The intersection of human empathy and machine intelligence is where the future of banking is being written. For Jessica Dawson, NAB’s Head of AI Enablement, this journey started when she worked in a contact centre while she was studying. From managing NPS programs to leading AI strategy at one of Australia’s ‘Big Four’, her career reflects the very nature of the industry – collecting and analysing data to create deeply human experiences.
In a recent sit-down, Jessica shared how National Australia Bank (NAB) is moving beyond the hype of generative AI to build an enterprise-wide engine of productivity, innovation and personalisation.
Why AI enablement matters
At a time when AI is projected to add hundreds of billions to the Australian economy by 2030, the challenge for large institutions isn’t just having the tech—it’s adoption and alignment. ”To understand the mandate for AI enablement, we need to take a step back and look at the context of what AI means for the economy, both globally and here in Australia. While global headlines often focus on trillion-dollar figures with productivity as the primary driver, it’s important to remember that the impact is very real and tangible for Australian businesses of all sizes, from SMEs to large enterprise organisations”, says Dawson.
Research from the CSIRO and the Tech Council of Australia shows that AI has the potential to add hundreds of billions to our economy annually by 2030. The sectors seeing the most significant impact will include manufacturing, retail, professional services, healthcare, and financial services.
“AI is not hype; it’s a strategic lever for growth, efficiency, and competitiveness,” Dawson explains. The AI Enablement function at NAB exists to build and coordinate the ecosystem so technology and business teams can grow AI sustainably.
“As the collective understanding of applied AI has matured, so has my role. Today, as Head of AI Enablement, my focus is on embedding AI capability across the entire enterprise. I’m responsible for ensuring our people have the tools, skills, and governance frameworks needed to use AI confidently and responsibly. Ultimately, it’s about driving better outcomes for our customers and staff—bringing me right back to my roots of wanting to understand exactly what drives customer satisfaction”.
The four pillars of NAB’s AI strategy
To move beyond experimentation and into true enterprise value, NAB focuses on four specific priority areas. This framework ensures that AI isn’t just a technical achievement, but a practical tool that serves every corner of the organisation.
1. Customer – the ‘one voice’ experience
The first pillar focuses on using AI to provide personalised, insightful, and human-centric experiences. This includes deploying advanced virtual assistants to reduce wait times and using AI to curate specific financial offers rather than generic ones. Under this pillar sits the ‘Customer Brain’ program, led by NAB’s Executive for Customer Decisioning & Data Science, Lisa Marchant.
The ‘Brain’ is an AI‑driven decisioning capability that draws on thousands of data points to help the bank deliver more personalised experiences across every customer interaction — whether that’s a notification in a customer’s mobile app or a conversation with a banker over the phone.
Marchant comments, “The NAB Customer Brain is a cornerstone of our strategy. As we work toward our ambition of being the most customer-centric company in Australia and New Zealand, it is the engine helping NAB deliver, timely, relevant, and personalised experiences at scale”.
“The design principle behind the ‘brain’ is straightforward – we use everything we know about a customer to decide the most helpful thing we can do for them. We do this consistently in every channel – real time when a customer interacts, and also when NAB feels it is important to proactively reach out. This ensures that a customer feels recognised and supported regardless of how they choose to interact with us,” Marchant adds.
NAB’s Customer Brain guides more than 100 million interactions every month leveraging over 3,500 AI models across both the Personal and Business Bank. “We use those insights to deliver the ‘next best action’, whether that is a helpful reminder, a timely nudge, or a simple moment of recognition. The Brain has consistently driven a 40% uplift in customer engagement over the first three years of the program. The system learns over time what customers like or dislike and adapts to their preferences, allowing us to stay relevant without overwhelming them”.
2. Colleagues: augmenting the workforce
AI is designed to take the ‘robot’ out of the human. By automating repetitive tasks and providing real-time insights, NAB enables its staff to focus on high-value problem solving and empathy-driven service. “On the colleague front, AI is certainly augmenting how our people work, supporting everything from design through to engineering and helping them move faster,” says Dawson.
NAB believes AI adoption will be most successful in organisations that invest in capability, not just tools. “Building future-critical skills and establishing a learning culture to support colleagues’ career continuity and growth is key”, Dawson adds. NAB has rolled out Responsible AI learning to all colleagues and runs an annual Data & AI Month that has seen more than 41,000 attendees and over 250 hours of learning delivered since its introduction in 2022.
3. Operations: efficiency and protection
AI is particularly effective in areas where speed and pattern recognition are critical. Dawson says, “AI is also strengthening our processes across critical areas like financial crime, scams, protection, and service operations. Ultimately, while efficiency is important, we also need to consider the exponential potential of personalised customer engagement and AI-enabled innovation. This is exactly why I’m so passionate about my role—it sits right at that juncture where AI meets better customer experiences”.
4. Software Development Life Cycle (SDLC)
By using AI-assisted development tools, NAB is radically accelerating the speed at which they can build and deploy new features. “By focusing on code generation, automated testing, and engineering design, we are achieving a significantly faster ‘time to market’ for new digital products while simultaneously improving overall code quality”, says Dawson.
Value realisation doesn’t come from any one of these pillars in isolation; rather, it is the sum of these parts working together that drives true impact. “On the customer side we are utilising AI to create deeply personalised, real-time experiences through the ‘Customer Brain’. This system, led by Lisa Marchant’s team in Customer Decisioning & Data Science, now guides the majority of our customer interactions and has already delivered a significant uplift in engagement”.
“On the colleague front, AI is actively augmenting how our people work—spanning everything from initial design to final engineering—enabling them to move with much greater speed and efficiency”.
Breaking the legacy barrier
One of the biggest barriers to scaling AI is a reliance on legacy, siloed systems; that simply weren’t designed for modern data access. To overcome this, organisations must transition toward a modern, reliable data system.
Dawson’s advice for this transition follows three key steps:
- Fix the foundations: AI at scale is only as strong as its underlying data. Standardising data models, creating consistent governance, and establishing reliable pathways are essential. If you aren’t sure where to begin, you can use AI itself as a starting point to help clean and improve your data.
- Modernise incrementally: You cannot replace every legacy system at once. Instead, look at standing up a parallel, cloud-based AI platform and focus on creating reusable patterns for data ingestion and transformation, taking it piece by piece.
- Embed governance and collaboration: Security, privacy, explainability, and auditability should be built into the platform from the start, not added as an afterthought. Utilising cross-functional hybrid squads ensures the platform connects seamlessly to drive real business value.
Build, buy, or partner?
One of the toughest decisions for any financial services institution is determining which capabilities to build in-house and which to outsource. Dawson outlines a pragmatic approach for this choice, “We scale AI by enforcing guardrails within a common enterprise platform, which then enables federated configurations. This allows individual teams to build the specific tools that differentiate us, while we maintain the flexibility to buy solutions where it makes sense.
“Ultimately, a plug-and-play architecture is more valuable to us than a rigid ‘build vs. buy’ stance. This approach allows our business domains to deliver value at speed, while central guardrails keep us safe, compliant, and interoperable”.
When weighing these options, Dawson recommends:
- Build when the capability is a true differentiator.
- Buy when the capability is non-differentiating.
- Partner to accelerate new patterns and uplift skills.
“It is important to remember that in this fast-moving market, what is a differentiator today could become a commodity tomorrow—and vice versa. Maintaining that architectural flexibility is key”, advises Dawson.
The road ahead
Transitioning from AI pilots to scale requires a shift in mindset. Dawson advises,” You shouldn’t implement technology for technology’s sake. Instead, choose a single, high-impact use case, measure the outcome (cost, time, or revenue), and bring your people with you. “When we think about scaling AI from initial pilots to enterprise-wide implementation, it requires making very clear choices across governance, risk, and security, as well as value measurement. These elements are essential to ensure consistent adoption across the organisation”.
Key leadership actions Dawson recommends, include:
- Establishing a clear vision: Ensure AI supports your broader business strategy rather than just implementing technology for technology’s sake.
- Identifying quick wins: Look for small-scale pilots that demonstrate early ROI. It doesn’t need to be a massive project to be meaningful.
- Managing risk: Implement considered governance around ethics, security, compliance, and transparency. Identify your non-negotiable compliance obligations from the start.
- Workforce engagement: Communicate early, encourage curiosity and digital experimentation, and build confidence across the workforce.
“While most organisations currently use AI to drive efficiency, customer service, and compliance gains, we are already moving toward a world of ubiquitous AI copilots. Looking further over the horizon, we anticipate the emergence of purely AI-driven business models. It is a truly exciting time to witness this transformation across both the Australian and global landscapes”.