By Daniel Wood
Four years ago, Nic Tyson (pictured) bought a brokerage that was still old-school and paper-based. Filing cabinets held the client histories and a renewal meant rifling through folders to reconstruct a client relationship before anyone picked up the phone. Today the director of The Advisers, a Taranaki-based broking firm in New Zealand, can look across the office and count almost no paper at all. The artificial intelligence (AI) tools now built into his systems are handing his brokers back the hours that filing once consumed. For any brokerage weighing where AI belongs, his experience points to a grounded starting place: get the data in order first and the efficiency follows.
Tyson inherited an operation that relied on a patchwork of disconnected tools, each holding a different slice of the client picture. "In the past we had a CRM system, a processing system, a document management system, and various other tools," he said. None of them spoke to each other, and the cost showed up in the slow, manual work of rebuilding a client's history from scratch.
So the brokerage consolidated, moving its data and documents into JAVLN's cloud-based Officetech platform. The appeal, he said, was less any single feature than the foundation of administrative efficiency it created.
"It's really a foundational piece of tech to build upon – one source of truth," Tyson said.
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That foundation, he argued, is what makes this AI useful rather than gimmicky. A reliable, consolidated record is the base layer any AI tool needs to draw on and it was exactly what the disjointed setup he started with lacked. The firm is already seeing the payoff through the platform's AI reporting function, which pulls a client's recent activity – file notes, correspondence, document changes and outstanding tasks – into a single summary on demand. It reflects a wider shift, with clean, consolidated data becoming a major draw for New Zealand brokers weighing up their technology stacks.
With that base in place, the clearest gain is time. "The core answer is that it's saving time for our team," Tyson said. "Prior to a client conversation, you might be spending upwards of 30 minutes just preparing and understanding what's happened over a 12-month period." Tyson said the new AI agent can cut that in half and provide a clear snapshot of what's transpired with a client.
The value, he says, runs in two directions. "The data is key, but there are two aspects I look at," Tyson said. "One is the data on what's happened with the client and the other is from a management point of view – the oversight of what's happening and what our advising force is doing." So when a colleague is on leave and their client calls, a manager can pull a concise overview of the account in moments rather than hunting through files.
"It's very quick to get that overview, even while you're on the phone having that discussion, to understand what's generally occurred," he said.
The pressure to adapt is not confined to New Zealand. In Australia, the National Insurance Brokers Association (NIBA) has identified technology and automation as the single biggest disruptive force brokers expect over the coming decade in its October 2025 report, Ready or Reacting? Shaping the Future of the Insurance Broking Profession, measuring that expectation against the profession's level of preparedness for each force. For firms on both sides of the Tasman, that gap between expecting disruption and being ready for it is exactly what a clean data foundation is meant to close.
That speaks to a broader argument about how AI can make insurance advice more human rather than less. Tyson's staff, he said, are "people people" who value client relationships over paperwork; stripping out preparation time lets them spend it on advice instead. Used this way, the technology frees brokers to do the work that drew them to broking in the first place.
"Having a tool like this to enable them to have real conversations with people is really powerful," said Tyson.
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None of this removes the harder questions. The Reserve Bank of New Zealand (RBNZ) has weighed those tensions directly: in a special topic within its May 2025 Financial Stability Report, the central bank set AI's upside – improved productivity, greater modelling accuracy and stronger cyber resilience – against risks including data-privacy concerns, errors in AI systems and a growing reliance on a small number of third-party providers. "There is still considerable uncertainty around how AI will shape the financial system," said Kerry Watt, the RBNZ's director of financial stability assessment and strategy, with regulated firms expected to manage those risks as part of their existing obligations. That backdrop feeds straight into the trust questions brokers face as AI enters client advice and it is why Tyson is following the platform's roadmap rather than rushing. JAVLN’s AI-powered, insurance-specific client management tool is flagged for release later in 2026.
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