear governance, reusable workflows and confidence that outputs will stand up to scrutiny.
Current use is concentrated in information-intensive activities such as financial analysis and valuation, commercial due diligence, risk and compliance, and market intelligence. These are sensible entry points because data volumes are large and cycle-time gains are visible. But they also carry material model, confidentiality and accountability risks. A faster answer is not automatically a better decision, especially if source data, assumptions or model limitations are opaque.
The greatest value may come not from reducing transaction timelines, but from enabling better investment decisions. The most useful governance model is neither unrestricted experimentation nor centralized control. It is a tiered system based on decision risk. Low-risk drafting and workflow tasks can be scaled quickly. Analysis that informs valuation, compliance or investment committee decisions needs stronger controls over data quality, assumptions, validation and human review. The board does not need to approve every use case, but it should understand how AI affects the evidence and accountability behind major capital decisions.
The goal is not a separate AI-enabled transaction function, but AI embedded across the deal workflow, with value measured and clear guardrails for when human judgment must take precedence. Organizations that achieve this will be better equipped to use acquisitions, divestments and partnerships as tools of continuous strategy and growth accelerators, rather than isolated events.
Next time, we would start preparing for due diligence considerably earlier. Buyers need detailed information on rights, contracts, artist arrangements and different revenue sources, and much of that information was spread across separate systems and teams. Pulling it together under time pressure created inconsistencies, and every inconsistency raises questions about the reliability of everything else. We need to have that information in a single and consistent form before starting the sale process.
CEO action agenda:
Explore key questions CEOs are asking about AI productivity, business growth and portfolio transformation
CEOs are not retreating from growth, but growth is becoming more demanding. Investment, productivity, skills and portfolio strategy can no longer be managed separately. AI and automation may expand capacity, chiefly through higher output rather than lower costs, but value will depend on continued transformation of legacy operating models, processes and systems. Partnerships and transactions can accelerate capability, while divestments can release capital and attention, provided organizations can integrate, govern and measure their choices. The imperative is a faster loop between strategy, execution and evidence. The best-positioned CEOs will retain conviction about direction while adapting the route to get there.
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