AI has been shaping conversations about the future of insurance for years. Early enthusiasm focused on automation, rules engines and predictive models – streamlining processes such as claims triage, pricing support and document management. 

Now, the conversation is evolving. Across the industry, insurers are looking to move beyond a seemingly endless cycle of pilots and proofs of concept. The ambition has shifted firmly towards enterprise-scale deployment and embedding AI across entire customer journeys, channels and business functions. 

Yet the results, so far, have been mixed. While some insurers are realising tangible business value, others remain stuck in experimentation mode or are quietly stepping back from initiatives that failed to deliver. 

What separates these outcomes is no longer access to technology and tooling. Instead, it comes down to how organisations design, govern and embed intelligence into their operating models. 

This is where agentic AI enters the picture. 

What Do We Mean by “Agentic” AI? 

Agentic AI marks a significant shift in how intelligence is applied. Rather than simply generating insights or automating isolated tasks, it is designed to act – either autonomously or semi-autonomously – within clearly defined guardrails. 

In an insurance context, this could mean: 

• Coordinating decisions across underwriting, pricing and referral workflows 

• Orchestrating multi-step claims journeys, including communications 

• Continuously adapting processes based on customer behaviour and emerging risk signals 

• Managing exceptions, escalations and hand-offs without disrupting flow 

Crucially, agentic AI is not about removing humans from processes altogether. Instead, it is about rethinking where and how decisions are made – carefully balancing automation with moments where human judgement, empathy or oversight are essential. 

Why Some Insurers Are Pulling Ahead 

A small but growing group of insurers is already translating agentic AI into meaningful outcome. And their success is not accidental.  

It reflects a different approach. 

They start with outcomes, not technology. Rather than being charmed by the latest tools, they focus on what they are trying to improve: faster resolution times, better risk selection, reduced leakage and stronger customer trust. Their decisions on technology follow the strategy they’ve set. Not the other way around. 

They design for real journeys, not over-simplified ones. This means anticipating complexity from the outset: understanding dependencies, recognising exceptions and identifying where judgement matters most.  

Too many initiatives have historically failed because they were designed for happy paths only, leaving common customer requirements that seemed outside of the norm on paper, as an afterthought.  

Time and time again Minimum Viable Product (MVP) didn’t really meet the minimum requirement, resulting in automations with too many exceptions and escalating manual workarounds.  

And perhaps most importantly, those insurers who are moving ahead, treat agentic AI as an operating model shift, not an IT upgrade. Governance, controls, monitoring and accountability are embedded from day one, rather than added later in response to risk concerns. 

Build vs Buy: A Strategic Choice 

As AI capabilities mature, insurers are facing increasingly complex decisions about how best to implement them – particularly when it comes to building versus buying solutions. 

Some organisations have chosen to build, investing in internal capability with the expectation that it will deliver long-term, enterprise-wide value. Done well, this can create strong differentiation and control. However, building everything in-house is rarely sustainable without significant scale, deep expertise and a sizable, sustained commitment to investment. 

At the same time, many common insurance use cases are now well understood, making a buy approach both viable and attractive. Off-the-shelf solutions have evolved rapidly and can significantly reduce both time to value and upfront investment – allowing insurers to move quickly and focus internal effort where it matters most. 

In practice, the most effective strategies strike a balance between the two. They combine: 

• Core platforms that orchestrate decisions across end-to-end journeys 

• Modular intelligence components that can evolve and be replaced independently 

• Clear organisational ownership of data, decision logic and outcomes 

Without this clarity, AI programmes may deliver technically – but struggle to deliver strategically, with fragmented capabilities and diluted impact over time. 

From Prediction to Orchestration 

Predictive models remain essential, but agentic AI requires a broader and richer approach to intelligence. 

Leading insurers are increasingly drawing on: 

• Behavioural data to understand customer intent, not just risk 

• Cross-journey insights to identify patterns over time 

• Real-time signals to dynamically adjust journeys in the moment 

This enables a fundamental shift – from asking “what is likely to happen?” to “what should we do next?”. 

In practice, this can transform how insurance works. Claims processes can accelerate straightforward cases while proactively supporting vulnerable customers with more complex needs. Underwriting can combine automation with targeted expert review. Service journeys can evolve dynamically, responding to customer needs as they change – without constant manual reconfiguration. 

The Workforce: Changing Roles, Not Replacing Them 

As agentic AI becomes more prevalent, its impact on the workforce is becoming clearer. 

Rather than eliminating roles, it is reshaping where value is created: 

• Underwriters focus more on complex risks and portfolio strategy 

• Claims handlers spend more time on empathy, investigation and exceptions 

• Operational teams shift from execution to oversight, optimisation and governance 

The insurers who succeed recognise this early. They invest not just in technology, but in skills, training and change management. Those who do not, often face resistance, inconsistent adoption and a loss of confidence in AI-driven decisions. 

Risk Aversion vs Momentum 

The market today reflects a spectrum of approaches. 

At one end are front-runners, scaling agentic capabilities across multiple journeys. At the other are more cautious organisations, only now beginning to move after years of observation. 

Both positions are understandable. What matters most is clarity of direction. 

A strong AI strategy should: 

• Define clear outcomes 

• Prioritise high-value, low-regret use cases 

• Start small, learn quickly and scale responsibly 

Without this discipline, organisations risk moving too fast and losing control – or moving too slowly and missing the opportunity altogether. 

Designing for Impact, Not Hype 

Agentic AI has the potential to reshape insurance in a profound way – but only if it is approached thoughtfully. 

The challenge is no longer whether to adopt AI, but how to embed intelligence meaningfully into journeys, decisions and operating models. Those who focus on outcomes, orchestration and human-centred design are already beginning to pull ahead. 

Those who focus on tools alone, or underestimate the complexity involved, risk repeating the missteps of earlier digital transformations. 

Ultimately, the future of insurance will not belong to the most automated organisations. It will belong to the most intelligently designed ones.

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