This blog explores the critical intersection of people and AI in the insurance sector. Drawing on insights from across the Dutch insurance market, it offers practical guidance for leaders navigating AI-driven workforce transformation. You will find:
Across the insurance sector, 2025 marked a decisive shift. AI is no longer a pilot on the horizon — it is already reshaping roles, redefining skills, and challenging long-held assumptions about how insurance work gets done. The conversation in boardrooms has moved from whether to adopt AI to how fast, how responsibly, and how to bring people with you.
The numbers reflect this shift. Leading insurers are now reporting that the majority of their workforce has completed foundational AI training. Thousands of employees are using AI productivity tools in their daily work. Contact centre agents are supported by generative AI that summarises calls in real time. Customer interactions handled by AI-powered chatbots and voicebots have grown by triple digits in a single year. These are not experiments — they are operational realities.
For employees, the implications are significant. Entry-level roles are changing as AI handles more transactional tasks. Middle management is evolving as AI takes over review and routing functions. Specialist roles in underwriting and claims are being augmented, with professionals spending less time on information retrieval and more time on complex judgement. Skills requirements are shifting faster than at any previous point in the industry's history.
For leaders, the question is no longer whether AI will affect the workforce — it is whether their organisation is ready to manage that change in a way that strengthens capability rather than hollowing it out. The evidence from leading insurers suggests that the answer depends less on technology than on culture, governance, and the quality of the people strategy that underpins AI adoption.
The most significant shift in how leading insurers approach AI workforce readiness is the move away from treating AI as the domain of data scientists and technology teams. The emerging standard is AI literacy as a universal baseline — something every employee needs, at whatever level is appropriate to their role.
This means foundation-level AI awareness training for all staff, tool proficiency for those who use AI regularly, and advanced capability for developers and data specialists. Insurers that have invested in this tiered approach are seeing measurable results: higher adoption rates, fewer errors, and greater employee confidence in working alongside AI. Those that have left AI capability development to individual initiative are finding adoption patchy and cultural resistance harder to address.
A consistent pattern across insurers that have successfully scaled AI is the deliberate dismantling of business unit silos. When each function develops its own AI approach in isolation, the organisation misses the opportunity to learn from what is working elsewhere and to share the infrastructure costs of AI deployment. The result is duplication, inconsistency, and a ceiling on the value that AI can generate.
Leading insurers are responding by creating shared AI infrastructure, centralised governance, and cross-functional teams that combine business knowledge with technical expertise. The insight is straightforward: if an AI tool that reduces processing time in claims can also reduce it in underwriting or disability, the organisation should be structured to make that transfer happen quickly. Multidisciplinary teams that experiment together, share feedback, and iterate continuously are the organisational design solution that several insurers have identified as central to scaling AI effectively.
Some insurers have gone further than skills training and tool deployment, identifying the specific workforce behaviours that make AI adoption succeed. These include openness to change, willingness to experiment, cross-functional collaboration, and the ability to speak up when AI outputs seem wrong or incomplete. These are not soft add-ons — they are the conditions under which AI can be adopted safely and effectively.
Where these behaviours have been defined explicitly and embedded in performance management, the evidence suggests faster and more sustainable AI adoption. Employees who understand what is expected of them in an AI-enabled environment are more confident, more engaged, and more likely to identify improvements. The insurers making the most progress are those that have treated workforce behaviour change as seriously as technology change.
Across the sector, responsible AI is increasingly understood not just as a regulatory or governance concern but as a people commitment. Employees need to trust that AI tools have been designed with appropriate safeguards. Customers need to trust that AI-assisted decisions are fair and explainable. Regulators, including under the European AI Act, require evidence that human oversight is built into AI processes, not added as an afterthought.
Leading insurers have responded by establishing responsible AI frameworks that span ethics, bias monitoring, explainability, and human oversight. Importantly, these frameworks are not positioned as constraints on innovation — they are positioned as the foundation that makes sustainable AI adoption possible. The message to employees is clear: we are adopting AI responsibly, and you are part of how we make that happen.
Several leading insurers have moved beyond voluntary AI training to structured, enterprise-wide programmes that reach the entire workforce. These programmes typically combine short e-learning modules accessible to all staff, more intensive training for frequent AI users, and advanced development pathways for those building or deploying AI tools. The ambition is not to turn every employee into an AI specialist — it is to ensure that no employee is left behind as AI becomes embedded in everyday work.
The results are encouraging. Insurers that have launched comprehensive AI literacy initiatives report higher adoption rates for new tools, better quality AI outputs — because employees understand how to prompt and review AI effectively — and lower levels of anxiety about job security. When employees understand what AI can and cannot do, fear gives way to curiosity.
One of the clearest practical examples of effective AI people strategy in insurance is the deployment of AI tools in claims handling. In personal injury claims, for instance, AI tools have been developed to help claims handlers navigate complex files more quickly — surfacing relevant information, generating smart summaries with source references, and reducing the time spent on administrative retrieval. The professional retains full responsibility for the judgement and the outcome.
The impact on the workforce has been positive: handlers report that the work is more engaging because they are spending more time on the substantive tasks that require expertise and empathy, and less on repetitive information management. This is the human-AI balance done well — AI handling the low-value cognitive load, the human retaining ownership of the work that matters. Strategic workforce planning in these organisations has explicitly modelled how natural attrition and talent development can absorb role changes over time, treating AI adoption as a managed transition rather than a disruption.
Contact centres are among the highest-activity environments for AI deployment in insurance. Leading insurers are using generative AI for real-time call summarisation, enabling agents to spend less time on post-call administration and more time on the next customer. AI-powered knowledge management systems give agents instant access to accurate policy and product information, reducing handling time and improving the consistency of customer responses.
The people strategy behind these deployments is important. Insurers that are getting this right are not simply automating as much as possible — they are designing for human escalation at every point where the customer interaction requires expertise, empathy, or complexity that AI cannot handle well. Low-impact contacts such as address changes are fast and digital. High-impact situations such as claims after an accident or complex coverage queries are routed to experienced humans. AI makes the distinction and the routing. The human makes the difference.
A recurring theme in insurers that have successfully accelerated AI adoption is the role of leadership immersion. When senior leaders engage deeply with AI — through intensive learning programmes, hands-on use of AI tools, and visible participation in AI initiatives — the signal to the wider organisation is unmistakeable. AI is a strategic priority, not a technology department project.
In several Dutch insurers, Management Board members have participated in multi-day AI learning programmes at leading business schools. This is not simply about equipping leaders with technical knowledge — it is about building conviction and fluency that shapes the strategic choices that follow. Leaders who have genuinely wrestled with what AI can do are better placed to ask the right questions, set the right ambitions, and support the workforce through the changes that follow.
The evidence from leading Dutch insurers points to a clear set of strategic considerations for any leader designing an AI people strategy. These are not abstract principles — they are the choices that distinguish organisations making genuine progress from those stalling at the pilot stage.
Translating strategy into action requires specific, measurable steps across five dimensions of AI workforce readiness.
The following framework reflects the maturity levels observed across the Dutch insurance sector. Use it to assess your organisation's current position and identify the most important next steps.
Awareness of AI's people implications is growing. AI pilots are underway in individual business units, but there is no formal workforce AI strategy. Training is ad hoc and voluntary. Culture is curious but cautious. Governance frameworks are being discussed but are not yet operational. Most insurers were at this level in 2023 to 2024. The priority at this stage is building conviction at leadership level and launching the first structured capability building initiative.
An AI people strategy is taking shape. Basic AI literacy training is being rolled out across functions. A responsible AI framework is in development. Some roles have been redesigned around human-AI collaboration. Leadership is aligned on a value creation narrative. The priority at this stage is moving from voluntary to structured learning, establishing cross-functional AI governance, and making the first explicit workforce planning commitments.
AI literacy programmes cover a significant proportion of the workforce. Company-wide AI tools are in broad use. Responsible AI governance is operational. New workforce behaviours are embedded in performance management. AI use cases are multiplying across business functions. The priority at this stage is ensuring adoption is deep and consistent, not just broad, and extending AI capability to parts of the organisation that have been slower to adopt.
AI workforce strategy is fully integrated with business strategy. Data literacy is a company-wide baseline. Human-AI collaboration is the default operating model. The organisation attracts and retains talent in part because of its AI culture. Hundreds of AI use cases are in production, generating measurable business value. The priority at this stage is sustaining the capability advantage, continuing to invest in responsible AI, and remaining ahead of the regulatory and competitive curve.
For most insurers, the immediate priority is moving from Level 1 to Level 2, or from Level 2 to Level 3. The key lever at each transition is the same: visible leadership commitment, followed by structured workforce investment, followed by sustained cultural reinforcement.
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