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 six 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
AI’s next phase: From scale to sustainable operations
Across banking, technology, and asset management and private equity, AI investment and agent deployment are holding steady as organizations shift toward more coordinated, enterprise-wide use. As multiple agents are orchestrated across workflows, the focus is sharpening on cost visibility, governance, and how work actually gets done. As AI adoption continues to scale, discipline and deliberate execution are enabling consistent, measurable value.
In banking, AI scale, speed, and value are defined by governance.
While investment remains steady and adoption accelerates across the banking sector, the ability to scale AI is enabled by governance, data readiness, and the realities of operating in a highly regulated environment. According to the KPMG US Q2 2026 AI Pulse – Banking survey, the institutions pulling ahead are those strengthening foundational capabilities, from improving cost visibility and governance consistency to preparing their workforce to operate AI effectively. As banks increasingly deploy AI agents to support cross-functional decision making, the challenge is shifting from ambition to control. The critical question is whether banks can build the infrastructure, oversight and discipline required to run AI safely and consistently at enterprise scale.
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Asset Managers and Private Equity firms begin to unlock cross-functional value from AI, while scaling remains measured.
While investment continues and firms expand beyond isolated use cases, asset management and private equity firms are taking a more deliberate approach to scaling AI, focused on cross-functional coordination. According to the KPMG US Q2 2026 AI Pulse – Asset Management and Private Equity survey, most firms remain in exploration and pilot stages, with leaders distinguishing themselves by using AI agents to align goals and performance metrics, provide shared insights, and support cross-functional decision-making, indicating early movement toward more integrated ways of working.
At the same time, scaling remains constrained by workforce readiness, governance maturity, and limited cost visibility, with employee adoption declining and resistance tied to skill gaps and workload complexity. The critical question is whether firms can build the alignment, discipline, and operating model needed to translate coordinated use into consistent, enterprise-scale value.
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At the same time, scaling remains constrained by workforce readiness, governance maturity, and limited cost visibility, with employee adoption declining and resistance tied to skill gaps and workload complexity. The critical question is whether firms can build the alignment, discipline, and operating model needed to translate coordinated use into consistent, enterprise-scale value.
For technology companies, AI scale is no longer just about deployment — it’s about coordination, control, and value.
While investment and agent adoption remain steady across the broader market, the technology sector is moving into a more advanced phase of execution. Cost visibility, cross-functional alignment, and executive accountability are becoming the true markers of scale. The leaders separating themselves are operationalizing AI as an enterprise-wide capability: using agents to align KPIs across functions, actively monitoring costs, and prioritizing outcomes over activity.
As prior technology cycles inform a more disciplined approach, the critical question is no longer whether companies can deploy AI — it is whether they can run it with discipline at enterprise scale.
Banking
In banking, AI scale, speed, and value are defined by governance.
While investment remains steady and adoption accelerates across the banking sector, the ability to scale AI is enabled by governance, data readiness, and the realities of operating in a highly regulated environment. According to the KPMG US Q2 2026 AI Pulse – Banking survey, the institutions pulling ahead are those strengthening foundational capabilities, from improving cost visibility and governance consistency to preparing their workforce to operate AI effectively. As banks increasingly deploy AI agents to support cross-functional decision making, the challenge is shifting from ambition to control. The critical question is whether banks can build the infrastructure, oversight and discipline required to run AI safely and consistently at enterprise scale.
Download PDF
Asset Management & Private Equity
Asset Managers and Private Equity firms begin to unlock cross-functional value from AI, while scaling remains measured.
While investment continues and firms expand beyond isolated use cases, asset management and private equity firms are taking a more deliberate approach to scaling AI, focused on cross-functional coordination. According to the KPMG US Q2 2026 AI Pulse – Asset Management and Private Equity survey, most firms remain in exploration and pilot stages, with leaders distinguishing themselves by using AI agents to align goals and performance metrics, provide shared insights, and support cross-functional decision-making, indicating early movement toward more integrated ways of working.
At the same time, scaling remains constrained by workforce readiness, governance maturity, and limited cost visibility, with employee adoption declining and resistance tied to skill gaps and workload complexity. The critical question is whether firms can build the alignment, discipline, and operating model needed to translate coordinated use into consistent, enterprise-scale value.
Download PDF
At the same time, scaling remains constrained by workforce readiness, governance maturity, and limited cost visibility, with employee adoption declining and resistance tied to skill gaps and workload complexity. The critical question is whether firms can build the alignment, discipline, and operating model needed to translate coordinated use into consistent, enterprise-scale value.
Technology
For technology companies, AI scale is no longer just about deployment — it’s about coordination, control, and value.
While investment and agent adoption remain steady across the broader market, the technology sector is moving into a more advanced phase of execution. Cost visibility, cross-functional alignment, and executive accountability are becoming the true markers of scale. The leaders separating themselves are operationalizing AI as an enterprise-wide capability: using agents to align KPIs across functions, actively monitoring costs, and prioritizing outcomes over activity.
As prior technology cycles inform a more disciplined approach, the critical question is no longer whether companies can deploy AI — it is whether they can run it with discipline at enterprise scale.
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