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Kristen Nunery, CEO of illumend, the next-generation AI platform redefining how companies manage third-party risk and insurance compliance, calls on organizations to rethink one of the most entrenched assumptions in Certificate of Insurance (COI) review: that because every submission looks different, the review process itself cannot be standardized.
Nunery makes that case in her article, “Every COI Looks Different—It’s Impossible to Standardize”: How AI Is Finally Solving COI Data Chaos. She argues that the real opportunity is not to force uniformity across certificates, endorsements, schedules, and attachments, but to create a more consistent way to evaluate them against actual insurance requirements.
The article comes as organizations face growing audit pressure, rising third-party risk, and heavier documentation volume across properties, projects, vendors, and business units. In many environments, COI review still depends on manual interpretation of fragmented paperwork. This creates reviewer variability, approval delays, rework, and added compliance exposure.
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According to Nunery, that ongoing friction stems from a flawed premise. Too many organizations, she says, treat COI review as a document collection exercise when it is really a contextual compliance decision. Collecting paperwork is only one step; the more important question, she says, is whether the submission actually satisfies the insurance requirements tied to a specific lease, contract, property, or project.
“Every COI does not have to look the same for review to be consistent,” said Kristen Nunery, CEO of illumend. “That is the shift. The opportunity is not to standardize the documents. It is to standardize the review layer. AI can read across fragmented submissions, surface the details that matter, and compare them to real requirements in a way that strengthens consistency, explainability, and defensibility without losing human oversight.”
In the article, Nunery explains that legacy systems have improved document collection and renewal tracking, but they have not solved the most difficult part of the workflow: interpreting what was submitted, matching it to requirements, and identifying what is missing or misaligned. She argues that this “interpretive middle” is where delays, inconsistency, and risk tend to build.
Nunery argues that AI is now practical for this work because it can read across certificates, endorsements, and attachments, then normalize key fields like coverage, limits, and dates. She says this allows teams to move beyond basic data extraction and toward true requirement-based review.
Looking ahead, Nunery argues that organizations that standardize the review layer will be better positioned to reduce approval friction, lower rework, strengthen audit readiness, and improve consistency across teams. Those that continue to rely on document collection and manual interpretation alone, she says, will face greater strain as complexity and scrutiny continue to rise.
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