Artificial Intelligence in the insurance world has evolved from futuristic fantasy to practical necessity. But the latest twist? It’s not just about smarter algorithms or more accurate predictions. Welcome to the age of Agentic AI — where AI systems act more like strategic collaborators than just tools. And trust us, it’s a game-changer for underwriting, claims, fraud detection, and beyond.
So, What Exactly Is Agentic AI?
At its core, Agentic AI refers to systems that are goal-driven, autonomous, and proactive, capable of handling complex tasks by orchestrating multiple sub-agents and tools. It doesn’t wait for instructions — it anticipates needs, solves problems, and adapts to changing situations.
Think of it like this:
Traditional AI = a calculator or document scanner
Agentic AI = a digital team leader that manages tasks, cross-checks sources, makes recommendations, and loops you in when needed
Here’s what Agentic AI typically handles:
Document and image ingestion
Rules-based and contextual decision-making
Dynamic workflow orchestration
Live updates across systems
Human-in-the-loop validation
It’s not a dashboard. It’s a partner.
Agentic AI vs. Traditional AI Tools: What’s the Difference?
As one speaker at the InsurTech NY Spring Conference said:
“Saying you have Agentic AI because you have a chatbot is like saying you built Amazon because you have a shopping cart.”
Real-World Insurance Use Cases: Agentic AI in Action
1. Claims Handling: Beyond Decision Trees
Picture this: A customer files a theft claim on their vehicle.
They upload documents, police reports, and images
Agentic AI scans for inconsistencies (e.g., a change in the vehicle’s description)
It flags the issue, provides an explanation, and suggests a follow-up investigation
It’s not just checking boxes , it’s evaluating and evolving with the process.
2. Fraud Detection: Intelligence Meets Intuition
One demo showed an AI agent examining property inspection photos. One was labeled “crawl space” — but had a dog in the shot.
The AI recognized the context mismatch
Flagged the photo as invalid input
Explained why it couldn’t make a recommendation based on it
In another instance, it refused to answer a financial risk query, stating the request was out-of-scope — showcasing its ability to maintain regulatory guardrails.
3. Code Deployment from a Sketch? You Bet.
Yes, really. One keynote showcased an AI agent handed a flowchart sketched on paper.
The result?
Backend code was generated
Unit tests were created
Integration testing kicked off
Synthetic data was prepped
What normally takes weeks was done in minutes. That’s the power of multi-agent orchestration.
4. Humans Still Matter: But Now They’re Supercharged
Despite the tech wizardry, every successful use case emphasized human-in-the-loop validation.
AI handles the grunt work:
Pattern recognition
Data matching
Form processing
But when it comes to ethics, exceptions, or high-stakes decisions, humans take the wheel.
It’s not automation replacing jobs. It’s augmentation, freeing people for higher-level thinking.
5. Data Governance: Explainability Is King
One of the biggest risks in AI for insurance? Hallucinations, when models make up information. In a regulated space, that’s a nightmare.
Agentic AI avoids this through:
Boundary constraints
In-scope queries only
Citations of source data
Audit trails for compliance
As one speaker said:
“If your AI system can’t explain why it said no — you don’t have a system you can trust.”
Why Agentic AI Fits Insurance Like a Glove
Insurance isn’t just complex. It’s messy, regulated, and interconnected.
Core traits of insurance processes:
Rules and compliance-heavy
Multi-modal (images, forms, voice)
Decision-centric workflows
Collaborative across departments
Time-sensitive
Agentic AI isn’t just “good enough” for this complexity — it was built for it.
What’s Next for Insurers?
We’re entering a post-app era.
AI won’t be just a feature — it’ll be foundational
Static systems are giving way to adaptive, evolving networks
Your operations won’t use AI — they’ll run on it
Want to start smart?
Here’s how:
Identify one pain point or use case (claims, fraud, onboarding)
Involve subject matter experts early
Implement governance from the start
Scale intentionally — not chaotically
Companies who build around Agentic AI — not just bolt it on — will win.
FAQs About Agentic AI in Insurance
Q: Is Agentic AI just another chatbot or smart assistant? A: Not even close. It’s a coordinated network of agents and tools working toward a goal with oversight, reasoning, and adaptability.
Q: Can Agentic AI really be trusted in regulated industries? A: Yes, if implemented correctly. Governance, transparency, and auditability are built into good Agentic AI systems.
Q: Does this mean insurance jobs will disappear? A: No. It shifts repetitive work to AI and frees up humans for higher-impact, judgment-driven tasks.
Q: How is this different from “AI-powered tools” we already use? A: Traditional tools act in isolation. Agentic AI thinks and acts like a collaborative partner, orchestrating across tools and contexts.
Wrapping Up: Time to Think Bigger
The future of insurance doesn’t belong to those who digitize forms. It belongs to those who reimagine workflows, collaborate with intelligent agents, and design systems that think with them.
Agentic AI isn’t the cherry on top, it’s the new foundation.
The question is no longer “Should we use AI?” but “How far can we go with Agentic AI?”
FiveM has extensive experience advising leaders on modernization initiatives, resulting in valuable insights and “The Five Key Lessons” for digital transformation.
