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AI adoption is accelerating: Is AI risk management keeping up?

AI adoption is accelerating across Europe, but AI risk management is lagging. Explore how Marsh helps organizations scale AI securely with strong governance.

Across Europe, generative AI has moved beyond experimentation. It is increasingly embedded in everyday business activity, supporting work from document drafting and customer service to IT operations, legal, HR, and finance.

But Marsh’s AI without blind spots: The 2026 Marsh European AI Opportunities and Risk Survey Report raises a more important question than where AI is being used: Are organisations managing AI risk at the same pace as they scale AI?

The evidence suggests there is room for improvement.

AI adoption is expanding rapidly across business functions. More than half of the organisations we surveyed already use generative AI in routine operational workflows, while IT and cybersecurity teams report the highest adoption levels. AI is no longer confined to innovation teams or isolated pilots. It is becoming part of day-to-day business operations.

However, widespread use should not be confused with organisational maturity.

Our survey shows that most European organisations remain in the early stages of deployment. A minority report AI as an enterprise-wide business capability, while many continue to operate limited rollouts or proof-of-concept initiatives. In other words, AI is spreading faster than the governance structures designed to support it.

This maturity gap becomes even more evident when organisations assess their AI risks.

Data privacy and security remain the dominant concern, followed by inaccurate AI-generated information and regulatory compliance, according to our survey. These are not abstract technology risks; they have direct implications for customer trust, operational resilience, financial performance, and legal exposure.

The challenge is not that organisations fail to recognise these risks. Rather, it is that risk management practices remain inconsistent.

Some organisations have introduced AI-specific risk assessment frameworks, while others continue to rely on traditional enterprise risk processes or project-by-project reviews. Some do no formal assessment at all. Likewise, governance measures vary considerably. Around half of respondents report providing employee training on AI risks, yet far fewer have implemented formal data governance, regular AI audits, bias monitoring, or independent third-party reviews.

This disconnect raises an important business question: if AI increasingly supports operational decisions, customer interactions, and core business processes, can organisations rely on governance models that remain fragmented and unevenly applied?

Managing AI risk is no longer simply about preventing failures; it is about enabling organisations to scale AI with confidence.

That requires moving beyond policies and awareness programmes towards repeatable governance embedded within business operations. Risk ownership should extend beyond IT and cybersecurity to include risk management, legal, compliance, procurement, HR, and business leadership. AI governance becomes most effective when accountability is shared across the organisation rather than concentrated within technical teams alone.

The organisations most likely to realise sustained value from AI will not necessarily be those that adopt it first. They will be those that build governance, accountability, and operational discipline at the same pace as adoption.

As AI becomes a core business capability, competitive advantage will increasingly depend not only on how quickly organisations deploy AI, but also on how effectively they manage the risks that accompany it.

Are you scaling AI with a control environment that can keep pace? Fill in the form below if you would like to contact us.

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Article

AI adoption is accelerating: Is AI risk management keeping up?

Are you scaling AI with a control environment that can keep pace? Fill in the form below if you would like to contact us.

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