Commercial insurers spend years expanding distribution, strengthening broker relationships and sharpening their risk appetite, only to discover that success creates a new constraint:
Too many submissions for underwriters to evaluate effectively.
That is the submission paradox. The industry has traditionally treated this as an operational problem: automate intake, extract fields faster, reduce manual work.
I believe that framing is too narrow.
The strategic problem is not how to process more submissions. It is how to identify, early and consistently, which submissions deserve scarce underwriting attention and capital.
Importantly, the objective is not simply higher conversion, but a profitable conversion, by responding faster to risks that fit appetite, meeting pricing expectations and improving the portfolio.
AIG's journey provides an excellent case. Its Lexington business saw submissions increase from approximately 30,000 in 2018 to 300,000 in 2024. Those 300,000 submissions translated into roughly 6,700 binds—a 2% submission-to-bind ratio—and approximately $1 billion in new-business premium. Behind those numbers sits a familiar commercial-underwriting reality. A submission is rarely a clean dataset.
It may contain applications, loss runs, schedules of values, spreadsheets, financial statements, engineering reports, broker correspondence, scanned documents and subsequent corrections. Before an underwriter can exercise judgment, someone must first assemble the risk.
AIG described its traditional submission-to-review process as taking approximately three to four weeks, involving manual extraction, validation, research, augmentation, rating and approval—and acknowledged that underwriters could not review every submission.
That is more than an efficiency issue. It creates a growth constraint. Because when underwriting capacity is finite, every hour spent reconstructing a submission is an hour not spent evaluating risk, negotiating terms, structuring coverage or engaging brokers.
And speed matters because an attractive risk has little value if the carrier responds after the placement has moved.
The Real Objective Is: Improve underwriting capacity
The obvious response is automation. But automating document intake solves only the first layer of the problem.
The more strategic question is: Can AI increase the number of opportunities an underwriter can intelligently evaluate—while improving alignment with risk appetite, pricing and portfolio objectives?
That is the direction of AIG's Underwriter Assistance / AIG Assist architecture. Its disclosed capabilities span:
Ingestion → Augmentation → Prioritization
The broader workflow goes further:
Submission → Structured Risk → Context → Appetite → Priority → Underwriter Decision
That is a fundamentally different ambition from automation.
The harder problem is preserving underwriting meaning, wherein capabilities such as separating and retrieving tabular versus textual information, reconstructing complex tables, maintaining source and page traceability, retrieval with error controls and preserving original-page visual context through multimodal techniques play a major role.
The hard problem is no longer extracting a number from a document. It is preserving the underwriting meaning surrounding that number. A $100 million value means little without knowing whether it represents revenue, TIV, payroll, limits, exposure or something else—and where the evidence came from.
It is the transition from document automation to submission intelligence.
Ontology: Where submission becomes part of the portfolio
Extraction alone still leaves individual facts disconnected. An ontology turns extracted submission data into underwriting context by connecting entities such as the insured, locations, exposures, limits, loss history, broker and policy structure through semantic relationships. It creates a machine-readable representation of how the risk fits together.
A location is no longer simply an address. It becomes an exposure contributing to a peril zone, an aggregation and ultimately a portfolio decision. That semantic layer is one of the foundations for moving from submission intelligence toward decision intelligence.
What happens when the underwriter gets time back?
AIG reported that its assisted workflow reduced submission preparation/review from approximately three to four weeks to less than one day and enabled review of 100% of applicable submissions in the disclosed deployment.
By year-end 2025, Lexington received more than 370,000 submissions, and AIG reported a 35% improvement in submission-to-bind for Lexington middle-market property, reducing underwriter time-to-quote by 55%.
The above journey provides a directional view on how P&C insurers can reimagine submission at enterprise scale to drive better risk-adjusted growth.
Three horizons to deliver the capabilities for P&C insurers
Horizon 1: Submission Intelligence
As learned from AIG's journey, RAG, source traceability, ontology, and human-in-the-loop controls help transform fragmented documents into an underwriting-ready representation of risk.
Ingest → extract → enrich → retrieve → prioritize → summarize → underwriter review
Horizon 2: Richer context
Submission packages contain engineering reports, which contain images such as a property's floor plan, protection layout showcasing sprinklers, smoke detectors, etc., and rooftop images. The ability to synthesize and corroborate the relevant risk details across text, images, layouts, and tables is highly relevant to creating a cohesive context for complex submissions.
This is where multimodal RAG techniques using vision-language models (VLMs) such as Gemini 3 Pro, Anthropic Claude 3.5, Alibaba Qwen3-VL, etc., play a critical role in chunking them, unifying the embeddings into vector databases with relevant identifiers to establish traceability, and optimally retrieving (multi-vector retrievers) relevant data for the context search.
Horizon 3: Underwriting Decision Intelligence
The richer context can then connect the submission to risk appetite, underwriting rules, portfolio concentrations and prior experience (via knowledge graph), enabling AI to reason over relationships rather than documents alone.
This is where the opportunity becomes transformational. When we connect submission intelligence with: underwriting appetite, pricing targets, portfolio concentrations, catastrophe accumulations, historical outcomes, broker relationships and underwriting authority.
The system no longer asks: "What information is in this submission?", rather augments the underwriter with intelligence:
"Given our appetite, portfolio and economics, should an underwriter spend time on this opportunity now—and why?"
And this is precisely why governance has become strategic
The Human+AI operating model should preserve clear decision rights.
- Hard rules can enforce appetite and authority boundaries.
- Predictive models can quantify risk, pricing and behavioral signals.
- Agents can interpret unstructured evidence, identify contradictions and synthesize context, but
Underwriters should retain judgment over risk selection, terms, exceptions and material decisions
The objective is not to replace underwriting judgment. It is to deploy that judgment where it creates the most value.
Governance becomes the architecture of trust
Once AI recommends which opportunities receive underwriting attention, governance becomes part of underwriting discipline. Every recommendation should be capable of carrying its evidence, confidence, data lineage, model or prompt version and decision path.
Every override should be attributable. Every action should operate within explicit authority. These controls determine whether an underwriter can trust the system enough to act on it. It is consistent with the principles reflected in NIST Generative AI Profile and the NAIC Model Bulletin on insurers' use of AI.
Without an evidence chain, AI may scale activity, but will not necessarily scale institutional confidence. And this cannot be owned as a technology program alone.
Underwriting defines appetite and decision rights.
The CDO industrializes data, context, and controls.
Business leadership owns conversion, portfolio quality and adoption.
The strategic question for P&C leaders
The winning submission platform will not be the one that processes the most submissions. It will be the one that helps the insurer recognize attractive risks earlier, respond while the opportunity is still actionable, deploy underwriting attention, and convert more of the business it wants to write at the right economics.
The progression is simple:
- Automation reduces work.
- Submission intelligence expands usable underwriting capacity.
- Decision intelligence determines where that capacity and ultimately capital should go.
The more consequential question for P&C leadership teams:
Are we simply applying AI to read submissions faster—or are we prepared to encode our appetite, expose portfolio context, and govern how machines influence underwriting attention and capital?
That distinction may determine who converts submission intelligence into profitable growth.
