Insurance has always been built on trust. Customers trust insurers to price risk fairly, protect them when things go wrong, and make decisions that are consistent, explainable and justifiable. Regulators trust insurers to operate responsibly, manage risk appropriately and treat customers fairly. Insurers trust their own data, systems and processes to support decisions that can carry significant financial and personal consequences.
That foundation is now being tested. As artificial intelligence, data-driven underwriting and automated decisioning become more embedded across insurance, the industry is entering a new phase of digital transformation. AI is already influencing how risk is assessed, how policies are priced, how claims are triaged, how fraud is detected and how customers are served. Used well, it has the potential to make insurance faster, more responsive, more personalized and more efficient.
The next step is even more significant. As insurers move from AI as an assistant to
The more AI becomes part of the decision-making fabric of insurance, the more important it becomes to prove how those decisions are made. That is why I believe 2026 will be defined by a new competitive discipline: trust engineering.
Trust can no longer sit at the level of brand promise, policy wording or regulatory response. It has to be engineered into the systems, data flows, operating models and governance structures that support the insurance business. In an AI-enabled market, it will not be enough for insurers to say their decisions are fair, compliant and customer-centric. They will need to provide evidence of it.
Governance is no longer a back-office issue
The EU AI Act classifies AI systems used for risk assessment and pricing in life and health insurance as high-risk. The European Insurance and Occupational Pensions Authority (EIOPA) has also made clear that responsible AI use in insurance requires strong governance, data quality, record-keeping, fairness, cybersecurity, explainability and human oversight.
For insurers, that creates an obvious compliance obligation. But compliance is the most interesting part of the story. The bigger point is that regulation is forcing the industry to confront a much wider operational question:
Can insurers understand, control and explain the decisions being made?
That question matters because many insurance organizations are trying to deploy modern AI capabilities on top of technology estates that were never designed for this level of transparency or control. Core systems are often fragmented. Data is spread across multiple platforms. Business rules are embedded in legacy processes. Decision logic can be difficult to trace. Manual workarounds still sit between systems that should be connected.
That creates a very practical problem. In many insurance organizations, even establishing whether the same customer holds more than one policy can be more difficult than it should be. Customer, policy, claims and billing data often sit in different systems, structured around products rather than people. If insurers struggle to create a single, reliable view of the customer, it becomes much harder to bring together the trusted data foundation required for governed, explainable AI.
In that environment, AI does not automatically create intelligence. In some cases, it simply accelerates complexity.
If an insurer cannot clearly understand the data feeding an AI model, the rules shaping a decision, the governance around that decision, and the human accountability attached to the outcome, then it will struggle to build trust. Not just with regulators, but with customers, partners, distributors and its own internal teams.
What trust engineering really means
Trust engineering is about designing the insurance enterprise so that responsible decision-making is built in from the beginning.
That starts with data. AI depends on data, but insurance data is often fragmented across product lines, channels, geographies and legacy systems. If data is incomplete, inconsistent or poorly governed, the decisions built on top of it become harder to trust.
Insurers need strong data lineage, clear ownership, consistent definitions and the ability to understand where data has come from, how it has been used and what role it played in a decision.
The second pillar is explainability. Insurance decisions affect real people. Why was a policy priced in a certain way? Why was a claim routed for additional review? Why was a customer offered one product and not another? The industry cannot afford black-box decisioning in moments that matter.
Explainability has to exist at both the technical and business level, so that decisions can be understood by data scientists, compliance teams, underwriters, claims handlers, regulators and, where appropriate, customers.
The third pillar is auditability. In an AI-enabled operating model, insurers need to be able to reconstruct decisions after the fact. That means maintaining records of data inputs, rules, model outputs, approvals, overrides and human interventions. Audit trails are not just a defensive compliance mechanism. They are the basis for learning, improving and proving that the business is operating responsibly.
The fourth pillar is human oversight. AI should not remove accountability from insurance. It should make accountability clearer. There will be areas where automation can improve speed and consistency, but there must also be clear thresholds for human review, clear escalation routes and clear accountability for outcomes. Human oversight cannot be a vague principle. It has to be operationally designed.
This becomes especially important as AI becomes more agentic. Autonomous insurance workflows need clear operating boundaries. What can the system decide by itself? What can it recommend but not execute? When must it escalate? Which data sources is it allowed to access? What permissions does it have? What happens when two models, rules or workflow agents produce conflicting recommendations?
These are not theoretical questions. They are control-plane questions, and they need to be answered in the architecture, not improvised after deployment.
The final pillar is adaptability. Regulation, customer expectations and AI capabilities will continue to evolve. Insurers need systems that allow governance rules, decisioning logic, product design and customer journeys to change without expensive, high-risk transformation programmes every time the market moves. Trust engineering is not a one-off compliance project. It is a capability.
The commercial value of governed intelligence
The mistake would be to view all of this as regulatory drag. When viewed through the right lens, there is a huge opportunity.
Done properly, trust engineering creates commercial advantage. It allows insurers to move faster because they can deploy AI with greater confidence. It reduces the risk of stalled innovation because governance has already been designed into the operating model. It helps insurers respond more quickly to regulatory questions, customer complaints and internal risk reviews. It also creates the conditions for more sophisticated use of AI over time.
In my view, the winners in 2026 will not simply be the insurers experimenting with the most advanced models. They will be the insurers that can operationalize AI safely, transparently and at scale.
That distinction is important. Many insurers are already testing AI in isolated use cases. Some are seeing promising results. But pilots are not the same as production-scale transformation. Moving from experimentation to enterprise-wide adoption requires a different level of discipline. It requires trusted data, governed workflows, integrated systems, clear controls and the ability to evidence decisions across the policy lifecycle.
This is where modern core architecture becomes critical. AI cannot be treated as a layer that sits separately from the insurance business. If AI is influencing underwriting, pricing, claims, service or risk management, then it has to be connected to the core systems where those decisions are executed. Otherwise, insurers risk creating a gap between intelligence and action, or worse, between decision-making and accountability.
A modern insurance operating model needs to bring data, rules, workflow, governance and customer interaction closer together. It needs to allow insurers to configure and adapt decisioning processes while maintaining control. It needs to support automation without losing visibility. It needs to make governance part of the flow of work, not a manual process that happens after the fact.
That is the real meaning of trust engineering. It is not about slowing innovation down. It is about creating the conditions for innovation to scale.
Trust will define the next phase of insurance transformation
The insurance industry has spent years talking about digital transformation in terms of speed, efficiency and customer experience. Those priorities still matter. Customers still want faster service. Insurers still need lower operating costs. The market still rewards agility.
But the next phase of transformation will be judged by a higher standard. Can the insurer explain its decisions? Can it prove its data is reliable? Can it show where human judgement was applied? Can it demonstrate that AI is being used fairly and responsibly? Can it adapt when regulation changes? Can it build confidence with customers at the same time as it improves efficiency?
These questions will increasingly separate the leaders from the laggards. For insurers, the challenge is not whether to use AI. That question has already been answered. The challenge is whether they can use AI in a way that is transparent, governed and trusted.
In 2026, trust will not be a soft concept. It will be a hard operational capability. The insurers that understand that now will be better placed to innovate, compete and grow in the market that is coming next.
