How to navigate a safe AI transformation

Artificial intelligence is no longer a future trend—it is reshaping the global economy at unprecedented speed. Adoption has moved from early experimentation to near-universal use across industries, transforming how organizations operate, make decisions, and serve customers.

For insurers, this shift is particularly significant. We are not just observers of AI—we are both users and enablers. We deploy AI across our operations while also underwriting the risks that AI creates for businesses and society.

This dual perspective puts the insurance industry in a unique position: to help ensure that AI delivers value safely, responsibly, and at scale.

From opportunity to responsibility

The benefits of AI are already tangible. Across insurance, AI is improving customer experience, accelerating claims processing, and expanding access to expertise. For example, AI-powered systems can significantly reduce handling times and increase customer satisfaction while freeing up human experts to focus on complex cases.

But opportunity comes with responsibility.

AI systems influence decisions that matter—coverage, claims, pricing, and customer interactions. That makes trust, governance, and accountability not optional, but foundational.

At Zurich, this means embedding AI governance into every stage of development. Systems are designed to be transparent, explainable, and continuously monitored, with clear ownership and oversight. High-impact applications are subject to stricter controls, including risk assessments, bias testing, and structured incident management.

Crucially, for decisions with meaningful consequences, a human remains in the loop—ensuring judgment, empathy, and accountability are preserved.

Because when it comes to AI, the goal is not to eliminate risk—but to manage it intelligently.

Understanding the AI risk landscape

To navigate AI safely, we must first understand the risks it creates.

Insurance offers a powerful lens here. Decades of experience in areas like cyber risk show that new technologies rarely introduce entirely new categories of risk—they reshape and amplify existing ones, while also creating new, harder-to-measure exposures.

The AI risk landscape can be viewed in three layers:

1. Known and manageable risks
   
   These are familiar challenges, now intensified by AI:
   
   • Bias and discrimination in decision-making
   • Incorrect or unsafe outputs
   • Data privacy and security risks
   • Intellectual property concerns
   These risks are already understood and can be mitigated through strong governance and controls.
2. Emerging risks
   
   More complex challenges are now coming into focus:
   • Limited explainability of AI decisions
   • Unclear accountability across complex supply chains
   • AI-enabled cyber threats
   Here, traditional frameworks begin to stretch, requiring adaptation and innovation.
3. Systemic risks
   
   At the frontier lie risks that could affect entire systems:
   • Correlated failures across similar AI systems
   • Concentration risk among a small number of providers
   • Cascading effects across interconnected systems
   These are the most difficult risks to measure—and potentially the most impactful.

Why insurability matters

At its core, insurance depends on one principle: risk must be understood and attributable to be insurable.

AI challenges this principle.

As systems become more complex—especially with the rise of autonomous and “agentic” AI—understanding how decisions are made, and who is responsible for them, becomes harder. In multi-layered ecosystems involving developers, platforms, and users, accountability can blur.

Without clarity on responsibility, risks become difficult to price—and difficult to insure.

This matters because insurance plays a crucial role in enabling innovation. Without mechanisms to transfer risk, adoption slows, and trust erodes.

Building trust in the age of AI

So what does it take to move forward safely?

Three priorities stand out:

1. Transparency and accountability
   
   People need to know when AI is influencing decisions—and have the ability to understand, question, and challenge outcomes.
   
   Clear rules on liability are equally essential. When responsibility is well defined, risks can be priced, insured, and managed.
2. Proportionate governance
   
   Not all AI systems carry the same risk. Governance frameworks must reflect that.
   
   High-impact applications require robust oversight, human involvement, and rigorous testing, while lower-risk uses should not be overburdened.
   
   At the same time, systemic risks—such as concentration or cascading failures—must be addressed collectively, including through international coordination and shared standards.
3. Investing in people
   
   AI will reshape work, and the transition must be actively managed.
   
   Reskilling, education, and workforce support are essential—not just to mitigate risk, but to unlock the full potential of AI.
   
   At the same time, human skills such as judgment, ethics, and creativity remain indispensable and must be preserved.

A shared responsibility

AI transformation is already underway. The question is not whether it will happen—but how.

The choices we make today around governance, accountability, and collaboration will determine whether AI becomes a source of trust and progress—or fragmentation and risk.

Insurance has always played a key role in enabling progress by managing uncertainty. The same principle applies here. The goal is not zero risk.

It is risk that is understood, governed, and proportionate—creating the foundation for AI to scale safely, responsibly, and with confidence.