AI is a people challenge before a technology one

Artificial intelligence is no longer a theoretical discussion in commercial specialty insurance. From bordereaux processing and exposure management to underwriting support and claims triage, AI is becoming embedded in day-to-day operations. Yet despite increasing investment, adoption remains uneven. The limiting factor is rarely data or tooling alone, but people shaped by different generational perspectives.

Across insurers, MGAs, brokers, and coverholders, attitudes toward AI vary widely. Different age groups bring distinct expectations, concerns and levels of trust, which directly influence how new technology is understood, engaged with or resisted. In my 25+ years working in the finance industry, I have repeatedly seen similar patterns emerge when major change is introduced without clear communication about how it will affect roles, responsibilities and careers.

why generational perspectives are important in commercial insurance

Commercial specialty insurance is a people-intensive industry. Judgement, experience and relationships remain central to underwriting complex risks, negotiating placements and managing claims. Many professionals have built decades-long careers on expertise that is tacit rather than codified.

AI challenges this identity. It introduces automation into areas historically viewed as human-only, raising questions about relevance, control, and professional value. These questions are felt differently depending on when and how individuals entered the workforce.

Understanding generational perspectives is therefore not about stereotyping but about recognising lived experience and career context.

baby boomers: experience, control and distrust

AI is often viewed by Baby Boomers in commercial specialty insurance through the lens of risk and disruption rather than opportunity. Having lived through multiple waves of transformation, digitisation, outsourcing, offshoring and large-scale system replacements. There is a natural wariness of initiatives that promise efficiency but frequently deliver upheaval.

Underwriting judgement is closely tied to professional reputation and authority. AI can feel less like an enhancement and more like an attempt to replace craft with computation. Disillusion is therefore rarely about AI as a concept, but about how it is introduced: opaque “black-box” models, top-down mandates with limited practitioner input and vendor-led narratives built on generic use cases that fail to reflect specialty complexity. When AI is framed as “doing underwriting” or “automating judgement,” resistance naturally hardens.

Engagement improves markedly when AI is positioned as decision support rather than decision replacement. Making expert judgement more visible, defensible and scalable. Tools that reduce administrative burden, such as data cleansing or bordereaux validation, are more readily accepted, particularly when models are transparent, auditable and explainable. For this group, trust is not driven by enthusiasm or novelty, but earned gradually through evidence, involvement and demonstrable respect for professional judgement.

gen z: fluency, optimism and hidden anxiety

Gen Z professionals in commercial insurance are generally more open to AI-enabled workflows and experimentation. They expect systems to be intelligent and assistive, are comfortable with rapid iteration and are willing to learn alongside technology. With less attachment to traditional role definitions, they are often viewed as natural champions of AI adoption.

Yet disillusion still exists but for different reasons. Beneath this apparent optimism sits anxiety about long-term career relevance in an increasingly automated industry. Uncertainty about which skills will truly matter and concern that early-career learning opportunities may be diminished by automation. In a sector where expertise has traditionally been built through on-the-job training and exposure to complex cases, there is a real fear that AI-enabled shortcuts could leave professionals underdeveloped while simultaneously accelerating performance expectations without sufficient support.

Gen Z is more likely to engage when AI is positioned as a learning accelerator rather than a replacement for experience. Engagement improves when tools are transparent about their limitations, allow for human override and are paired with clear skill pathways and progression models. The central question for this generation is not “Will AI take my job?” but “Will I still have a meaningful career path?”

reframing AI: from replacement to partnership

Successful AI adoption requires a deliberate shift in narrative. Away from efficiency alone and toward resilience, where AI is used to scale expertise rather than strip it out. Away from pure automation and toward augmentation, with AI supporting stronger human judgement rather than replacing it. And away from a generational divide toward generational transfer, where AI helps capture institutional knowledge while accelerating the development of new talent.

So, some practical examples of how AI can be used in partnership:

• Using AI to pre-validate bordereaux while underwriters retain final judgement and accountability
• Embedding explainability so senior experts can interrogate, challenge and refine AI outputs
• Leveraging AI outputs as learning tools to support training and skill development for junior staff

leadership actions that matter

Leaders must be intentional in how AI is introduced and discussed. AI should be framed as enhancing capability rather than replacing roles. Experienced practitioners should be actively involved in model design and validation, ensuring solutions reflect real-world complexity and judgement. AI logic, limitations and accountability must be made explicit, not implied.

Equally important is defining what “good” looks like in an AI-enabled role at every career stage, supported by clear development pathways. Space must be created for challenge, scepticism and iteration as part of adoption. Ultimately, AI implementation is as much an organisational change programme as it is a technical one.

trust is the true adoption metric

In commercial specialty insurance, AI will ultimately succeed or fail on trust across generations, roles, and levels of seniority. Baby Boomers need reassurance that their expertise is recognised, respected and amplified. Gen Z needs confidence that AI will enhance, rather than undermine, long-term career development.

Both require clarity around accountability, purpose and value. Organisations that treat AI as a human transformation, not merely a digital one, will be best placed to navigate this shift. Those that fail to address generational disillusion risk investing in powerful capabilities that few people truly trust or want to use.