Acrisure’s AI Strategy: A Deep Dive Q&A with Benjamin Funk

For the modern enterprise, AI has transcended its status as a mere utility.

Following years of exponential growth, the financial sector’s rapid adoption of the technology is no longer just about driving efficiency or trimming overheads. Instead, it has now secured a permanent seat in the boardroom.

That shift in perception is palpable. From HSBC appointing David Rice as Chief AI Officer to industry heavyweights like Lloyds Banking Group's Rohit Dhawan and Starling’s Harriet Rees advising government policy, AI now dictates the growth trajectory of global finance.

In this Q&A with InsurTech Digital, Benjamin Funk, Chief Technology & AI Officer at Acrisure, explores how AI serves as the fundamental architecture of their operational strategy.

Can you tell us a bit about yourself and your experience?

Before joining Acrisure, I spent 12 years at Palantir building its commercial business and scaling the insurance bucket from first clients to partnerships across the value chain. 

The lion’s share of my last decade was operating at the edge, performing forward-deployed open-heart surgery on insurance organisations – embedding directly with the front line, understanding core workflows from first principles, and co-developing impactful software solutions in lockstep with each partners’ doers and leaders. 

At Acrisure, I’m now leading technology, innovation, and AI across our engineering, data, analytics, product, sales tech and agentic automation teams.

Acrisure sits in a uniquely powerful position with the opportunity to become a true one-of-one organisation. 

As the preeminent advisor and provider for non-discretionary services and software, Acrisure is nestled between both our clients and carrier partners of all shapes and sizes. We have broad reach across the SMB market, and our fintech genes bring a relentless ambition for creative innovation. 

I joined Acrisure to apply everything I’d learned at Palantir to where it matters most. Not as an overlay, but as a critical and core engine for how a business ought to run.

What has been the highlight of your career? 

There hasn’t been a single role or product launch. What stands out are slices in time where real impacts and outcomes were delivered in the face of crisis – when the pressure and stakes were at their highest, imperfect information was pervasive, and there was no time for clear plans or deliberate decision-making. Most importantly, though, urgent execution against the crisis genuinely impacted people.

Across the early 2020s, whether it was live, front-line responses to natural disasters combating bushfires outside Sydney, earthquakes and tsunamis in Japan, the wildfires in the Palisades outside Los Angeles, or navigating the world-changing virality of COVID, each of these experiences reinforced a core belief for me: impact comes from proximity.

You must be close to the problem and only then are you accountable – for results, not for ‘activity’.

Those moments taught me how to lead with urgency and empathy. You can move fast without losing people. You can make hard calls while staying grounded in trust. Collectively, these crisis experiences define how I think about leadership, technology, and impact today.

What exciting things will AI support for Acrisure in the immediate future? 

We’re using AI to rearchitect how Acrisure operates. Not as a bolt-on tool or experiment, but as a fundamental transformation of our core operating engine. Plenty of organisations talk about pilots, minimum viable products and AI labs. That’s the easy path, and it rarely changes outcomes.

At Acrisure, we’ve chosen to operate on hard mode by embedding AI directly into core, productive workflows that must bear real weight across the value chain. If the industry is clearly moving toward agentic orchestration – first within enterprises and then across them – there’s no reason to wait. 

Hard mode transformation is the way to get there.

Our approach is rooted in a forward deployed engineering model. Our technical teams embed directly with brokers, operators and business leaders in the field, experiencing the work as it happens – all the messiness, friction, detail and constraint.

Real transformation only occurs when there’s no separation between strategy and execution, and when teams share ownership of both development and outcomes.

Project LeftSeat is a clear example. We’re using AI to connect client interactions, enrichment data, routing rules, and carrier integrations into an end-to-end engine that delivers aviation quotes in minutes instead of days. 

Across our ecosystem, we’re extending AI by building cross-functional platforms that unite our colleagues with the latest in AI across core propositions – premium placement, client experience, broker collaboration, carrier coordination, policy servicing claims and loss. 

Our goal is simple: give our brokers, servicers, and placement professionals a cybernetic ‘Iron Man and Woman suit’ to empower them.  AI doesn’t replace judgment; it amplifies it. That’s how we win.

Could you expand a bit more on what digital client experience means? 

When we talk about a digital client experience, we’re not talking about a new interface or a slick portal. We mean redesigning the client journey from the ground up by rebuilding the underlying workflows that support engagement, placement, servicing, renewals and digital advisory.

The pace of change is accelerating, and simply digitising existing processes is no longer enough.  Instead of layering new technology on top of legacy processes, we’re leveraging AI directly as the key component in core workflow overhauls.

AI agents capture and interpret communications, autonomously enrich this with timely data, coordinate across systems and partners, and move work forward with far less friction. The result for clients is faster turnaround, more consistent outcomes and a smoother experience end to end.

The next frontier will be agentic commerce – and, it’s here. Embedding experiences directly into this emergent AI surface has echoes of Google Search from the late 90s.

Tomorrow, the newest systems won’t just respond, but they’ll proactively orchestrate due diligence, selection and execution for products and services. The foundations that a few select organisations are building now will determine who meets the inevitable rise in client expectations and who falls behind.  

How can innovation be fostered in fintech? 

A fintech, by definition, is a tech-forward financial services org. If you’re truly a ‘fintech’, innovation is not optional; it is the reason you earn the label.

True fintechs are consistently embracing the most cutting-edge technologies and adapting to what’s new, what’s possible and how creative, recurring applications of tech can generate alpha; and, re-earning the fintech medal repetitively each day, week, month and year. 

Innovation is fostered by building the organisational expectation that the operations of the business as it is, is never enough. Innovation thrives when teams are then empowered to act and continuously change the way they work with new tech in tow.

It’s no surprise that to do this well means spending time at the front where the value is created, understanding pain-points in day-to-day processes, and giving people permission, support and encouragement to challenge how things are done. And then, of course, build and allow the change.

Innovation sticks when it becomes part of the organisational culture. When truth-seeking is rewarded, failure is treated as data, and success is tied to results; innovation sheds its episodic shell and becomes part of the organisational DNA.

What advice would you give to other fintechs adopting AI strategies? 

My advice here is blunt and simple – quit theorising, stop tinkering and start with the work.

Spend time with colleagues at the coalface. Identify where judgment meets inefficiencies and where repetition breeds. Build from there.

Prioritise systems that can coordinate end-to-end workflows, not just automate tasks. Just as important, design for reality: compliance, permissions, and accountability matter from day one. Seek others who are ‘actually’ doing it, link arms, and build together. Momentum compounds.

At the end of the day, the question is simple: Why operate on easy mode? Bringing AI into core operations means treating it as a real bottoms-up engineering exercise, not as an add on. It requires weight-bearing expectations and a willingness to take on hard problems.  

If you want meaningful outcomes, you must do the work.