KPMG AI Quarterly Pulse Survey
From AI ambition to value: Confidence fuels scale
Executive Insights:
A stronger case for AI value emerges with increase in governance, financial accountability and workforce adoption.
Enterprise AI programs are entering a more mature, disciplined phase, according to the latest KPMG AI Quarterly Pulse Survey. Nearly 6 in 10 organizations can now demonstrate quantifiable business value from their AI investments across multiple operational and financial dimensions—from productivity and accelerated decision-making to enhanced stakeholder experience and stronger financial performance.
In tandem, leaders are continuing to embed rigorous governance and financial accountability directly into their AI programs. As cost reviews, usage budgets, and monitoring dashboards become standard practice, confidence in AI risk management is increasing, even as AI agent deployment scales. Today, 73% of leaders report confidence in their existing capabilities to manage AI risks at scale, up from 57% last quarter.
Given stronger guardrails, organizations are progressing more confidently into the next wave of AI innovation: the percentage of surveyed executives reporting deployment of AI agents increased from 53% to 62% since last quarter. Notably, the percentage actively developing or implementing multi-agent systems climbed to 25%, compared to only 6% in the last two quarters; and enterprise workforce adoption quadrupled over the past year.
How do organizations define AI value?
While initial AI deployments centered primarily on efficiencies at the task level, organizations are now capturing value across the broader enterprise value chain. Productivity gains remain strong (55%), but the value profile is diversifying:
- 49% report faster decision-making, as AI compresses business analysis and execution cycles.
- 38% cite better customer and employee experiences via AI tools that personalize engagement and elevate everyday workplace satisfaction.
- 37% boast stronger financial performance, e.g., revenue gains and cost efficiencies directly attributable to AI implementations.
How can organizations govern AI risk and costs at scale?
As AI scales across functions, governance must evolve from overarching policy guidelines into day-to-day operational oversight. Enterprise leaders are boosting their financial discipline, operational tracking, and guardrails for human/AI interaction.
- 74% of leaders require cost reviews during project approval, up from 61% last quarter.
- 70% of enterprises use real-time monitoring dashboards to assess performance.
- 43% enforce token or compute usage budgets to manage spending.
- 49% have defined high-risk scenarios where autonomous AI decision-making is prohibited.
How are organizations scaling autonomous AI agents responsibly?
Organizations are scaling autonomous AI agents responsibly by moving beyond siloed experiments and embedding them in workflows with clear controls over costs, data access and decision-making. With these safeguards in place, adoption is broadening from individual agents to coordinated multi-agent systems:
- 62% of organizations are currently building, testing, or deploying AI agents, up from 53% last quarter.
- 25% of organizations have deployed multi-agent systems, up from 6% across the previous two quarters.
How are organizations driving workforce adoption of AI?
Organizations are driving workforce adoption by extending AI beyond small pilots and embedding it into everyday workflows. The results show both the growing reach of AI across the workforce and its evolving role in employee productivity:
- 44% of organizations now report significant employee adoption of AI, a more than fourfold increase from the 10% reported just one year ago.
Source: KPMG US, AI Quarterly Pulse Survey, Q3 2026 (September 2026)
INDUSTRY FOCUS
Confidence in AI governance is driving enterprise scale
Across technology, banking, and asset management and private equity, organizations are realizing AI's value as confidence in governance continues to grow. As AI becomes more embedded in core business operations and AI agent adoption expands, leaders are investing in trusted data, cost discipline, and workforce readiness to scale AI responsibly across the enterprise.
Banks are scaling AI, strengthening the data, governance, workforce capabilities, and human oversight needed to support adoption at enterprise scale.
Known for balancing innovation with rigorous risk management, banks are seeing measurable returns as AI moves deeper into the business, with CEOs taking greater ownership and agents advancing into deployment. At the same time, banks remain disciplined in how they scale, concentrating on investments in risk and compliance, operations, and cybersecurity.
Data readiness, privacy and cybersecurity remain significant challenges, and leaders are sharpening their focus on governance, trusted data and human oversight. Banks are also formalizing how employees work with AI, embedding new skills into workflows and elevating human judgment and creativity as AI takes on more tasks.
Asset managers and private equity firms are realizing AI value while investing in the workforce skills and governance needed to support broader adoption.
Asset managers and private equity firms are seeing AI generate value across a range of business priorities, including productivity, decision-making and financial performance, as AI becomes increasingly integrated into business operations.
As AI agent deployment expands, firms are becoming more focused on how adoption is managed and supported across the organization. Firms are teaching AI skills, integrating AI collaboration into existing roles and preparing employees for changing workflows. At the same time, organizations are strengthening oversight through data-controls, monitoring capabilities and enhancing human review of AI-agent activity. The next phase for many firms will be ensuring these workforce and governance capabilities continue to mature alongside AI adoption.
As nearly all tech leaders see AI value, scale is the next test
With 92% of tech leaders seeing tangible returns from AI, the industry is raising the bar for what success looks like. Cost reviews are becoming standard practice, CEOs are actively owning AI’s outcomes, and organizations are establishing clear safeguards around higher-risk use cases for autonomous agents.
The next challenge is scaling those gains across the enterprise while maintaining trust, security, and a workforce strategy that keeps pace with the technology. That means knowing where AI creates the greatest impact, managing the costs of increasingly sophisticated models and capabilities, and ensuring governance keeps pace with adoption.
Banking
Banks are scaling AI, strengthening the data, governance, workforce capabilities, and human oversight needed to support adoption at enterprise scale.
Known for balancing innovation with rigorous risk management, banks are seeing measurable returns as AI moves deeper into the business, with CEOs taking greater ownership and agents advancing into deployment. At the same time, banks remain disciplined in how they scale, concentrating on investments in risk and compliance, operations, and cybersecurity.
Data readiness, privacy and cybersecurity remain significant challenges, and leaders are sharpening their focus on governance, trusted data and human oversight. Banks are also formalizing how employees work with AI, embedding new skills into workflows and elevating human judgment and creativity as AI takes on more tasks.
Asset Management & Private Equity
Asset managers and private equity firms are realizing AI value while investing in the workforce skills and governance needed to support broader adoption.
Asset managers and private equity firms are seeing AI generate value across a range of business priorities, including productivity, decision-making and financial performance, as AI becomes increasingly integrated into business operations.
As AI agent deployment expands, firms are becoming more focused on how adoption is managed and supported across the organization. Firms are teaching AI skills, integrating AI collaboration into existing roles and preparing employees for changing workflows. At the same time, organizations are strengthening oversight through data-controls, monitoring capabilities and enhancing human review of AI-agent activity. The next phase for many firms will be ensuring these workforce and governance capabilities continue to mature alongside AI adoption.
Technology
As nearly all tech leaders see AI value, scale is the next test
With 92% of tech leaders seeing tangible returns from AI, the industry is raising the bar for what success looks like. Cost reviews are becoming standard practice, CEOs are actively owning AI’s outcomes, and organizations are establishing clear safeguards around higher-risk use cases for autonomous agents.
The next challenge is scaling those gains across the enterprise while maintaining trust, security, and a workforce strategy that keeps pace with the technology. That means knowing where AI creates the greatest impact, managing the costs of increasingly sophisticated models and capabilities, and ensuring governance keeps pace with adoption.
AI’s value story is getting sharper. The clearest sign that AI is maturing is where the value is showing up: better experiences, faster decisions and stronger financial performance. That is putting AI at the center of business strategy.
Todd Lohr
Vice Chair and Head of Client Technology & Innovation, at KPMG LLP
What are the key findings of the Q3 2026 Pulse Survey?
- 58% of organizations now report measurable business value from AI initiatives.
- 74% incorporate formal cost reviews into their AI approval processes.
- 73% of leaders express confidence in their governance and risk-management capabilities at scale.
- 62% are actively building, developing, or deploying AI agents across enterprise workflows.
- 25% are developing or deploying multi-agent systems.
- 44% report significant workforce adoption.
Dive into our thinking:
AI Q3 2026 Pulse Survey: Key findings
AI business value comes into focus
Confidence to scale AI continues to grow
AI agents are moving from pilots to enterprise reality
Looking ahead, how can organizations continue to drive AI value?
To continue to bridge the gap between AI ambition and durable value, leadership teams should prioritize the following guidelines:
- Tie AI projects to multifaceted business metrics: Move beyond measuring time savings to measuring decision velocity, customer sentiment, error reduction, and financial impact.
- Treat governance as a “work in progress”: While existing enterprise controls are likely an effective baseline, autonomous and multi-agent architectures require continuous oversight, active model evaluation, and regular stress-testing and enhancements.
- Formalize cost architectures: As agent usage accelerates, implement dynamic token budgeting, chargeback models, and real-time observability dashboards to prevent cost overruns.
- Foster human-agent collaboration: Pair technical rollouts with targeted workforce upskilling, clear employee guardrails, and change management programs to maximize organic workplace adoption.
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Todd Lohr
National Managing Principal of Clients & Markets, KPMG LLP
Todd Lohr
National Managing Principal of Clients & Markets, KPMG LLP