The financial services sector has never been shy about technology, from automated trading to fraud detection, the industry has used advanced tools for decades. So it’s no surprise that when AI entered the mainstream, finance started experimenting early. 

In our recent AI survey, 40.3% of financial services respondents said their organisation is still in the experimental or pilot stage. And while that might sound slow for such a tech-forward sector, the reality is more nuanced. 

Grassroots AI adoption 

According to JP Browne, Practice Manager from our Talent office in Auckland, “AI adoption in financial services hasn’t been a top-down strategy. It’s started with individuals experimenting and then leadership scrambling to wrap governance around it.” 

This first wave of AI in finance has come from analysts automating data reviewal processes, teams using AI to parse documents and support in decision-making, and customer service teams testing AI-powered call transcription and insights. 

In many cases, these use cases weren’t part of a formal plan but were initiatives driven by curious employees. 

The security and compliance squeeze 

Financial services hold some of the most sensitive data in the economy, so it makes sense why security and compliance concerns are front and centre. 

“You either let people dabble under controlled conditions or you lock it down completely. In financial services, both approaches are happening and sometimes within the same organisation,” observes JP. 

And our survey data backs this: 

• 46.2% said security or compliance concerns were their biggest obstacle 
• 38.3% said their organisation has restrictions or policies in place limiting the use of external AI tools 

Some organisations are also bringing services back on-premises to reduce external risk and exposure and maintain control over sensitive data flows. 

Productivity gains are real but targeted 

The early AI wins in the financial services industry aren’t flashy and focused on speeding low-risk repetitive processes, freeing human experts for high-value and complex decision-making, and improving data analysis and insight generation. 

JP shares, “Insurance has been using automation for years and AI just extends what’s possible, like approving low value claims instantly or extracting insights from thousands of documents.” 

The AI strategy gap 

Despite early adoption, many financial organisations still lack a unified AI strategy and this gap creates risk; tools proliferate without integration, data duplication drives up cloud costs, and security and compliance posture can’t keep up with usage. 

According to Jack Jorgensen, General Manager of Data, AI & Innovation at Avec, “Leaders see AI as the golden ticket but getting there means having to build secure data foundations first, and that’s where the real work is.” 

So, what should leaders in financial services do next? 

• Map existing AI use across the business, including shadow AI 
• Set clear ownership for AI strategy 
• Invest in secure data infrastructure before scaling 
• Pilot with measurable outcomes in customer experience, operations, and compliance 
• Train teams regularly to keep pace with the evolving risk landscape 

Financial services leaders know the stakes: move too slow and you lose competitive edge, move too fast and you risk regulatory breaches. The winning path is deliberate innovation by balancing productivity gains with ironclad risk management. 

Want to explore what the survey discovered? Access the full report. 

If you’re looking to build secure AI capability or hire financial services tech talent, get in touch with our team. Or if you’re ready to deliver a secure and compliant AI or data project, drop a message to Jack’s team at Avec.