Banking on it.

I interviewed Amanda Tay (General Manager of SME at ING, an ethical AI enthusiast, all-round good human) and asked her how the banking sector has been implementing safe, secure and effective AI technology into their operations.

JK: Amanda, I know you as someone who gets excited about the possibilities of AI, but who also spends a lot of time thinking about risk, governance and responsible use. How do you balance that tension when the technology is moving so quickly?

AT: Tension is exactly the right word. And I believe that tension needs to exist. The opportunity with AI is enormous, but so are the responsibilities that come with it. The speed we want has to be balanced with governance, risk management and ethical considerations. Together, that's what creates sustainable progress. It's a bit like feng shui when those things are in balance, everything works better.
You've probably heard the phrase “go slow to go fast” and I think that's particularly true with AI. When something touches customers, employees or their data, you can't just rush in because the technology is exciting. You have to stop and ask some basic questions: Does this genuinely add value? Is it something people actually want? Have we thought through the risks and unintended consequences?
So, looping back to your question, I don't see the tension as a problem to solve. I think it's a really healthy thing to have. It forces you to pause and ask whether you're solving a real problem, whether people actually want it, and whether you've thought through the risks.
The organisations that will get the most value from AI won't necessarily be the fastest adopters. They'll be the ones that can innovate responsibly, bring people with them, and stay focused on delivering outcomes that genuinely add value for customers.

JK: Picking up on that… trust in finance is already hard-won, and there’s a lot of emotional baggage in banking. How do you keep on top of it? Do you survey people, track the national sentiment work, run design working groups?

AT: Trust is everything. If you don't have trust, you don't have anything. We're fortunate to have a strong and well-regulated banking system in Australia, and that's important because people are trusting us with some of the most significant financial decisions in their lives.
When it comes to AI, I think the starting point has to be the customer. We spend a lot of time listening to customers and understanding what builds trust, what maintains it, and where their concerns are. Just because something is possible with AI doesn't mean it's something customers want or value.
Internally, we've created a lot of opportunities for our people to learn, experiment and build their understanding of AI. That's a really positive thing. But as you move closer to customer-facing use cases, the stakes become much higher. You're dealing with things like privacy, personal information and the customer experience itself.
That's why trust has to remain at the centre of every decision. Trust can be lost very quickly. For me, it's not about using AI for the sake of using AI. It's about being really clear on where it genuinely adds value, where it improves outcomes for customers, and where it helps solve a real problem.
AI is still evolving, and people are rightly asking questions about accuracy, reliability and transparency. So rather than trying to do everything, I think the better approach is to focus on the areas where AI can create meaningful value while continuing to earn and maintain customer trust.

JK: It might help readers to understand what AI looks like at ING, internally and externally – share as much or as little as you like. People might be averse to AI in the abstract and then realise, oh, I’ve actually used that. Then they see AI a little bit more clearly and learn to trust and use it more.

AT: There's still a lot of experimentation and learning happening, but we're moving out of the hype phase. The conversation is becoming much more practical. It's less about the technology itself and more about where it can solve real problems and create value.
We've also invested a lot in helping people build their understanding of AI. There are learning programs available to everyone, regardless of their technical background, and we've got an AI Guild that brings together people who are curious, want to learn, and want to experiment with ideas, in a controlled environment.
What I've loved seeing is that some of the best ideas aren’t coming from technology teams. They come from people who understand customers, processes and everyday frustrations. Give them the tools and the opportunity to experiment, and they come up with some really creative solutions.
So, when I think about what AI adoption looks like, it's not really about the technology. It's about people becoming more confident, more curious and more willing to try things. That's where the value starts to emerge.

JK: Internally it sounds like adoption has been strong. Have people raised philosophical or existential concerns about AI – and if so, how has ING dealt with that?

AT: Most of the conversations I see are much more practical than philosophical. We're talking about the future of work, how customer expectations are changing, and where AI can genuinely add value. More broadly, it feels like the conversation across the market has shifted from “Should we be using AI?” to “How do we use it responsibly and get the most value from it?”

JK: What messaging has come from leadership about job losses? How have you helped people feel safe and secure enough to embrace the change?

AT: The conversation has been about meaningful work rather than job replacement. AI can take care of some of the repetitive or administrative tasks, which gives people more time to focus on the things humans are uniquely good at building relationships, solving problems, thinking critically and supporting customers.
A big focus has also been education. We're investing in helping people understand what AI is, how to use it responsibly and where it can help them in their role. The ask isn't to become an expert overnight. It's to be curious, practise, and build confidence over time.
The reality is that the way we work will continue to evolve, just as it always has. The opportunity is making sure people have the skills, support and mindset to evolve with it.

JK: The flip side – when everyone’s gung-ho, how do you keep the line on slop and hallucinations, or people spinning up hundreds of agents?

AT: That's where governance becomes really important. We have guardrails around how AI is used, including things like brand, customer communications and the development of agents. But people will always need to apply judgement. AI can be incredibly useful, but the accountability remains with the person using it.

JK: You’ve seen the full cycle – fear and uncertainty, experimental hype, disillusionment. A lot of organisations are saying: we’ve thrown money at governance, at training, at the tools, and nothing’s working. What actually gets you into the value sweet spot?

AT: If you're saying “nothing's working”, that's the exact problem - you don't know what you're working towards. Have a goal first. Too often organisations start with the technology instead of the problem they're trying to solve. In my experience, when AI initiatives struggle, it's usually less about the technology and more about data maturity, processes and clarity of purpose. Start with the outcome you're trying to achieve, then work backwards. If you don't know what success looks like, it's very difficult to create value.

JK: And to the leader who says: “I have no idea where to start, I feel so behind?”

AT: Start by understanding your obligations and your data. Then get curious. You don't need a massive AI strategy on day one. Play around with the tools, learn what's possible, and start thinking about where it could genuinely add value in your organisation. And don't worry if you're feeling behind. Most of us are still learning. The important thing is to engage with it, understand it and keep building your confidence over time.

JK: When leaders hear “governance” a lot of them think: a policy or an assessment we did once or have to do. You’ve operationalised it at ING… can you break it down into a couple of practical examples?

AT: Governance becomes real when it changes how people make decisions. For example, understanding what data you're using and whether it's appropriate to use it. Or making sure people are applying judgement rather than blindly trusting the output. You don't need to know how to build an AI model, but you do need enough understanding to ask questions and challenge the results. That's what good governance looks like in practice.

JK: I’d love to know what you’re seeing as the exciting stuff for small businesses. What does it look like to be a small or medium business in the AI era – and how can AI light a fire under our economy?

AT: What excites me most is that capabilities that were once only available to large organisations are now accessible to small businesses. Whether it's marketing, content creation, customer service, research or administration, there are tools that can help small businesses do more with less. But the flip side is that AI doesn't remove responsibility. Small business owners still need to think about things like data privacy, cyber security and intellectual property. If you're using external tools, you need to understand where your information is going and how it's being used. I think there's a real opportunity for organisations like banks, technology providers and industry groups to help small businesses navigate that safely. The opportunity is enormous, but so is the need for education and awareness.

JK: And how else can you support small businesses in the AI era?

AT: For me, it always comes back to understanding customers and what they're trying to achieve. Small business owners are time poor. They're juggling cash flow, customers, staff and growth. The more we can remove friction, support better decision making and meet customers where they want to be met, the more valuable we become as a partner. What excites me is that AI has the potential to make expertise more accessible. It can help people solve problems faster, spend less time on administration and more time focusing on growing their business. The key is using it to think better, not think less. The best use cases I've seen aren't about replacing people they're about helping people spend more time doing the things they're uniquely good at.

JK: Despite it being around for more than a decade, my parents still don’t trust digital banking. They just don’t understand the technology and they’re kind of putting their heads in the sand about it even though I’m trying to teach and show them how to use banking apps for transfers and simple transactions for years. In some ways, the banking sector has had an advantage in digitising and creating social license for digital banking products – you kind of know what to expect as you do the same in implementing AI-enabled banking products.

AT: I think trust is earned over time. Banking has already been through that journey with digital channels and digital banking. What felt unfamiliar eventually became normal because it solved real problems and delivered value. AI isn't really any different. The technology will keep evolving, but the fundamentals stay the same. Listen to customers, understand the risks, focus on where it genuinely adds value, and bring people with you on the journey. That's how you build trust - not by having the latest technology, but by using it responsibly.

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