Discussions about AI are continuing at pace, in industries across the globe. For the insurance sector, the debate is largely no longer about whether AI will shape the future of businesses, but how to use it now and prepare for the future without weakening the foundations of firms’ business models.
We know that AI can help firms improve the efficiency and effectiveness of an increasing number of tasks. However, alongside these benefits, firms must be confident that their use of AI remains commercially viable, operationally resilient, properly governed, and consistent with good customer outcomes.
Responsible adoption of AI is a business model imperative
AI capabilities are advancing at extraordinary speed, including towards more autonomous and agentic applications. At the same time, firms face geopolitical uncertainty, cost pressures, cyber threats, and growing third-party dependencies. In this environment, standing still is not a risk-free option: firms that fail to develop effective AI capabilities may weaken their competitiveness, service quality, and adaptability.
Equally, adoption without disciplined control is likely to create new and heightened risks, opaque decision-making, and unmanaged dependencies. We must not forget that maintenance of a suitable business model is a Threshold Requirement for regulated firms regardless of whether that business model is AI-native, makes very limited use of emerging technologies, or lands somewhere in between.
Insurers are already applying AI across claims processing, fraud detection, customer servicing and internal productivity activities. We are seeing all these themes and more in our work with firms, in addition to rapidly growing interest on topics such as using AI to enhance the efficiency and effectiveness of activities in second line risk and compliance functions.
The central challenge for senior leaders is to make AI adoption a core part of the firm’s business model and strategy, rather than treating it as a collection of disconnected technology projects.
Regulators have therefore understandably moved beyond recognising AI’s transformative potential for individual processes and are increasingly focused on how firms are managing related, emerging risks to strategy and business models. Existing principles-based and outcomes-focused frameworks will continue to apply: the PRA and FCA have been very clear that technological innovation does not dilute boards’ accountabilities for strategy, governance, resilience, or customer outcomes.
As the application of AI continues to expand and develop, we recommend firms should assess the suitability and sustainability of their business models at three connected levels:
- use case;
- enterprise-wide operating model; and
- wider ecosystem (i.e., platforms, data, infrastructure and third parties on whom delivery depends).
Business model suitability & sustainability must be evidenced
In our view, the firms most likely to benefit will not simply deploy AI fastest; they will connect adoption to clear business objectives, risk appetite and measurable outcomes. Boards and senior management need a current view of where AI is used, what value it creates, which risks and dependencies it introduces, and whether investment, controls and skills remain proportionate as capabilities evolve.
Crucially, firms must not only maintain confidence that their business model remains suitable and sustainable as AI is deployed…but be able to provide convincing evidence of the fact. In our discussions with firms, we have emphasised the importance of using current regulatory expectations as a core input to strategic reviews of how they will manage the risks and opportunities presented by AI.
Boards and senior management should be asking pertinent questions such as:
- Where can AI create durable value in our existing (or enhanced) business model, and where might it add complexity without proportionate benefits?
- Can our governance and oversight arrangements, skills, capabilities and resources keep pace with increases in AI autonomy and scale?
- Do we have the ability to capably identify and manage model, data, infrastructure and supply chain dependencies?
- Can outcomes from our use of AI be explained, challenged and corrected when this affects pricing, claims or customer engagement?
- How do amended arrangements fit with our existing accountabilities and arrangements for evidencing ‘reasonable steps’ under the Senior Managers Regime?
- Does our management information allow the Board to evidence that use of AI remains within agreed strategy and risk appetite?
Maintaining trust during change
We are at a critical inflection point for how the businesses of tomorrow will be structured and run, and the future is arriving at pace. The opportunities and risks of this transformative era apply to insurers as much as they do other firms, in the UK and globally.
The success of the insurance industry has always depended on trust: that risks are understood, that decisions are fair, and that firms will deliver when policyholders need them (often over long timescales). AI-enabled transformation of business models must therefore reinforce rather than erode that trust. To succeed, firms should act now to align their AI plans and related topics including operational resilience, third-party risk management, data governance, and talent initiatives with their overall business model and strategy.
The firms that do this well will not only create a defensible evidence base to enable control and compliance as they navigate these changes; they will develop and maintain business models that can exploit AI’s opportunities, withstand its risks and remain demonstrably suitable and sustainable as the technology and market continues to evolve.