Why insurers have to stop talking about AI in 2026

With artificial intelligence (AI) agents dominating tech headlines and major insurers announcing autonomous deployments amid mounting pressure to digitise, 2026 is shaping up to be the year when manual claims processes finally give way to AI-driven automation. Research shows that only 8–10% of businesses are truly AI-ready, and this year will be the make-or-break for insurers to capitalise and get ahead of the competition.

Crucially, 2026 has to be the year that insurers stop talking about AI. Each year that passes widens the gap and sees the insurance sector falling behind. The sector has had the time to talk – what matters now is making the decision to be part of this transformation.

What stage of AI is the insurance sector in?

Large banks and organisations in the financial services space have been the most visible AI movers in the past 12 months, particularly in deploying customer-facing AI solutions. And, as insurers watch their banking counterparts embed AI into their customer service, compliance and decision-making, they have to recognise a simple fact. Layering new technology onto old ways of working isn’t a strategy.

Transformation takes more than buying from the right vendor, and speed of adoption goes well beyond pressing ‘go’ on the technology. The fastest-learning insurers are those whose leaders understand that real progress demands change management, process redesign, and workforce transformation. Meanwhile, those falling behind remain stuck in perpetual pilot phases, treating
AI as merely a productivity tool rather than acknowledging that success depends on people and processes as much as the technology.

How fast will change really happen?

Insurers are piloting AI, but what’s stopping them from scaling successful AI pilots to full deployment? The main barrier keeping companies from scaling successful AI pilots is a misunderstanding of what real transformation requires. Take Gartner’s prediction that over 40% of agentic AI projects will be cancelled by 2027, and the Massachusetts Institute of Technology’s discovery of a 95% failure rate at enterprises for generative AI pilots.

The challenge is that turning technology into bottom-line impact depends primarily on people and processes. Around two-thirds of the effort is in traditional change management, such as redesigning workflows, redefining roles, and helping people adapt. Real transformation means rethinking roles, responsibilities, and processes from the top down. Without that, there are no real P&L gains.

What AI use cases will be most effective for the insurance sector?

The rise of reasoning models will make them a key part of insurers’ AI arsenal in 2026. These models have brought agent-like capabilities into everyday use, enabling features such as document analysis, automated decisions, data querying, and other autonomous task handling. More importantly, they create entirely new information networks, allowing AI agents to work with tools, data sources, and workflows in ways that were previously impossible.

The biggest value emerges when AI is used to replace human reasoning and translate the traditional human-to-document, document-to-human workflow patterns into genuine document-to-document workflows. In insurance, for example, the entire process traditionally relied on humans to interpret unstructured data – a customer describes an event, which is recorded as a document, then someone in the insurance company reviews it, using reasoning powered by caffeine to deduce what the insurance company should think about the document, e.g. is a claim event covered by the underlying policy? Independent of the outcome, the human then creates another document, e.g. claims decision. This human reasoning has been the only way to handle these networks, leading into an operations design where humans sit in the intersection of the nodes.

The biggest value emerges when AI is used to replace human reasoning and translate the traditional human-to-document, document-to-human workflow patterns into genuine document-to-document workflows

This is not because the caffeine-powered brain is superior for the task but because there has not been any other option – the decision cannot be reduced into simple, deterministic rules. Now reasoning models change this fundamentally, and the biggest disruption we are going to see is as simple as the emergence of a document-to-document information network where there are no humans needed in the intersections anymore. This disrupts industries that were previously unautomatable, like insurance, where the core reason humans are in the process is that their brains are needed to translate documents through the information network. Understand this paradigm shift and it becomes clear that the value is in redesigning operations around the assumption that certain human roles aren’t necessary. However, this value only materialises when organisations commit to the complete transformation, not just the technology deployment.

What about AI agents?

Applying AI in an insurance setting that is willing to implement change, educate its workforce, and rethink its operational design is where the real transformation happens. AI agents can sift through immense volumes of data with speed and consistency, spotting patterns that are difficult to spot for humans, for example surfacing deliberate fraud, long before it turns into costly payouts. Think what the value would be if you could scrutinise every claim like it would be the only claim your company processes that day.

Consider a travel insurance claim arriving in the middle of the night, when claims handlers aren’t working. An AI agent can analyse the event description in real time, review the member’s claims history and underlying policy, and determine coverage – all within minutes. By morning, the customer could have confirmation of coverage, and the claim has already taken the first steps towards being settled.

The claims handler isn’t starting their workday with a backed-up log, and the customer isn’t waiting for a resolution. What once took days now happens while the policyholder sleeps, which means insurance professionals can focus on genuinely complex cases and exceptions rather than routine processing.

The time for talking about AI is over

In 2026, insurers have to stop talking about AI’s potential, and take action on that potential. Value has been demonstrated in other sectors, and pilots and concepts have been developed for a range of insurance use cases. Industry front-runners are already using specialised AI agents, offering operational leaders a real alternative for recruiting. These digital workers are able to handle tasks that once required teams of people, transforming how policies are priced, claims are settled, and risks are understood.

For operational leaders the question is who is using this moment to redesign their operating model, free from the traditional human-to-document pattern. When emails disappear, handoffs collapse, and data flows directly from document to document through the process, the impact is immediate and measurable. What looks like innovation today will very quickly become the baseline – and by the time that’s obvious, the advantage will already be gone.