Agentic AI isn’t just the latest buzzword. It represents a real shift in how insurance companies will operate over the next decade and beyond. Rather than simply using AI as a tool, agentic systems can act with a level of autonomy, making informed decisions, optimizing workflows, and learning over time. But not every organization is ready to hand over the reins. That’s why understanding your position on the agentic AI maturity model is essential.
Scaling AI responsibly in insurance doesn’t happen all at once. It’s a progression. Each step builds on the last, and trying to skip ahead usually causes more harm than good. Below, we break this journey into five clear stages to help insurers assess their current position and where they need to go next.
1. Awareness and Experimentation
At this early stage, AI is mostly experimental. Insurers may run pilots, like using AI to automate simple claims triage or customer service responses. But efforts tend to be siloed. There’s interest, but no real strategy.
Here, success means getting hands-on. Develop an understanding of what agentic AI is, its capabilities, and its applications. For example, using AI to answer common customer queries might reduce call centre volume, but it’s not yet “agentic” if human oversight is still needed at every step.
This is also where insurers begin to consider the long-term impact. What does responsible AI look like in a highly regulated environment like insurance? At this stage, that’s still an open question.
2. Operationalization and Tool Adoption
AI becomes more embedded in daily workflows. You might use artificial intelligence to detect fraud patterns or automate underwriting. Here, teams move from experimentation to implementation.
Agentic systems are introduced on a small scale. For instance, some firms are leveraging AI-powered chatbots that can resolve common queries and escalate only when needed. The goal is not full autonomy but reducing workload and improving efficiency.
3. Integrated Intelligence
AI is no longer a side project. It’s embedded in core operations, making independent decisions within defined guardrails. Agentic AI at this stage might, for instance, analyze incoming claims alongside external data (like repair shop estimates or weather reports) and determine who handles the claim, whether to pay it out immediately, flag it for manual review, or negotiate a payout amount.
This level of automation is dynamic. So while traditional AI would apply rules and generate similar answers each time, agentic AI will adapt its behaviour based on how accurate and successful it was with the previous actions it took.
Real-world examples are emerging. Some insurers are deploying AI-powered efficiency models that adjust their handling of low-value, high-frequency claims with near-total autonomy. Over time, the system learns which claims can be auto-approved safely, thereby reducing manual intervention and improving turnaround times.
At this point, AI not only makes processes more efficient but also smarter and faster, helping insurers scale without sacrificing accuracy.
4. Strategic Autonomy
This is where AI begins to play a strategic role. Systems don’t just support decisions, they make them within agreed parameters. AI models might adjust pricing in response to real-time market data or dynamically allocate resources during disaster events. Where strategic autonomy differs from integrated intelligence is really in the level of independence and impact. So, AI isn’t just improving processes; it’s actively shaping business outcomes in real time, with minimal human oversight.
Security becomes crucial here. Trust in AI decisions must be earned. Financial services firms are already recognising the importance of AI-driven security, particularly as customer data and critical operations are increasingly managed by autonomous systems.
This stage requires clear guardrails and governance. Without them, autonomy can turn into risk. Transparency, explainability, and ethical oversight are essential.
5. Agentic Optimization
At the final stage, agentic AI is embedded across the enterprise. It collaborates with humans, adapts continuously, and learns from every interaction. Systems optimize themselves over time, anticipating needs, correcting inefficiencies, and flagging emerging risks without being told to.
This is not about replacing people. It’s about using AI to unlock capabilities that were previously out of reach. Insurers at this stage see massive gains in speed, accuracy, and customer satisfaction. But they’ve also done the hard work: training data properly, managing risk, and aligning AI behavior with business values.
To reach this point, insurers often partner with specialists. Whether that means working with consulting experts or exploring targeted solutions to enhance business AI, success depends on strategy, not just tools.
Final Thoughts
Every insurer is somewhere on this maturity curve. The key is knowing where you stand and what’s next. If your AI journey is still in its early stages, focus on foundational use cases. If you’re already scaling, ensure your systems are secure, explainable, and aligned with your long-term business goals.
Agentic AI isn’t about pushing a button and walking away. It’s a partnership between technology and people. And for insurance companies ready to lead, it’s the future of competitive advantage.
Learn more about what’s possible in financial services and how the right AI strategy can make it happen.