As generative AI moves from experiment to everyday tool in Australian workplaces, insurance brokers are among many sectors that are grappling with existential questions concering their future in this brave new world. The uptake is no longer anecdotal. Deloitte research says genAI use in Australian workplaces has risen from 32% to 38% of employees in less than a year. The shift is also visible in the SME base that many brokers serve. The Federal Government’s AI Adoption Tracker reported 40% of Australian SMEs are currently adopting AI, up 5% on the previous quarter.
That backdrop is fuelling two competing narratives inside insurance broking: one that AI could hollow out entry-level learning and undermine risk expertise; and another that AI could hand time back to brokers to do more of what clients actually pay for - interpreting risk, explaining trade-offs and managing tough conversations when coverage, price and appetite collide.
David Leach, CEO of insurance software firm JAVLN, sits firmly in the “augmentation” camp.
“Ultimately, AI will create super powered insurance brokers and financial advisers," said Leach.
In his view, the early value is less about replacing advisers and more about stripping time out of the administrative drag that slows the market: document sorting, first-draft submissions, chasing and comparing wordings and converting messy client inputs into clear underwriting information. Done well, that kind of workflow support doesn’t change who owns the advice - but it can change how quickly a broker can reach an informed recommendation and how consistently a team can present a risk.
Leach’s argument is that capability comes from practice, not access.
“Over the next five years, we will see the rise of brokers with superhuman powers – the early adopters who’ve invested the time in training, in practising how to be expert level at prompting and using AI tools, and who’ve done the homework to use these trade tools at an expert level," he said.
That’s also where the risk picture sharpens. Brokers may be moving fast because clients are moving fast, but insurers and risk leaders are warning that speed without governance can create new exposures. In its Risk Barometer, Allianz warned that AI adoption is often “moving faster than governance, regulation, and workforce readiness can keep up,” pointing to operational, legal and reputational risks as AI scales.
The more interesting development may be the performance gap opening between brokerages that operationalise AI and those that keep it at the “interesting pilot” stage. The pitch from AI proponents is straightforward: if you embed practical, repeatable workflows, you can respond faster, reduce rework and free senior time for higher-value client discussions - not because you remove humans from the process, but because you remove low-value admin from their days.
Leach framed that advantage as compounding at a team level.
“If you take one such broker and then multiply that across a team of 10 or 15, you have an entire brokerage of super powered employees," he said. "That’s a massive competitive advantage.”
But the constraints are real. Allianz has flagged that scaling AI can expose firms to data-quality constraints, integration hurdles and shortages of AI-skilled talent.
And there’s a cultural risk for broker leaders: if the “time saved” is treated purely as a cost play rather than reinvested in file quality, client communication and staff development, AI could accelerate the wrong outcomes - faster processing, weaker advice and more E&O anxiety.
AI’s biggest impact might be less about replacing brokers and more about reshaping roles. Allianz reported that companies are leaning on education, retraining and upskilling as their main workforce response to increased AI adoption.
For broking, that creates a practical leadership test: how do you keep training pathways intact if juniors do less of the “grunt work” that used to teach them the basics? How do you maintain strong checking disciplines when AI is doing more drafting and summarising? And what does “human in the loop” actually look like on a file when speed becomes the default expectation?
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Leach’s view is that the upside is achievable - but only if tools are used to lift the quality of work, not merely to do more work.
“If you sit on top of these tools and learn to use them to their full potential, you’ll see in the insurance industry – and many other industries – that humans will be able to massively uplift their productivity and speed, and also improve the quality of their work," he said.
Clients may not care whether a broker used AI to tidy a submission - but they will care if service improves: faster turnaround, clearer explanations, fewer back-and-forth emails and better anticipation of underwriter questions. With SME AI adoption rising and workplace usage increasing, expectations are unlikely to slow down.
The more grounded version of the “super broker” idea, then, is conditional: brokers who treat AI as craft - with training, controls and a clear “human in the loop” standard - may end up looking more human to clients, not less. Those who treat it as a shortcut may simply move faster toward avoidable errors and possibly oblivion.
