How nCino’s Will Jung Is Rewriting AI Strategy for Banking

AI adoption is accelerating across banking, but the focus has primarily focused on incremental improvements to internal operations and cost savings. Customer experience? Still built on yesterday's technology.

Will Jung wants to change that. As CTO of nCino, he sees a bigger opportunity: not just to make systems faster, but to make them smarter for the people who use them. “Experience has been one where I thought AI could make a big difference,” he said. “I think there is a real opportunity to rethink how experiences are provided for different industries.”

In this episode of Keep Moving Forward, he shares how nCino is building digital partners that augment expertise rather than attempting to replace it, how explainability unlocks speed in regulated industries, and what it really takes to become an AI-native financial services company.

  • 34 min

How nCino’s Will Jung Is Rewriting AI Strategy for Banking

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Why Banks Conflate Optimization with Transformation

Most banks see AI as an optimization tool. Cut costs. Speed up operations. Automate the back office. Will thinks that misses the point.

The problem isn't just inefficiency. It's that banking experiences are still built on old technology. "A lot of times, experiences today have been based on yesterday's technology," Will says. Banks put the burden on customers to do manual grunt work — data entry, form submissions, document uploads — over and over again, even when they're already customers.

AI makes it possible to flip that. Voice-activated workflows. Conversational interfaces. Pre-populated insights that verify rather than require input. The goal isn't automating the bank's systems. It's making banking adaptive, intuitive, and aligned with how people actually live.

How Human Augmentation Thrives With Trust-Based Design

nCino's AI strategy is built around digital partners — persona-based agents designed for specific roles. Executive partner. Risk management partner. Processor partner. Each handles a defined set of jobs, not the entire workflow.

Will's framing is simple: the problems banks solve don't change. What changes are the processes. Digital partners handle the repetitive work — monitoring credit portfolios, pulling insights from multiple sources, and identifying patterns. That frees bankers to do the relationship work AI can't touch.

Explainability and auditability aren't optional in banking, though. If a banker doesn't trust how a decision was made, they'll reverse-engineer it. That kills speed. "If you build your system with that trust and with a way that a bank can follow it and understand how the system made that decision, that will actually give you the speed that you're desiring."

Will also knows when not to overcomplicate. Credit monitoring benefits from agents that synthesize data and surface insights. But policy enforcement is rules-based and deterministic. That's where simple automation beats another layer of AI. 

Building an AI-Native Organization Through Strategy, Context, and People

Will breaks down what it takes to become AI-native into three pillars: strategy, context, and people.

Strategy means making clear choices. What problems are you solving? What are you ignoring? Everyone needs to understand the mission and how their role contributes. Without that clarity, you get output but no outcomes.

Context is the second piece. Will connects this to AI architecture — context engineering matters for agentic systems, but it's just as critical across the org. "Context across your organization is so important," he says. Every role needs to be on the same mission and understand how their work contributes to that goal. Misalignment wastes effort no matter how much tooling you deploy.

The third pillar is people. Training matters. Critical thinking matters. First principles reasoning matters more now than ever. AI gives teams faster tools, but only if they know how to apply them strategically.

Will advocates for a combination of top-down urgency and bottom-up engagement. Leadership signals priority and provides access to approved tools, but adoption comes from teams experimenting, sharing what works, and questioning existing processes.

Will's approach to AI leadership is grounded in a simple truth that technology is powerful, but the thinking behind it determines whether it creates value or just complexity. 

Transcript

Will Jung:

Experience has been one where I thought AI could make a big difference. If you look at the way that large language models works and the way that it can translate between the different modes of communication between video, text, and images now, I think there is a real opportunity to rethink how experiences are provided for different industries. A lot of times experiences today have been based on yesterday's technology is how I look at it. And a lot of that has been you put the impulse on the customer to provide data, to do a bit of the manual grunt work, data entry, provide you forms and documents, sometimes over and over again, even though you're already a customer of the bank. And so, if I think about the opportunity that AI has is you can rethink those experiences.

Gemma Versace:

Hey everyone, and welcome to Keep Moving Forward, the podcast from X-Team for tech professionals who are passionate about growth, leadership, and innovation. I'm your host, Gemma Versace, Chief Customer Officer at X-Team. In every episode, we sit down with leaders who are redefining how technology teams work, grow, and lead. People who understand that performance begins with connection. Today, I'm joined by Will Jung, Chief Technology Officer at nCino. Will has spent his career at the intersection of banking and technology, moving from engineering and transformation roles inside a major financial institution to leading the global engineering team at nCino. As a former customer who now guides the product and technology strategy, he brings a rare view into how banks are navigating AI-driven change from both sides of the table.

In our conversation, Will talks about the real meaning of AI transformation inside regulated industries, why automation and agents should start with the problem rather than the hype, and how human augmentation, not replacement, is the foundation of nCino strategy. He explains how AI is reshaping credit analysis, customer experience, risk posture, and operational intelligence. He also discusses why banks must design for trust, explainability, and speed at the same time. Will also shares the cultural insights he's gained leading globally distributed teams, the organizational changes required to become an AI-native company, and the mindset the leaders need to stay curious, grounded, and focused on value as technology shifts around them. 

Let's get started.

Hi, Will, and welcome to Keep Moving Forward. Thanks for joining us today.

Will Jung:

Hi, Gemma, pleasure to be here.

Gemma Versace:

Wonderful. Well, let's get straight into it. And I think a great place to start would be asking if you don't mind walking us through your journey into tech leadership and what has led you to your current role at nCino.

Will Jung:

Yeah, so I started my career as an engineer at a bank working in the technology division and quickly shifted roles into transformation and product. And as part of that, got into a leadership position at the bank, managing technology and product there. Obviously worked with a lot of partners and technology partners like nCino. I was a customer at nCino before joining them recently, about a year and a half ago, as a GM of product. And then about eight months ago, I got the opportunity to lead the technology engineering team globally here and now the States side in Wilmington, North Carolina.

Gemma Versace:

Yeah, fantastic. And it's great to also hear about another Australian coming over to the US and particularly in the tech space is absolutely fantastic. And you've mentioned nCino and with nCino works at the intersection of finance and technology, two industries that are currently being reshaped by AI. What does AI transformation mean in that context today for you?

Will Jung:

Yeah, it's a good question because I think there's probably two ends of the spectrum. One, I think when we look at those two industries and technology and finance, AI transformation seems to be another word for optimization. And there's been a lot of opportunities to use AI to optimize those industries, whether it's through engineering capacity or whether it's to reduce cost and total cost of ownership across technology. So that's been a key side. I think the other side for the AI transformation really, and what I think is a bit underbaked to date is the experience side of AI. It's really using AI to transform how we actually provide the services that across the technology and especially the finance industry has been providing to its customers.

Gemma Versace:

Yeah, fantastic. As part of the research for the podcast today, obviously we've been listening to some of the other podcasts that you've been involved in and some of the content that you've shared. And one of the things that you've said is that AI experience is under-hyped, and you just touched on that in your answer there as well. Can you give me a little bit more detail on that? What do you mean by that? And especially in a sector like banking where accuracy and trust is just so incredibly paramount?

Will Jung:

Yeah, I think when I first saw AI come out a few years ago, and then obviously recently with consumer AI really taking off, experience has been one where I thought AI could make a big difference. If you look at the way that large language models works and the way that it can translate between the different modes of communication between video, text, and images now, I think there is a real opportunity to rethink how experiences are provided for different industries. And if I think about banking specifically, we think of banks as partners to your financial life, financial wellbeing. And I know a lot of banks think that way in terms of providing those services to the customers. So really, a lot of times experiences today have been based on yesterday's technology is how I look at it. And a lot of that has been you put the impulse on the customer to provide data, to do a bit of the manual grunt work, data entry, provide you forms and documents sometimes over and over again, even though you're already a customer of the bank.

And so if I think about the opportunity that AI has is you can rethink those experiences. So a lot of times, even digital banking, it's just a paper form digitized, but now there's a real opportunity to rethink. Maybe it's not a digital form anymore. We've seen the chat experiences and maybe it's chat with some more insights pre-populated, more verification by the client, or it could be even voice activated as an example. So I know a lot of people are on the road, they don't want to type and sit on the front of a computer anymore. I know a lot of people don't own computers anymore. A lot of things they do is on the mobile. And so in that case, typing on a mobile is pretty hard. So if you use your voice again to be able to initiate those experiences, have those experiences with your bank, I think that's where really AI can do, transform that sector in terms of the experiences they provide to their customers.

Gemma Versace:

What a fabulous viewpoint also and focus point for somebody like yourself in a CTO position. Because when we think about AI, a lot of people that want to adopt it and feel the need to be constantly including more tools for their teams, it is more about getting the internal process improvements or internal optimization. Whereas the way that you are looking at it, which is just such a fabulous viewpoint and somebody who is the chief client officer here at X-Team, it's music to my ears is that for you the focus is absolutely on the experience of your customers. And how easy it is for them to be able to utilize the AI advancements that you're giving them for the ease of use, but also such a positive experience. And that is just such a wonderful way to look at it. And no doubt, you're getting some fantastic feedback back from your clients having such a focus on that.

That's fantastic. And I guess, a little bit of a lead-on question from that is with so much pressure to adopt agentic AI across the industry, across the banking industry, you've also cautioned though against using AI for AI's sake. Where do you see that it's being misapplied potentially?

Will Jung:

It's almost like the classic hammer and nail problem. You've got a big hammer nail that can put in a lot of nails and everything looks like a nail to you. And so, we're seeing that across the industry. A lot of times, I think when people understand how to build enterprise scale, agentic applications, a lot of that does come at a cost. There is a cost to it, whether it's an inference cost, whether it's an experience or latency cost. And there's different types of architecture you need to provide. There's different things that are needed fundamentally. Your data architecture needs to be quite solid in the way that you provide context to your agents.

And so, if you understand those, I guess, constraints or the real principles required around agentic architecture, you do need to think through what problems you're solving with that hammer and it needs to be fit for purpose. And sometimes the cost and the effort behind it isn't worth the ROI.

Gemma Versace:

Yeah, thanks for that. It's a really good call out. And I guess, an example, if you'd be able to share an example of, say, a real life example where simple automation delivered better outcomes than a more complex AI solution. I know you've just called that out as being something that it can and does happen. Are you able to give us a little bit more detail around an example or a potential experience that you've had where that has been the case?

Will Jung:

This comes from principles we look at when we look at agentic, it's what is a complex repetitive pattern matching needed across different data sources? And that's generally where we find really the agentic workflows and agents are really proving their value here. And so our example is, I'll use credit monitoring as an example because that's something that we've looked at. So in banks with credit monitoring, we look at different types of information. 

There's financial statements we look at, previous cashflow and the current cashflow of the businesses, some macroeconomic factors. And all the different data sources we use that a credit analyst would generally have to use and look at really to make a decision. And so, really that's where we found agents were really good at pulling the data, understanding the insights, finding the patterns, and providing suggestions to our credit analysts to really go against that and to find actually what activities should the bank have with the client.

There's conversation that they needed, is it a simple extension of a review, et cetera? So that's a classic example. 

Now, where you can get complicated is then if you add another agent on top of that goal, okay, why don't you go against the credit policy for me as well? But a lot of times, policy is very rules-based and this can just be automated. And so rather than having another agent and the complexity of, I guess, a non-deterministic rules processing versus a deterministic rules process, especially in a regular environment, that's where you need to think, well, actually another agent here wouldn't make sense. This is where a simple automation policy that's automated with rules that's a lot better coupled with an agent that's giving you the insights, gives you the credit analyst, then gives you as a credit analyst, you get the best of both worlds.

Gemma Versace:

It's a really good point. And you were talking about earlier around there being so much hype around high ROI AI adoption. And how everybody's first instinct is to say, not how can we do this better, how do we do this differently using some sexy AI or some really complex AI that we can then take as a reference story to show the market how good we are. How do you guide your teams and your clients as well away from some of that hype and really more towards around the really specific high ROI AI adoption that you can deliver to not only internally at nCino, but also for your clients as well?

Will Jung:

I think we always anchor back to the problem we're solving, and I think that's really important. So even our clients, they appreciate that we understand banking, that we understand the regulation, the compliance requirements that they go through, and the burden of proof that they need to provide to regulators that they've thought through of the architecture, and the data lineage, et cetera, et cetera. So I think that's a great start. And then we show how not that AI will increase the burden of compliance and regulation, but how it can make it easier. So I think that's a real key point in the sense that AI not only does it do the sexy things, but it can also make your mundane things that we need to do as an industry to make sure that we are protecting our clients' money and doing the right thing, but we can do it in a smarter way and a faster way.

So if you anchor back to that problem and if you anchor back to, hey, this is also continue to help your risk appetite, this is actually helping your risk posture even better. That's a great way to show banks and also internally to show, hey, we are solving real problems with banks. And that's when you see the light bulbs click, the adoption clicks because it's not just, "Hey, we're going to give you more AI tooling on top of everything and we just plastered AI everywhere." It's, "No, we are solving a real problem for you. We thought about this deeply and we're using the best technology available to solve your problem in the best way possible."

Gemma Versace:

Yeah, that's so important and such a reason why clients would have so much trust in nCino is around, you talk about making sure that their risk posture is in good shape. I think that it's a really good point to be able to bring home the fact that, as you said, you are providing opportunities for some of the more interesting and sexy enhancements. But really making sure that you are doubling down and supporting them from a risk profile and risk posture perspective, is just so hugely critical and such a great value proposition that nCino offers for your clients as well. Changing these a little bit, you've described nCino's strategy as human augmentation, even using the term digital partners, which I think is just an absolute genius marketing move on you guys to be doubling down on the digital partner side of things. Can you unpack what that means in practice?

Will Jung:

Yeah, so we've got a strategy across, I guess, the agentic side of our platform to really be the digital partners. So we have persona-based agents. So as an example, there's an executive partner, there's a risk management partner, there's a processor partner as well. And these digital partners, what we've done is, again, going back to the core proposition of a bank is to, again, provide financial advice to make sure that customers can trust the finance institution they're partnering with with big life decisions. If you think about it, a lot of the finance is really driving a lot of life decisions where you're buying a home, growing your business, et cetera. So with that in mind, we know that the problems that the banks are solving for customers don't change. The jobs they need to do don't change, the way that they need to make sure that proper digital lenses is done, making sure that the money's safe, making sure that we are doing a proper risk assessment. And making sure that the customers are in the best financial position as possible. That doesn't change.

The processes around it can change because a lot of the processes that we've developed over the years in the industry is based on, again, yesterday's technology. So with today's technology, the processes need to change, I think. And with our digital partner strategy, it's really taking that persona and focusing on what are the core problems, what are the core jobs to be done that those personas across the bank need to do? How do we make sure that all those things are done in a trusted regulator way, in a smarter way using agents, which then unlocks the people. Because I think this is where as an industry, we've promised personalized banking for about 10, 20 years now with the digital transformation. I think we're still a long way to go in terms of personalized banking. And this is where you unlock the people to really develop relationships with the customers, divide that personalized service because banking is emotional.

It's again, big life decisions. There's a lot of trust that you need with your banker. So if your banker isn't spending so much time doing the administrative burrowing mundane work and letting process overtake their day-to-day by allowing them to focus on customers, I think that's a win-win for everyone.

Gemma Versace:

Yeah, definitely. And you're so right as well, that being able to free up the individual bankers to be able to have more connectivity, more engagement, develop more of that customer development relationship is only going to serve better for the client but also, ultimately, the bank. Because there is that greater trust, there's that higher likelihood that customers will invest more, bring more into the bank and be a lot more trustworthy of any advice, and unique, and specific, tailored advice that's coming back their way. So it's super critical to be able to give your banking clients a lot more comfort that they're going to have the time to be able to give that unique service to create that wonderful, trusting partnership that is just so important between a client and their bank.

How do you balance the need for explainability and auditability with the push towards smarter, faster decision-making? Because as you mentioned, everything kind of comes back to wanting to create the best experience for your client, and you want to be able to move it at rapid speed. You want to be able to give advice or decisions very quickly to the client as a bank. But you also need to obviously have that audit trail and the opportunity to be able to explain why and how this decision was made. How do you balance delivering on those two things?

Will Jung:

Yeah. So I don't think auditability or explainability a choice. I think in a regular environment like banking, that's a non-study you have to have, that's the basics. And it's interesting because you might think that going for the speed to decision and going back to audit will explain a bit later, it can be a choice. But what ends up happening is the bankers don't trust that decision. And so they spend time trying to work out how the system made that decision. And so you actually end up losing the speed. But if you design it upfront with the explainability and auditability, and they can trust that decision making, that's when you get to speed. And so in a way, if you build your system with that trust and with a way that a bank can follow it and understand how the system made that decision, that will actually give you the speed that you're desiring.

Gemma Versace:

Yeah, great. And I think you're exactly right, is that the two aren't mutually exclusive from each other. One, they both help the other in developing an overall really good experience, but also helping, as I said, to continue to develop the trust piece as well is just so critical. What organizational changes are needed, in your opinion, to truly become an AI native company? And that is beyond just adopting tools. What's your opinion on that?

Will Jung:

We thought hard about this because that was a journey that we started a couple of years ago, and really there are three things that I think about. The first one is the problems that you're solving still matter. And so, if you think about it, it could be a strategy that we think through. And strategy is really, the way I think about it is just choices. Choices that you make on what you are doing and what you're not doing, because everyone only has a limited capacity. And so, being crystal clear on that strategy and the choices that you're making as a company, that is critical because getting everyone online, understanding the customer problem, getting closer to the customer, nothing, no amount of technology or difference in technology can change that, and that's how you win. And so that's part one. The second part is context.

And so we know that context engineering is a big thing across agentic architectures, making sure that you get the right information at the right time, but that goes beyond just the technology and architecture. Context across your organization is so important. So we've got to talk about strategy. Every role in your company needs to be on the same mission and understand the goal and how their role contributes to that. And so if you're not clear on that, no matter how much AI you've got, no matter how much code you're developing, I think you're just going to get a lot of code, but no real outcomes. And so, that's critical with that context part. And then the third part is your people. Again, I think people are the driving force of your organization. Your organization is your people. And so, how you train them up in the sense of not just being aligned with the strategy and the context, but then being equipped with the tools, being equipped with first principles thinking, the criticals thinking, I think that's even more important now with the tools available.

And so having that as an organization, no matter what size your organization is, I think is critical. I think that plus now, then you've got the tooling like AI across the different ways of now faster coding, faster design, faster product management, and faster customer success and support. All of that then comes into that strategy, and context, and the people.

Gemma Versace:

Yeah, it's a really good answer. And I think that your point about really making sure that you've got really good breadth and depth of buying across all parts of the business is just so hugely critical. And I was recently speaking to the CTO at Thomson Reuters and he was talking about the fact that they have had 80% adoption with their AI tooling because they did mandate it from the top. And because they wanted to be able to show their clients exactly how to go about trying to get that buy-in.

I guess, as an extension on that, what advice would you give to a CTO or VP of engineering who's currently feeling a little bit behind in AI and doesn't really necessarily know where to start. Whether it be around how you specifically engage to be able to get that buy-in and excitement across not only your team, but the broader business. Or what some of the strategies, if you're comfortable sharing nCino has implemented, whether it be like the example from TR where they very much mandated it to try and get the buy-in and they've had success with that. What's some advice that you'd give to a CTO or VP of engineering listening who really just wants to know how to bounce the ball on implementing an AI strategy for their business?

Will Jung:

Yeah, so I think it needs to be twofold. I do agree with the top-down approach in the sense of there needs to be a sense of urgency given in terms of the tooling and being equipped and educated on what the new technology can do. So that I definitely agree with. And we've done that at nCino to an extent as well. The other part as well is you also do need bottoms-up engagement. And that's where really listening to your engineers, a lot of engineers are using AI, whether you know it or not, whether it's personally or in the office. And we found that the same. And this is where we landed on a few partners and tools using AI IDE especially. But really a lot of that came through feedback from our engineers themselves. I've been using this, I've been using this. And then balancing that across our strategic partnerships, we landed on a few tools and going, "Yep, company-wide, this is what we're doing."

And I think making the tools available company-wide is to show that there is a real investment here and then just sharing. I think creating culture of collaboration and sharing. Every Friday we have something called Innovation Fridays at nCino where you don't have to be in engineering, you can be in customer success, support, sales as well, and just showing how you're using the AI tooling and what innovation you're doing in your day-to-day work. And just seeing your peers being able to do that with approved tooling, but also being able to innovate to make their lives better. And getting the license to question the current processes and changing the processes with the new tools, that's always powerful as well. So a combination of that top-down and bottom-up engagement and just seeing your peers look at it and being able to really 10X themselves or whatever it might be is really powerful.

Gemma Versace:

Yeah, that's fantastic. And I speak from experience here at X-Team about how we are doing just that as well, is that there's certain parts of the business that I look after that we're getting some of those automations, those quick automations done. But now we're getting access to some really fantastic additional AI tooling and being able to see the uptake. But then the successes across the team, not only within my team, but more broadly across X-Team, I definitely agree that it's a little bit from column A and a little bit from column B. It's a combination of the two.

You've worked across many regions, obviously now sitting out of the US and with a distributed global team. Do you notice any unique strengths or challenges in how AI innovation is adopted between Australia, or the US, or Europe? Are there any nuanced differences that you're happy to share?

Will Jung:

I do think culture plays a big part. And so Gemma, you'll know this being Australian as well. Australians have this tall poppy syndrome where you don't talk about yourself, you always laugh at yourself. That's a big thing in Australians. And so, I think what that we see is it's a lot more grounded in the sense of show me, prove to me first and then I'll get on board. I find in the US, it's very different. US is, I guess, they call it the land of opportunity for a reason. And so a lot of it is based on, hey, there's this opportunity, so let's get on board to see what this opportunity is like.

Gemma Versace:

Yeah. Have a crack.

Will Jung:

Yeah, exactly. So really I see that difference a lot. And even when I speak to customers, when I speak to CIOs and CTOs in Australia, a lot of the questions, a lot of the discussion is around risk. It's around audits and security, which by right we talk about. And not saying that United States, we don't talk about that at all, but United States, we talk more about the opportunity and the value provided. And we talk about both things in both regions, but where I spend more time speaking to really, I think, kind of reflects that culture and it's a bit different in that sense. So I think culturally that is something that I've been aware of and just understanding, okay, where am I talking to? And again, it's based on the regulation and the market, et cetera as well. So being aware of that and just being exposed to that has been really interesting for sure.

Gemma Versace:

Yeah, fantastic. You've mentioned previously that nCino is building anonymized benchmarking and portfolio intelligence. Can you tell us more about that work and where it's headed?

Will Jung:

Yeah, so we've got a rich dataset of 13 plus years in the industry across thousands of banks globally. And there are banks that partner with us who have provided consent to use, again, anonymized data to improve our product. And also, we have started to provide intelligence back to those customers as well. So customers can see their operational analytics across the platform to see how their process is working on our platform, where the rework's happening, where the bottlenecks are, and really fine-tune that, and also being able to see benchmarking across their peers. And again, it's all anonymized. You can't see who your peers are, but it does give you a sense of, okay, how am I doing the market? What are my peers doing from an operational perspective? And that's really interesting for us as well.

And from a product perspective as well, we use that data to make sure, "Hey, are we focused on the biggest problem of our customer bases?" Or, "Hey, we're noticing this step of the workflow is an area customers are really struggling with, let's deep dive on that. Let's have some customer interviews, understand where are the problems, what are they going through? Is this something in the system that we can help them with? Is it regulation? Et cetera." It just allows us to have a richer data-driven conversation with our customers and also just provide back to our customers because I think that's where they're really getting that value from nCino, is that industry understanding and expertise.

Gemma Versace:

Yeah, absolutely. Talk about providing a win-win, a win for your clients, as you said, with such a wealth of data being king, available to them for that comparison purposes, but also for the upskilling purposes within them and their business. But also on the flip side, for the data that then you can consume from nCino to then translate that into decisions that are going to help with continuous improvement or further value creation for your clients using the data to determine what those initiatives and potential new enhancements and products could look like. So yeah, fantastic way to be able to make sure that there's a win for your client, but also a really fantastic win for nCino to continue to drive and create that value for your client base as well. Last question, and it's a bit of a big one, but how do you personally keep moving forward?

Will Jung:

That is a big question. So for me, very simply put, it's I think never staying still. And I think always understanding that there's always so much to learn. And for me, if I'm not learning something, if I'm not solving a new problem, it does, I think for me, I get a sense of, am I really developing here in the current role, or current company, or wherever it might be? So that's really the motivation. I love solving your problems. I love learning. And I think that's just an inner motivation that I do have. And I think you get motivated by having the same people around you. And so being in a technology company, you get the benefit of a lot of curious people and a lot of curious thinkers and innovators, and that always keeps you on your toes and with different perspectives. And that diversity I think is another important part.

It's making sure that you have diverse people around you. Sometimes it's easiest for us, especially in technology and even in banking, to some extent, to be in an echo chamber. We were all talking about the same thing over and over again and just revalidating our own thoughts. And so, just having diverse perspectives around not just in work, but in life, with people with completely different occupations. My wife, as an example, she's in the art side. She has no idea what I do, but that's different perspective is just great on life and just in general. So that keeps me going.

Gemma Versace:

Fantastic. And now living in the States, there's a whole new level of different that you get to learn and experience and be a part of as well. So what are such a fantastic attitude. I love that, that everybody is there to be able to learn from and work with and only make everybody stronger through the diversification of different thoughts, and people, and minds. So thank you so much, Will. I have thoroughly enjoyed this conversation and I know that our listeners will have as well. So thanks so much for your time and effort and all the wonderful answers and intel that you've been able to share with us today.

Will Jung:

Thanks, Gemma. It's a pleasure.

Gemma Versace:

My conversation with Will highlighted a powerful truth about applied AI. The tools matter, but the thinking behind them matters most. Will showed how the most successful AI strategies begin with clear customer problems, strong context, and teams who are aligned around purpose and outcomes. His approach to digital partners and human augmentation reframes AI as something that elevates expertise rather than replaces it, especially in industries where trust, compliance, and accuracy drive every decision. We also talked about AI adoption. Will was very clear that becoming an AI native company takes both top-down urgency and bottom-up curiosity. It requires equipping people with new skills, giving them the space to experiment, and ensuring that the systems they build are transparent enough to earn real trust. What stood out most was his mindset. Keep learning, keep solving new problems, keep surrounding yourself with the people who bring different perspectives. That is what helps teams move forward with confidence in a moment of rapid technological change.

Join us next time for more conversations with technology leaders who inspire us to grow, lead, and innovate. You can find us on Apple Podcasts, Spotify, or YouTube Music. If you enjoyed this episode, please share it with your network.