KPMG AI Quarterly Pulse Survey

Executive Insights:

As organizations embrace scaled AI deployment, the challenge is doing so with disciplined execution. This Q2 2026 report examines how leaders are managing the economics of running AI at scale, orchestrating agents across workflows, and turning usage into measurable enterprise impact.

Where do organizations stand in their AI journeys?

AI has entered a new phase defined by the shift from scaling adoption to managing execution with greater discipline. Organizations realize that AI is integral to future business viability and growth: deployment is holding steady above 50%, and leaders plan to invest an average of $202 million over the next 12 months.

However, as scale increases a new challenge is emerging: Only 26% of organizations have real-time visibility into the cost of running AI. Disciplined execution is now a core constraint.

What defines the next stage of AI maturity?

The defining shift for organizations is from deploying AI to coordinating its functionality across workflows.

While 53% of organizations are using AI agents, the percentage orchestrating multiple agents across workflows has doubled (9% → 18%). This reflects an accelerating trend away from optimizing individual tasks and toward connecting decisions, data, and processes across the enterprise.

• Why it matters: It has become clear that AI value is created across workflows, not within them. Organizations that remain siloed will struggle to translate adoption into meaningful enterprise impact.

How can companies track and manage the cost of AI at scale?

As AI usage scales, organizations need a clearer way to track costs, the priorities that are being funded, and the degree to which AI investments are yielding value. The issue is no longer simply whether organizations are investing in AI, but whether they can see, explain, and manage the economics of that usage as it moves across teams and workflows.

Many organizations have put foundational controls in place: 66% use dashboards and 61% require approvals. However, only a quarter have real-time visibility into the cost of running AI. At the same time, 35% of leaders cite gaps in economic literacy, including token and inference pricing, as a barrier. 

This confluence of factors creates a management cost-visibility gap: leaders may know AI is being used, but not always whether costs are increasing and which use cases are generating value.

• Why it matters: Without granular visibility across the enterprise, organizations can’t link AI usage to value. This creates a blind spot where costs can rise faster than outcomes, requiring leaders to reframe their view of AI from a technology investment to a system of capital allocation.

What ultimately separates high-performing organizations?

High-performing organizations are beginning to separate themselves through how effectively their people use AI. Nearly half of leaders (47%) say employees who use AI effectively already outperform their peers, making AI fluency—the ability to partner with AI to drive outcomes—a key determinant of individual and organizational performance.

Organizations are responding by investing in upskilling and reskilling (65%) and paying a 6–15% salary premium for strong AI talent. At the same time, 55% expect roles to evolve for employees who do not adopt AI capabilities, while 54% say social and interpersonal skills are now more important than purely technical ones.

• Why it matters: The advantage is no longer access to AI; it is the ability to use it well. Organizations that build workforce fluency and align skills, incentives, and collaboration to measurable outcomes will extract more value from their AI investments.

Source: KPMG US, AI Quarterly Pulse Survey, Q2 2026 (June 2026)