Walk through any insurtech conference today and you'll hear "agentic AI" mentioned at every turn. Every vendor booth promises autonomous systems that can think, act, and learn. But when you examine these solutions more closely, many turn out to be large language model (LLM) implementations with intelligent automation added. These are valuable advances, certainly, but not the autonomous agents they claim to be.
This terminology inflation creates a fundamental problem. When insurance executives hear every vendor claiming to have "agentic AI," the market becomes so cluttered that companies that invest in building these new capabilities get lost among rebranded automations.
Defining Agentic AI
Part of the problem is that there's no uniform definition of what makes AI "agentic." Different experts emphasize different aspects: Some focus on autonomous decision-making, others on learning capabilities, and still others on goal-directed behavior. But while the exact boundaries remain fuzzy, we can certainly identify what agentic AI is not.
It's not just a chatbot with a fancy prompt. It's not a series of LLMs strung together with if-then logic. And it's definitely not traditional automation rebranded as AI.
One proposal is that true agency requires at least three core capabilities:
- Tool usage - the ability to navigate and interact with different systems
- Memory - maintaining context and learning from past interactions
- Real-time adaptation - adjusting approach based on results when something unexpected happens
The coding assistants like Cursor and Claude Code offer a useful reference point. These tools represent the current state of the art in AI, and most industry observers would comfortably label them as "agentic." If these are our benchmarks for genuine agency, the gap with most "agentic AI" solutions in insurance becomes clear.
This distinction matters because it reveals a spectrum. On one end, you have simple automation following predetermined paths. On the other end, you have fully autonomous systems that set their own objectives and continuously evolve. Most of what's being called "agentic" in insurance today sits firmly at the automation end, despite the marketing claims.
Current Market Examples
The evidence for this is everywhere. Take one major claims administrator's recent announcement of their "agentic AI" solution. Dig deeper, and it's a bundle of voice bots, intelligent document processing, and some alerting.
Another prominent vendor markets six different "AI agents" as part of their agentic platform. Remove the marketing speak, and you find a data layer with LLMs for document routing, a chatbot that accesses internal data, and template generation with compliance checks. These are often solid implementations that deliver real value, but they're a far cry from being truly "agentic."
The Market Distortion
The pressure to appear cutting-edge creates an arms race of terminology. When every vendor feels compelled to claim "agentic AI" to stay competitive, an insurtech that invested heavily in genuine foundations—tool usage, memory, and real-time adaptation—gets lumped in with one that simply added "agent" to their chatbot's name.
This creates an unfortunate dynamic. Insurance executives face an impossible task: evaluating solutions when every vendor uses the same terminology for vastly different capabilities. Even sophisticated buyers struggle to identify which systems will grow into true agency as AI matures versus those that are essentially dead ends with fancy names.
When implementations fall short of vendor promises, it naturally reinforces skepticism about AI investments. The insurtechs building for the future get caught in this backlash, making meaningful transformation even more challenging. Everyone loses: Buyers miss out on genuinely transformative technology, real innovators struggle to differentiate themselves, and the industry's digital evolution slows to a crawl.
The Path Forward
The insurance industry doesn't need to claim false sophistication. Current AI applications can provide tremendous value. Intelligent document processing saves countless hours. Well-designed chatbots genuinely improve customer experience. Predictive analytics enhances decision-making in measurable ways. These are powerful tools that augment human capabilities. The industry benefits when we accurately describe what these tools accomplish and match them to appropriate use cases.
For those evaluating solutions, start with a more fundamental question: Do you actually need agentic AI? If your goal is to reduce document processing time by 80%, intelligent automation might be exactly what you need. If you want to improve first-call resolution rates, a well-designed LLM-powered chatbot could be the perfect solution. These aren't agentic, but they solve real problems with proven technology available today.
Reserve the search for true agentic capabilities for problems that actually require them: complex claims that need dynamic investigation across multiple systems, underwriting decisions that must adapt to unique scenarios in real-time, or fraud detection that needs to evolve its approach as schemes change. For these use cases, ask the hard questions: Can this system actually use tools to solve problems? Does it maintain context across interactions? Can it adapt when things go wrong?
As agentic AI capabilities mature, they will transform how we handle claims, assess risk, and serve customers. But we'll only realize that potential if we're honest about where we are today and deliberate about where we're investing for tomorrow.
As buyers and builders, we all have a role in maintaining clarity about what AI can actually accomplish. This ensures that success goes to companies building on real capabilities rather than marketing claims, while preserving confidence in AI's genuine transformative potential.
