Turning AI Ambition Into Real Business Value

[00:00:04] How do businesses move beyond AI experiments and start creating real value? Well, my guest today is Tom Alexander. He's the Head of Innovation and Transformation at Cross Country Consulting. And Tom is someone that works closely with executive teams and CFOs who are all feeling the pressure to adopt AI,

[00:00:27] but often unsure where to begin, what to prioritise, or why their early efforts are falling short. So today, Tom will talk about why so many AI projects stall, why the real issue is often transformation rather than technology, and why leaders need to stop chasing isolated tools, start thinking about AI as a part of a wider business programme.

[00:00:52] And we'll also discuss accountability, ownership, and what it really takes to connect strategy, operations, technology, risk, governance, all of these things in a way that delivers measurable results. So if you or your business are trying to cut through the noise and understand how to make AI work inside a real business, this episode is for you. So enough from me. Let's get Tom onto the podcast now.

[00:01:23] So thank you for joining me on the podcast today. Can you tell everyone listening a little about who you are and what you do? Neil, thanks for having me. So my name is Tom Alexander. I'm the Head of AI Innovation and Transformation at Cross Country Consulting. If you don't know Cross Country, it's a big four boutique focused on serving the office of the CFO, the office of the CIO. And we're seeing that strategic partnership kind of continue on and play a big role with AI.

[00:01:52] So my role is that I help clients cut through the noise and get on a practical path to value creation, solving real business problems, and doing that with AI and many other ways as well. It is not the only solution, of course. And one of the things that I do and the team that I have is that we stood up in Innovation Lab. So I lead that Innovation Lab. And what we're trying to do is stay on top of the latest and greatest, keep our leadership team informed,

[00:02:20] train our consultants, our clients, and help them drive execution. And I do want to shout out our amazing lab team out there if they're listening. And we really just pride ourselves, Neil, on strategy through execution. So that's been a very successful endeavor. We'll talk a little bit more about that as we go. And a second shout out to that lab team there. And one of the things that I love about what you said was a practical path, solving real problems.

[00:02:47] And I think one of the reasons that is so important is over the last couple of years, there's been so much talk around stalling AI projects, caught in AI purgatory, struggling to get things into live, and lack of ROI from expensive AI projects. So I'm curious, from what you're seeing, what are the most common challenges that executives and CFOs and organizations at large are facing when adopting AI in their business? And what leads organizations to stall?

[00:03:16] Is it even the tech or is it the culture that they've not thought about? What are you seeing here? Neil, I love where you're going there and what you're calling out. So this is not a new problem. I'm sure there are plenty of folks on the phone that have tried to implement a new system. A new ERP tried to run a large transformation project, tried to implement RPA 10, 15 years ago. And we know that 70% of those kind of transformations fail, period.

[00:03:45] And there have been plenty of studies here recently about, you know, we watched the office of the CFO very closely and how finance progress has stalled and why that is. The MIT study from last summer, which seems just ages ago at this point, 95% aren't getting ROI. All of those reports. And it's really just about core transformation, principles, culture, change management. Do you have the right executive steering committee overseeing this and sponsorship? Do you have the right investments? Do you have the right accountability?

[00:04:14] It is core transformation that's at the center of all of this. And engaging the human is so important in that. Is there a carrot to come up with a big idea? Is there, you know, is there executive leadership who truly understands the technology? So I think there's some of that as well. I would also tell you that there's really a lack of creativity.

[00:04:39] If you don't understand the solutions, then you can't be as creative with how you're solving for some of those outcomes. And one of the interesting tidbits that we like to share with our clients, actually, I'll give Gartner a lot of credit for this, is they would say, don't necessarily have someone from the existing process come try to think through how the AI-based solution should work.

[00:05:03] Maybe take a blank sheet of paper and get some new people involved because sometimes you're so indexed in how this used to work or the manual nature of how you do it today that you can't think creatively about how to use the technology. So creativity is something that we see in a big way there. And then I would just say, you know, AI runs through your processes, through your business, in and around your data sets.

[00:05:29] It is not kind of rote or single-dimensional at all. So how are you driving one kind of interconnected strategy through one funnel of prioritization and tied to your strategic plan and making sure you're not solving these problems and solving with AI in functional silos? And I think for the last three years, many leaders have almost been spellbound by the shiny new AI solutions that they keep seeing.

[00:05:58] But I think we are getting back to problem-first mindset, which is much welcomed, I think. And to give every person listening somewhat of a valuable takeaway, how can they as leaders take a step back and focus on more the business process, the problem where automation and AI can create an impact and get back to that business problem first? But what should they be doing?

[00:06:22] Yeah, a couple of different things to kind of find the right areas to focus on. So I would go back to how well do these businesses track operational excellence KPIs? Do they have internal and external benchmarks that they're tracking? External benchmarks being what is the competition doing? So you're looking at business intelligence and where are you falling behind?

[00:06:48] Where are you seeing maybe some of its marketing, but where are you seeing some of the competition pull ahead with solutions? What's on their website even could be a huge differentiator for someone choosing them over you. So it's about KPIs. It's about measurement. It's about operational costs you might be carrying because your core systems aren't doing the work for you or your team prefers to do the work in a manual way or they don't know any other way to do that.

[00:07:16] And you don't end up having real-time visibility to category spend or the optimal license cost to pay because you haven't thought about what the KPIs are, what the benchmarks are, or what should you pay for something like that. So where are you delivering on time? Where are you falling behind the competition? Those are the things you should be thinking about for identifying where the use cases are.

[00:07:42] And oh, by the way, those benchmarks that I'm mentioning, just take those, throw them out the window, and just make sure that you're incorporating a 30% to 50% increase in what those benchmarks should be. Because I am feeling the speed of work just pick up every day at this point. Clients are asking for answers more quickly than ever before, and everything's moving faster.

[00:08:08] So the benchmarks will change as well, and the cost that you'll spend on your infrastructure will change as a result. Yeah, I completely agree. I was talking to a guest recently, and they were also saying that focus on goal one, because very often it's easy to suddenly start having a little peek at goal number two, and you get completely distracted from goal number one.

[00:08:29] But when I was doing a little research on cross-country consulting, one of the things that stood out to me there is you see AI as an enterprise program rather than a point solution. Tell me more about that mindset and what you advise your customers. Yeah, Neil, I've got so many examples of that. Maybe just to tell it in the context of a client story. Yeah.

[00:08:54] So lead to cash is an end-to-end process that we look at very deeply. We talk to clients about the revenue cycle, and it's tied directly to their strategy. So competitive advantage and growth, KPIs, goals to hit. What is slowing clients down? Is your pricing competitive as part of that process? Where aren't you potentially hitting sales targets? Where are customers churning, and you're losing those customers, and why is that?

[00:09:23] And maybe even within that process is cash flowing to you in the way that you would expect. Are you getting paid for the services and what you expect based on your billings and collections? So the story is working with a software company. We were seeing a bunch of customers churning from the software company. So we were engaged to kind of think through this issue with the leadership team.

[00:09:48] How could we solve this, and how could we potentially use AI to help us with that solution? So what we found out was that whole leadership team was meeting on a biweekly basis to kind of get underneath this problem, going customer by customer, talking about what the facts were, and then going back to their respective functions and businesses to try to, you know, fact find it and put a plan in place. And what we told them was, you know what?

[00:10:16] An agent needs to be running this meeting that you're having. An agent that is steeped in data, has the insights, points you in the right direction, tells you the next 10 clients you're going to lose based on those trends, needs to be sitting in the middle of that meeting. And one of the ways we solved this problem, and it's kind of multifaceted, and this kind of gets back to running through the enterprise.

[00:10:42] We came up with a solution of a group of agents that one that's monitoring customer sentiment out there on the software. What are the pain points and software features that are showing up where people may be dissatisfied that they have not added into the mix? This will trigger sales to go attack the angle of what could they sell this new and different?

[00:11:07] Or if you haven't added that feature that we're talking about that a lot of other companies that aren't churning really like, they should go be selling that feature to clients and doubling down on that. We also tied this into insights that triggered, if you believe it, software development engineering. So where are competing tools out there in the market adding features that you're not responding to? So now the competition is getting ahead of you.

[00:11:33] So can we trigger software engineering to consider what some of those new features could be in and around the tool? And all of that kind of tied into then the analysis and the meeting at the leadership level, where we were providing the CEO insights and advanced analytics on what was happening out there. Some of the sentiment, some of the software features and angles there, all while giving trends, advanced analytics and details on the next clients that were going to be at risk

[00:12:03] so that he could lead the company and drive the strategy that way. So it's interesting in that you could definitely feel the data and AI touching all aspects. And after all this data got cleaned up, you can only imagine how much easier it made it on the FP&A team to kind of drive insights to the CEO, probably to close the books on the back of that. So it starts cascading the value that you see through the organization

[00:12:29] because you're positioning your data to be used in a way that you never have before. And to further bring to life that approach there and the kind of value that it brings, how does that tie into other everyday business activities from strategy, operations, technology, risk, governance, all those topics that we possibly don't talk about enough but are critical to successful outcomes?

[00:12:57] Yeah, I would say on the back of that story, I think you need to ask yourself, how are you designing your lead to cash process to produce the right data, right analytics, to drive competitive advantage across your enterprise? And you hit on a couple of different aspects of who would care about that and what that touches, right? And I think you need to ask more from your process and your people involved in them. We have kind of taken this strategy. I'll make it about our lab team for a second.

[00:13:27] So you talk about strategy, operations, technology, risk, and governance. We assembled our lab team with cross-service line professionals because we know that AI is touching on all of these things. So sitting within the lab, we pulled from our risk professionals, our tech-enabled accountants, transformation people, data strategists and experts, and even design thinkers. We have a design thinking function at Cross Country that helps people think about the user experience

[00:13:55] for using certain technologies and thinking about designing for the persona because what people use these solutions over the course of time. So our view somewhat is that engineering is being a little bit commoditized through AI and business acumen. Change management are going to be the differentiators, and you need to bring that cross-functional team to the table to solve for it. You need to think about all of those aspects you named to get that done.

[00:14:24] And this isn't new. When you talk about strategy, operations, tech, risk, governance, these are all principles that you had to think about anyway when you were putting together your data strategy. And hopefully you've done that by now. But if you haven't, you're late to the game. And this is where a lot of companies are saying, hey, I made an investment three to five years ago in my data lake, my warehouse, my snowflake, my Databricks.

[00:14:50] And now they're building agents on top of that data strategy, and they're moving ahead. So data is a big component of that too. And those were things you should be thinking about when you're setting up your data strategy as well. Yeah, I completely agree with you. And I do wonder if many organizations almost lost sight of those fundamentals or at least some of them.

[00:15:11] And we now find ourselves adding into a world of agentic AI, agents, digital co-workers, and their ownership challenge in AI. Who's accountable and how to align with stakeholders, et cetera. What are you seeing there? Yeah, so ownership challenges are definitely a thing. And it gets back to where I started with transformation principles. So this all starts with your strategic plan.

[00:15:40] And you should have one funnel of strategy, transformation that you're looking to drive, investments that you're looking to make. And that needs to go through your executive team. And those investments is what you're tying to achieve your strategy, align your functions, and drive the return. And AI is driving huge returns or can drive huge returns if you set this up right. So that strategy should cascade down to your business, to your functions.

[00:16:09] And, you know, we would say, and we've spent some time on some of these executive committees as well as people got stood up. Bring a third party to provide their perspective on do you have, are you having the right conversation around one funnel and prioritization and getting that right out of the gate so that you get and are attacking the best outcomes? And you're not bringing kind of, you know, one use case from each silo or each function to the table and try to solve more broadly.

[00:16:37] Go after the big outcome so you can solve the rest of your project. So let's get back to ownership. You've got this kind of executive steering committee that is absolutely a must have. And, you know, orgs that do this well have CEO sponsorship. They have business-led ideas, CEOs sitting at the top. They're thinking at the executive level, what are the big ideas and big outcomes we need to drive to drive competitive advantage? And you need to send that through a transformation office.

[00:17:06] And this is where we like to have the CFO and CIO aligned to the transformation office because they're so deep in the business. They're so deep into the investments and tracking of that, how this will play into the existing data architecture and strategy. So don't break AI and the strategy around AI from your transformation investments that you're making because you need to think about it holistically there. And I would say, you know, our engagement approach brings this together.

[00:17:35] We like to bring, you know, after rapid discovery where, you know, we kind of go through use case or interconnected use cases to try to get to an outcome. We like to bring everyone together in a workshop. And we bring executives. We bring technology representatives. We bring, you know, folks from the business and the functions where the use case is touching. And it's designed to get alignment on those stakeholders. Who owns this?

[00:18:01] What's the RACI chart on the roadmap where we're going to implement this and then monitor going forward? Who has access to the data that this will produce? This is how you're going to scale your program. You need to stack hands and move forward together with prioritization and ownership and make sure you're attacking the right priorities. So we like to do that. We like to move fast and get folks into a hundred-day plan that is very clear on responsibilities.

[00:18:26] And I suspect over the last few years, you've seen so many good examples of where enterprises get it right. And I suspect a few where they've made a few simple mistakes that has derailed a project entirely. If you take all those experiences, what is it you think matters most when implementing AI into their business? And any big advice there that you'd offer too? Yes. Glad yes. And I think it's a combination.

[00:18:54] And really getting back to what I said about engaging the humans, I think that's so important. So one, go back to the executives. You are on point to have the top-down vision, to understand the technology, where this can take you, what the opportunities are. So there has to be some kind of executive point of view on how this all ties to the strategy. But I would not overlook the bottoms-up engagement and kind of ideation, carrots that you could put out there for your team.

[00:19:24] So executives need to be in the know. That's great. You don't want your folks out there fearing AI. That is what is going to make a huge difference to your organization. And that's what will ultimately allow you to win, is if you have the masses that are thinking about the big ideas for where this can take you. What they're also doing is getting an understanding of tools in the meantime.

[00:19:48] So making that investment is important, and I'd love to hit on training a little bit later here. The other thing that is important to this that is super critical to making sure that you execute is just committing to a foundation. So we did talk about transformation principles, but doubling down on that, do you have an intake process for some of the ideation? That it assesses the current operating model, people process data and technology.

[00:20:19] I guess this is yes, a solution. Do you take the time to say, what's the measurable outcome that we're going to achieve if we implement this solution? What is the risk rating on this agent? And who do I need to route to in the risk organization? Kind of a red, yellow, green concept of a risk rating on some of the solutions that you're putting together. Where does the data live? What's the state of that data? Does it be cleaned up to even make this AI kind of agent effective? And where should that data live?

[00:20:49] So you're going to have an impact on your tech architecture, APIs, your user experience, all that stuff. If you don't take the time to consider those design principles and requirements, you will fail. Just like many tech implementations, you know, before that. So set the foundation for when things come into your file and intake process, what you will do to immediately make sure that you're driving the right requirements and then move forward into the future state.

[00:21:18] Well, thank you so much for coming on and sharing your invaluable insights today. And for people listening, maybe they want to better harness AI to its full potential. They could be cautious, nervous, just feeling overwhelmed with all the noise there. Any takeaways for those people listening just to help them harness AI? Yes. So a couple of thoughts. So data, maybe an obvious one. Data is so critical to unlocking the value of AI.

[00:21:47] But I would just advise don't create forever projects on your data strategy. You know, you can vibe code and kind of build proofs of concept that compartmentalize your data strategy for that solution and end up informing the broader solution. So what we like to do is, you know, send folks in, some vibe coders in at the front end of our engagements during discovery. And we start asking questions. We go right at the outcome.

[00:22:16] I'll just give you an example, you know, building a cash flow forecast. And we get into the cash flow forecast. What data do I need to do this? What is the state of that data? You know, what of this data is maybe ineffective and needs to be cleaned up so that I can get at it? What are the next agents that I should consider building on the back of this that might help me either clean up the data or drive better solutions and billings and collections that impact this cash flow forecast?

[00:22:42] What are external data sets around customer sentiment or vendor trends that might impact me longer term that I haven't really thought about? Because I'm just looking at accounting information and other numbers. So there's so many things to think about like that. So go in, don't create forever projects, but use that proof of concept to inform you on what your data strategy can be. And that's a quick way to get at it. I would always say like define the business problem. Don't lead with the technology.

[00:23:11] That's an easy one. And then, you know, something that I want to share with all the listeners out there is we have spent a lot of time on training. And it's very important because we need to stay ahead of our clients, you know, understand what's going on out there and really be on the cutting edge. And we split up an innovator training program across country.

[00:23:34] And what that looks like is that we have consultants that go through kind of strategy, governance, risk, all of the things we talked about with transformation principles, what you need to be thinking about. They learn how to vibe code proof of concepts. And you can make it very far there. They then learn about agentic AI, how you get to scale and how these agents might orchestrate and work with each other and how they kind of weave through organizations and data.

[00:24:03] And how you should think about your data sets strategically longer term. And then they go into kind of a capstone project where they present to a group of executives. And we have had such success with folks coming out of that training, coming out and building platforms, reconstructing our own website and thinking about different ways to do that or cleaning up data on the front end of projects in no time at all. So I would just advise, like, train people up or go find some training to go to.

[00:24:33] We have an amazing program. We'd love to talk to any potential clients out there about because this has been such a major hit. And when you're not helping other enterprises stay on track and harness AI to its full potential, I've got to ask, what's your focus at cross-country consulting this year? What are you focusing on? Anything you can share around what you're working on this year? I would say in this world, agility is so key.

[00:25:01] So the tools keep changing. They change every other week. They're leapfrogging each other on the power of them. How you get to scale is changing because there are many of these companies that are building enterprise platforms now where agents can live. So we need to stay agile. That's one thing we need to do. We are very hyper-focused on the pathway to scale for our clients right now. And the reality is that I'm seeing very few companies out there that are on the cutting edge, that have multi-agent examples.

[00:25:31] They very much have point solutions out there. But there's so much more that they could be thinking about and interconnecting the business. So that's a big focus is how do we get to scale with this? We've kind of viacoded to proof of concept land. And now how do you go from point solutions to kind of orchestration of agents?

[00:25:51] And maybe one other thing to mention is we've thought a lot about just the industry use cases here and getting into real business conversations and outcomes in that way.

[00:26:02] So we've got pods of solutions and what we call agent clusters that are very much aligned to industry solutions, functional solutions, and just thinking about productization in that way so that we could paint the picture of what's possible for the clients in a bigger way and get them to see how this is kind of an enterprise solution. So we've got a unique approach to do all of this, Neil, at the very front end of our engagements that we like to call Jumpstart.

[00:26:31] And that's where we send in the Vibe Coders. We send in this cross kind of service line team that I mentioned, you know, folks from a good representation of our service lines, risk experts, data experts, transformation experts with the Vibe Coders to come up with the best well-rounded solution for you and the proof of concept. And then the pathway to scale from there. And that's where we get agreement in the workshop.

[00:26:56] So we've had so many great conversations, you know, 60, 90 minutes with clients talking to them about what we're seeing in the market and how our Jumpstart approach could really bolt them forward. So combination of all of those things, it's been very, very busy. As you can imagine, you have to stay on top of this every day because if you're not reading the news, then you're a day behind. So, you know, we're sourcing that from various outlets and staying on top. Absolutely love that.

[00:27:23] And obviously, we've covered so much in a short amount of time today. For anyone listening just wanting to find out more about you, your work, how they can work with you or have a conversation or keep up to speed with some of the announcements, where would you like me to point everyone? Yeah, Neil, thanks for asking that. So the best place to start is our website at crosscountry-consulting.com. You can learn more about our AI innovation practice, the lab that I mentioned.

[00:27:51] You can explore so much thought leadership that we've pumped out there as we've learned along the way here. You'll see examples of how we're helping organizations apply AI in practical, responsible ways, as we talked about at the beginning of the call here. Also, reach out on LinkedIn. You know, find me on LinkedIn, Thomas Alexander. I'm out there. We regularly share insights. I've got my own podcast where we've published, you know, both audio and video around end-to-end process.

[00:28:18] So we really focused on record to report, procure to pay. We've got a lead to cash episode coming up very soon. And we're starting to do those with technology vendors and clients as well. So more to come on that front. But some of the exciting things happening. Reach out. We'd love to have a conversation. Awesome. I'll put a link to everything there, including the podcast. What's the name of the podcast again? Field Notes. Awesome. We're syndicated now, Neil. So we're across a bunch of different platforms. Find us everywhere.

[00:28:48] Awesome. Well, I'll add a link to that. And we've covered so much today, especially around how leaders can take that step back and focus on business processes where automation and AI can create the biggest impact. And also what matters most when implementing AI into a business and how to harness AI to its full potential and give people listening a valuable takeaway or a number of valuable takeaways here. So thank you so much for sharing your story today. Thanks for having me, Neil. Really appreciate it.

[00:29:17] There was a lot to take away from this conversation with Tom today. But one particular point really stayed with me. And that is successful AI adoption rarely comes from chasing the next tool. It actually comes from understanding the business problem and then aligning the right people and then building a plan that can actually scale.

[00:29:38] And what I loved about Tom's perspective here is that he brought the conversation right back to fundamentals, leadership, accountability, training, data and change management. These things might not be the flashiest parts of AI, but they are often the difference between momentum and another stalled initiative that stuck in pilot phase.

[00:30:00] So whether you are a CFO, a business leader or just someone trying to make sense of where AI could fit in your organization, I hope today's conversation gave you a slightly clearer path forward. And finally, over to you. What would happen if your business stopped asking, hey, where can we use AI and started asking, where can AI genuinely improve the way that we work? As always, let me know your thoughts. It's techtalksnetwork.com.

[00:30:29] Keep your insights and experiences, everything you're seeing and hearing out there. I want to hear from you. So let me know. But that's it for today. So thanks for listening as always. And until next time, don't be a stranger.