Asset managers and private equity firms are projecting average AI spending of $103 million over the next 12 months, even as the industry grapples with slowing adoption of AI agents and deepening concerns about cost control, according to KPMG's Q2 2026 AI Quarterly Pulse Survey.
The survey, which polled 100 US-based C-suite and senior executives from firms with annual revenues of $1 billion or more, found cybersecurity the dominant near-term spending category at 35%, followed by data and analytics at 29% and research and development at 27%.
Commitment to the technology appears durable. Just over half of respondents said AI would remain a top investment priority even in a recession scenario over the next 12 months, a finding that signals the sector views AI less as discretionary spending and more as structural infrastructure.
Data security and privacy concerns topped the list of factors shaping near-term strategy, cited by 91% of respondents. Pressure from investors or boards to demonstrate value came second at 87%, followed by access to lower-cost large language models at 84%, workforce limitations at 82%, and the need to improve client experience and engagement at 81%.
Despite the scale of investment, actual deployment of AI agents remains limited. Only 19% of firms are currently scaling or orchestrating agents across multiple functions, while more than half are still in the piloting phase. Employee adoption fell sharply quarter over quarter, dropping to 22% from 37%, and more than a third of firms reported outright resistance from staff.
Skills and capability gaps drove that resistance in 67% of cases, followed by job security concerns and increased workload complexity, each cited by 60%. Trust and transparency issues accounted for 43%. Against that backdrop, 83% of organizations said they are either already investing in reskilling programs or plan to do so. More than half said they would pay between 6% and 15% more for candidates who can demonstrate strong AI capabilities.
The barriers to scaling agents are predominantly structural. Data readiness and access was flagged by 57% of respondents as a primary obstacle, while AI cost and economic literacy and the complexity of agentic systems were each cited by 49%.
Governance and cost visibility present an additional challenge. While 63% of respondents said AI operating costs are at least partially visible, only 4% reported full visibility. Monitoring dashboards are in place at 61% of firms, and 58% incorporate cost review into AI approval processes, but usage or token budgets have been adopted by just 18%.
Accountability for AI-driven decisions sits heavily at the top of the organizational chart. Nearly six in ten respondents attributed primary responsibility to the CEO or executive committee, while 25% named another C-suite executive.
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