Insurance broking is under pressure from all directions; rising client expectations, regulatory complexity, and now the rapid acceleration of artificial intelligence across every industry.
It's tempting, in that environment, to feel like change is happening to you rather than something you're shaping.
But here's a more useful frame: while you can't control how fast AI evolves, you can absolutely control how ready your business is to benefit from it.
That readiness doesn't start with picking an AI tool. It starts much earlier than that.
The gap between expectation and preparation
The insurance industry is well aware of what's coming. NIBA's research* identifies technology and automation as the top disruptive force brokers expect over the coming decade.
83% percent of brokers believe AI will have a significant impact on their business by 2035. But only 61% feel prepared for it.
That gap, between recognising the change and being genuinely ready for it, is where the real work lies. It’s something the whole industry is grappling with.
Accenture research** found that while 90% of insurance executives plan to increase AI spending in 2026, a quarter cite talent shortages as their main constraint, and 24% identify weak alignment between AI initiatives and core business strategy.
Only 24% have embedded continuous learning programs focused on AI, and just 5% are redesigning job roles to reflect new ways of working.
Across the industry, intent is outrunning action. The brokerages that close that gap first will carry a meaningful advantage. The question is how.
Start with what you can control
The instinct when AI enters the conversation is to look outward - for the right tool, the right platform, the right feature.
But the most impactful decisions you can make right now are internal ones. Here's where I'd focus first:
Get your data in order. AI is only as useful as the information feeding it. If your client records, policy data, and compliance documentation are scattered across disconnected systems - or living in spreadsheets and email threads - no AI tool will fix that.
Structure your data now, before you need it to power anything more sophisticated. Think of it as laying pipe before you turn on the tap.
Reduce your browser tabs. One of the most counterproductive things a brokerage can do is layer more point solutions on top of an already fragmented technology stack. Every additional tool is another place your data lives, another login, another integration to maintain.
Consolidate around your core platforms, the ones that manage your most critical workflows, and make sure they're genuinely connected. Fewer, more integrated tools will serve you far better than a browser with 20 open tabs.
Know who's holding your data, and whether you can trust them. As AI becomes more deeply embedded in how software works, your data becomes more valuable and more sensitive. The technology partners you choose matter more than ever.
Ask your core providers directly: what is your AI roadmap? How are you investing in these capabilities? What security certifications do you hold? Look for providers who can demonstrate SOC 2 compliance, proper encryption, and clear data governance practices.
A platform you can trust with your data, built by a team with the scale and expertise to develop AI properly, is worth significantly more than a collection of standalone tools that each hold a piece of your business.
Build AI fluency across your team. Almost a third of financial services and insurance businesses are already using AI for administration, documentation and sales analytics.
Your team should be experimenting too - but safely and with purpose. The distinction matters: intentional learning means every interaction with a new tool is a goal-oriented experiment, not a trend to chase.
Before your team picks up anything new, ask a simple question - what specific problem are we actually solving? Then experiment, and periodically reflect on whether the tool is genuinely freeing people for higher-value work, or simply adding to the digital noise.
Create a culture where learnings are shared openly, and where failure is treated as part of the process. That starts with leaders setting the tone, but it doesn’t stop there.
Use AI tools in your own work. Share what works, what doesn’t, and what surprised you. When that behaviour is visible across the business, capability builds faster.
Make AI learning ongoing, not a one-off. Capability doesn't come from a single workshop. It comes from practice, repetition, and regular exposure.
Build AI literacy into how your business operates - not as a project, but as a habit.
The opportunity hidden inside the chaos
It would be easy to view AI as daunting. I'd argue it's the opposite. Precisely because so many brokerages are overwhelmed, uncertain, or simply waiting, the bar for differentiation is lower than it might appear.
The businesses that make clear-headed decisions - about their data, their platforms, their people, and their partners - will be operating from a fundamentally stronger position as AI capabilities mature.
You can't control the AI revolution. You can't control what your competitors are doing, or how quickly the regulatory environment will catch up.
But you can control your data discipline, your technology partnerships, your team's capability, and your own willingness to intentionally lead through change rather than around it.
That's what it means to be intentional: using AI to take care of the repetitive, data-heavy work - so your people can invest their energy into the things technology will never replace: the judgement calls. The client relationships. The advice that matters when it's most needed.
That's where the work is. And for brokerages prepared to do it, the upside is real.
References
* NIBA Ready or Reacting Report, October 2025
** Accenture Pulse of Change report, January 2026
David Leach is the keynote speaker at ANZIIF New Zealand's inaugural Executive Connect, 21 April in Auckland. Make sure to hear him speak.
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