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The Hard Part of AI Starts After the Demo Works
The Hard Part of AI Starts After the Demo Works Enterprise investment in artificial intelligence continues to accelerate. Recent McKinsey surveys show that more than
February 3, 2026
Recent McKinsey surveys show that more than 90% of large organizations plan to increase AI spending, often significantly. At the same time, fewer than 1% of enterprises describe themselves as AI-mature, meaning AI systems are embedded in core operations, governed consistently, and delivering repeatable outcomes at scale. This widening gap between investment and impact reflects a deeper structural problem in how organizations approach AI once it moves beyond the proof-of-concept stage.
Early AI initiatives tend to progress quickly. Small teams work within limited scope and operate with a high tolerance for uncertainty. Proofs of concept deliver encouraging results, and pilots demonstrate technical feasibility, creating the appearance of healthy adoption. As AI systems are expected to operate continuously in production environments, influence real decisions, and withstand regulatory, financial, and operational scrutiny, the conversation shifts away from innovation and toward risk containment: avoiding disruption to existing operations, ensuring compliance, and defining clear responsibility for how the system behaves over time.
Notably, once AI tools are deployed, organizations speak far less about the experience of running them in production than about the promise that preceded adoption.
Operationalizing AI at scale introduces a different set of constraints than experimentation. Systems must run continuously, comply with regulatory expectations, and operate within clear accountability structures, requirements that pilots are rarely designed to satisfy. As AI becomes part of day-to-day operations, the primary challenge shifts from proving technical feasibility to ensuring operational stability: integrating systems without disrupting existing processes, managing usage-driven costs, and maintaining auditability over time. Decisions that could be deferred during experimentation, like who is responsible for system behavior, how compliance is enforced, and how risk is managed across the lifecycle, become unavoidable once AI supports real business outcomes. It is at this stage that many organizations discover the gap between building AI systems and being able to run them reliably in production environments.
A central issue that surfaces here is accountability. In most enterprises, responsibility for AI systems is structurally fragmented. Capability, infrastructure, delivery, and oversight are distributed across multiple actors, each optimized for a narrow function rather than end-to-end operation. External providers contribute models, platforms, or integration work without retaining accountability for how systems perform over time. Internally, governance is split across security, compliance, finance, and operations, with each function managing a specific risk exposure but none exercising authority over the full lifecycle of the system. This arrangement is workable during experimentation, when scope is limited and consequences are contained. In production environments, however, the same fragmentation becomes a constraint, slowing decisions and complicating accountability precisely when continuous operation and regulatory scrutiny require the opposite.
The risk profile of AI has also shifted materially. Industry data shows that roughly one in five organizations has experienced security or data incidents involving unmanaged or “shadow” AI. Breaches associated with shadow AI are significantly more costly on average, reflecting not just remediation expenses but regulatory scrutiny, delayed deployments, and loss of executive confidence. In this environment, AI decisions are no longer led by early adopters or innovation teams. They are increasingly gated by legal, compliance, security, and finance leaders whose primary mandate is risk containment.
These dynamics are especially pronounced in Europe. European enterprises operate under stricter regulatory regimes, heavier legacy IT environments, and higher sensitivity to operational failure. AI systems are expected to meet production standards earlier in their lifecycle, with clear auditability, traceability, and accountability. As a result, execution weaknesses surface sooner. This often creates the impression that European organizations are slower to adopt AI, when in fact they are encountering the limits of experimentation-led models earlier and more visibly.
Organizations that succeed at scale tend to converge on a common pattern: accountability for AI execution is centralized, even when delivery is distributed. Authority over architecture, governance, cost control, and lifecycle performance is explicitly assigned, allowing AI to function as production infrastructure rather than perpetual experimentation.
Our whitepaper, Why AI Slows Down When It Reaches Production, examines this operational gap in depth. Download it to understand why ownership, governance, and execution, not model capability, now determine whether AI investments translate into durable enterprise value.
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