10 min
The Saturday Fraud Strategist
The AI Adoption Journey for Fraud & Risk Teams
If you’ve been following fraud on LinkedIn for any real stretch of time, you are probably familiar with Brian Davis and have been reading his posts. He has been the first fraud hire at many companies across physical goods, e-commerce, marketplaces, and fintech. Today he sits at the center of it all running deep dive retreats through Safeguard.
I have talked with Brian before about AI adoption for fraud and risk teams, and he said something that stuck with me. There’s a real difference between AI activation and AI enablement. Most organizations think they’ve done the second when really they’ve only done the first. I wanted to bring our conversation to all of you, because I think fraud teams may underestimate or overestimate where they sit on this journey.
What you’ll hear in this episode:
- Why AI activation vs AI enablement is the distinction most companies get wrong, and what it actually looks like when you throw a tool over the fence with no guidance.
- Brian's four pillars for real AI enablement are clear policies, actual training, dedicated incentives and time, and a feedback loop that doesn't die after three weeks.
- Why change management for fraud teams is really a people management problem wearing a technology costume.
- Brian's full five-stage framework, covering AI blocked, AI aware, AI enabled, AI first, and AI native, and how to honestly assess where your own team sits.
- Why AI enablement leadership buy-in has to start at the top, and how executives showing their own AI usage removes the imposter syndrome holding everyone else back.
- Why so many fraud teams try to go big on day one, transaction monitoring, KYC, and end up frustrated, when the smarter path is workflow design for AI adoption that starts small.
- Concrete, non-technical AI use cases for fraud, including pattern analysis, internal reporting, and OKR alignment with sales and marketing.
- How reducing engineering dependency with AI is changing what fraud analyst upskilling with AI actually looks like day to day.
- Brian's personal framework for fraud practitioner AI use cases, including his own daily habits and how he thinks about build versus buy AI fraud tools.
You should listen to this episode if you:
- Are a fraud or risk leader trying to figure out whether your team is actually AI enabled or just AI activated
- Are responsible for fraud team AI training or building out fraud team AI governance policies from scratch
- Are a fraud analyst wondering how to build AI literacy without waiting for your company to hand you a roadmap
- Are trying to motivate a team through fraud team change management without losing the people who are cautious about the change
- Are comparing AI first fraud organizations against the fully rebuilt AI native fraud teams and wondering which one is actually the realistic goal
Episode notes & key takeaways
AI activation is not AI enablement, and that gap is where most teams get stuck
Brian's framing here really landed for me. AI activation means buying the licenses, rolling out the tool, and giving your team zero guidance beyond "go use it." AI enablement is everything that has to happen after that. It means clear policies on what you can connect to, real training on the dos and don'ts, dedicated incentivized time to actually practice, and a feedback loop that makes sure this isn't just a three-week burst of excitement that fizzles out. Without all four of those pieces together, you get exactly what Brian described. A handful of power users emerge, a much larger group stays cautious and barely uses the tool, and a third group sees no reason to change what already works for them.
Leadership has to go first, or nobody else will
I loved Brian's point about psychological safety here. When executives visibly use AI themselves, imperfectly and learning in public, it gives everyone else permission to do the same without fear of looking foolish. Without that example, you end up with the worst version of top-down AI mandates, where leadership demands the team become "AI native" while having no real idea what that requires operationally.
The five stages run from AI blocked all the way to AI native
Brian's five-stage model is the backbone of this whole conversation, and I think it's genuinely useful as a self-assessment tool. AI blocked means you can't use AI yet, for any number of reasons, whether that's missing data, missing tooling, or unclear policy. AI aware means you've dabbled inconsistently, maybe tried it once, without ever really coming back to it. AI enabled means you're using AI for a specific use case, consistently, on a recurring basis. AI first means AI has become core to your decision-making and team structure across multiple use cases. AI native means the entire function has been rebuilt around AI from the ground up, which realistically only applies to companies without much legacy infrastructure to begin with. Most fraud teams right now sit somewhere between AI aware and AI enabled, whether they realize it or not.
Start small, and resist the urge to go straight for your biggest, riskiest use case
This is the part I really want fraud leaders to sit with. The instinct when you get excited about AI is to go straight for transaction monitoring or KYC, your highest-stakes, most customer-facing processes. Brian's advice runs the opposite direction. He recommends starting in lower-stakes places first, mapping your existing workflows and running pattern analysis and building internal reports. Let your team build real AI literacy and confidence before you ever put it in front of a customer.
AI is changing fraud team economics, not just fraud team output
Brian said something here that I hadn't quite articulated to myself before. AI is reducing fraud and risk teams' historical dependency on other departments, especially engineering, to get anything built. That's a genuine motivation lever, since fraud teams have always had a lot of ideas and very little budget or headcount to execute them. Reducing that dependency on engineering means fraud practitioners can prototype, test, and even ship internal tools without waiting in someone else's roadmap queue.
The echo chamber is lying to you about how far behind you are
Brian's closing point might be the one I keep coming back to. If you're on LinkedIn feeling like everyone else is light-years ahead of you on AI, that's an echo chamber talking, not reality. If you're actually spending time learning and experimenting, you're already ahead of the vast majority of the market. That reassurance matters, because FOMO and imposter syndrome are two of the biggest silent blockers standing in the way of a fraud team actually progressing through these adoption stages.
Final takeaway
If there's one thing I want you to walk away with, it's that AI adoption for fraud and risk teams isn't a switch you flip. It's a journey with real stages, and buying a license doesn't put you at the finish line. It puts you at the starting line. The teams that actually get value out of this aren't the ones that moved the fastest, they're the ones that built policies, training, incentives, and feedback loops in alongside the technology. Wherever you honestly sit on that five-stage journey today, the goal isn't to compare yourself to some imagined version of the market that's already ten steps ahead. It's to take the next real step, and then the one after that.
Resources & links
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Connect with Brian Davis | LinkedIn
VP at Safeguard
Founder, The House of Fraud
Advisory Board Member, Association of Certified Fraud Examiners
Connect with Chen Zamir | LinkedIn
Host of The Saturday Fraud Strategist
Helping fintechs build smarter fraud defenses
Co-author of “The Fraud Fighter’s AI Playbook”
Episode transcript
Brian Davis
00:00
Once I get hired, I'm lazy. Full stop. Here's a tool. Go use it. We're giving you no guidance. You might get in trouble. You might not get in trouble. One team could see that they're AI native. And then another team in the same organization will say we're AI aware, meaning we're not doing anything, but we should. The simplest one I do is,
Chen Zamir
00:31
Welcome everybody to another episode of The Saturday Fraud Strategist and with me today Brian Davis in the house the Brian Davis. Uh actually this is how I need to introduce you. My assumption is that if you're listening to this podcast uh you probably know who Brian Davis is. That's my assumption. But for the two three folks who are listening to us and somehow didn't come across you uh tell us a bit about yourself.
Brian Davis
00:52
Absolutely. I've been in the fraud and risk space for a little over a decade. Um I wanted to get into fraud because I got a little bit of like education within my college and my master's program even though I had a masters in accounting placed out I did a 5-year program so I placed out a lot of my courses. So I ultimately in my master's program only took entrepreneurship and fraud. Although I got a piece of paper that said I'm really really good at budgeting. Good luck getting anything else. Fast forward a year, I was finally able to get into fraud. Didn't know what that meant, what it really would entail. I wanted to work in e-commerce. Didn't really know what that meant. A decade ago in Boston, there weren't really many jobs in e-commerce and fraud. So, I found one company, convinced them to hire me that I studied a little bit about fraud, so I should be your first fraud hire. Um, and haven't looked back since. My weird little quirk is I like to see how different business models are abused by fraud. So I've done physical goods, e-commerce, marketplaces, fintech, consulting, vendor side. Now today I sit in the center of the ecosystem putting on uh AI deep dive retreat. So now in events and community building. So it's I've seen it from a lot of different lenses. It's given me appreciation of how different tactics are used in different industries, but there are little nuances or the type of data or what you can or can't do for controls, kind of impacts how you can mitigate different uh types of risks. So like a fintech is going to handle that a lot differently than a digital subscription uh for onboarding and fake accounts and circumvention and like I like there isn't really necessarily enough information on the digital subscription to say do we have synthetic identities? I don't know. It's just a fake account at that point because we don't collect enough information. However, are they building something to ultimately use down the line? So being able to see how these different tactics evolve and are just tested from platform to platform has uh been a way that I like to challenge myself on top of always being the first fraud hire. So I like to build I'm the zero to one person who comes in and says none of you want to deal with fraud for some reason. I do. Let me come in. I'll build out the team, the tools, the rules, everything sort like that. I'll help hire the team, build out the function, and at that point, there's too much structure in place for me, and I go on to the next one.
Chen Zamir
03:22
That's super, you know, super interesting because I remember, I don't remember exactly when I bumped into you, obviously on LinkedIn. It was, I'm guessing, very early into my own uh LinkedIn journey. And I remember two things. I remember one that you know we we both had very different careers in fraud, but also very similar uh to an extent. Meaning we are kind of like hovering around this problem from all sorts of different perspectives and wearing all sorts of different hats. Uh and I think especially I don't know a lot of other folks who have, who who transition from being a practitioner to being, sorry I'm you know I'm using a very bad word towards you and towards myself as well, but being a marketer. Uh so that's that's one that's interesting. Secondly, I also remember that you know reading your post and you're you didn't mention that you're very active on LinkedIn for many many years right now. Oh yeah, many years. Um and I remember when I first started uh writing on LinkedIn and and producing content, I remember trying to understand like okay, who who can I copy from? Like who can I get inspiration from? And there was literally no one. There was you.
Brian Davis
04:38
Yeah.
Chen Zamir
04:39
Mainly on LinkedIn and there was Karisse who hadn't, still has her podcast. Um so obviously you know I I bumped into you very early on. Uh and I remember reading your post still uh till today and agreeing with everything that you say. Which is incredibly annoying Brian. Incredibly annoying. Uh and many times you say before I before I have the chance to say that. Yeah. So uh no it's really a pleasure to to you know to have you on the podcast. We always have like great conversations. So this one we'll also share with the crowd. I want to start where you know we had we had a call I think it was like two three weeks ago. Uh I think you were on vacation since, I was on vacation since. Um and during that call we talked about AI. I mean what can you talk about in the fraud space uh in 2026 right? Um and and specifically we talked about the kind of like the adoption journey of AI, that we see in the market. And and I remember listening to you and you know a lot of the insights that you get. I think uh you know you've seen a lot, because of your role at at Safeguard event. Um and I remember that you you raised this very interesting concept and I think you kind of like distilled it very nicely. Uh, and I remember you saying there's a difference between AI activation and AI enablement. So maybe we can start there and and you can kind of like walk us through like what's the difference and and how you see it.
Brian Davis
06:17
I think a lot of people are thinking about how can I use AI today. I'm very pro AI. I'm always looking for ways to make my life easier. I've this has always been my mentality. I've always told people once I get hired, I'm lazy. Full stop. So, I'm going to try to not, I'm going to try to find ways to automate my role. Try to find ways to say we keep investigating the same patterns over and over and over. What data points missing for me to increase rules? So, it's always been my mentality. Then it became like process improvement, setter level excellence. For me, I'm lazy. I'm just trying to find better ways to make my output, my mentality, kind of like be able to see the full complexity of whatever business or platform I'm working on today. So that's like the root foundation of all of this. So when it comes to like AI, it's a again it's a tool. And a lot of people don't necessarily know how to use it, don't necessarily have the time to sit down and use it. And it's changing so fast, you ultimately have to be unemployed to keep up with the different models. What do I use this for that? Do I just stick with one platform versus the other platform? Do I self-host? Do I use the cloud? Do I like, what do I do? It's very overwhelming to start. So I love the initiative for a lot of companies be like we want to find ways. We want to empower our our own employees to design their workflows of like, what do you hate? What, where can we use AI? However let's throw it over the fence and hope for the best. That is AI activation. That is buying licenses, getting tooling that's already like involved with your contracts, or tools that you already use. Just kind of like you could be handcuffed of like what you have. And you just kind of take the easiest path to say, we use AI at insert company. Then the employee doesn't really know what to do. They haven't been trained. They don't know the, I'm generalizing. I'm generalizing the person who doesn't really have the time that I, doesn't dedicate any any bit, and don't really have maybe the comfort or feeling of support to test, in house. So what does that mean? A lot of companies are unclear what can you connect it to? Can I connect it to my email? Can I connect it to Zendesk? Can I connect it to Stripe, Adyen, Checkout? Whatever platform you use. Uh because payments in our space is a lot of great indicators. Can I connect it to any other tooling? How do I connect it to the tooling? Do I need an engineer? Do I need a product manager? Can I do this? Do I have access to do this? What can I or can't I do? So, the first step is, be clear around the boundaries and constraints of your own policies internally of what can your employees do. I know some companies that can connect it to everything. I know some companies who can't connect it to anything. So then the power of what you're really going to be able to do, kind of changes what you can. It's just like automation or rule-based systems. You have data, you put those as inputs and then that will dictate the outputs you're able to get. If you don't have the inputs, you're can't get the outputs. It really kind of still comes down to that foundation of being able to have that data. So that is AI activation. Here's a tool. Go use it. We're giving you no guidance. You might get in trouble. You might not get in trouble.
Chen Zamir
10:02
And we take the box. We take the box for our board. Right.
Brian Davis
10:05
Exactly. To say, "Yes, we're doing it. Yes, we rolled it out. Yes. Yes. Yes. Yes. Yes. Sounds good." Then you're going to get a couple things out of that, I guess, before we get to AI enablement. You're going to get people, some a couple power users. And they don't know if they're doing anything wrong. They might, and they might get in trouble. Then they're not going to be power users. You have some people who could be power users but are cautious and careful about what they do, what they connect, what they say. So they end up barely using it and going back to the other ways. And then the other people are like, well, the way I'm doing it works. Why do I have to change what I'm doing now? It's just change management right there. Uh again, if you throw over the fence, you're going to fall into those three pools. The first person, they might get in trouble, they might not. That second group which is your biggest opportunity to get power users, are cautious and careful and are not using it not using the full-time. And then that third pool of like they don't, there's no incentive. There's no reason for them to ultimately start using these tooling. The way that it works, it works. I've been doing this for 20 years, 10 years, 3 years. Doesn't really matter, it just works. I don't need this tool. And then what's AI enablement really look like. Comes from a lot of different forms. But really it starts with communicating your expectations, your policies, your boundaries and constraints. Being clear of what you can connect to, how you can use it, what you can put into it, what you can't do, what you can't connect to it. Just being very clear so you know what you're operating in as an individual and an enduser. Then that's one. Train them of the dos and don'ts of using whatever platform you are. This is platform agnostic. It doesn't really matter because they all offer a lot of this functionality and you need the data. Without the data, can't really do much. Then how do you actually get people to have time? There's two two ways to kind of do it. One, incentivize. How can you incentivize people to look for ways to play with this tool? To learn it, to test it, to build it, to push things into production, to build internal tools They have to be incentivized. It could be through OKRs. It could be through little hackathons where there is prize pools. There could be recognition. There could be uh just new channels spun up in either your teams, or Slack, or for you something else of like just show and tell. Of just getting the comfort of allowing people to test, build, and then show off. So you're incentivizing. Then the other aspect of that, where does the time come from? You have to be able to allow your employees and set dedicated time to ultimately talk about it. Show off things, or just have heads down time to just pick one process, or one report, or connect one data flow. To be able to actually do it. Because if you work 40 hours a week, and then in order to use AI they need to work 41, 42, 50, 51, you're going to lose a lot of people in that depending on the size of your company. So one, it has to be incentivized. Two, you have to have time. And three, you have to have boundaries and constraints and teach people how to actually use it. And then they have to all go together. And the final one, this is where usually any change management dies. Like this is an AI adoption. This is just change management in general. You have to let that run, see how it does, and then how do you reinforce and reassess. So, the feedback loop. How do you really make sure that this wasn't a 3-week blip of excitement? Maybe great for team bonding, maybe great for production for a little blip. But how do you carry that momentum forward? And ultimately make sure these new processes, these new ways to investigate, these new ways to build reports, these new ways to communicate actually end up being adopted. Again, it can be incentivized. Again, it can be built into OKRs. Again, it can be built into like monthly or quarterly syncs or reviews for if you want to call it AI task force. Someone to review across work or own your own team to say, are we making progress with AI? How and what are, what is the improvement impact? Be loud about that. That's how you ultimately how to get people bought in. Show off. Um, so those are kind of the ways that I think about the difference between AI activation and then how do you actually enable AI within your team?
Chen Zamir
14:51
That's great. And I I would ask, or I would say this. I'm guessing that the the organizations that are stuck in the act activation stage, uh I'm guessing most of them uh you can split into two categories. Uh one are the unaware. They simply don't know that it's not enough to buy tokens. Right? Uh and say to their employees that they have I don't know like a token badge. Uh they just they're not aware that you need all of these things. Uh the other group um are probably not motivated. Uh meaning they are aware that they are using AI as kind of like you know a veneer, uh you know. Um but um they are probably not motivated enough to invest all of these things. All of this effort that you talked about. And before we kind of like deep dive a bit more uh into this kind of like entire journey, um like how how do you, like when you speak to such organizations, how do you uh tickle their motivation nerves or whatever? Um and I'm guessing that like like there are two layers to it. There's like the personal motivation. Why should I do this change if everything works? Why should I actually like go invest in in AI? And also as an organization, what do we get as an organization from enabling AI, not just activating it?
Brian Davis
16:34
I've coached basketball also for over a decade now that I can say that. I think about this with a lot of same mentalities of that. There's different teams. You have different players who have different personalities. One year you can motivate a team and get them bought in and kind of like giving their full effort with certain tactics. The next year those tactics might not work. So part of it is understanding who's within your company and who's within your team. And ultimately to get true adoption and to get true motivation, it has to start at the top. So when executives are using it and showing how they're using it and what they're connecting to, one, it'll give people ideas, but two, it'll give people this comfort level of, okay, they're doing it. They're not perfect. They're learning. It removes a lot of the impostor syndrome of like, well, I don't want to show this. People are going to make fun of me. I don't want to show this. People are going to think this is stupid. So it removes a lot of that and kind of creates a little bit of psychological safety within your company. So, one, set the the tone and goals at the top. If they're unaware, they don't know how to use it, then like uh what you're going to typically get is you're going to find people who are using it a ton and then execs are going to keep saying, "I want to be AI native. I want to be AI-pilled. I want to keep using more AI, more AI." But if they're not using it, they don't really understand it either. So, one, set the goal, set the tone at the top. Um that ultimately will give the direction and strategy of what tactics ultimately work us up into there. What incentives can we use? Can we use incentives as a company level? Can we use incentives on a team level? Does teams have budgets to incentivize? So, there's kind of like all these little bits. How do you want to kick it off? Make if you want it to be a big thing, make it a big moment. Company hackathon, uh company training session, team like yes, it takes time. Yes, it stops overall um in short-term production of the company, but if you're making a big bet on AI and you roll it out week, probably either going to take way longer to adopt or just fail. So, you got to make it a moment within the company to get people excited to get people bought in. That's still not going to cover 100% of people. Now, you ultimately need to get like who are your coaches and assistant coaches, how I think about it. That might be your managers, be your directors, depends on your team structure. But you're going to have certain like pods across all the different orgs. Even if you roll it out as a company, one team, here's what I'm seeing. One team could see that they're AI native, and then another team in the same organization will say, "We're AI aware”, meaning we're not doing anything, but we should. So there's going to be a huge variance depending on the size of the company within, maybe not even depending on the size of the company, but huge variance between the perspectives of where they are on the AI journey. Just two teams apart. So proximity is close where you can learn from each other. So now whoever owns that pod, the group, a function, the team, you know your people on that team better. How can I motivate the majority of the people with one specific tactic? There's usually one motivation lever that you can get 60 to 70% of your team. Then there's going to be a couple of the people that like drag their feet, just don't do it. It's going to take my job. I don't want to use this. I don't want to learn this. I'm afraid. Um, so how do you motivate those people on an individual basis? Ultimately, the goal is, you will be typically have one-on- ones and meetings with people. Make it a moment to talk about, make it a moment to help people. How can you get the group bought in? And then who are the individuals that need a little extra love, who need a little extra support? And then how do you get that love and support for them? To ultimately make them feel comfortable to remove those fears, remove those hesitations, understand what is actually blocking them and work with them to remove those blockers. That's very hard to do at a large scale company. It's very hard to do when you're responsible for hundreds of people. So there are ways around that. But you got to kind of break up and segment your teams in the pods or whatever it may be to ultimately find what those individual incentifications and support systems look like to get people to just feel comfortable trying it out. Understand it, it isn't going to take your job. Uh the way that I think about it though of like, you're going to have the people who are top performers who are going to use this and become even top, even more top performers. You're going to have a bunch of people in the middle, who weren't top performers with this that will become top performers. Then you're going to have the people who probably are going to end up on pips performance plans anyway, and this could help them get out of that bucket to average performer. And then there's going to be people that drag their feet that they were underperforming to begin with, that they're going to be underperforming here. Pre or post AI, that's an entirely different conversation. But I see AI as a tool to either speed up those processes to learn to move people up on that performance chart for yourselves. Of like the quality of output. And then the transfer of knowledge is a huge component to this, of the transfer of knowledge. What are our 1% on our team doing? How can we leverage AI to feed that back into the people we are figuring out our own upskilling plans for them? How can we leverage AI into this to move the average group? Which that's where most people fall in in most companies. And average means something entirely different at every spot, every team. I'm not saying average to be average, just that's math. Of how it the the bell curve works. And then how can you move people up on that?
Chen Zamir
22:20
Yeah. I I love how very quickly you kind of like draw the line from like a technological adoption effort, right? Well, we're talking about core technology. Supposedly, we should speak about should we use Chat GPT or Claude? Should we use this model or that model? How many like what's the token budget per team member? But very quickly you kind of like go, and kind of like connect it to a transformation journey. And then very quickly into people management, uh into people in general. And it's fascinating to see how a project that seems like a technological project first and foremost, is actually first and foremost um a people management project. Maybe all projects are. I wonder though what happens, or I'm guessing that you also meet sometimes, I don't know maybe maybe you don't. Um leadership teams that themselves don't really understand why they should, or what's for them to gain other than say yeah we we do that too. Um, what like when you're thinking about teams that actually enabled AI, and went very far with it, how would you kind of like present their gains, their kind of like the fruits of their efforts to these skeptics that say, "Yeah, AI is another hype train and it it will pass and it will be something else. I'm not going to invest time and money in that."
Brian Davis
24:01
It's a tinker tool to me honestly. Once people start to play with it, it allows a lot of non-technical people, not going to point any fingers. But nontechnical people get over those hurdles of like well I need product, I need engineering, I need a fight for roadmap, they don't understand this problem. Uh there's a bunch of different use cases, like the one that I talked about a lot of. Like if I can build a prototype, it's going to be a lot easier for me to communicate with product and engineering what I'm trying to communicate. And a visualization of like this is what I envision, this is what I'm thinking, this is the portal layout, this is the functionality. You can build all of that without even connecting it to any data. Let's say you're in one of the companies that have don't connect to anything. You can still design your workflows. You can still design the what you want to work out of. You could still design reports. And then hand it off to somebody. That's going to be a lot easier of an ask. It's going to take a lot less mental load, for whoever is sizing that from an engineering scope, to ultimately say, "Oh, I thought this was way harder looking at this. We could do this." And then that becomes a lot easier to get dedicated resources consistently through it. So again, that is just an example of using AI as a tool for communication to different teams of how they think and see differently that you're always negotiating with of, I need more support. There's never enough engineers. And even with AI there's still not enough engineers. There just maybe some engineers that their output is getting insane. Maybe their token usage as well. But that goes into the trade-offs and budgeting and some visibility into like the economics behind it. I'm not going to speak on the economics today. Uh but it is another lever into all of this, of like the the equilibrium across teams of like what is the right usage levels for everybody. But I mean that's one way that I kind of start to think about it. [Ad Break (25:53): Hey folks, I want to take a quick break to speak about today's sponsor, me. If you're finding this useful, do me a small favor, like and subscribe. It really helps with the algorithm. And if you're not already on my newsletter, The Saturday Fraud Strategist, you should definitely check it out. Every Saturday, I break down fraud trends, real cases, and strategies from my own experience of building and operating fraud prevention systems. Whether you're new to the space or a system practitioner, I'm sure you'll find it interesting. Check the link in the description. Takes 2 seconds. You'll thank yourself next Saturday. Now, back to the video.]
Chen Zamir
26:26
You know, I've seen so many risk teams. And you know, obviously I've led risk teams. And it's always like risk teams always have so many ideas of what can be done and so little budget and resources, because risk usually doesn't grow the business. You touched a very interesting motivation lever that was right there. And I never kind of like articulated it to myself. So I I yeah I love it. Um let's talk a bit about, so okay so we talked you know generally about activation versus enablement and kind of like how to get the the team there. When you look at teams today, and you look at the journey, and where they are on that journey, like how do you see that. What are the like common steps that you see teams go through when it comes to AI enablement?
Brian Davis
27:21
I know we were just talking about this. And I gave you a similar answer a couple weeks ago. But even from the conversation, so as like I kick off Safeguard, I talked to up until the event, I'll talk to about 200, 300 practitioners, senior leaders who are thinking about this. And I talked to everybody from anti- AI to I don't want to imagine life without AI. So I see the full spectrum of all of this. And how I, when you asked me this as we're prepping for this call right before I hop on recording, I kind of changed my answer to you. Like it's still similar, and I still got five stages, and I'm going to lay them out, and then you kind of like poke at which one you want to explore. We can explore all of them one by one. But um as I'm kind of talking to all these people and getting a better understanding of where they sit, I kind of see it in five buckets. One is AI blocked. I just can't do it. It could and this is like a honestly a catch-all category. It could be we don't have the data, we don't have the tools, we don't have the supporting company. Uh just for whatever reason, we just can't start it. They could be aware, they could be unaware. So it doesn't really segment between that and just for a multitude of reasons, just can't use AI. AI aware. So this will be companies who are a little bit earlier in their journey. Of they're learning, maybe experimenting, maybe thinking about getting tooling or deciding between tooling, haven't really started using it yet. Um really haven't rolled it out across the company, but are learning and educating themselves of we should probably be using AI. How could we be using it? They're going to be starting to answer some of those questions, pre-tinkering. And then AI enabled. So I think about this on a company level. Honestly, it can roll out to like a team level too. But I think about it on a company level. Where AI is not really everywhere. Not everyone's really using it. They might be having a little bit of a challenge with adoption, or usage and enablement. But they are using AI somewhere in a specific use case across the company. So it is being used. It might be a small use case. It could be a large use case, but is being used somewhere specific, but not everywhere. AI first. I think this is where most people, especially companies who are probably realistically older than 5 years want to be. AI first. So it's really kind of becoming core to product, core to decision-making, core to team structures, core to processes and programs. So I think this is where most people want to be. You're going to probably find most people today in AI aware or AI enabled for the most part. I think we've in the last 12 months moved a lot of people from AI blocked to AI aware and AI enabled. But I think most companies' goals today, just due to the structure and the tooling the legacy when companies are built, AI first is where I think a lot of people ultimately are setting their goals today. We want it core to everything. And then there's AI native. This is the company is built entirely around AI. Really, it's not many companies have like redone the entire plumbing to be entirely AI native. These are going to be typically more of your companies who have launched in the last couple years. Uh so they don't have as much legacy tooling, processes, people, products, onboarding, etc. So it's a little bit easier for them to build the entire company around AI. So those are the five parts of the journey that as we speak today, like if we speak in a month, I might have either another journey. Bucket them. Uh but as I answer this today, we're recording on September 1st. Can't believe it. We're already in September, but as of we're recording September 1st, this is where I sit today with um where company AI journeys are in the five stages.
Chen Zamir
31:29
And tell me, you mentioned that within these five stages, you perceive the market mostly to be and when I when we say the market, we mean fraud teams, right? And fraud organizations. Most fraud organizations sit somewhere between step two and step three, AR, AI aware or AI enabled. And would you would you think that the kind of like the normal fraud practitioner out there would answer the same? Would they also say about themselves that they are probably somewhere there and that most of the market is also there?
Brian Davis
32:09
It's weird. Um I just from conversations with people I'll see what they're saying, or how what they categorize themselves, and then what they explain is different. So and it goes in both ways. Some people are underestimating where they are and some people are overestimating where they are. I won't say everybody's overestimating. I won't say everyone's underestimating but people also aren't clear with this of like where they actually sit. So I think people this also goes into the learning of it. Of like what really is the adoption AI journey stages. What are the methodologies that should be transferred from company to company? Like there really isn't a true standard yet. So like I might categorize this as like this is where I see the five stages today. Someone else might say you're wrong. These are the stages simpler blah blah blah. Uh but the way that I think about it today is most companies, and this is I think different than from 12 months ago. I think a lot of people were AI blocked for a multitude of reasons, uh to use it. I'm seeing from conversations more people are falling in tier 2 and tier three. Probably a little bit heavier in tier 2 than tier three today. But I am feeling that there's more companies in the next like six, I won't even say 12 months, 6 to 9 months that will move up to that uh third tier. Just from like what their plans are. What they've been sharing with me and what they want to execute on. And if some of these companies can execute on their own road maps and get that delivered, that would bump them up from my perspective to that tier three. Uh but I think there's definitely a lot of learning, a lot of experimenting. The space and the tooling is moving so fast. Um if you follow any of the AI bubble, um there's a lot of people who stay focused as like are in the AI bubble. Like I am. I know I am. I know I'm pro, but I'll have conversations sometimes where someone will be like what is Claude? What is an agent? And I have to take a step back. Sometimes it's different. Sometimes it's just a conversation outside of the industry that aren't necessarily in tech. Uh but I think it's still cool that people are trying to understand. And just shows that the widespread variance of where people actually are today on the AI journey. And there are some companies that would surprise you that are higher. And there are some companies that would surprise you that are lower. And I think that also goes with like fraud protection as well. You'll learn about some companies being like, I would have sworn you have way more in place and way more mature on your fraud journey. Same thing goes with AI.
Chen Zamir
34:55
Yeah, it's interesting. Because I mean I I see it the same and differently. Meaning, and I don't know what you what you would think about it. And maybe I'm just you know a cynic. But the the way that I explain this uh this kind of like this this state where teams at the same time are um overconfident or overestimating and underestimating themselves. Is that on one hand when you would ask teams, um like are you adopting AI? Um most teams would would overestimate themselves. They would think they are AI enabled. Where in the best case maybe they are AI activated. Um, but I think that when you would ask them, not necessarily straightforward, but when they would need to kind of like rate themselves against the market, and when where they are in this journey compared to the rest of the market, I think many teams experience I don't know if FOMO or an imposer syndrome are the correct uh.
Brian Davis
36:06
I think FOMO appropriate here. Cuz if you just log on to any social media to try to read anything, one, there are people that are well beyond everybody. And then two, there are people who just take those and then rewrite it even though they don't do anything in it. So then there's more of an echo chamber than people actually doing it. But if you're trying to research and learn that echo chamber becomes dangerous. Because it's like, am I behind? Am I so far behind? And if you're caring about this today, you are ahead. And you're doing the right things to educate and learn, how can I better use this for my own personal career to put myself in a better position in 12 months from now? So I am a top performer in my company and in the industry. So if you're thinking about this, if you're researching and trying learning, if it's hard. Honestly, this stuff is confusing to everybody unless you're actively building it. Like inhouse at one of the frontier labs. Everybody else is on a learning journey of understanding this and how to best use it. I will admit my simplicity sometimes. My best way to learn it is say, "Hey, Mr. LLM, Mrs. LLM of your choice. How can I do this? This is my goal. I don't know if this is possible.” And I like to use voice dictation because sometimes I'll rant and ramble. I'll give it a lot more context. And I'll just sit back, and I'll just kind of like ramble. And say, "These are my goals. This is what I'm trying to do. This is like the data I have. Am I missing something here? Can we do this? What's the best way to do it?" And then I'll go back and forth a little bit with my LLM. And then ultimately from that I can come to a decision of, is this possible or not? And then if it is possible I can probably build it with my LLM together uh of using it. And then this is where the different types you have like Rockbot, uh more of like a personal agent. You could have co-work or ChatGPT work. Where it's like a little bit more non-technical for more everyday users to like implement and automate more in your work. And then you have Codex and Claude. These are kind of like your two main platforms from OpenAI and Anthropic, of like what you're more comfortable building, being a little bit more technical. But as a non-technical person, just being able to use either voice dictation or just typing out, and using your native language to ultimately achieve the outcomes that you're trying to accomplish.
Chen Zamir
38:42
So, okay. So, so far we've been kind of like high level bird's eye view of uh kind of like the industry, and the state, and kind of like the state of AI adoption, and so on. Let's get a bit more kind of like nitty-gritty and really give some uh um concrete examples of how all of this would look like specifically in a fraud team. Because a lot of what you said I'm guessing that you know would apply to to any any kind of team. Yeah. And you know starting at the bottom, like how would you know, like how like going through each of these stages. How do you recognize and self kind of like identify that this is where you are? What is probably blocking you from going over to the next stage? And what is likely, or what are the likely things that you probably need to do or think about to solve these blockers? And I'm guessing that you've seen you know like live examples or real examples of on of all of these stages. So I'm guessing that you can, without naming names, give some some concrete examples.
Brian Davis
39:51
Yeah I think um the way, the blockers. Let's talk blockers. They're people, policies, data. If you don't have data, not going to really be able to do much. Policies, if you aren't clear what you can or can't do, people, especially in our industry, tend to be a little bit more cautious on taking risk. Naturally, I think that's a good mentality. But if you aren't clear, people individuals are typically going to be a little bit more cautious in pushing boundaries of what they can or can't do. That won't be everybody, but it'll probably be just due to the nature, and behaviors, and psychological factors in the industry we work in. Will be safer. And then people. People goes into the executive setting the tone. People goes into people not wanting to do it. People go into just being afraid to show off what they're doing, to get feedback. So those are going to be your your blockers, ultimately to all of that. Uh and then all that goes into tooling and budget. Uh but tooling and budget. Tooling can kind of go into data. Budget goes into people assigning the budget and winning them over. So I kind of lump those into the data. Uh because data could be tooling in itself. It could be buying data. It could be your own data. It could be some hybrid of the two. Uh so those are going to be your obstacles. And each one of those has its own way to ultimately kind of like overcome those. I think we've kind of hit a little bit on those already earlier. So, I'm not going to go deeper into that. Uh once you get past the obstacles, then that will give you a little bit more of an understanding what you can or can't do with building, what tools you have. It could be a third party vendor. It could be the LLM directly. It could be something self-hosted by your company. There could be that once we overcome those obstacles, you'll have a clear understanding what you can and can't do and what tools you're actually going to be using for it. Um, for this exercise and me talking through, I'm going to be talking about the LLM uh specifically again platform agnostic. This can be done across any of the platforms, but platform agnostic. The mentality. Now it's mentality. After all this now, it's mentality of the individual could kind of go into blocker but I put this in a different spot. A lot of people think when, especially in our space, when conversations, when I want to do something, building agents, I I should go big. Transaction monitoring, KYC, all wonderful use cases. All ripe in opportunity. So the mentality of this is my biggest core of my work, we should start here, that's going to be more expensive. It's going to be a lot more risky to roll it out in production with customer facing. It's going to take probably additional tooling, something you're probably not going to be able to build in house right away, with um the tools where you have. So I think it for me that's like where you work up into. Or if you already have a solution provider that you have an agreement with. They might have functionality that you can work with them. Either from like a customer advisory spot of like we're your customer. We want to do this. We don't want to build this in house. We're already buying and paying for you. We think you could do this. Could we build this together? A lot of companies are going to probably take on that conversation. It's mutually beneficial at that point. Or there are vendors who have already rolled out agents or sell agents. So then like it's a build versus buy at that point. Do we need to build it?
Chen Zamir
43:28
Yeah. No, I'm sorry to interrupt. It's just so interesting that you're raising this point. Because I think in reality what happens is exactly the opposite, right? Meaning the like this the teams that are early in the journey, and are just starting out with AI, are one very excited. And two, they are unaware of the complexities. And then what happens is that they think they can do that. They think they can build. They think they don't need to buy. And actually this like we see a lot of teams that are just, you know, uh you know just starting this kind of like build journey. And invest a lot of effort and budget into it. And then fail and then you know it kind of like reflect uh on the entire kind of like AI adoption journey. Oh we we kind of like we failed. While the journey itself is not the issue. It's just the strategy.
Brian Davis
44:26
Yeah. Now, I think you know where I'm going with this one. And the it's unsexy and it's boring, but it's been a major component of my career of everywhere I go. Is I'm assessing the entire customer journey. I'm assessing the different workflows. I'm assessing the different processes, internal and external. And that workflow design, nothing to do with AI, is ultimately giving me indicators and bets for where can we actually use and adopt AI. And the internal external piece is where I start to kind of like help people communicate like where do I start? You could start anywhere. So this is just my recommendation because I want people to build up AI literacy and AI confidence. And then kind of like team camaraderie around it. So those are the three pillars I'm honestly working for. So if you start big, you might get people a little bit frustrated. You're gonna it's going to take longer to see like outputs. And you're going to probably create a little fear with your team of, oh if we do agents here am I going to get fired? And then that's people you're going to start to pull away. So where I usually recommend, again those three little pieces, of you you've done the the unsexy non AI, you could use AI honestly to do the workflow of help insert my URL. Help me walk through what onboarding look like. What fraud risk do I have? You don't have to put in anything at that point that is unique. And keep it all external. And just get a sense of what would use, that as consultant, what would a consultant kind of assess our business risk profile and our different workflows? Help me prioritize the impact, blah blah blah. And you're kind of working through this logic of, okay, help me map my company. Help me map the work to our risks. And then from there, now you have lots of different pockets of opportunities for AI. It could be a report, it could be an alert, it could be an escalation, it could be rule assessments, it could be surface area. The simplest one I do is, and again, I have a little bit more uh clarity in what I can and can't do in my own role. Uh, and this isn't fraud specific, but it helps me show up better, one for my family, show up better, two, for my company. I like a lot of people, you get out of bed, you open up your phone, you check what, who texted me, what emails, what's the weather. Before I know it, I'm on my phone for way too long. So, every morning it sends me a message at 7:30 a.m. uh local time, and it will send me what my calendar is, a little note, and then the weather, and I'm just in one. I don't have to open up my uh multiple tabs. I don't even have to go into my phone. I can look at it, phone away. I have kids. It allows me to be more present, get out of bed a little bit faster, help with breakfast, help with a lot of different things in my house. But, it allows me to be prepared of what does my day look like? Is there anything outstanding? Are there any emails or Slack messages that are higher priority? Or I owe someone a response. So it gives me little insights to that so I don't drop the ball on other aspects. I build a lot of little things like that. Um so that's one. And then I use it for like a lot of pattern analysis of I might have a hunter hypothesis. So I'll take the data again I have control of putting and clarity that there is some data that I can put into my LLM. Uh so I can use that specific data to look for patterns or trends for the work that I do. And then either create into visualizations for other people to use or for my own reports. I can upload specific um data, and put like one create a one-time internal local cue for myself to work through of the work that I need to do. And then it's just dead after that. It's not it's self-hosted on my computer. There is no uh URL. It's just only on my computer, but it helps me do whatever that mini project is. That I don't need to build custom software around, but helps me stay focused and productive. So, pattern analysis is always a good one to start with. Internal tooling or reports or executive reports or your power like presentations, building out use cases, aligning uh OKRs. Your OKRs to other companies OKRs. Fraud team usually combats a lot with sales and marketing and product. So, help me better align or create my OKRs that I can be an ally to marketing. So I can be an ally to sales. Not uh competitive or seen as a constant blocker to sales in marketing. So I can not just be here's Brian to tell us no, fraud's just a cost center. So how can I work on my own presence? All right, I'll pause there. I think you got the wheel spinning. I just rapid fired a couple different use cases. We could also talk about uh teams doing it for like chargeback representation, and building their own compelling evidence, or sorting through all of these reason codes, and getting clarity around what are the true buckets based on the data we have, and having better visibility faster visibility into some of your chargeback and fraud trends as well.
Chen Zamir
49:38
So I think there's an interesting distinction to make here. And I'm wondering like whether like did we, are we still at AI aware? Which is phase two. Or are we already at phase three? Which is AI enabled, in your mind.
Brian Davis
49:52
If you're doing that, that's AI enabled. Those are specific use cases. If you're using that, and you have those running on a recurring basis, not a one-time little project, if you're doing that on a recurring basis. Some of these could be monthly, some of them could be weekly, something that could be daily. Again, I don't need to do, when I was working in my roles, I didn't need do chargeback reporting every day. I needed to be aware of chargeback trends daily, but I did not need to report out unless anything significant on a daily basis. So, I could build in my report creation based on my data collection, and help me find better segments, and take feedback from other times, so I can create a easier to comprehend uh report. So if you're doing this on a recurring basis, that would be for me, enable. You're using it for a specific use case, on an ongoing basis.
Chen Zamir
50:45
I think I'm starting to to grasp uh how you see this model. And I and I want to kind of like you know like uh uh play it back to you and see if I got it right. So AI blocked, first phase. Okay, you're not doing anything with AI, or at least not anything of importance. Uh maybe you're taking
Brian Davis
51:03
They can’t, for whatever reason. I think you just can't. You don't have the tools, you don't have the comfort to try them, you haven't tried them, you don't have the data to do anything. Uh you just can't. And there are people who are still in this uh bucket. They might want to. They might not know even about LLMs. Those aren't really um, don't really matter for this bucket. Just aware, or unaware, just can't do.
Chen Zamir
51:30
Yeah. Then we get to the two two later stages. Which is AI aware, phase two. And AI enabled, phase three. And if I if I got you correct, the difference between how you use AI would be as follows. In phase 2 AI aware, you're using AI, but you're not using AI for uh like fraud specific uh tasks. You're using it for productivity. You're using it to just be a more efficient employee. And that doesn't really matter if you're a fraud analyst, or a developer, or a marketing specialist or whatever. While in AI enabled, you're already starting to tackle the first actual kind of like use cases for automation. This is not no longer productivity. This is like really like taking processes and automating them. Uh like fraud processes and automating them. Whether these are reports or these are whatever labeling or classification work or whatever. Um is that is that how I should look at it or did I miss it?
Brian Davis
52:44
I think pretty close. The depending on productivity, could make you higher leverage and focus on other work. I think it it comes for me the consistency. So productivity could still be user case. Especially if you're a program manager, a people manager, depending on your work. The admin work, and that type of work, can help you focus on higher leverage work. Um AI aware, for me, it would be like they can. They just haven't. Or they've did it once and haven't done it. They might do it ever so often, but it's not the same task.
Chen Zamir
53:19
Got it.
Brian Davis
53:20
More of like that phase. Of maybe start, stop, start, stop. I'm not really doing anything. I want to learn how to be better. I'm trying to consume information. I'm trying to learn what other people are doing. But we really haven't consistently done anything with ourselves. So, productivity could be like the start stop. I did this one thing, I don't really use it. I tried it, but we're not doing anything. Uh if you build out the use case specifically, could be admin oriented, it could be escalation oriented, it could be research oriented, it could be assessment oriented, it could be transaction monitoring, it could be like detection prevention and monitoring uh oriented. Those are use cases. So if you're now tackling one of those, but not all of those, that would be enabled. Like we are using it for a use case. We're using it consistently and it's helping us. We are going to continue using it like this. That would be enabled. We're solving a specific use case. We're not solving maybe all of our use cases. Uh and then uh, that's now as you start to compound more and more onto that. And now the mentality goes into like our decision making, our escalations, our when it becomes more core to all of that, that's now when you would get to the next tier from there. And then AI native was like everybody's, like everything is built on the foundation of AI. Not necessarily pre AI.
Chen Zamir
54:54
So at which point, at which of these stages, like I'm thinking there's usually with uh with technology that you can do two things, at least when we're speaking about automation. One you can take an existing process and automate it. I'm doing, uh I don't know, like whatever. I I'm I'm reporting something weekly, now I can do it daily, and I also don't need to spend time. Okay, so that's straightforward automation. But there's also another layer. Which is really to enable completely new processes, completely new capabilities, completely new efforts. Uh the the the use case that I always like to harp on, is you know like uh fraud operations teams that don't necessarily have data analytics, uh skill set or experience. And you can use uh and and can use AI to do data analysis, write rules and so on. Would that be in AI first or is that AI native?
Brian Davis
56:06
Not AI native if everything's not built on it. Uh if you're kind of like building into that functionality, for me that would be AI first. AI native would be you've redone your entire function. And everything operates within AI, uh foundationally and into all of your processes. Decision- making, everything sort like that. AI first, that would be a good example of AI first. Assuming, well if you get to that point you've probably also have done other tasks. Have built out other functionality, other automations, built out agents or research. So my assumption is to get to that point you've probably done other work. So that would for me bump you up to AI first. Now you're thinking about, okay, we've opened up more time, more productivity, more output, higher leverage work. Now we're looking at pushing our team forward. Now that would be for me classification and AI first. I'd imagine most teams aren't going to start with that mentality, of like that specific example. So I'm going also on the assumption that that's not the only thing they've done. That's the only thing they've done. Then that would be enabled. Like you're probably going to make good progress. It is a specific use case, one specific use case. You're not really adopting and evolving it through your entire function. But if you're starting there, you're probably going to speed up pretty quickly through other aspects. But in that snapshot in time, kind of just depends. So my assumptions if you're doing that, you've probably have done other things, and now you're kind of pushing your team forward. You have more comfort, confidence to create new systems, create new processes, use new data or data you have in different ways. You're probably moving along in that journey. Based on kind of like assumptions I would make just from conversations I have and how most of the markets kind of think about it. Most people start with what's what's familiar or what do I hate doing? Most people start there. So just due to normal human behavior, my assumptions that I'm making based on your example, I would put that into first.
Chen Zamir
58:14
So while we're on this point, maybe my last question. So we just went through the entire kind of like uh uh transformation journey and the five steps that you recognize uh different teams going through today. Would you see the same journey um as one who is relevant to individuals? And specifically not like human beings in general, but specifically to fraud fighters. Should I as a fraud fighter look at my journey with AI in the same terms and in with the same phases?
Brian Davis
58:53
Probably similar phases. Uh probably label it differently. Uh realistically, you'd probably label it slightly different, but the overall sentiment would be um probably pretty similar within the usage, adoption, understanding. You might go a little bit closer instead of like AI blocked, AI unaware, aware, you might go through a little bit more of that traditional of like product discovery phase, would probably be closer to that journey. And kind of like progress through there. The only thing I would add to all of that is, control aspect of like what do you actually have control with as an individual? And what do you want within your own career? And is this a tool that can help get you there? If it is and you believe in that, I'm pro. Again, I'm my biases is yes, which is why I spend and dedicate time weekly if not daily on trying to do something. Um, my challenge for myself, I use a specific LLM. I know what I can and can't do on it. Uh, my goal is to never leave that LLM. So, why when I have to leave that LLM, why do I need to? Is there uh data I'm missing that's not connected? Is something broken? It doesn't quite work. That manual is still better. Uh, is there something that just I can't get the data into it? So for me, when I'm thinking about my own workflow design, my own system design, my personal challenge to myself as like a mental checkpoint is why am I leaving this right now? And do I have to? And sometimes the answer is yes. But when the answer is no, then that's change management, that's behavior management, or that's getting access to data. So how can I improve my process so I can work within one tab and I can create moments of my own decision. It's not necessarily uh finding ways to offload to agents and automation LLMs. It's where should I be spending my mental load, my human intelligence and why do I have to leave another tool to get access to making a decision. So that's my own personal challenge. So controlwise always go on the assumption the company is not going to give you enough time to educate. Carve out an hour a week, 3 hours a week, whatever you can do consistently and just get into the tool. If you can't, if you know you can't do things or can't upload data, fine. Work on a use case of designing a report of what it could look like. Use dummy data, use consulting of like assess my fraud risk risk and help me gauge what AI could be here. Like there are still ways you can work these tools to get comfortable like AI literacy, AI confident in the tooling. So as you play more, it's going to give you more ideas. Can ask it give me ideas for other ways I could be using you. I just used Grockbot as one that's kind of getting a little bit of internet momentum right now. And when I first got on it, I was like, these are my goals. This is what I've struggled with other tools. Is this something you can do? Help me understand what agents that is a way for me to actually be able to build this. And then through a little bit of back and forth, a little bit of like I just have it interview me. It then repeats back and says, "Here's what I think you want to do. Is this correct? Here's what I would recommend." And then we're just kind of going through that process. And then it spun up agents to ultimately solve the goal I was trying to accomplish. So if you don't know or if you just are stuck in this mentality of like I don't know how to push myself yet, just ask it. Ask it to challenge you. Ask it to push you, ask it to roast you. Like tell it to do that. It won't necessarily do it on its own, but if you prompt it to do it, then it'll allow you. And I also always recommend uh doing voice dictation. You're going to say more, you're going to say it faster. It doesn't necessarily need to be a perfect prompt. Now, as models advanced, it doesn't necessarily need to follow specific prompt structure guidelines. Yes, it can help in some scenarios. In some other scenarios, not necessarily. So, my personal preference in those is just hold down, I have a hotkey. Hold down the hotkey, talk for two, three minutes, and say, "Help me figure out this mess. What am I actually trying to do?"
Chen Zamir
63:29
100%. Um, and by the way, I'll mention since you didn't. That Brian over the, you know, better part of a year or two now has, you know, posted uh, more than a few posts with several examples of prompts to do exactly the kind of things that he just talked about. Like how to do, you know, like threat assessment of your business, how to um, like basically ask questions that you would ask a consultant. And and I think these can be like really great starting points to to get into not just using AI, but also to use AI in the context of uh fraud prevention or fraud fighting. Uh so yeah check check uh check out Brian's uh LinkedIn. Uh he has he has these posts peppered out throughout the the last two years I think. Um
Brian Davis
64:21
Also we're going on five years now. AI specific, definitely not five years but uh I've been writing now. Yeah. Yeah, those um started for myself probably honestly about two years is probably accurate. Like really trying to challenge myself to learn it. I saw this as, I see this is where work is going, the future of work is going. I don't necessarily know will this, what we're experiencing today, be what it's like in 12 months, 24 months. I don't know. But I do challenge myself to find as much time weekly if not daily to understand where the market's kind of going. And then understand what does that mean for my current role. And then what are other aspects or skills that I can use this tool to teach me. Again for like uh one that came up in conversation, this can be my last kind of example today, is I will admittedly I know SQL is something that has always been a job wreck for my entire career. I stink at SQL. I can reverse engineer it. My goal was always, I know I need probably six well-written SQL queries for me to do my job on an ongoing basis. Now, I need to know how to reverse engineer those. So, I would have people write those for me. Build my allies, get help, have them write me better queries that I know I just can't do. And then learn how to reverse engineer them. Well, now with AI, I don't need to have that ask. I don't need to waste my own human capital internally for these low-level ask for other people that I can get up and running with these queries that 12 months ago probably AI couldn't have done this well. I still couldn't do it this well. I can select all with the best of them. But really writing a solid query, not that that was never my skill. And I was always honest in my interviews. And some companies opted out with me. But the companies that don't necessarily understand how you can make an impact, especially as a first fraud hire. Like SQL wasn't necessarily 80% of my role. I could get by by knowing how to reverse engineer. AI now allows me to write way better capable queries and then teaches me what they're doing. So now I have a better understanding of what am I actually doing, why am I doing this and what are different aspects of this query that I can alter or do I even need to alter? I can just go back to the LLM of choice. But for me, my way that I feel like I'm at least stimulating my brain and learning is I will ask it to reverse engineer or teach me what it's done versus just entirely offloading it. Sometimes I can't. Sometimes I'm just run it. I need it. Let's get going. I don't have time.
Chen Zamir
67:18
Yeah. Choose your battles. Yeah.
Brian Davis
67:19
But um. I do this is one way of like people, one argument with AI anti AI work space is your intelligence is going to go down because you're going to offload all the thinking. For me it's about designing the workflows understanding where my brain is most used where it's most needed. And focus my mental loading capacity at those points. There's different skills that I'll learn and develop all along the way but this is a tool to speed up my own personal learning for different skills that I probably would have never taken the time to learn, honestly.
Chen Zamir
67:53
Yeah, I agree. By the way, I don't take the time to memorize phone numbers anymore since uh you know, like smartphones and so on. I'm not stupider because of it. But yeah, anyway, uh Brian, this has been fantastic. I want to kind of like go over some of the takeaways that we went through in this conversation kind of like quickly. Uh we started talking about uh AI activation versus AI enablement. I really like your take and I think I kind kind of like captured four points that you mention that the the four things that you want to have in place to not only say that you have AI. Uh uh you know like budgeted and and and part of the company, but it is actually enable it's actually like bringing uh value. You mentioned four points policies um in terms of what how what do you connect to how do you use it and so on. Training people you actually want to train your people to use it how to use it when to use it and so on. Thirdly, uh you talked about budget. Both in terms of like incentivization but also in terms of time. I mean you know the the the the the resources in the end the time that you have to invest in AI, either it comes on the expense of your personal time or the company needs to budget time for it. And fourthly, you talked about uh the process uh and making sure that this is not just, you know, like um an announcement or a hackathon or a one-time kind of like thing. You know, we have like the AI month, but there are actually processes in place where we are running this iteratively and with feedback loops and all of these other terms that we fraud fighters love very much. We talked a bit about how to instill motivations. Um, on the kind of like team side, we I I really like the fact that you mentioned how it's the leadership's uh job. And this means that a they need to kind of like be first. They need to be front line with a adoption. Uh they cannot just say, "Okay, you get the tokens. Now make something out of that.” You need to like really show and put yourself out there. Um, and the second thing is that this is, you said it so many times throughout the conversation. I cannot agree uh enough. This is uh a a change management process. This is a transformation process and this must be a very carefully managed process. Um, which I highly highly agree. On the leadership side, we also talked about the fact that AI is, especially for fraud teams or for risk teams, AI reduces the, or can reduce can reduce, the historical dependency that risk teams always had when it comes to external resources. Uh starting with engineering but not only engineering and being able to do more uh with less. It's uh you know this uh uh it's like a gag reflex sentence uh at this point in time but it is true uh at least when you frame it like that we talked a bit about the, not a bit we talked a lot, about the journey and how you see the journey and the five steps in the journey. AI blocked where either you're unaware of AI or you're aware but you're like you're physically blocked and you're not doing anything with AI yet. Then you have AI aware where you are starting to experiment with AI with very simple use cases. But mainly I liked your take you doing it inconsistently. Maybe you've done it once and you didn't come back to that. Uh as we talked about before uh you're not enabled yet. Uh and maybe you're doing it half-hazardly. Sometimes you do. Sometimes you opt to just do it the old fashioned way because it's faster. Uh at least right now. We, the next phase would be AI enabled where you're starting to do things consistently. Uh more and more uh use cases maybe especially around automating processes that already existed. The fourth stage stage would be AI first. Where not only that you're doing it consistently and across multiple use cases, but you're also starting to build new capabilities and you're pushing your team to do more. This is not only do better. This is not only to do faster. This is not only to do uh with less. This is really about doing new things that AI enables. And lastly AI native. Where the transformation has been completed. At this stage, this is not only about what you're doing, but it is also how you are set up. How you are organized. It's how your team looks like, it's how your platform looks like, it's how you how your budget looks like and so on. Love it. And you did mention one thing and we kind of like we went through all the phases but I think that uh is a very very sharp point. And it's probably it's probably somewhere between phase two and phase three. And that is to start small. And a lot of teams I think maybe are excited, maybe they have this FOMO, maybe they have the uh imposer syndrome. But basically they feel this pressure that they need to build in house, and they need to go big, and they end up biting more than they can chew. Uh, and I've seen it plenty of times and if I've seen it plenty of times, you speak to more practitioners about this topic than I am, then I'm sure you've seen it dozens of times. Um, and I I don't know if it's like a build or buy uh question necessarily, but it is definitely like how do you start, and how to do pilots, and how to do proof of concept and so on. Brian, it's as always it's been a pleasure uh just hanging out for an hour and a bit. Uh and also talk about my favorite topic AI and fraud.
Brian Davis
74:08
I think it's, obviously everybody's talking about it, everybody wants to learn, but things like this is what's actually going to move our industry forward. Actually having conversations, actually letting people understand that there is this whole journey to AI adoption and using and being more comfortable and literate within AI. Just because you read something on LinkedIn, just because someone gives an example, just because you go on other social media platforms and look at them, doesn't mean you're really far behind. If you care about this stuff and you're putting in an effort to learn how you can better set yourself up and your company up and you're taking control in your own self-education, it's a wonderful time uh for you to actually speed up where you are on that journey where your own comfort confidence is. When you look at the general market, everybody underestimates where they are. In the general world, if you're caring about this, you're probably in the top 10% of the world. Uh just think about that within numbers of what you actually where you actually put yourself within the tech bubble. I understand I understand we hear a lot of things but in the general world general population you are ahead of way more people than you think and realize just keep going keep learning and find new ways to make the future work easier.
Chen Zamir
75:27
Absolutely. I uh I think the echo chamber point is so sharp. I uh I think fraud fighters might uh might think that in order to be a fraud fighter you need to be bald, bearded, and wear black. Uh but you don't. You don't.
Brian Davis
75:40
Uh, it’s a good start. I will say I’m biased again here, but it’s a good start.
Chen Zamir
75:45
Yeah. Uh Brian, thank you very much. And I want also to thank our audience. And I'll see you next Saturday.
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