September 25, 2025
Artificial Intelligence (AI) holds substantial potential in Australia’s banking sector. From advanced fraud detection to hyper-personalised customer experiences, AI could transform the way banks operate and compete.
As we explored in our first blog in this series, banks and financial institutions in Australia are leading the way with AI adoption, with numerous pilots and initiatives underway. But the road to success with AI relies on more than just picking up and running with the latest tech.
Organisations first need a solid foundation across data, strategy, governance and skills. Banks that leap into AI adoption without the right groundwork across the organisation risk AI pilots stalling, investments going to waste, and compliance risk growing, with the business value of AI left unrealised.
Every AI initiative is only as good as the data that informs it, and to see the real benefit of AI, data must be clean, reliable and interoperable. For banks, that data could be across everything from customer transaction records and behavioural insights to market data and operational signals.
But, according to a recent study, 46% of banking leaders say they are struggling with siloed or unreliable data. When data is locked away in different teams, business functions or platforms or if it can’t be relied on as a source of truth, organisations can’t access insights or power AI initiatives. Within financial services, a lack of robust data pipelines can also cause security and compliance risks.
This is exactly the challenge we helped an Australia-based financial services group overcome when improving the data pipelines within its security vulnerabilities team. Its existing monolithic system meant every change was risky and time-consuming, with up to 80% of deployments being rolled back due to errors. But, by introducing the concept of “pipelines as products”, breaking down the monolith into smaller, purpose-built pipelines and upskilling the organisation’s data team, we made deployments safer, faster, and more reliable. It not only delivered immediate operational value but also created a robust and secure data platform foundation that can be used in future AI-adoption. Read the data pipelines case study
Technology leaders across all sectors are feeling pressure to “do something with AI”, and do it quickly. But worryingly, 27% of banking executives admit their organisation lacks a clearly defined AI strategy. Without a clear strategy, banks risk chasing hype rather than solving meaningful problems. As industry leaders highlighted at our recent AI-focused Expert Talks event, the right place to start isn’t with the technology itself; It’s about finding the right business problem for it to solve.
Beyond strategy, good governance is vital in responsible AI adoption. Trust is a vital currency for banks and financial institutions, with customers and regulators demanding transparency and security. With just 30 per cent of Australians believing the benefits of AI outweigh the risks, banks cannot afford to treat governance and security as just checkbox exercises.
Without a formal AI governance framework in place, banks run the risk of unsecured data, access issues and introducing bias in decision-making. With regulators promising to come down strongly on organisations that don’t prioritise AI security and customer needs, organisations without strong governance could find themselves with regulatory and compliance challenges in the future.
That’s why we created our AI Governance Framework, a comprehensive structure to guide organisations on the journey to responsible and secure AI adoption. It includes guidance on governance, risk management, high-level AI stage gates, AI portfolio management, AI Ops and technical architecture. Download your copy of the AI Governance Framework.
Even with the right foundation across technology, data and strategy, an organisation’s culture can still mean success or failure in an AI initiative. As with any new technology, workers and teams are concerned about what it will mean for their day-to-day operations. Trust in management, fear of redundancy, and changing workflows are often more difficult to overcome than upgrading technologies or defining a strategy. According to recent research, 83% of banking executives identify employee resistance to change as a barrier to AI adoption, compared to 73% in other sectors.
Strong leadership is required in setting the vision and inspiring trust in new ways of working. Similarly, with new technologies comes the need for new skills – organisations that invest in the right expertise will be able to run faster with AI.
As our Global Head of Data, Simon Case, recently put it: “Capability is more than skills. It’s confidence, ownership, and the power to make good decisions.” At Equal Experts, we focus on delivering long-lasting value to our clients by ensuring we leave behind a legacy of learning, knowledge sharing and self-sustaining growth.
For example, we supported HMRC to create the next generation of talent with the creation of a data engineering academy. With our technical expertise and history of coaching in-house teams, we designed a tailored academy approach combining technical training with real-world hands-on experience in placements within HMRC. Growing from 7 to 47 engineers in 18 months, the first 20 graduates secured permanent roles within the government department, embedding within HMRC the data capabilities needed for the future. Read the full HMRC Data Academy case study.
It’s tempting to view AI as a silver bullet. But on the road to an AI-enabled bank of the future, financial organisations need to start the journey by focusing on their foundations first. Banks that invest in their data infrastructure, strengthen governance, and build cultural readiness will be the ones to scale AI safely and profitably. They will not only improve efficiency and customer service but also protect their most valuable asset—trust. Those who neglect these foundations risk failed pilots, mounting compliance costs, and missed opportunities.
At Equal Experts, we’ve seen this first-hand. From building secure data platforms to embedding responsible AI practices, we bring the experience, pragmatism, and technical depth needed to turn AI proof-of-concepts into value-adding software in production.
Contact us today to find out how we can help your organisation lay the foundations for lasting business value from AI.
This blog reflects the personal experiences and opinions of the author and it should not be taken as formal Equal Experts methodology.
Nick Williams is Technology Principal at Equal Experts APAC. With delivery experience across the UK, Europe and Australia, Nick has held a mixture of technology, platform and architect consultancy roles. Across his career, he has led a variety of complex projects, including embedded systems in the warehousing and logistics industry, supporting the design and development of the UK government’s largest data platform and driving technology transformation in finance and superannuation.
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