KPMG has warned that most organizations are scaling AI faster than they are redesigning the enterprise to support it, leaving many transformation programs stuck in localized productivity gains instead of delivering enterprise-wide results.
Its new Transforming the Enterprise 2026 report, based on a February 2026 survey of 1,750 senior transformation leaders across 20 countries and territories, found that only 14 percent of organizations see themselves as top performers relative to peers. Just 26 percent strongly agree that AI has helped them achieve growth objectives.
That gap is the core story. Enterprises are investing in AI, digital tools, and operating model change, but many still struggle to turn that activity into coordinated performance across the whole business.
KPMG’s findings matter for CX leaders because customer experience often exposes these failures first. Front-end AI may improve analytics, personalization, or service interactions, but weak orchestration behind the scenes can still leave journeys fragmented, slow, and hard to scale.
Framing the challenge, Adrian Clamp, Global Head of Consulting Strategy and Investment at KPMG International, argued:
“Real value from AI requires operating as an intelligent enterprise, aligning strategy, decisions, and execution. Yet, most organizations have not redesigned themselves to do so, with complexity rising faster than performance. As a result, many risk scaling AI without delivering sustained enterprise impact or meaningful returns.”
AI Productivity Is Rising, But Enterprise Impact Is Not
KPMG’s report shows that AI use is now broad across the enterprise. More than half of respondents reported scaling operational AI across customer analytics, operational efficiency, and decision support.
Yet full embedding remains limited. For example, while 57 percent said AI is in scaling operational use for customer analytics and personalization, only 27 percent said it is fully embedded in operations.
The same pattern appears elsewhere. AI is helping firms automate processes and support decisions, but it is not consistently reshaping how work moves across functions or how the enterprise executes as a system.
That helps explain why the gains remain modest. KPMG found that 48 percent of respondents described AI-driven productivity gains as incremental, while 40 percent reported significant improvement and only 11 percent reported substantial improvement.
The report also found that organizations still measure AI value mainly through efficiency. Thirty-nine percent track increased productivity, 36 percent track time saved, and 33 percent track operational cost reduction. Far fewer measure outcomes tied to revenue, competitive position, or new business models.
Why AI Transformation Still Breaks at Scale
KPMG argues that many enterprises are applying AI to old structures instead of redesigning those structures around AI.
That includes fragmented data, legacy workflows, and operating models built for stability rather than continuous change. It also includes transformation efforts that run in parallel without enough integration across priorities, systems, and decisions.
The scale of that challenge is clear in the report. Only one percent of organizations said they are not currently undergoing transformation, while the average organization is managing 3.5 concurrent transformations.
At the same time, 43 percent said they now operate hybrid AI ecosystems, combining third-party platforms with internal capabilities. That may add flexibility, but it also raises coordination and governance demands. Clamp put the point more bluntly later in the report:
“Most organizations have an AI value creation problem. Their AI deployment is moving faster than their pace of operating model change.”
That line lands hard for CX teams. A company can deploy AI in customer engagement, service automation, or recommendation engines, but if fulfillment, compliance, operations, finance, and workforce processes remain disconnected, the customer journey still suffers.
The Operating Model Is the Real Constraint
One of KPMG’s strongest findings is that the bottleneck is not ambition. It is execution.
Only 12 percent of organizations said they can move a new initiative from concept to execution in less than three months. More than half need six months or more.
That matters because AI compresses decision cycles. It creates pressure for faster handoffs, tighter alignment, and more responsive workflows across the enterprise.
Still, most organizations have not structurally enabled that speed. KPMG found that 87 percent of respondents believe operational agility creates competitive advantage, yet many still rely on functional silos, sequential approvals, and disconnected accountability. In an assessment, Svilena Tzekova, Global Head of Corporate Services at KPMG International, warned:
“In our experience, transformation is rarely constrained by ambition or technical possibilities. It is typically constrained by how work moves across the enterprise. Fragmented operating models can create isolated improvements, but struggle to deliver coordinated enterprise performance.”
That observation helps explain why AI success often looks better in pilots than in production. The technology may work, but the enterprise around it still cannot absorb intelligence at speed.
Trust and Governance Now Shape Performance
KPMG also makes a sharper point about governance than many AI studies do. It argues that trust is no longer a compliance layer added after deployment. It has become part of how performance gets delivered.
Only 24 percent of organizations said they have proactively integrated AI risk management into strategy and the technology lifecycle. Most still rely on reactive, siloed, or partially integrated approaches.
The report found another red flag in how organizations handle AI failures. Forty-five percent said unexpected or biased outcomes are typically handled quietly within technical teams, while only 32 percent treat them as a learning opportunity with transparent review.
That matters because governance now affects speed, confidence, and scale. If leaders cannot trust the system, they will struggle to deploy it across critical workflows.
Asked what now separates stronger performers, Samantha Gloede, Global Head of Risk Services and Trusted AI Leader at KPMG International, emphasized:
“Trust is no longer just a safeguard, it is also a prerequisite for performance. As transformation scales across interconnected systems, organizations should be able to rely on decisions, not just data.”
KPMG’s numbers back that up. While 60 percent of organizations now see trust as either a strategic differentiator or core competitive advantage, only 28 percent measure operational or revenue outcomes linked to trusted AI.
Why This Matters for Customer Experience
For CX Today readers, one line in the report stands out more than most. KPMG describes customer experience as the clearest signal of enterprise orchestration.
That makes sense. Every customer interaction cuts across data, workflows, systems, policies, and decision rights. When orchestration is weak, customers repeat themselves, wait longer, and hit avoidable friction. When orchestration is strong, experiences become faster, more adaptive, and more predictive.
KPMG states the point directly in the report: where orchestration is weak, experiences fragment. That has big implications for enterprises buying AI tools in customer service, CRM, analytics, and automation.
That is why this report lands as more than another AI adoption survey. KPMG is arguing that the next phase of AI transformation will be won by the companies that can redesign the enterprise around execution, trust, and end-to-end coordination.
And for CX leaders under pressure to prove ROI, that may be the most useful takeaway of all. AI may still improve the front end first, but customer experience will reveal whether the rest of the enterprise has caught up.
Discover more about this subject here Why Enterprise AI Governance is Set Up to Fail
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