KPMG - Global AI Pulse Q2 2026: From deployment to value realisation
Key takeaways
AI adoption is accelerating faster than proven ROI. The share of organizations in the driving-adoption phase rose from 13% in Q1 to 22% in Q2, yet only a small minority describe themselves as having reached established ROI.
Confidence remains high even as expectations become more pragmatic. 76% of senior leaders say AI is already delivering meaningful business value, 78% are confident their AI strategy can be future-proofed and 79% say AI would remain a top investment priority even in a recession.
Accountability is emerging as a differentiator. Executive sponsorship is common, but organizations with clearly defined accountability report materially stronger confidence, value realization and established ROI.
AI economics is becoming a leadership issue. Nearly half of organizations have rephased AI-agent deployments when expected costs began to outweigh anticipated value, while only around one-third report full visibility into AI operating costs.
The next phase is about redesigning work, not simply deploying more technology. Human-AI collaboration, governance, resilience and ecosystem capabilities are rising in importance as organizations move from isolated use cases to enterprise-wide operating models.
Executive summary
KPMG’s Q2 2026 Global AI Pulse shows an enterprise AI market moving quickly from experimentation into adoption, but not yet converting that activity into established financial return at the same pace. Planned spending remains steady at a weighted average of US$188 million over the next 12 months, confidence has risen across every tracked measure and the proportion of organizations in the driving-adoption phase has increased sharply. The central challenge is now less about proving that AI can create value and more about proving that value consistently, at scale and with economic discipline.
Three themes dominate the report. First, accountability: CEO sponsorship is widespread, yet responsibility for AI-informed decisions remains fragmented. Second, economics: leaders are paying closer attention to operating costs, usage-based pricing, model economics and whether deployment costs remain aligned with expected value. Third, workforce integration: organizations increasingly recognize that scaled AI requires role redesign, AI fluency, clear intervention rules and practical governance embedded into everyday workflows.
The report’s strongest signal is that more AI deployment does not automatically translate into better outcomes. Organizations reporting stronger results are distinguished by clearer ownership, better cost visibility, stronger governance and a more deliberate approach to where AI is embedded in the business.
Key stats and quotable claims
AI delivering meaningful business value: 76%, up from 64% in Q1
Confidence in ability to future-proof AI strategy: 78%, up from 70%
AI remains a top priority even in a recession: 79%, up from 74%
Planned AI spending over the next 12 months: US$188m weighted average
Organizations in the driving-adoption phase: 22%, up from 13%
CEO actively owns AI as a strategic priority: 75%
CEO or executive committee ultimately accountable for AI-informed decisions: 24%
Organizations that have rephased AI deployments when costs outweighed expected value: 49%
Organizations with full visibility into AI operating costs: 35%
Established ROI with full cost visibility vs. without it: 15% vs. 3%
Established ROI where accountability is clearly defined vs. less/not accountable: 14% vs. 4%
Making good progress toward a fully integrated AI-human workforce: 71%
Significant employee adoption of AI agents: 28%, up from 25%
“One of the clearest indicators of AI maturity is that the questions change. Early adopters ask whether they should move forward. Leaders ask how to scale. They spend less time debating the technology and more time figuring out how to make it part of the business.”
Simon Benson, Regional AI Lead for ASPAC, KPMG Australia
“The next challenge is not deploying AI. It is redesigning work for a world where humans and AI operate together. That’s not a technology exercise. It’s a rethink of roles, workflows and how value gets created.”
Benedikt Höck, Regional AI Lead for EMEA, KPMG in Germany
“Clear accountability does more than manage risk. It creates confidence. When people understand who owns decisions, how AI should be used and where responsibility sits, organizations are better able to move quickly and scale responsibly.”
Priya Emmanuel, Regional AI Lead for the Americas, KPMG in the US
Overall summary
The report describes a transition from AI deployment to AI management. Organizations are still investing, confidence is strengthening and adoption is moving deeper into the enterprise. But the questions leaders are asking have changed. The emphasis is moving toward where AI produces measurable value, who is accountable for outcomes, how work needs to change and whether the economics remain sustainable as usage scales.
KPMG links stronger outcomes to three operating disciplines. The first is long-term business value, with investment directed toward infrastructure, security, governance and other foundations that support scale. The second is clear accountability, including explicit decision rights, ownership of risk, human override rules and responsibility for AI operating costs. The third is economic visibility, so leaders can see usage, cost and value together rather than managing AI through fragmented or delayed financial views.
The wider implication is that enterprise AI maturity is becoming less about access to models and more about organizational capability. Governance needs to move from policy into operating practice. Workforce adaptation needs to move from training into workflow redesign. Cost control needs to move from after-the-fact billing into active monitoring and decision-making. The organizations that can make those shifts are better positioned to turn adoption into sustainable value.
Deep dive
Chapter 1: Pragmatic AI: Adoption accelerates, expectations rise
22% have reached the driving-adoption phase of AI maturity, up from 13 percent in Q1.
76% say AI is delivering meaningful business value.
79% would maintain AI investment even in a recession.
71% report making good progress toward becoming a fully integrated AI-human workforce.
Confidence remains strong as AI moves into deployment
More than three-quarters of senior leaders now express confidence in AI’s long-term value, strategic relevance and ability to withstand economic uncertainty. Notably, confidence increased across every tracked measure this quarter, with the largest gain among the confidence measures occurring in perceptions of meaningful business value. Organizations are becoming less focused on proving AI’s potential and more focused on scaling it effectively across the enterprise.
Organizations are becoming more confident in AI’s strategic value
I am confident in my organization’s ability to future-proof its AI strategy: Q1: 70%; Q2: 78%; Change: +8 pts
AI will continue to be a top investment priority for your organization, even if a recession occurs in the next 12 months: Q1: 74%; Q2: 79%; Change: +5 pts
AI is currently delivering meaningful business value for my organization: Q1: 64%; Q2: 76%; Change: +12 pts
To what extent do you agree or disagree with the following statements? Percentage who agree/strongly agree. Q1 (n=2110), Q2 (n=2145).
Source: Global AI Pulse Q1 2026 and Global AI Pulse Q2 2026, KPMG International, April 2026 and June 2026 respectively.
Broad adoption outpaces maturity
The share of organizations in the driving-adoption phase rose significantly this quarter, increasing from 13 percent to 22 percent, representing the largest movement observed anywhere along the AI maturity curve. Yet relatively few organizations identify as having reached the established-ROI stage. Organizations are moving rapidly toward broader deployment, while fewer have reached the most mature phase of the AI journey.
“One of the clearest indicators of AI maturity is that the questions change. Early adopters ask whether they should move forward. Leaders ask how to scale. They spend less time debating the technology and more time figuring out how to make it part of the business.”
Simon Benson
Regional AI Lead for ASPAC
KPMG Australia
The largest movement this quarter occurred in the driving-adoption phase
Research and development: Understanding the technology and its potential: Q1: 11%; Q2: 9%; Change: −2 pts
Experimentation: Proof-of-concepts or pilots, identifying areas of improvement opportunity: Q1: 22%; Q2: 19%; Change: −3 pts
Strategic planning: AI roadmap, KPIs and data infrastructure: Q1: 21%; Q2: 21%; Change: No change
Scaling the technology: Early adoption and optimization with measurable outputs: Q1: 26%; Q2: 22%; Change: −4 pts
Driving adoption: Embedding AI across the organization: Q1: 13%; Q2: 22%; Change: +9 pts
Established ROI: Experiencing meaningful business outcomes with tangible growth opportunity: Q1: 8%; Q2: 7%; Change: −1 pt
Which of the following best describes the phase your organization is in its AI journey? Q1 (n=2110), Q2 (n=2145).
Source: Global AI Pulse Q1 2026 and Global AI Pulse Q2 2026, KPMG International, April 2026 and June 2026 respectively.
AI maturity
The extent to which an organization has moved beyond experimenting with AI to embedding it across operations and generating measurable business value, assessed here across six progressive phases from research and development to established ROI.
AI priorities are broadening as deployment scales
Planned AI investment remains steady at US$188 million* over the next 12 months, effectively unchanged from Q1. While spending levels remain stable, organizations are becoming more deliberate about where they invest and what capabilities they prioritize.
Productivity and faster decision-making remain important priorities, but both declined this quarter. In contrast, ecosystem partnerships, governance, resilience and human-AI collaboration gained importance. As AI adoption expands, organizations appear to be shifting their attention from individual use cases toward the capabilities required to embed AI more broadly across the enterprise. The focus is increasingly moving from proving AI can deliver value to building the foundations needed to scale it successfully.
“The next challenge is not deploying AI. It is redesigning work for a world where humans and AI operate together. That’s not a technology exercise. It’s a rethink of roles, workflows and how value gets created.”
Benedikt Höck
Regional AI Lead for EMEA
KPMG in Germany
AI priorities are expanding beyond productivity and efficiency
Faster, better decisions: Q1: 41%; Q2: 36%
Productivity gains: Q1: 42%; Q2: 35%
Cost reduction: Q1: 31%; Q2: 29%
Human-AI collaboration and fluency: Q1: 28%; Q2: 30%
Responsible AI and governance: Q1: 26%; Q2: 28%
Adaptability and resilience: Q1: 18%; Q2: 20%
Ecosystem and partnerships: Q1: 12%; Q2: 16%
Which of the following are key AI priorities for your organization? Q1 (n=2110), Q2 (n=2145).
Source: Global AI Pulse Q1 2026 and Global AI Pulse Q2 2026, KPMG International, April 2026 and June 2026 respectively.
Weighted average: Average reflects a weighted mean based on reported planned investment across respondents, adjusted for sample representation by organization size and region.
Economics are becoming a strategic consideration
AI strategy is being shaped by a broader set of considerations than at any point in our research. Cost and efficiency, competitive pressures, energy use and macroeconomic conditions are all rising in importance. Growth, competition and economics are becoming more intertwined in AI decision-making.
Access to lower-cost, high-fidelity models is the fastest-rising influence on strategy, increasing by 7 points quarter over quarter, while energy use, carbon concerns and macroeconomic conditions each rose by 6 points. Nearly half of organizations report having rephased AI deployments where costs began to outweigh expected value. Senior leaders are increasingly weighing AI operating costs, token usage, sustainability and growth as interconnected decisions. As AI becomes more deeply embedded across the enterprise, the strategic agenda is expanding beyond productivity and use-case deployment to include the economics, governance and long-term sustainability of AI at scale.
The human side of AI scaling comes into focus
Organizations recognize that scaling AI will require changes in how people work. More than three-quarters of senior leaders (78 percent) expect AI fluency to become more important and believe roles will change for employees who do not develop these capabilities. At the same time, 71 percent report making good progress toward becoming a fully integrated AI-human workforce.
The shift is visible in employee behavior. Significant employee adoption of AI agents rose from 25 percent to 28 percent globally, while resistance declined modestly to 14 percent. The exception is the United States, where resistance increased from 5 percent to 20 percent. The contrast highlights that workforce adaptation may not progress uniformly as AI becomes more embedded in day-to-day work.
For many organizations, the next challenge is not deploying AI, but embedding it into how work gets done. Realizing value at scale will depend on workforce adoption, capability development and the redesign of work around human-AI collaboration.
Security remains the main concern, but economic factors are rising fast
Data security/privacy/risk concerns: Q1: 33%; Q2: 33%
Pressures to demonstrate value to investors or our board: Q1: 19%; Q2: 24%
Macroeconomic factors (GDP growth, inflation, tariffs, etc.): Q1: 18%; Q2: 24%
Limitations on hiring, upskilling initiatives: Q1: 18%; Q2: 22%
Need to manage an increased workload or volume of tasks: Q1: 17%; Q2: 22%
Access to lower-cost, high-fidelity technology/LLMs: Q1: 15%; Q2: 22%
Increased energy use by my organization and its impact on the environment: Q1: 16%; Q2: 22%
Balancing AI scaling and carbon emissions/sustainability impact: Q1: 16%; Q2: 22%
To what extent are the following factors influencing your AI strategy within the next 6 months? Percentage saying ‘Great concern’. Q1 (n=2110), Q2 (n=2145).
Source: Global AI Pulse Q1 2026 and Global AI Pulse Q2 2026, KPMG International, April 2026 and June 2026 respectively.
Chapter 2: Governance: The foundation of AI at scale
3x higher established ROI with clearly defined accountability.
75% say their CEO actively owns AI as a strategic priority.
Only 24% identify the CEO or executive committee as ultimately accountable for AI-informed decisions.
AI governance is evolving from principle to practice
The Q1 report highlighted the growing importance of governance and trust as foundations for responsible AI adoption. As organizations scale AI across the enterprise, Q2 suggests the conversation is shifting from governance itself to how governance is operationalized. The question is no longer to what degree governance matters. It has to be embedded into day-to-day decision-making.
Around three-quarters of organizations say their CEO actively owns AI as a strategic priority. Yet ownership and accountability are not the same thing. As executive ownership becomes the norm, organizations reporting the strongest outcomes are those that translate leadership commitment into clear accountability, decision rights and governance practices.
Accountability remains widely distributed
While executive ownership is common, accountability for AI outcomes remains fragmented. Just 24 percent of organizations identify the CEO or executive committee as ultimately accountable for AI-informed decisions, while 29 percent point to a named C-suite executive.
For many organizations, accountability is shared across business leaders, functions and governance committees. Shared responsibility can support collaboration, but it can also make it more difficult to determine who owns outcomes, manages risk and has authority to act when issues arise.
Few organizations have a single point of accountability for AI-informed decisions
The CEO or executive committee: 24%
A named C-suite executive accountable for AI outcomes (e.g. COO, CAIO, CIO): 29%
The business unit or function leader using the AI: 15%
A centralized AI governance or risk committee: 13%
Shared accountability across multiple roles or teams: 13%
Accountability is unclear or not formally defined: 3%
AI systems operate autonomously within approved limits: 1%
Not sure: 2%
Q2 (n=2145).
Source: Global AI Pulse Q2 2026, KPMG International, June 2026.
“As AI becomes woven into the fabric of the enterprise, governance can no longer sit on the sidelines. The organizations creating the most value are building accountability into the way decisions are made, risks are managed and work gets done.”
Samantha Gloede
Global Head of Risk Services and Global Trusted AI Leader
KPMG International
Governance must extend beyond leadership
Executive accountability is important, but governance ultimately succeeds or fails through day-to-day operating practices. Organizations need clear rules for when employees can intervene, who owns AI-related costs, how AI outputs are reviewed and what happens when systems fail.
While most organizations report having at least some governance mechanisms in place, relatively few describe these practices as fully embedded. Across a range of governance activities, only about one-third say roles, responsibilities and processes are very clear and well managed. Many organizations are still working to translate governance principles into operational discipline.
Governance foundations are in place, but operational clarity remains uneven
Who is accountable for AI data quality and data refresh cycles: Very clear/well managed: 35%; Somewhat clear: 40%; Unclear/gaps exist/not addressed: 26%
Where humans are expected to override, review, or correct AI outputs: Very clear/well managed: 35%; Somewhat clear: 40%; Unclear/gaps exist/not addressed: 25%
How easy it is for staff to intervene or pause AI-driven decisions when needed: Very clear/well managed: 35%; Somewhat clear: 38%; Unclear/gaps exist/not addressed: 27%
Understanding the ongoing operating costs of AI once embedded in workflows: Very clear/well managed: 34%; Somewhat clear: 39%; Unclear/gaps exist/not addressed: 28%
For each statement, please select the option that best reflects your organization’s current situation. Q2 (n=2145).
Source: Global AI Pulse Q2 2026, KPMG International, June 2026.
“Clear accountability does more than manage risk. It creates confidence. When people understand who owns decisions, how AI should be used and where responsibility sits, organizations are better able to move quickly and scale responsibly.”
Priya Emmanuel
Regional AI Lead for the Americas
KPMG in the US
Clear accountability is associated with stronger outcomes
Across multiple measures, the pattern is consistent. Organizations with clearly defined accountability are more likely to report confidence in their AI strategy, stronger business value and established ROI. The strongest relationship appears in ROI, where organizations with clearly defined accountability report established returns at more than three times the rate of those without it.
The findings suggest that governance is becoming an important enabler of successful AI adoption. Accountability alone does not create value, but organizations that define it clearly appear better positioned to realize value consistently.
The strongest outcomes are reported where accountability is clearly defined
Confidence in organization’s ability to future-proof its AI strategy: CEO accountable: 60%; CEO less or not accountable: 22%
AI is currently delivering meaningful business value for my organization: CEO accountable: 57%; CEO less or not accountable: 21%
Established ROI: CEO accountable: 14%; CEO less or not accountable: 4%
To what extent do you agree or disagree with the following statements? Percentage that strongly agree. Q2 (n=2145).
Source: Global AI Pulse Q1 2026 and Global AI Pulse Q2 2026, KPMG International, April 2026 and June 2026 respectively.
Chapter 3: The economics of AI: From deployment to management
49% have rephased AI deployments when expected costs outweighed anticipated value.
15% vs. 3% established ROI among organizations with full cost visibility versus those without it.
+7 points increase in the importance of lower-cost, high-fidelity models as a strategic influence.
Managing AI economics becomes a leadership priority
As AI scales across the enterprise, senior leaders are paying closer attention to what it costs to operate, sustain and grow. Managing AI economics is emerging as a key challenge this quarter, as organizations face growing pressure to demonstrate value while controlling costs.
The economics of AI are also becoming more difficult to manage. One-third of senior leaders cite AI cost and economic literacy skills as a challenge to deploying AI agents, while 29 percent report difficulty understanding and controlling operating costs as systems scale. As usage-based pricing models become more common, many organizations are still building the capabilities required to forecast, monitor and manage AI spending effectively.
The shift is also evident in strategic decision-making. Access to lower-cost, high-fidelity models is the fastest-rising influence on AI strategy, increasing by 7 points from Q1. Nearly two-thirds of organizations say they would consider incentives designed to encourage greater AI usage. Yet as deployments expand, organizations need more visibility into how usage translates into cost and whether those costs are generating meaningful business value.
Deployment decisions are becoming more selective
Many organizations are becoming more deliberate in how they deploy AI. Nearly half report having rephased AI-agent deployments when expected costs began to outweigh anticipated value. Around one-quarter have scaled back deployments, while a similar share have delayed or paused them. Another fifth report questioning deployment decisions altogether.
These actions do not signal reduced confidence in AI. Rather, they suggest a growing willingness to evaluate where AI creates meaningful value and where it does not. Organizations appear increasingly focused on concentrating investment where expected returns are strongest.
Nearly half of organizations have rephased AI deployments when costs outweigh expected value
Yes — we have scaled back or narrowed deployment: 24%
Yes — we have delayed or paused further rollout: 25%
We — have questioned the decision but not made changes: 22%
No — costs and value remain aligned: 24%
Not applicable — AI agents not yet deployed: 5%
Costs outweigh AI value: 49%
Has your organization questioned, delayed, or scaled back the deployment of AI agents because the expected costs began to outweigh the value generated? Q2 (n=2145).
Source: Global AI Pulse Q2 2026, KPMG International, June 2026.
Economic visibility remains limited
Managing AI economics depends on understanding them. Yet only around one-third of organizations report having full visibility into their AI operating costs and actively monitoring them. Most continue to rely on partial, delayed or fragmented views of AI spending.
Organizations with stronger cost visibility are also more likely to have governance controls in place, including formal review processes, usage monitoring and spending controls. As AI deployments grow more complex, effective cost management depends on the processes and controls needed to monitor spending, evaluate trade-offs and understand where value is being created.
Most organizations lack full visibility into AI operating costs
Fully visible and actively monitored: 35%
Somewhat visible: 42%
Visible only after billing: 13%
Largely invisible: 8%
Not sure: 3%
How visible are the operating costs of your AI systems today? Q2 (n=2145).
Source: Global AI Pulse Q2 2026, KPMG International, June 2026.
Understanding AI economics is becoming a competitive capability
Across the findings, the pattern is consistent. Organizations with full visibility into AI operating costs are substantially more likely to report established ROI than those without it. Those with full cost visibility are five times more likely to report established ROI than organizations without full visibility (15 percent versus 3 percent).
Effective AI management depends on visibility. Visibility alone does not create value, but it gives senior leaders the information required to allocate resources, evaluate trade-offs and scale investments more effectively.
The disciplines required to manage AI economics are still emerging
Managing AI economics requires more than visibility into costs. It also depends on the controls, processes and operating disciplines organizations use to monitor spending and manage value over time.
While many organizations are paying closer attention to AI economics, relatively few have implemented the full set of capabilities required to manage them effectively. Just over half report incorporating cost reviews into AI approval processes or using dedicated cost-monitoring dashboards, while fewer have established token budgets or architecture standards designed to improve efficiency.
Many organizations have yet to implement the core disciplines required to manage AI economics
Cost review as part of AI approval processes: 54%
AI cost-monitoring dashboards: 53%
Usage or token budgets: 40%
Architecture or prompt design standards: 39%
None of the above: 5%
Not sure: 3%
Which of the following are currently in place to manage AI usage costs? Q2 (n=2145).
Source: Global AI Pulse Q2 2026, KPMG International, June 2026.
Conclusion: Turning adoption into value
Confidence is rising, investment remains steady and organizations are moving rapidly from experimentation toward enterprise deployment. Yet established ROI remains concentrated among a relatively small group of organizations. As AI scales, the challenge is shifting from deployment to execution: translating adoption into measurable and sustainable value.
Three priorities consistently distinguish organizations reporting stronger outcomes.
Together, these priorities help organizations move beyond deployment toward sustainable value creation. Strong foundations support scale, clear accountability helps translate ambition into outcomes and economic visibility enables senior leaders to make better decisions as AI deployment grows.
1. Prioritize long-term business value
Direct investment toward the foundations that support long-term value creation, including infrastructure, security and governance. As AI matures, focus on the capabilities required to scale value across the enterprise.
2. Clarify accountability
Define who is accountable for AI outcomes, who manages risk and who has authority to act. Make decision rights explicit, including who can override AI outputs, how employees intervene and who owns AI operating costs.
3. Build economic visibility
Create visibility into AI costs, usage and value from the outset. Establish the monitoring and controls needed to ensure AI can be scaled sustainably and deliver measurable return.
The bottom line
The defining signal this quarter is not whether organizations are investing in AI. It is how they are managing the challenge of turning adoption into value. Organizations are becoming more deliberate about where they invest, how they govern AI and how they manage its economics. Those reporting the strongest outcomes are not necessarily deploying more AI. They are building the capabilities required to realize value more consistently as AI scales.
