AI is no longer a future ambition for insurers—it’s here, transforming how submissions, claims, and operations are managed every day. But with adoption comes a critical question: Can teams trust the outputs? This is where explainable AI (XAI) comes in.
By making AI-driven insights transparent and auditable, explainable AI ensures decisions are traceable. Insurance teams can see what data was used, how it was interpreted, and why it led to a certain result.
A growing number of MGAs, carriers, and reinsurers are investing in AI, with the percentage of insurers fully embedding it into their value chain jumping from just 8% last year to 34% in 2025. But alongside this adoption comes a challenge: trust.
Many AI solutions operate like a “black box.” They deliver outputs, but users don’t know how the AI came to the conclusions it did. Teams are left asking: Where did this information come from? Why was this decision made? Can I trust it?
In an industry that is both heavily regulated and highly dependent on accuracy, trust is essential. Without transparency, AI can be perceived as another risk factor—something insurers are reluctant to embed in their core workflows.
Explainability closes this gap. By surfacing the reasoning behind every AI-driven decision, explainable AI builds confidence, accelerates adoption, and enables faster, more reliable decision-making across insurance operations.
Explainable AI (XAI) refers to artificial intelligence systems that make their decision-making processes understandable to humans. Instead of simply presenting an output, explainable AI shows:
This focus on traceability—the ability to link an output back to its source—sets explainable AI apart from “black box” models.
It’s also important to distinguish between interpretability and explainability. Interpretability means a model’s structure can be understood in theory—for example, a decision tree splitting on variables. Explainability ensures that in practice, teams can trace and validate an output after it is generated.
For insurers, this distinction matters. Explainable AI ensures operations, underwriters, and claims teams can trust and act on results.
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Insurance operations involve many moving parts—distribution, product development, operations, claims, and underwriting. Explainable AI gives all of these teams confidence by showing how outputs were generated and why they can be relied on.
As a heavily regulated industry, insurance requires that every decision be defensible—whether to auditors, regulators, or even in court. Explainable AI provides the transparent trail needed to validate decisions, justify outcomes, and ensure results stand up to scrutiny. This not only reduces risk but also builds confidence that AI can be safely embedded into core insurance workflows.
Teams are more likely to embrace AI when they can see how it works. By providing transparency into reasoning and results, explainable AI reduces resistance and accelerates adoption across the organization.
Explainable AI is most powerful when applied to real insurance workflows. By making outputs transparent and auditable, it ensures teams know exactly where information came from and why a decision was made. For example:
Each of these examples reflects the same principle: explainability transforms AI from a black box into a transparent partner, helping insurance teams move faster while staying in control.
Brisc’s Submissions Agent illustrates explainability in practice. It allows underwriting teams to process broker submissions in seconds while maintaining full visibility into how data was processed and extracted, from where, and why.
Explainability isn’t just about making AI transparent—it translates into measurable business value for insurers:
As insurers embed AI more deeply into their operations, explainability will determine which solutions earn lasting trust and adoption. From bordereaux reconciliation and claims triage to underwriting and submissions, explainable AI ensures that every output is transparent, interpretable, and auditable.
For insurers, it’s the foundation for faster decisions, stronger collaboration, and sustainable growth.
Brisc’s Submissions Agent is one example of explainability in action, combining speed, accuracy, and transparency to help insurance teams make better decisions, faster.