TL;DR: Despite being heavily regulated, the financial industry in Australia is leading the charge in the secure, compliant large-scale adoption of enterprise AI solutions. From leveraging AI assistants and tools to building custom in-house solutions that are disrupting the sector, financial companies are doing it all. This blog explores key principles and best practices behind AI success in the financial sector and what other industries can learn to accelerate adoption.
At V2 AI, we are working with multiple clients across insurance, banking, accounting, and more who are all embracing AI at a rapid pace. We are seeing multi-agent solutions being launched with digital AI agents working collaboratively alongside humans to
Accelerate the sales cycle
Dynamically allocate portfolios
Deliver more personalised customer service at speed and scale
Introduce operational efficiencies by taking over administrative tasks
And much, much more.
How are these organisations keeping pace with innovation despite stringent compliance requirements? The secret lies in the following seven principles generally observed across the sector.
# 1 Go Digital Before Building AI
The financial services industry is, at its core, a purely white-collar sector. It is a thought-centric environment where most jobs have already been codified into processes and systems. Banks and insurers have been investing in IT and tech infrastructure for decades. They are not only digitised at the back of house, for tasks such as calculating interest rates, mortgage repayments, and processing forms, but also at the front of house, where customers interact directly with digital interfaces.
Finance customers could already self-service, report fraudulent transactions, and complete entire workflows online before AI came along. This deep, long-term digitisation has created the foundation for AI success.
Lesson: Before AI can transform your business, your processes must already be digital. That’s when your data becomes accessible, giving you the ability to experiment, innovate, and introduce automation that directly impacts business outcomes.
Ask yourself:
How digitised is your backend?
How are customers interacting with you via digital channels?
Are you collecting feedback, identifying pain points, and tracking metrics to pinpoint where AI can truly move your KPI needles?
#2 Think Small to Move Fast
Financial organisations have long mastered the art of finding quick wins with disruptive technology. Decades before the AI boom, banks and insurers were already identifying small, high-impact areas to test and automate. One early proof-of-concept involved using basic image recognition to speed up loan approvals. A customer’s identity could be verified online by matching their webcam image with their ID photo. What once required a branch visit and manual checks can now happen online in minutes.
Today, the same principle applies. Likely, your employees are already quietly experimenting with AI, using it for meeting transcription, research, summarising, and feedback and quality checks. This “shadow AI” shows where the real productivity gains are hiding. Instead of replacing jobs, these tools free people to focus on higher-value work.
Lesson: Success with AI doesn’t come from trying to automate everything at once. Break down workflows, identify time sinks, and target the 20% of effort that delivers 80% of results. Knowing your team is the fastest way to find your AI quick wins.
Ask yourself:
Where are employees already using AI tools on their own for personal productivity gains?
Which parts of a process eat the most time but add the least value?
How can small automations improve quality, not just speed?
#3 Use Compliance Literacy as a Competitive Advantage
Financial institutions approach any innovation with a regulations-first approach. Regulatory guidelines often allow considerable flexibility in implementation details, as long as broader security measures are in place.
For example, when the Australian Prudential Regulation Authority (APRA) first released its cloud guidelines (CPS 234 and CPS 231), some assumed they couldn’t move workloads to the cloud at all. But the fine print said otherwise. APRA didn’t forbid cloud adoption but merely required organisations to assess risks, maintain data visibility, and ensure accountability for outsourced services. Once banks recognised that compliance meant control, not restriction, they moved fast, building secure, auditable cloud environments years ahead of others.
Today, AI presents the same opportunity. The regulators are not banning innovation; instead, they are just expecting accountability.
Lesson: The safest AI strategies come from knowledge, not fear. Instead of second-hand interpretations, use AI itself to understand regulations in your sector. Upload a 400-page regulation, get it summarised, search it semantically, and discuss your compliance questions with context.
Ask yourself:
Which frameworks shape what “secure” means in your industry?
Have you verified your compliance assumptions, or are they inherited myths?
How can AI tools make understanding regulation faster and more collaborative?
#4 Build Safety Nets as You Scale
AI adoption isn’t about zero risk but more about controlled risk. Banks operate under APRA’s guidance, but that does not mean checking every regulatory box before you begin. When it comes to AI, they prefer action over perfection. It is about building safeguards, one system, one rule, one layer at a time.
Airlines can fly with minor faults, but never with engine failure. Similarly, the acceptance criteria can be met gradually, provided critical risks are mitigated upfront.
Lesson: Start by codifying your business rules, documenting workflows, and adding control gates where risks actually exist. Strengthen guardrails with each iteration so progress never comes at the cost of trust or compliance.
Ask yourself:
Which AI risks could truly put you out of business?
Are your controls built for innovation, or just for inspection?
Where can safety mechanisms evolve alongside your AI systems?
#5 Treat AI as a Team Sport
Financial organisations frequently assess the blast radius of any new tech adoption, assessing how it affects customers and the wider industry beyond the organisation itself. The mindset is simple: protect the individual, protect the industry, and you will automatically protect yourself.
Of course, the technology team alone can’t hold all that context. You need cross-functional collaboration that blends risk, compliance, domain expertise, and engineering judgment.
This culture of collective accountability makes AI in finance safer and smarter. It ensures that ethical, operational, and security concerns are surfaced early by the people closest to them.
Lesson: AI is a collective responsibility of the entire organisation, not just the technology team. AI becomes sustainable when everyone owns a piece of the safety net. Build teams that question impact and the process itself, not just the output.
Ask yourself:
Does your AI team include cross-functional experts?
How do you implement accountability chains for AI-driven automation?
When do security, legal, compliance, enablement, and marketing experts step into your AI lifecycle?
#6 Bring in Experience When You Need It
Most industries evolve slowly, while AI moves at cloud-era speed. It is challenging for organisations to keep up with both, changing AI capabilities and the tech stack around it. The financial sector is well known for bringing in the experts it needs for the duration of projects, and you can do that too.
There is no compression function for experience. However, you can ease the learning curve by working with a mentor. External specialists bridge the gap by transferring both tools and ways of thinking.
Lesson: Borrow expertise when you’re facing an unfamiliar problem, then internalise that knowledge as you go. External experts, like V2 AI, can help you move faster by showing your team the problem-solving process rather than just giving the final solution. On the journey of AI self-sufficiency, you want to partner with experts who set you up for long-term success over just getting the job done quickly in isolation.
Ask yourself:
What expertise gaps slow down your AI adoption today?
How are you ensuring your AI team learns and not just delivers?
If you already have external help, are they teaching your team or just giving you solutions?
#7 Invest in Innovation, Not Just Efficiency
Financial institutions have always treated innovation as an investment in future revenue streams, not just as a cost-saving measure. Long before AI, banks built venture divisions and innovation arms, like Westpac’s Reinventure and Commonwealth Bank’s X15 Ventures, to test disruption from within. They have invested in new structures, from standalone brands to incubated startups, so innovation evolves naturally without internal resistance.
Lesson: Treat innovation (and associated expenses) as an ongoing insurance policy for your future business model. You dont want others to redefine your customers’ experiences. Rather, you need to design an ecosystem where new products and ideas can safely grow without being constrained by legacy processes or fear of external disruption.
Ask yourself:
Do you have a structure that allows innovation to thrive without bureaucracy killing it?
Are you investing in your next revenue stream before the current one declines?
How can you isolate experimentation without isolating the people behind it?
Final Words
AI disruption is just the next chapter in a long history of human progress. It is the next tool that multiplies human capability.
A farmer with a horse could plough one field. A farmer with a tractor does exponentially more. Just as machines transformed agriculture, AI is transforming knowledge work and amplifying what people can produce.
The true value of AI lies in the space it creates for new possibilities. Human time saved by AI can be reinvested into creativity, experimentation, and problem-solving, the kind of work that drives new revenue.
The real differentiator is how you use this AI tool to rethink the role of your teams, your business units, and even your industry.
