CEOs are investing in AI, talent, and portfolio transformation to convert productivity gains into growth. The latest EY-Parthenon CEO Outlook survey reveals an emerging productivity upswing, with almost all CEOs reporting productivity gains over the past 12 months, and nearly half reinvesting these gains to support future growth, innovation, and transformation. However, these gains do not always translate into enterprise performance, as nearly a quarter of CEOs report that productivity gains are offset by operational complexity, regulatory obligations, risk management requirements, and increasing work demands.
CEOs are navigating a complex backdrop of slower growth, geopolitical disruption, and technology change. Many increasingly view inaction as the greater risk, investing while building flexibility into how and when capital is deployed.
Key findings and insights:
- AI and productivity: AI can create capacity, but CEOs must decide where to reinvest it. Without operating model change, gains risk being absorbed by complexity and friction. The real AI dividend is not simply lower cost but greater and better output.
- Productivity reinvestment: The productivity question is becoming less about whether improvement is possible and more about whether it can be measured with confidence. 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.
- Barriers to value creation: Legacy systems and organizational complexity are the biggest barriers to converting productivity gains into measurable business value. Outdated operating models preserve handoffs and controls designed for a slower, more fragmented information environment.
- AI economics and talent: Organizations that align AI spending, workforce redesign, and skills development will be best positioned to realize long-term value. Only 16% of CEOs have detailed, real-time visibility into AI costs and high confidence in assessing return on investment.
- Workforce implications: 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. Reskilling already ranks among the leading contributors to productivity gains, but only 15% say productivity gains typically fund reskilling and capability building.
- Portfolio strategy: CEOs are pursuing growth through a broader range of transaction strategies, combining product, service, and business model innovation with alliances, M&A, and joint ventures. The proportion of CEOs planning acquisitions over the next 12 months has fallen to 51%, but deal activity remains well above historical averages.
- Divestitures: Divestments are on the rise, with more than half of CEOs (51%) intending to divest a part of their business in the next 12 months. AI and advanced analytics can improve the divestiture process most where judgment begins, not just when financial analysis concludes.
- AI in transactions: AI is moving from experiment to embedded discipline in transactions, being used to improve analysis, accelerate execution, and strengthen decision-making. The greatest value may come not from reducing transaction timelines but from enabling better investment decisions.
CEO action agenda:
- Reinvest capacity deliberately.
- Redesign workflows.
- Measure outcomes, not adoption.
- Fund capability from productivity gains.
- Measure full AI economics.
- Redesign work before resizing.
- Unbundle roles, remove obsolete tasks, and define where human judgment and accountability remain essential.
- Protect learning time, reskill domain experts, and use hiring, partnerships, or deals to close gaps.
- Choose the capability route.
- Stay divestment-ready.
- Build a transaction-AI roadmap.
- Link AI to deal performance.
- Govern by decision risk.
- Apply stronger validation, data lineage, and human review where AI informs valuation, compliance, or capital approval.
- Track decision quality, diligence coverage, cycle time, value realization, and post-deal learning.
- Prioritize repeatable use cases, common data standards, and measurable outcomes across the deal lifecycle.
