For me, it was a dizzying 3 days, a little overwhelming to be honest. But I knew from experience what to expect at ITC Vegas. True to its theme, “The Horizon of possibilities,” the 2026 edition, revealed the sheer breadth of innovation across 600+ technology providers, each with a distinctive and dolled-up presence, competing to attract the 9000+ participants representing 1900+ insurance carriers.
I sensed a great collaboration opportunity between Insurtechs and service providers like Trigent, with both combining forces and offering a full-suite of comprehensive services to large insurance carriers.
But most attendees I spoke with, though impressed by the AI novelty, expressed their doubts about the solutions’ efficacy: How well would these polished AI demos perform in real-world environments?
The fears are not entirely unfounded if you consider the AI fatigue stemming from recent pilot non-successes. However, a few solutions had demonstrated superior value for the business, proving the problem was not with the technology, but with how AI was executed.
Trigent presented three proven AI solutions at ITC Vegas:
- A Superfast Claims Processing Solution with AI-assisted intake, policy validations, and exception handling
- An Underwriting Engine with risk analysis, submission summaries, and actionable insights
- Document intelligence that identifies obligations, risks, and insights across contracts in seconds.
Attendees delved into the demos, equally interested in understanding what it would take for AI pilots to become sustainable, repeatable deployments.
And rightly so, Trigent presented ArKos, an enterprise AI workbench to help leaders validate AI pilots before committing capital.
Over 3 days at the event and through conversations with 100+ attendees, I observed an interesting dilemma.
There is a genuine interest in AI solutions and a clear need to see quick wins. However, this is tempered by the defensive notion that moving too fast in a regulated environment could also expose the company to unknown risks.
Insurance as an industry depends on deterministic outputs. In other words, determinism is crucial to insurance decision-making. Hence, probabilistic AI models must be complemented by a deterministic approach through adequate guardrails and human-in-the-loop systems.
That said, AI governance must not stall progress. Low-risk, low-impact use cases can be evaluated differently from high-impact, high-risk use cases.
Towards the very end of the event, I was certain about one fact: insurers are convinced that the seemingly new risks posed by AI, including increased attack surfaces, will be outweighed by the benefits of secure, scalable solutions. The solutions that will clearly succeed are those that require minimal changes to the existing operations, while being fully capable of demonstrating measurable value at every turn.
In short, 3 north-star questions will determine success:
- How effectively does the solution integrate with the enterprise’s existing tech stack?
- How seamlessly does it align with current business operations, incrementally modernizing processes instead of demanding an overhaul?
- How effectively does it demonstrate value across the enterprise in the short run?
