Global AI investment is accelerating, yet KPMG data shows the gap between enterprise AI spend and measurable business value is widening fast.
The headline figure from KPMG’s first quarterly Global AI Pulse survey is blunt: despite global organisations planning to spend a weighted average of $186 million on AI over the next 12 months, only 11 percent have reached the stage of deploying and scaling AI agents in ways that produce enterprise-wide business outcomes.
However, the central finding is not that AI is failing; 64 percent of respondents say AI is already delivering meaningful business outcomes. The problem is that “meaningful” is doing a lot of heavy lifting in that sentence, and the distance between incremental productivity gains and the kind of compounding operational efficiency that moves the needle on margin is, for most organisations, still substantial.
The architecture of a performance gap
KPMG’s report distinguishes between what it labels “AI leaders” (i.e. organisations 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, said: “The first Global AI Pulse results reinforce that spending more on AI is not the same as creating value. Leading organisations are moving beyond enablement, deploying AI agents to reimagine processes and reshape how decisions and work flow across the enterprise.”
Among AI leaders, 82 percent report that AI is already delivering meaningful business value. Among their peers, that figure drops to 62 percent. That 20-percentage-point spread might look modest in isolation, but it compounds quickly when you consider what it reflects: not just better tooling, but fundamentally different deployment philosophies.
The organisations in that 11 percent are deploying agents that coordinate work across functions, route decisions without human intermediation 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 percent of AI leaders are using agents to accelerate code development versus 64 percent of their peers. In operations, where supply-chain orchestration is the primary use case, the split is 64 percent versus 55 percent. These are not marginal differences in tool adoption rates; they reflect different levels of process re-architecture.
Most enterprises that have deployed AI have done so by layering models onto existing workflows (e.g. a co-pilot here, a summarisation tool there…) without redesigning the process those tools sit inside. That produces incremental gains.
The organisations closing the performance gap have inverted this approach: they are redesigning the process first, then deploying agents to operate within the redesigned structure. The difference in return on AI spend between these two approaches, over a three-to-five-year horizon, is likely to be the defining competitive variable in several industries.
What $186 million actually buys—and what it does not
The investment figures in the KPMG data deserve scrutiny. A weighted global average of $186 million per organisation sounds substantial, but the regional variance tells a more interesting story.
ASPAC leads at $245 million, the Americas at $178 million, and EMEA at $157 million. Within ASPAC, organisations including those in China and Hong Kong are investing at $235 million on average; within the Americas, US organisations are at $207 million.
These figures represent planned spend across model licensing, compute infrastructure, professional services, integration, and the governance and risk management apparatus needed to operate AI responsibly at scale.
The question is not whether $186 million is too much or too little; it is what proportion of that figure is being allocated to the operational infrastructure required to derive value from the models themselves. The survey data suggests that most organisations are still underweighting this latter category.
Compute and licensing costs are visible and relatively easy to budget for. The friction costs – the engineering hours spent integrating AI outputs with legacy ERP systems, the latency introduced by retrieval-augmented generation pipelines built on top of poorly structured data, and the compliance overhead of maintaining audit trails for AI-assisted decisions in regulated industries – tend to surface late in deployment cycles and often exceed initial estimates.
Vector database integration is a useful example. Many agentic workflows depend on the ability to retrieve relevant context from large, unstructured document repositories in real time. Building and maintaining the infrastructure for this – selecting between providers such as Pinecone, Weaviate, or Qdrant, embedding and indexing proprietary data, and managing refresh cycles as underlying data changes – adds meaningful engineering complexity and ongoing operational cost that rarely appears in initial AI investment proposals.
When that infrastructure is absent or poorly maintained, agent performance degrades in ways that are often difficult to diagnose, as the model’s behaviour is correct relative to the context it receives, but that context is stale or incomplete.
Governance as an operational variable, not a compliance exercise
Perhaps the most practically useful finding in the KPMG survey is the relationship between AI maturity and risk confidence.
Among organisations still in the experimentation phase, just 20 percent feel confident in their ability to manage AI-related risks. Among AI leaders, that figure rises to 49 percent. 75 percent of global leaders cite data security, privacy, and risk as ongoing concerns regardless of maturity level—but maturity changes how those concerns are operationalised.
This is an important distinction for boards and risk functions that tend to frame AI governance as a constraint on deployment. The KPMG data suggests the opposite dynamic: governance frameworks do not slow AI adoption among mature organisations; they enable it. The confidence to move faster – to deploy agents into higher-stakes workflows, to expand agentic coordination across functions – correlates directly with the maturity of the governance infrastructure surrounding those agents.
In practice, this means that organisations treating governance as a retrospective compliance layer are doubly disadvantaged. They are slower to deploy, because every new use case triggers a fresh governance review, and they are more exposed to operational risk, because the absence of embedded governance mechanisms means that edge cases and failure modes are discovered in production rather than in testing.
Organisations that have embedded governance into the deployment pipeline itself (e.g. model cards, automated output monitoring, explainability tooling, and human-in-the-loop escalation paths for low-confidence decisions) are the ones operating with the confidence that allows them to scale.
“Ultimately, there is no agentic future without trust and no trust without governance that keeps pace,” explains Steve Chase, Global Head of AI and Digital Innovation at KPMG International. “The survey makes clear that sustained investment in people, training and change management is what allows organisations to scale AI responsibly and capture value.”
Regional divergence and what it signals for global deployment
For multinationals managing AI programmes across regions, the KPMG data flags material differences in deployment velocity and organisational posture that will affect global rollout planning.
ASPAC is advancing most aggressively on agent scaling; 49 percent of organisations there are scaling AI agents, compared with 46 percent in the Americas and 42 percent in EMEA. ASPAC also leads on the more complex capability of orchestrating multi-agent systems, at 33 percent.
The barrier profiles also differ in ways that carry real operational implications. In both ASPAC and EMEA, 24 percent of organisations cite a lack of leadership trust and buy-in as a primary barrier to AI agent deployment. In the Americas, that figure drops to 17 percent.
Agentic systems, by definition, make or initiate decisions without per-instance human approval. In organisational cultures where decision accountability is tightly concentrated at the senior level, this can generate institutional resistance that no amount of technical capability resolves. The fix is governance design; specifically, defining in advance what categories of decision an agent is authorised to make autonomously, what triggers escalation, and who carries accountability for agent-initiated outcomes.
The expectation gap around human-AI collaboration is also worth noting for anyone designing agent-assisted workflows at a global scale.
East Asian respondents anticipate AI agents leading projects at a rate of 42 percent. Australian respondents prefer human-directed AI at 34 percent. North American respondents lean toward peer-to-peer human-AI collaboration at 31 percent. These differences will affect how agent-assisted processes need to be designed in different regional deployments of the same underlying system, adding localisation complexity that is easy to underestimate in centralised platform planning.
One data point in the KPMG survey that deserves particular attention from CFOs and boards: 74 percent of respondents say AI will remain a top investment priority even in the event of a recession. This is either a sign of genuine conviction about AI’s role in cost structure and competitive positioning, or it reflects a collective commitment that has not yet been tested against actual budget pressure. Probably both, in different proportions across different organisations.
What it does indicate is that the window for organisations still in the experimentation phase is not indefinite. If the 11 percent of AI leaders continue to compound their advantage (and the KPMG data suggests the mechanisms for doing so are in place) the question for the remaining 89 percent is not whether to accelerate AI deployment, but how to do so without compounding the integration debt and governance deficits that are already constraining their returns.
See also: Hershey applies AI across its supply chain operations
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