The future of insurance isn't about specialists wrestling with complex core systems. It's about insurance teams conversing with the core as naturally as they talk to each other, thereby reducing the cost of change, accelerating time to market, and creating more space to focus on customers.
AI is often framed as a threat to jobs. In reality, its greatest potential lies in freeing people to focus on high-value work while intelligent systems handle complexity, coordination, and routine tasks. Few industries stand to benefit more from this shift than insurance.
Like businesses in many sectors, insurers understand that AI is key to reducing costs, accelerating service, and driving smarter decisions. But what many are discovering are the limits of simply layering AI models onto legacy systems. The real breakthrough won't come from adding more AI. It will come from deploying it differently.
This is where agentic AI becomes truly disruptive. When embedded in a modern, cloud-native, API-first architecture, agentic AI enables insurers to move beyond today's bolt-on chatbots and narrow automation. Instead, they can create what I call the Conversational Core — a platform where intelligent agents orchestrate workflows across policy, claims, billing, and distribution, and business users leverage the system freely, engaging with it in natural language.
The Power of Agentic AI in Insurance
Agentic AI, where intelligent agents collaborate across systems to enable automation of complex, high-volume tasks, marks a step change in organizational effectiveness. By orchestrating across workflows, teams, and channels in real time, agentic AI can unlock new levels of automation, efficiency, and service. But only if supported by modern architecture.
Today, the majority of AI implementations in insurance are limited to chatbots — useful proxies for human-led conversations that answer basic questions or route requests. Helpful, but narrow. They make existing processes more efficient, yet fail to fundamentally change how the business operates.
Agentic AI is different. Embedded directly into the core, intelligent agents don't replace judgment. Instead, they take on the high-volume, complex tasks that slow people down, while humans stay in control. They can be applied across the full insurance lifecycle to handle what I consider to be low-hanging fruit:
- Smart quoting and file intake.
- Census and enrollment automation.
- Intelligent OCR for documents.
- Billing reconciliation.
- Risk assessment and fraud detection.
- Case and work automation.
In each case, agentic AI augments human workflows, reduces errors, speeds up admin-intensive tasks, and improves accuracy.
Beyond these foundations, more advanced functions are emerging, from collecting all the information required for underwriting to adjudicating complex claims, where AI agents can monitor events, suggest next actions, and execute workflows under human oversight.
The real driver here isn't automation for its own sake but orchestration: enabling insurers to coordinate decisions and processes across modules, channels and partners. While the most advanced scenarios are still developing, the foundational use cases are already within reach. Yet, in practice, few insurers have taken the leap.
From Bolt-On AI to the Conversational Core
Much of what's called AI in insurance is still machine learning: algorithms optimized for narrow tasks. Generative models are beginning to appear, but the real breakthrough will come when intelligent agents combine ML's predictive strengths with GenAI's orchestration power and insurers can interact with them conversationally across the core. Crucially, this must be embedded at the core, not bolted on at the edges. This isn't about evolving previous features, it's about creating new opportunities.
To unlock this potential, GenAI must become a native part of the operating core: acting on real-time data, triggering workflows, and collaborating with humans where it matters most. When the platform is enabled as an agentic AI framework, every service can be orchestrated by intelligent agents.
Rather than tweak existing processes, this approach establishes a new operating norm for insurance: Configure-Test-Deploy. What is standard in digital-native industries like Amazon, Uber, and Netflix now becomes possible in insurance and accessible to business users through natural conversation.
As with the platforms run by the digital giants, delivering this requires a MACH-based, cloud-native, API-first, AI-native, and data-ready architecture. With these foundations, agents can securely connect to any module, retrieve and act on real-time, enriched, contextual data, and coordinate decisions across the entire value chain.
What's more, when the platform is natively enabled as an agentic AI framework, insurers and partners can build and integrate their own intelligent agents. These aren't limited to single functions. They can span underwriting, claims, billing, policy servicing, and distribution in one coordinated flow. These agents draw on enterprise data from across the platform, execute tasks through secure application programming interfaces (APIs) and event-driven interactions and provide results to business users conversationally.
Critically, governance is built into the fabric of the platform. Intelligent agents acting across underwriting, claims, billing, policy servicing, and distribution not only operate more efficiently but also safely, compliantly, and transparently with auditability and human oversight at every step.
This is the essence of the Conversational Core. Not bolt-on features, not incremental chatbot upgrades, but a new operating model for insurance where intelligence is embedded at the heart of the core and insurers no longer operate their systems, they converse with them.
The Legacy Roadblock to Intelligent Insurance
The challenge for most insurers is structural. Their core platforms were never designed for an AI-enabled world. Many are still powered by monolithic systems that don't support native integration of GenAI and lack the openness needed for intelligent agents to interact with data across the business. Instead, AI is bolted onto isolated processes while data remains siloed, inaccessible and out of sync.
Monolithic systems are like walled castles: secure in their time but closed, rigid, and costly to maintain. Modern business requires open cities that are connected, adaptable, and designed for constant exchange.
This rigidity has two consequences. First, every attempt to introduce AI becomes a bolt-on, limiting its impact to narrow use cases. Second, the cost and complexity of change skyrocket. Even simple improvements can take months or years. For AI agents that need to orchestrate across underwriting, claims, billing, and servicing in real time, these constraints are a structural blocker.
In short, legacy systems don't just slow insurers down. They prevent them from unlocking the very technologies that could help them compete in a digital-first, data-driven market.
Building the Foundations for Intelligent Insurance
The shift away from monolithic architectures is not new. Across industries, enterprises have already embraced cloud-native, modular, API-first platforms with AI-ready data fabrics because they enable agility, cost efficiency, and continuous innovation. The same principles that transformed digital leaders in e-commerce and beyond now provide the blueprint for insurers ready to take the next step with agentic AI.
Let's be clear. Agentic AI isn't just another technology trend. It is the enabler of something bigger: the Conversational Core. A fundamental shift in how insurers configure, operate, and orchestrate their businesses to innovate, and serve their customers. The real question is not whether it will become part of the industry landscape, but how quickly insurers can create the foundations to take advantage of it. Those who act now will be the first to turn automation into orchestration, insight into action, and insurance into a truly intelligent enterprise.
