AI investment remains a top priority, but the standard for performance is rising. Our latest survey of 3,000 C-suite executives and 3,000 employees, fielded between July and August 2026, shows that widespread adoption is raising a new challenge: how to deliver value at scale.
Leaders are sharpening their focus on returns, AI economics and cyber resilience. Employees are using AI more frequently, working more alongside agents and feeling more secure in their roles.
Leaders are more measured about the year ahead, while employees are optimistic.
Leaders are more measured about the year ahead, while employees are optimistic.
Leaders are moderating their expectations about growth and hiring. Employees meanwhile are optimistic as nearly two-thirds now feel secure in their roles, up sharply since January.
71%
of C-suite leaders expect domestic revenue growth over the next 12 months, down from 90% in January
59%
expect faster revenue growth heading into 2027, down from 73%
61%
expect hiring to increase over the next 12 months, down from 74%
66%
of employees feel secure in their role, up from 48%
Explore the data: See how results compare on revenue growth expectations by industry or geography.
Against the macroeconomic backdrop, leaders continue to commit capital to AI.
Against the macroeconomic backdrop, leaders continue to commit capital to AI.
Spending is consistent as organizations continue familiarizing themselves with the technology, its economics and associated impacts on growth.
75%
of C-suite leaders plan to increase AI investment over the next 12 months
32%
see signs of an AI bubble, down from 51% in July
49%
would keep increasing investment even if a bubble burst
76%
see AI as more beneficial to revenue growth than cost reduction, consistent over the past two years
42%
primarily fund AI through new, dedicated budgets
As AI use expands, leaders are shifting from adoption economics to operating economics.
As AI use expands, leaders are shifting from adoption economics to operating economics.
FAQ
What is tokenomics?
The Tokenomics Foundation defines AI tokenomics as the discipline of converting energy and capital into AI capabilities and efficiently consuming that intelligence across the organization, to realize measurable business value. Tokens are the units of data that AI models process. Every prompt, response, retrieval step and agent interaction consumes them.
Tokenomics has become part of how organizations govern, fund and optimize AI at scale.
69%
of C-suite leaders scrutinize AI operating costs more closely than a year ago
41%
actively manage tokenomics and 51% are beginning to track it
57%
optimize systems for efficiency, whereas 48% train employees on responsible use
80%
of employees say they are familiar with the cost impact, but only 32% received clear guidance
Explore the data: See how results compare for organizations’ approaches to managing AI token spend by industry and geography.
Daily use is growing faster than end-to-end process redesign or scaled deployment of AI agents.
Daily use is growing faster than end-to-end process redesign or scaled deployment of AI agents.
There’s a gap between widespread adoption and the deeper organizational change needed to realize value at scale.
The next gains will depend on turning frequent use into connected workflows, redesigned processes and new operating models.
53%
of C-suite leaders expect measurable ROI within 12 months, rising to 87% within 24 months
41%
of leaders and 43% of employees use AI daily, up from 27% and 30% in July, respectively
15%
of leaders redesign end-to-end processes and only 20% are actively deploying agents across functions
25%
report widespread and sustained value from AI, up just two percentage points since July
Explore the data: See when leaders across the C-suite, industries and geographies believe they will achieve ROI expectations from AI.
As organizations scale AI, cybersecurity is becoming both a source of value and a test of readiness.
As organizations scale AI, cybersecurity is becoming both a source of value and a test of readiness.
With AI expanding the type and scale of cyber risks, readiness matters for investment. Greater confidence in managing AI-related risks is now the single most cited reason (24%) for increasing AI investment.
70%
of C-suite leaders rank cybersecurity a high strategic priority over the next 12 months, but only 49% are highly concerned about attacks
48%
rank cyber defenses and resilience among the areas delivering the greatest business value to date
54%
identify securing physical infrastructure and operational assets as a primary cybersecurity objective
55%
cite AI-generated phishing as a top cyber threat, and 53% cite AI-assisted malware
56%
of employees are confident in their ability to detect AI-powered threats in their work, while 58% say they have received the training needed to identify risks
Explore the data: See how organizations are adapting cybersecurity strategy to address threats by industry, geography and C-suite role.
AI is changing how work gets done faster than it is changing jobs.
AI is changing how work gets done faster than it is changing jobs.
As use deepens, employee behavior and trust are shifting with it. Productivity and satisfaction are rising, but so are the pace and demands of work.
Organizations will need to redesign how work gets done so that productivity gains remain sustainable.
64%
of employees say they try AI tools before asking a colleague for help
50%
regularly work with AI agents, up from 44% in July
53%
of employees would trust an AI agent to complete tasks from start to finish
82%
say AI increased their productivity, while 75% say it has increased their job satisfaction
50%
say AI has increased the expected speed of work, as well as the need to review AI outputs
48%
say rapidly changing AI tools make it difficult to keep current
Explore the data: See how employees are describing their experience with AI agents that can plan, decide or act with minimal human input by industry and geography.
What's next?
AI commitment is holding, but widespread adoption alone will not deliver value at scale. Leaders and employees have arrived at roughly the same place: using AI more often, broadly optimistic about its potential and cautiously confident about the year ahead.
Maturity will increasingly be measured not only by how widely AI is used, but by how effectively organizations control its economics, direct it toward valuable work, redesign processes and equip employees to work with AI safely and effectively. The challenge is increasingly organizational as well as technical, requiring new ways of working, stronger preparedness and greater clarity for employees on how AI will reshape their work.
Here’s what organizations should consider next:
- Widespread use is not the destination. Organizations need to turn adoption into sustained business value by connecting AI to high-value work and end-to-end processes. Our research on “The age of co-intelligence" finds that more than 50% of working hours are expected to be impacted by AI agents.
- AI costs need active management. With token consumption volume expected to grow 78% over the next 24 months, organizations need to select the right models for the right work, manage AI economics and introduce appropriate governance and controls.
- Cybersecurity must be embedded. AI can strengthen resilience, but only when governance, detection, training and accountability keep pace with the threat environment. Frontier AI models have narrowed the gap between discovery, weaponization and exploitation from weeks to hours.
- Work must be redesigned. Use must translate into connected workflows, end-to-end process redesign and effective human–AI collaboration. Employees also need the skills, guidance and support to adapt as AI changes how work gets done.
Methodology
Accenture conducted two complementary global surveys between July and August 2026. The first surveyed 3,000 C-suite leaders from organizations with annual revenues greater than $300 million across 19 industries and 21 countries. The second surveyed 3,000 non-C-suite employees from organizations of the same scale, also across 19 industries and 21 countries. Together, the surveys capture perspectives on the current business environment, including the drivers of disruption, preparedness to respond to change and the impact of emerging technologies such as Al on talent.
We use generative AI in our research production process. Our research experts review and validate the generative AI outputs with traditional research methods where possible, applying Accenture’s Responsible AI standards.
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