WEBINAR | What Nobody’s Telling You About Agentic AI✦

Click here to watch the full webinar. #

What you’ll learn #

• Why 92% of North American life insurers are investing in GenAI — but most are still optimizing static processes rather than transforming how they assess and act on risk
• What the “architecture trap” is, why embedding AI inside core systems creates hidden strategic constraints, and how the most forward-thinking insurers are designing around it
• The difference between AI-enabled and AI-native ecosystems — and why intelligence increasingly needs to live outside the system of record to drive real-time decisioning
• Why the proof-of-concept-to-production gap isn’t just a technical problem: what drift management, explainability, and human-in-the-loop design actually look like at scale
• The three-stage model (assess, predict, execute) that moves AI from cost reduction to revenue growth — and the data infrastructure decisions you need to make today to get there

Practical advice for insurance executives on where to start: from governance ownership and employee adoption to vendor selection criteria for production-ready agentic AI

In this session from the Equisoft Accelerate series, Celent Principal Analyst Fabio Sarrico and Equisoft AVP of Product Management Ghassan Karam cut through the noise to examine what’s actually happening with agentic AI in life insurance and what it takes to get from proof of concept to production.

From the architecture trap that quietly limits most AI initiatives, to the data wall that blocks decisioning use cases, to the change management challenges that derail even technically sound deployments, this webinar covers what the industry still isn’t saying out loud — and what you need to hear before your next AI investment.

Key insights from this session #

GenAI investment is real — but most of the industry is still in efficiency mode.

Celent’s 2026 CIO survey shows GenAI’s share of AI/ML budgets has doubled year over year, and 92% of North American life insurers are investing or planning to within two years. But the majority of production use cases — document summarization, submission triage, internal co-pilots — are optimization plays. They don’t change how insurers assess or act on risk. That shift from efficiency to decisioning is where the real transformation begins.

The architecture trap: why embedding AI in your core system is a strategic mistake.

Many insurers introduce AI into environments never designed to support it. When AI is embedded inside a system of record, it inherits that system’s limitations — batch processing, static lifecycle triggers, no real-time response. The result is a probabilistic AI operating inside a deterministic environment. The insurers getting this right are designing AI to live outside the policy admin core, connected through event-driven architecture and governed by clear human-in-the-loop logic.

AI-native vs. AI-ready: where intelligence lives matters more than whether AI is present.

An AI-native PAS is a meaningful upgrade — but it’s still fundamentally a system of record: built for stability and consistency, not adaptation. An AI-ready ecosystem treats the PAS as the heart (the system of truth) while decisioning moves to a cognitive layer outside the core. That intelligence layer can observe signals, respond continuously, and evolve over time without contaminating the deterministic logic of the core.

The POC-to-production gap is not just technical — it’s organizational.

Most insurers can build a compelling proof of concept. The hard part is making it reliable at scale: measuring drift over time, managing model versions, building override logic, and structuring the feedback loops that make AI systems improve. Just as important is managing employee perception. If your workforce believes AI is replacing them, their feedback will be guarded — and the system will never improve. Shadow deployment, cohort testing, and human-assisted recommendation is the gradual approach that actually works.

The real data barrier isn’t availability — it’s the 85% you aren’t governing.

Most insurers govern roughly 15% of their data: structured records from core systems. The other 85% — documents, emails, call logs, historical decisions — is unstructured, ungoverned, and inaccessible. Moving from efficiency use cases to growth use cases requires solving that gap. Insurers serious about AI-driven decisioning need to think about data lake architecture, unstructured data governance, and how they handle PII and PHI in an agentic environment.

Deepen your knowledge: Agentic AI in life insurance