What Your PI / E&O Insurers Care About When Thinking about AI

As generative and agentic AI rapidly transform how professional services are delivered, PI/E&O underwriters are asking a simple question: how do you ensure AI raises quality without quietly increasing risk? With AI adoption outpacing formal risk management, firms must still convince clients, regulators and insurers that they use AI in a controlled, transparent and defensible way.

Insurers are not anti AI. But they are looking for firms to show they approach AI using controlled, transparent and defensible methods.

Differentiate your firm in professional indemnity insurance negotiations by understanding the following areas of underwriter focus.

AI Perils for Professional Service Firms

Before looking at controls, underwriters want to understand what can go wrong.

AI can introduce specific PI/E&O perils for law, accounting and consulting firms, including:

• Hallucinated or fabricated content embedded in advice, opinions, reports or submissions.
  
  
• Biased or incomplete analysis based on skewed data, flawed prompts or models that are misaligned with local law or market practice.
  
  
• Unauthorized reuse or leakage of client information in prompts, training data, logs or shared tools.
  
  
• AI enabled IP infringement, where generated content is based on sources you have no rights to use.
  
  
• Over automation or “agentic” workflows that misroute, delay or mishandle tasks — for example, an AI assistant that automatically drafts and sends client updates based on incomplete context, or workflow agents that close matters or move funds without appropriate approvals.
  
  
• Inconsistent application of methods or standards, where AI‑generated work product diverges from agreed methodologies or firmwide quality expectations.
  
  

Each of these perils can lead to typical PI/E&O consequences: negligence claims, breach of contract, confidentiality or privacy violations, IP disputes, regulatory inquiries and costly remediation.

Where and How You Use AI

Underwriters want a clear, non technical view of where AI fits into your services:

• Use cases – drafting or reviewing documents and correspondence; research, modelling, analytics and forecasting; audit/assurance analytics; client facing tools - such as chatbots, portals and self service tools.
  
  
• Materiality – board and investment decisions, valuations and expert opinions, contracts and transaction documentation, regulatory filings, or financial statements.
  
  
• Type and autonomy – narrow assistive tool, generative system that substantially shapes the content of advice or analysis, or agentic workflow that can initiate or execute tasks (preparing drafts, routing documents, updating systems) with limited human intervention.
  

What ‘good’ looks like

You can map where AI is used across your matters, engagements and internal processes, distinguish high‑stakes from low‑stakes use cases, and explain how those differences drive your controls.

Governance, Ownership and Vendors

Insurers take comfort when AI is governed as a core business risk, not as an experiment:

• Clear ownership – a senior sponsor or committee responsible for AI strategy, risk and accountability.
  
  
• Policies and standards – firmwide rules on permitted and prohibited AI uses, human oversight, third‑party vs. in‑house tools, and how client data may (or may not) be used in prompts or training.
  
  
• Lifecycle management – defined processes for approving new tools and workflows before deployment, updating models and retiring what is no longer appropriate.
  
  
• Third‑party providers – due diligence on security, privacy, compliance, IP and reliability; contract terms that support your client commitments; contingency plans if a key AI provider fails or changes terms.
  

What ‘good’ looks like

AI risk is owned at the right level, with documented policies and structured vendor management, and not subject to ad hoc decisions.

Human Review, Quality Control and Data Protection

For PI / E&O underwriters, the crucial question is:
Who checks the AI, and how rigorously?

They look for evidence, with core elements including

• Minimum review standards – outputs fully reviewed by qualified staff for factual accuracy, regular spot checks, and nothing going straight to clients without human oversight.
  
  
• Risk based controls – stricter checks for legal, financial, tax or regulatory advice, and for high‑value or high‑profile matters.
  
  
• Training and culture – staff understand AI can be wrong, biased and over confident and they remain responsible for professional judgements.
  
  
• Data quality and confidentiality – clarity on what data trains or feeds AI tools, how you manage accuracy, relevance and bias, and how you prevent client information being reused or exposed across matters or clients.
  

What ‘good’ looks like

AI is always supervised when it affects client work and you have strong technical, contractual and procedural safeguards around client data.

Explainability, Documentation and Incidents

When something goes wrong, insurers and clients will ask:
How did you reach this conclusion, and what role did AI play?

Underwriters are reassured if:

• Decisions are explainable – you can explain how AI was used and why its outputs were considered reasonable at the time.
  
  
• Files show AI’s role – engagement or matter records show when AI was used and what human review or adjustments were made.
  
  
• Incidents are managed – clear pathways for detecting, escalating and recording AI related errors; a thought‑through approach to client notification, remediation and communication.
  
  
• There is a learning loop – issues feed back into model changes, policy updates, training and improved controls.
  

What ‘good’ looks like

You can reconstruct and defend your process in front of an insurer, regulator, court or client, and you treat AI incidents as learnable events, not isolated mishaps.

Titrating AI Risk Management: Not Too Much, Not Too Little

AI perils are dynamic and fluid. Over‑regulating every use of AI will choke innovation and waste scarce resources. Under‑regulating will leave firms exposed to claims and disputes.

A practical approach titrates AI risk management by scaling oversight, documentation and insurance response to the maturity and criticality of each use case:

• Higher‑risk, higher‑autonomy AI (AI influencing legal positions, valuations or regulatory submissions) should face stricter governance and approvals, strong human‑in‑the‑loop controls, and deeper documentation and testing consistent with standards (NIST AI RMF).
  
  
• Lower‑risk AI can be managed with lighter processes, clear but simple rules and periodic spot checks.
  

Feedback loops, including user trust, incident rates and complaints, can provide data to show underwriters that the firm continuously tunes its AI governance rather than having it set once and then forgotten.

What This Means for Your Insurance Story

Firms that can tell a credible story in these areas are more likely to:

• Secure stable capacity and more favorable terms.
• Build deeper partnerships with insurers as their AI use and understanding expands.
• Use AI confidently, knowing that governance and controls support both innovation and risk management.

If you can show where AI is used, how it is governed, how it is checked, how data is protected, and how you respond when things go wrong, you will be well positioned in conversations with both PI/E&O underwriters and clients.

For further information on risk titration review Aon’s AI Checklist for PI/E&O.

Other Aon AI Resources

• AI Risk is Outpacing Insurance: What Organizations Need to Know in 2026
• AI Risk Diagnostic
• 2026 Intangible vs Tangible Risks Comparison Report
• AI Risk 2026: What Business Leaders Need to Know

About Aon

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