Independent examination can make AI control representations more defensible in underwriting discussions, but it cannot determine an insurer’s acceptance, price or coverage.
Evidence cutoff: 15 October 2026.
Executive takeaways
- Identify the exposure: establish which deployed AI systems can affect customers, professional advice, confidential information or consequential transactions before discussing control effectiveness.
- Make the control claim examinable: connect each material representation to the deployed configuration, observed execution records, appropriate tests and explicit exclusions.
- Separate assurance from insurance decisions: independent examination can qualify what evidence supports; only the insurer can decide underwriting terms, and policy response depends on wording, facts and applicable law.
The renewal decision is about the system actually operating
An enterprise may tell its insurer that AI-generated advice is reviewed before reaching a customer. That statement requires a defined operating boundary: the systems and channels covered, available bypass routes, the validity of approval after content changes, and records of what the reviewer could actually inspect before release. Without those details, management cannot establish how widely the representation applies.
These are questions management can answer with system-specific evidence rather than policy documents alone. Before renewal, leadership should confirm that each material representation about a consequential deployment is accurate, bounded and supportable. The aim is not to promise an underwriting outcome; it is to reduce unsupported claims and make remaining uncertainty visible.
This discussion concerns underwriting risks arising from an insured organisation’s use of AI, not an insurer’s use of AI to make its own underwriting decisions. Its public market example concerns professional indemnity/errors and omissions (PI/E&O) insurance for professional-services firms; other lines, jurisdictions and policy forms may raise different issues.
The source establishes underwriting concerns, not a universal requirement
In September 2026, Aon’s Professional Services Practice published its account of what PI/E&O underwriters consider when professional-services firms use AI. It addresses fabricated or incomplete advice, misuse of confidential information, agentic workflows, governance, qualified review, documentation and the ability to reconstruct AI-assisted decisions. Aon frames these as practitioner observations and recommendations for insurance negotiations, not as a universally binding insurer questionnaire. [1]
Aon’s companion September 2026 checklist asks professional-services firms to map significant AI use cases, distinguish assistive, generative and agentic systems, clarify decision rights, document review practices, review vendor relationships and record incidents or near misses. For evidence on an individual file, it asks firms to document that AI was used and at what stage, alongside key prompts or parameters or model versions where relevant. Its wider checklist also addresses human-oversight standards. Those items can help reconstruct an AI-assisted decision, although their presence alone does not prove completeness or control effectiveness. The checklist is a broker-produced preparation aid; it does not impose a legal test, certify a system, or show that an insurer accepted particular evidence. [2]
A separate US policy-wording development reinforces the need to distinguish insurance lines. In October 2025, the Independent Insurance Agents & Brokers of America reported that Verisk’s Insurance Services Office (ISO) had developed optional generative-AI exclusion endorsements with January 2026 edition dates for specified commercial general liability and products/completed-operations coverages. [3] These are not PI/E&O forms. The association described the endorsements, but their original wording was not examined for this article, and their availability does not establish adoption by any particular insurer or their application to any claim. The reason to examine them here is limited: organisations should verify actual policy wording rather than presume that every line of cover treats AI-generated harm alike.
Jurisdiction and transferability: Aon’s Professional Services Practice material names US-based contacts and discusses professional-services PI/E&O. It should not be treated as an empirical US market survey, nor as a global rule. The reported ISO forms are explicitly US general-liability developments. Whether their implications transfer elsewhere depends on local policy forms, practices and applicable law.
Four questions must remain separate. A control may reduce the route to error without eliminating it. A technical failure does not by itself establish a financial loss; a loss does not by itself establish legal liability; and liability does not settle whether a particular insurance policy responds.
Insurer to organisation: specify the proposition before requesting records
A general request for an AI policy may establish that a policy exists. It does not establish that a consequential workflow follows it. ODA3 Institute’s analytical proposal is to begin with the underwriting question and express it as a proposition capable of examination: During the identified review period, could this deployed agent issue an external client message without the prescribed approval?
The evidence request should identify the deployment, version, interfaces, permitted tools, period, relevant control and decision being informed. It should also identify who supplies the records, how completeness is assessed and what cannot be checked. The following is a proposed evidence approach, not a requirement attributed to Aon.
| Executive or underwriting question | Evidence to examine | What it can show | What remains uncertain |
|---|---|---|---|
| Which external actions can the AI initiate? | Deployed tool permissions, network routes, integrations and sampled executions | Accessible action paths within the examined configuration | Unexamined, transient or changed paths |
| Is human approval a release condition? | Enforcement code or configuration, approval states and attempted bypass tests | Whether tested releases were blocked absent valid approval | Behaviour outside tested conditions |
| Can approval be reused after material changes? | Binding between approved content, reviewer identity, version and released output; change tests | Whether the tested approval applies to the released object | Unobserved changes or different interfaces |
| Did the reviewer see the material being approved? | Actual case view, available source context, displayed version, context requests and intervention records | Whether the displayed material matched the released object and whether challenge was possible in tested cases | Whether the reviewer understood the material, or untested paths hid context |
| Can an incident be reconstructed? | Correlated input, model/tool version, review, decision, action and change records; provenance and integrity checks | A bounded sequence for captured activity, subject to validation | Whether records are complete, unaltered, correctly time-aligned or representative of unexamined paths |
There is no virtue in collecting logs merely because they exist. If the decision requires proof that approval was enforced at the action boundary, an aggregate dashboard of review counts may not answer it. Conversely, demanding unrestricted prompts or personal data may create confidentiality and privacy risks. The request should be proportionate to the claim and governed by appropriate access and retention controls.
Organisation to insurer: show what supports the actual representation
Management owns the control design, implementation and accuracy of its representations. Technical owners should connect the claim to observable operations; risk and legal teams should ensure that its scope and qualifiers survive translation into insurance submissions. Independent assessors, where engaged, should challenge the evidence rather than simply repeat management’s description.
Whether to use internal validation or independent examination depends on the exposure, necessary expertise, potential conflicts, intended reliance and cost. Organisation-funded examination is not automatically compromised, but commissioning, scope-setting, reporting relationships and potential conflicts should be disclosed and managed. The parties should establish who may receive and rely on a report, and for what purpose; insurer reliance cannot be presumed. A release-control test does not establish that the underlying professional advice is sound, and a point-in-time result may no longer apply after material deployment changes. Where an insurer’s decision turns primarily on the insured’s activities, loss history or policy wording, operational-control evidence may have limited additional value.
A useful evidence package has four layers. First, it states the control proposition and the business consequence of its failure. Second, it maps the system boundary, including relevant versions, tools, people and external dependencies. Third, it presents tests and production records sufficient to support a bounded conclusion. Finally, it records exclusions, failed tests, missing evidence and changes after examination.
An assessor might therefore conclude: For the tested deployment version and paths, attempts to release a modified client message without renewed approval were refused, and corresponding action records were retained. That conclusion is meaningful precisely because it does not say that every possible route was tested, that advice was accurate, or that an insurer must offer cover.
Records that are unavailable produce an unresolved question; they are not affirmative evidence that a control failed. Conversely, a verified bypass or contradiction can support an adverse finding for the examined condition. Keeping those states separate is essential to credible assurance.
Illustrative scenario — synthetic, not based on an examined system
Consider a professional-services firm using an AI agent to draft client advice. The agent can submit the message to a reviewer and, after approval, send it externally. Management proposes to represent that every externally released message requires approval by a qualified reviewer.
The plausible failure route is not limited to a hallucinated answer. A tool integration might permit direct transmission; an approval might be reused after the message is edited; or a reviewer might see a condensed summary that omits decisive source context. If incorrect or confidential advice reaches a client, the firm may face remediation costs, client disputes or other consequences. Whether a compensable loss, legal obligation or insured event results remains a separate question.
The relevant control therefore has two separate propositions: release enforcement (the system actually prevents release without valid approval) and review fidelity (the reviewer sees sufficiently faithful, decision-relevant content and can intervene before release). For release enforcement, testers could attempt to send without approval, after approval expiry and following content modification. For fidelity, they could compare what the reviewer saw with the underlying material, test access to omitted context and examine whether an intervention still prevented release before the action became final.
Suppose every tested unauthorised release is blocked and the collected records link reviewed content to transmitted content. That supports a bounded statement about those test conditions. It does not establish comprehensive factual accuracy, reviewer competence across all cases, continuous production effectiveness or a particular insurance outcome. If a relevant connector was not tested, the published result should say so.
The important chain is explicit: deployment → plausible failure route → potential business consequence → enforced control → examinable records and tests → bounded finding → remaining uncertainty. Independent examination can challenge each link and disclose where the chain breaks; whether an underwriter relies on its findings is a separate, unverified question.
Board oversight should attach an owner to every material claim
The Board need not review tool logs. It should expect management to distinguish a policy requirement from evidence that the requirement actually operates, and it should ask whether external representations match the deployed system. That is a governance and disclosure discipline, not an attempt to substitute directors for the insurer or the technical examiner.
| Action | Accountable owner | Decision or evidence needed | Condition that changes the approach |
|---|---|---|---|
| Select consequential deployments for evidence review | Chief Risk Officer with business owners | Inventory of AI actions, affected parties and plausible loss routes | Lower materiality or autonomy may justify a narrower sample; increased autonomy triggers re-evaluation |
| Approve the scope and accuracy of insurance representations | General Counsel and insurance/risk-transfer lead | Control claims tied to specific systems, periods, channels and evidence | If a material claim cannot be substantiated, qualify or correct it before submission |
| Test release enforcement and reviewer-context fidelity | Chief Information Security Officer (CISO) and accountable system owner | Test results, deployment versions, content-approval binding, reviewer views and action records | A verified bypass requires remediation and re-testing; missing records require qualification and evidence repair, not an assumed failure |
| Choose internal validation or independent examination | Audit or Risk Committee | Consequence, expertise, conflicts, intended recipients and reliance terms, scope and cost | Low materiality or credible internal testing may justify internal validation; intended external reliance or significant conflicts may favour independent examination |
| Check actual policy wording across insurance lines | Insurance lead with broker and legal advisers | Relevant definitions, endorsements, exclusions, notification conditions and local law | A different insurer, jurisdiction or attached endorsement changes the policy-specific analysis |
Financial, operational, legal and reputational consequences should be assessed against the actual exposure, not assumed from the presence of AI. Management decides what evidence to create and what to represent; underwriters decide appetite, pricing and terms.
Notably Absent
The public sources and analysis reviewed here do not establish:
- That insurers require independent AI assurance, or that any insurer has accepted an independent AI assurance report as an underwriting credential.
- That independently examined controls produce lower premiums, broader capacity, reduced claim frequency or favourable coverage decisions.
- That a customer system was examined, a real bypass attempt was performed or an actual insurance claim was adjudicated for this article.
- That one professional-services broker’s September 2026 material represents all insurers, countries, insurance lines or renewal practices.
- That all possible execution paths, reviewer decisions or production periods can be inferred from a bounded test.
- That the optional US general-liability AI exclusions described by the independent agents’ association were included in a particular policy, or that their original form text was independently reviewed here.
- That an insurer questionnaire, executed policy, claim decision or on-record underwriter response to independent AI assurance evidence was examined.
- The frequency or severity of AI-attributed PI/E&O claims; no public data on this was located in the sources checked.
Make the next renewal statement testable
Leadership can now make a disciplined decision: identify consequential AI deployments, narrow the representations made about their controls, and require evidence capable of challenging those representations before they are supplied externally. The unresolved question is whether insurers will find particular forms of independently examined operating evidence decision-useful in specific lines and markets. That requires direct underwriting feedback and observed outcomes, not assertion.
References
[1] Aon, Professional Services Practice. “What Your PI / E&O Insurers Care About When Thinking about AI.” Aon, September 2026. https://www.aon.com/risk-services/professional-services/what-your-pi_eo-insurers-care-about-when-thinking-about-ai-final (accessed 15 October 2026).
[2] Aon, Professional Services Practice. “Risk Titration AI Checklist for PI/E&O.” Aon, September 2026. https://www.aon.com/risk-services/professional-services/risk-titration-ai-checklist-for-pi-e-and-o (accessed 15 October 2026).
[3] Nancy Germond. “Verisk to Roll Out New General Liability Exclusions for Generative AI Exposures.” IndependentAgent.com / Big “I” Virtual University, 21 October 2025. https://www.independentagent.com/vu_resource/verisk-to-roll-out-new-general-liability-exclusions-for-generative-ai-exposures/ (accessed 15 October 2026).
Methodology note
Source-bounded editorial analysis of two Aon professional-services resources and one secondary US policy-form report; proposed deployment-specific control-evidence review; synthetic professional-services advice-agent scenario; independent field validation not performed
Notably Absent
The public sources and analysis reviewed here do **not** establish:
- That insurers require independent AI assurance, or that any insurer has accepted an independent AI assurance report as an underwriting credential.
- That independently examined controls produce lower premiums, broader capacity, reduced claim frequency or favourable coverage decisions.
- That a customer system was examined, a real bypass attempt was performed or an actual insurance claim was adjudicated for this article.
- That one professional-services broker's September 2026 material represents all insurers, countries, insurance lines or renewal practices.
- That all possible execution paths, reviewer decisions or production periods can be inferred from a bounded test.
- That the optional US general-liability AI exclusions described by the independent agents’ association were included in a particular policy, or that their original form text was independently reviewed here.
- That an insurer questionnaire, executed policy, claim decision or on-record underwriter response to independent AI assurance evidence was examined.
- The frequency or severity of AI-attributed PI/E&O claims; no public data on this was located in the sources checked.
