Risk Manager's Guide to AI in COI Tracking: What's Real, What's Hype
Every COI vendor now claims AI capabilities. Here's a practical breakdown of what AI is actually doing in the category — and where the marketing is ahead of the engineering.
Every COI tracking vendor in 2024 and 2025 claims AI capabilities. The marketing copy is interchangeable: "AI-powered verification," "intelligent automation," "machine learning at the core." If you took ten platforms' AI claims at face value, you'd assume every problem in the category was already solved.
It isn't. Some platforms have meaningful AI engineering. Some have AI logos on a 15-year-old codebase. Most are somewhere in between.
Here's a grounded look at what AI is actually doing in COI tracking — and how to tell the difference.
What AI legitimately does in this category
Three areas where AI is producing real, measurable value:
1. Document extraction and parsing. OCR alone can read text from a certificate. AI models can interpret that text in context — knowing that "GL" in one section refers to general liability, parsing policy limits even when formatted irregularly, understanding endorsement language, and flagging ambiguous fields for human review.
The best AI extraction in the category is meaningfully better than rule-based or pure-OCR alternatives. Errors are lower. Edge cases are handled more gracefully. The platform learns from corrections over time.
2. Compliance rule evaluation. Once data is extracted, AI can compare it against your contractual requirements at scale. "Does this vendor's GL meet the $2M minimum required by the MSA, including the additional insured endorsement requirement?" AI doesn't replace human judgment on edge cases, but it handles the high-volume, clear-cut comparisons reliably.
3. Anomaly detection. AI can flag patterns that humans miss — a vendor's certificate that doesn't match historical patterns, language in an endorsement that suggests modified coverage, dates that look slightly off. These flags don't auto-resolve; they route to human review. But they catch things that pure rules miss.
When you hear AI claims, listen for which of these three categories the platform is talking about. Specific is good. Vague is bad.
Where AI hype outruns reality
Three areas where AI marketing tends to overpromise:
1. "Fully autonomous compliance." No platform in the category has fully autonomous compliance, no matter what the marketing says. Every serious platform has human review queues somewhere in the workflow. The question isn't whether humans are involved; it's how efficiently the AI front-loads work so humans focus on exceptions. Vendors claiming "AI does it all" are either lying or shipping bad outputs without review.
2. "AI predicts risk." Predictive risk modeling in COI tracking is much earlier than the marketing suggests. Some platforms have basic risk scoring (vendor with policy lapse history, geographic risk, industry vertical risk). Few have meaningful predictive capability beyond that. If a vendor pitches "AI predicts which vendors will become non-compliant," ask for the model's accuracy rates. The number, if real, is usually modest.
3. "Generative AI handles communication." Generative AI is a 2023+ phenomenon. Platforms claiming generative AI for vendor communication are mostly using basic LLM API calls to draft emails, which is fine but not magic. The output quality varies; the human-review-before-send model still applies. Don't pay a premium for "generative AI" when what you're getting is GPT API access in a wrapper.
The "AI from day one" question
There's a genuine architectural difference between platforms built with AI/ML in their core data architecture from the beginning vs. platforms that bolted AI onto an existing rule-based system.
Platforms with AI from day one can do things like:
- Continuously improve extraction accuracy as they process more documents
- Cross-reference data across customers (with appropriate privacy controls) to validate vendor info
- Learn from every human correction and feed it back into the model
- Handle edge cases through model adaptation rather than rule additions
Platforms with bolted-on AI are usually:
- Running AI as a parallel service, not a core capability
- Less able to incorporate corrections into model improvement
- More fragile when document types diverge from the training distribution
- Less able to compound capability over time
TrustLayer is positioned as an AI-first platform — built with AI/ML in the core architecture from inception, not bolted on. Some legacy platforms (myCOI's Illumend rebrand notably) are openly bolted-on, despite the marketing. The difference shows up over time: AI-first platforms get better; bolted-on AI plateaus.
How to evaluate AI claims during demos
Three questions:
1. "What specific AI tasks are running on this document, and what's the published accuracy rate for each?"
You want a list: extraction (X% accuracy), classification (Y% accuracy), endorsement parsing (Z% accuracy). Specific numbers means real engineering. Vague claims means marketing.
2. "Show me how the platform handles a borderline-quality document — a certificate with unusual formatting, partial data, or ambiguous language."
You want to see the platform attempt extraction, flag uncertainty, and route to review. Platforms with mature AI handle this gracefully. Platforms with weak AI either confidently produce wrong answers or give up entirely.
3. "When the AI is wrong, how does that get corrected, and how does the correction improve the model?"
You want a feedback loop. User corrects a mis-extracted field; correction logs in the system; model learns from corrections over time. Platforms with this loop compound capability. Platforms without it stay where they started.
The bottom line on AI in COI tracking
AI is real and useful in this category. AI is also massively over-claimed. The platforms with substantive AI engineering are pulling away from the platforms with AI marketing on top of legacy code, and the gap will widen over time.
When evaluating, weight AI capability — but be specific about which AI capabilities matter for your use case. Document extraction and rule evaluation are universally useful. Predictive risk and generative communication are nice-to-haves with mixed delivery.
Our comparison tool factors AI/automation into the algorithm. The platforms with serious AI engineering will surface in the recommendations; the platforms with marketing AI will land lower. Three minutes of your time, useful answer at the end.