KPMG: How AI Agents Drive Enterprise Profitability

Global investment in artificial intelligence (AI) is accelerating, yet KPMG data reveals that the gap between enterprise AI spending and measurable business value is widening rapidly. According to KPMG's first quarterly Global AI Pulse survey, while global organizations plan to spend an average of $186 million on AI over the next 12 months, only 11% have reached the stage of deploying and scaling AI agents that deliver enterprise-wide business outcomes.

However, the central finding is not that AI is failing; 64% of respondents claim that AI is already delivering meaningful business outcomes. The challenge is that the term 'meaningful' carries significant weight, and the distance between incremental productivity gains and the type of operational efficiency that truly impacts margins remains substantial for most organizations.

KPMG's report distinguishes between what it labels 'AI leaders'—organizations that are scaling or actively operating agentic AI—and everyone else. The gap in outcomes between these two cohorts is striking. Steve Chase, Global Head of AI and Digital Innovation at KPMG International, stated, 'The first Global AI Pulse results reinforce that spending more on AI is not the same as creating value. Leading organizations are moving beyond enablement, deploying AI agents to reimagine processes and reshape how decisions and workflows operate across the enterprise.'

Among AI leaders, 82% report that AI is already delivering meaningful business value, while among their peers, that figure drops to 62%. This 20-percentage-point spread may seem modest in isolation, but it compounds quickly when considering what it reflects: not just better tools, but fundamentally different deployment philosophies. The organizations in that 11% are deploying agents that coordinate work across functions, route decisions without human mediation at every step, surface enterprise-wide insights from operational data in near real-time, and flag anomalies before they escalate into incidents.

In IT and engineering functions, 75% of AI leaders utilize agents to accelerate code development compared to 64% of their peers. In operations, where supply chain orchestration is the primary use case, the split is 64% versus 55%. These are not marginal differences in tool adoption rates; they reflect different levels of process re-architecture.