Motley Fool Hidden Gems Investing - The Old Software Moat Is Dead — How to Spot the Enterprise AI Companies That Are Actually Winning

Starting point is 00:00:01 It's like handing someone the best camera and calling them a photographer. We would never do that. So handing someone this amazingly powerful technology and all of a sudden saying they are an AI expert or that this system is going to go, you know, automatically overnight change how we do business, I think is is absolutely incorrect. That was Adam Field, Chief AI Officer at Tungsten Automation, on why handing companies the most powerful AI tools in the world still isn't enough. Tungsten serves over 25,000 organizations, including 40% of the Fortune 100. And Adam has spent decades watching enterprises succeed

Starting point is 00:00:46 and fail at exactly this kind of transformation. I'm Motley Fool analyst Rachel Warren. In this conversation, Adam and I dig into what's really separating the companies getting ROI from AI from the ones burning the budget on it, including a surprisingly simple signal you can use to tell them apart just by watching their hiring. We hope you enjoy. When we talk about the massive capital expenditures surrounding artificial intelligence, the conversation almost always defaults to microchips and raw foundational models. but for the massive enterprises that power the global economy, the true competitive advantage isn't just about renting a model. It's about automating the billions of complex workflows

Starting point is 00:01:26 and transactions and documents that keep those businesses running. Joining us today is Adam Field, Chief AI Officer at Tungsten Automation. Adam brings decades of deep expertise in software automation and AI, leading global product vision and enterprise-wide AI strategy for a company that has been a giant and digital workflow transformation for four decades. Tungsten Automation serves over 25,000 global organizations, including 40% of the Fortune 100. Now, Adam is here to break down how the world's biggest brands are turning dark data into actionable revenue, why the unflashy layer of infrastructure is the real cash cow for a lot of enterprise software businesses, and what all of this means for the stocks in your portfolio or

Starting point is 00:02:07 the ones you might be watching. Adam, welcome to the show. Oh, Rachel, thank you for having me. It's great to be here. I really want to lay the foundation for our conversation today. You know, you've spent decades leading product vision and software automation. And for investors that might be trying to understand this space, what is kind of the fundamental difference between the old school modes of digital automation, what an AI-driven workflow engine can actually do today? So when you think about 30 years plus of software development, you know, we like to talk about it as being very deterministic, meaning you built software

Starting point is 00:02:44 automations, process automation. Some may have heard of RPA, robotic process automation bots. You basically told them what to do and they went and repeated that. There was some, there's been artificial intelligence and machine learning for a very long time, but generally the same inputs got the same outputs. Now what we're able to do with agents, you know, everything's called an agent nowadays, is give it a task, give it an output, give it a and it will use the tools and information at its disposal to go and get something done. And that's really the fundamental shift. There's obviously a lot of nuance under all that, but that's the basic shift.

Starting point is 00:03:21 I think we're in a time where the market's very focused on, obviously, the chip companies, the companies building the massive LLMs, and certainly that's an exciting area. But I think there's sort of a tendency to ignore a lot of that infrastructure beneath. So I'm wondering from where you sit, why is that automation layer where some of that real value is being created? Well, you've got kind of three layers. You've got the chip manufacturers, the NVIDIAs, the AMDs of the world. You've got the model companies that we know, Anthropic, OpenAI, and a bunch of open source models. And then you have the application layer.

Starting point is 00:03:57 Some of those like NVIDIA are playing in both spaces. But, you know, really where software automation, the models themselves have generally for many, I would say like 95% of what my employees at Tungsten do every single day, the model really doesn't matter. Now, if you're coding and you're doing heavy duty coding and you have lots of agents running in loops, fixing code, the model matters. And there's some that are just far better than others at those tasks. But for a lot of things we do, which is document heavy workloads at Tungsten, processing an invoice doesn't require Fable 5, doesn't require eating all of those tokens. So the model itself becomes a bit of a commodity. So I think it's what you build on top of it, the industry foundations that you build into it, the data that you give it, that's what the difference is, the data that you give it. Otherwise, everyone has access to these same models. I think another sort of interesting thing to look at is also the evolution of AI. I mean, obviously, we've been hearing about it so much the last few years. But as you noted earlier, AI is not new. Its current iteration obviously has changed a lot. Maybe you could walk our audience through that journey a bit. I, Rachel, I always find it funny when people ask me, analysts, customers, you know, investors, and they ask, are you doing AI in your product? I say, yeah, for 40 years, we've been doing AI in our product. So, you know, what is, what does that really mean? Well, machine learning, and here's what's unfortunate. Unfortunately, I think a lot of that traditional AI has sort of gotten kicked aside and everyone's focused on the latest and greatest in generative AI. But companies like Tungsten, we really pride ourselves in providing the right AI for the right job. And I think we need to be thinking about that a lot more. There's still a tremendous amount of value in machine learning technologies. It's faster. It's often more cost effective. It's more environmentally friendly because it doesn't take these large GPUs to power it to answer questions.

Starting point is 00:06:04 So we think bringing together traditional machine learning methods and the latest in generative AI and large language models to use the right AI to solve the right problem is where organizations are going to get the most value. So platforms that can provide that and on the fly figure out what technology to use to solve the question that's asked, the problem that's in front of it, I think that's where you get your greatest win. Blend wellness into your coffee routine with the Coffee Plus range, infused with functional benefits. Choose the coffee you love with added B vitamins, like Coffee Plus B12 to help support immune function and Coffee Plus B6 to keep your day moving. Or go with the flow and choose Ginseng Delight, our new double espresso with ginseng extract. Whatever lies ahead, don't change your morning. Let your morning change you. Discover Coffee Plus on Nespresso.com.

Starting point is 00:07:04 I've seen you use this phrase to describe the information trapped inside enterprise systems, dark data. And maybe, you know, for an investor that's trying to evaluate a legacy giant or a company, how does a business's ability to unlock data, whether it's trapped in contracts, invoices, and so forth, how does that translate directly to the trajectory of their growth story? Sure. Well, I mean, first to define dark data, most analysts say that it's 80 or more percent of information inside of an organization is, quote unquote, dark data. So what does that mean? In our world, it means most of that, 80 percent of that 80 percent is trapped inside of documents, things like contracts and annual reports. But the term document itself has even evolved over the last few years to mean things like an email. An email is a document. It's unstructured. Contracts are unstructured. No two contracts look the same. No two emails look the same. You think about the transcripts from phone calls coming into your contact center, that's all unstructured, dark data. And what that means is the reason they call it dark data is because those transcripts are sitting there, those contracts, those annual reports, those emails, those invoices are inside of your organization in documents, mostly now digital, but some still paper stored

Starting point is 00:08:25 somewhere or scanned and stored somewhere for reference later, but no machine or human are making use of it. And that's the difference now. So machine learning traditionally had made really good use of structured information. What are your customers buying? What are your suppliers supplying you? How much are they charging? What's the line items in your invoice?

Starting point is 00:08:44 We've been able to take advantage of that information for a very long time. Just recently now, we've been able to take advantage of all of this dark data because we can read these highly unstructured, multi-hundred page contracts, annual reports, millions of emails and begin to break them up and put them in a place that now when you're serving your customer or you're making an investment decision as a bank or you're trying to evaluate risk as an insurance carrier, you can now look at all of that unstructured information in these documents alongside all of the information you used to look at and make better decisions. And that's what these models are capable of doing. So that's what's really exciting and new. I think it's fascinating

Starting point is 00:09:22 too, because I think sometimes it's easy for a lot of people to hear vague conceptions of what AI can do. And I think those are really practical, you know, real world applications where we're seeing the value now. You know, it's interesting, obviously, you can look at metrics like a company's cash conversion cycle, as well as others to really see, you know, how much that growth story is accelerating. From what you see at Tungsten, obviously, the company you serve, many publicly traded companies, many of the large household names that a lot of people know, How drastically does something like automating document workflows shrink that cash conversion cycle? You know, what kind of capital efficiency does it bring about for these big corporate entities?

Starting point is 00:10:01 Well, look, I think in areas, let's say banking, for instance, we all know that any little bit of friction will increase the odds that this bank loses a customer. Let's talk about even personal loans, commercial loans, any little bit of friction. So the more they can understand about their customer and make decisions often in real time will, I think, very greatly, or I don't think, we've seen it proven multiple times over, that it increases conversion rates, you know, leading to obviously better customer acquisition and better customer retention. And quite often, these organizations can't make these decisions because they spend time reviewing things quite often that are still in documents. And Rachel, I should spend a second to say, I've said the word document probably a thousand times so far, and I'll say it a thousand more times before the end of this conversation. yes documents are still very prevalent in our world even though we think it's 2026 and documents should be gone they're not and so you know back to the banking example in your question you think about a large commercial loan there are so many documents involved in let's say it's a property being assessed income verification the quicker that a bank can process all of these

Starting point is 00:11:19 things, pull the relevant information out, look at the risk, see if there's any anomalies, see if they have any overlapping risk in places, because it might be buried in a paragraph somewhere. The quicker they can make that decision, the quicker they can convert the customer and write better business, of course, too. I want to talk a bit about sort of the mechanics of why enterprise AI implementations tend to stall and where they succeed. You know, there's sort of this widening gap in the corporate world between running a successful AI pilot and deploying AI in actual scaled production. I wonder, you know, if I could hear your thoughts, why do so many enterprise AI projects fail to make that leap past the pilot phase? Because I think quite often

Starting point is 00:12:03 these pilots will focus on a specific scenario. And we use the 80-20 rule a lot, right? They'll focus on the 80%. In our world, look how quickly I can extract information from a document. And we get this a lot. We talk to, you know, many of our customers and they have very intelligent, highly funded AI centers of excellence, and they might use Claude or ChatGPT or services from the hyperscalers and say, look, I threw a document at it and I get 100% extraction accuracy. Now, when you step back, though, and look at the other 20% and what the real world is like, how often do those documents come? We have customers where, you know, people are taking photos of their documents and IDs in a darkly lit basement, you know, that are sideways. How do you handle that scenario? How do you fix that? How do you handle the exceptions?

Starting point is 00:12:58 Even if you have perfect quality, how do you handle the exceptions? Well, you have to be able to process that accordingly. How do you handle when the large foundational model companies decide on a whim that they're going to deprecate a model so that they can put the hardware to their latest and greatest model? Now you have to have that team go and start retesting everything. Well, that's what companies like Tungsten do. So I think in a pilot scenario, quite often things look like they're going to work really well. Costs seem contained. But when you start to factor in the human capital that's necessary to maintain them, all the testing that goes into it, the exceptions, I have a chart that I built.

Starting point is 00:13:40 You know, the industry that we're in is known as intelligent document processing. And there's probably 125 different little squares on that that are bits of functionality that a well-formed IDP platform provides our customers. Most pilots aren't considering all of those pieces. And then the last bit, as I mentioned very quickly, is cost. It's easy, I think, in a vacuum to look at cost and say, you know, this document to extract, it's going to cost this many pennies. But when you start, again, factoring in the support, the token usage for the exceptions, not the rule, it really can go off the rails fast. So that's why we see more organizations not necessarily trying to decide if they're going to build it themselves or buy it from someone like Tungsten, but using these platforms to do what organizations like Tungsten do well and then build on top of it and then use their clever AI experts to go differentiate, take that data and go differentiate your organization with it. New from Nespresso. Blend wellness into your coffee routine with the Coffee Plus range. Infused with functional benefits.

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Starting point is 00:15:48 Because we have your number. One of the things as well I want to talk about, obviously, AI is profoundly changing the software as a service industry. I'm sure that's true in the private markets. Certainly in the public markets, we have seen the software meltdown in 2026. I'm sure we will continue to see that. Obviously, not all of these businesses are created equal, but it is very much a time where software is easier, cheaper to build in some cases. And I think there's been this concern that code itself is becoming commoditized and some of the generic software functionality, you know, could be spun up in days rather than quarters. So what happens to the competitive moats of a lot of these

Starting point is 00:16:28 traditional SaaS businesses? What are your thoughts on that? Well, you're referring to the SaaSpocalypse, right? And I looked at Google, I looked at Google Trends reports, you know, starting about 18 to 24 months ago, that term started to increasingly get Googled. And then, you know, OpenAI will make a big release and it spikes and then it goes back down and then open claw comes out and it spikes again and then comes back down. I've thought a lot about this. I've read a lot. I've sat back and thought about it because even though we're a privately held company, we're a SaaS company. And one of the questions I get very candidly from my customers is, will you guys be around by the end of the term of the contract that we're signing right now, right? I mean, it's an honest question.

Starting point is 00:17:09 They see the big behemoths, publicly traded companies and their valuations going down. Here's what I think. I think the old moat of I'm your system of record, therefore you can't replace me, that moat is gone. It's easy to point. When I first got a cursor subscription, I rewrote a 20-year-old personal productivity app of mine in three days. and the easiest part of it was pointing it at the database saying look at the old database the new database and move my data over i went to go make a t and i came back and it was done so moving the data like that's easy that's not a moat anymore but do i think like these big sas companies are going to go away and these reduced valuations do i think that there's you know a reason for it versus just, you know, people are worried. I think it's more the latter. Here's the thing. I think if you create great technology that solves a real problem, I think if you have many years of

Starting point is 00:18:08 know-how and you most importantly take risk away from an organization, then I think those SaaS companies will survive. You can't replace 30, 40 years of know-how. One example, when I say transferring risk. We're in a business that processes invoices, accounts payable, compliantly in 140 countries. To get the license to do that in 140 countries would cost millions of dollars and take years to do. So we take that risk away from organizations. I think those SaaS applications and those companies will survive. I think another kind of interesting question that comes to mind as well, you know, we're at a time where a lot of companies will say that they are leveraging the power of AI, they're integrating AI. Sometimes they're very

Starting point is 00:18:52 vague in what those claims are. Obviously, there are a lot of quality businesses that are, in fact, doing that. But it can be sometimes difficult, I think, for investors looking at the public markets to discern the value from the hype. And from your vantage point, you work with a lot of these major enterprises that are actually implementing these workflows into their daily operations. So I'm just kind of curious, what are the hallmarks, the signs that one can look for to actually see whether a company and institution is truly leveraging AI in a way that's actually going to bring meaningful growth to the business rather than just weighing on the balance sheet. Yeah. It's so interesting and such an important conversation right now. For fun, a few months ago,

Starting point is 00:19:34 I went in the internet Wayback Machine and looked at some organizations' websites and then compared it to their website just a few months later. And the same things that were called bots or nothing, you know, six months prior were called agents, six months later, even though I'm very convinced nothing at all had changed in their product or, you know, what they were doing. So it is really, really difficult to cut through that hype. But I coined a term, you know, some months ago that I call boring AI. And what I meant by that was, I think what you got to look under the covers in the organizations that are doing the foundational work. And what boring AI means is you can't just go roll out this technology for the sake of rolling it out and think you're going to get

Starting point is 00:20:20 some results. It takes a lot of time to build that data foundation, to build that agentic orchestration foundation in order to do things right. And the example that I'll give you is even internally at Tungsten, I took on this new role as chief AI officer in January and I pulled together a team. We're just now six months later rolling out some AI and agentic tools to our entire employee base. We spent six months getting that data foundation right, access to all of our systems, all the security, the redaction to make sure that we're in compliance. So those things take time. So I think what you really need to look for is not the hype and the flash and all of the words in the marketing term, but ask about the outcomes. Like what outcomes are they actually

Starting point is 00:21:08 achieving. If the end of their statement doesn't say, and we reduced this by X, we increased this by Y, we grew. Here's the other thing too, is you asked earlier about, is there a ceiling to automation? There may with many of these processes only be so many pennies or clicks or people that you can squeeze out of it. Well, we don't talk enough about at all in this world. We do talk a lot about efficiency. We don't talk about proficiency. We don't talk enough about revenue generation with these technologies. We talk about how many people we can replace, which is just an absolutely horrible way to go about running a business. We talk about how many clicks we can reduce, which, you know, those are finite. But what I think you'll find in companies that I want

Starting point is 00:21:55 to invest in are the ones that are actually creating better product. And you see they're reacting faster. They're changing their strategy as the world changes. And that's not necessarily a bad thing in today's day and age. They're driving more revenue. There are actually some studies have shown that they're hiring more people, not reducing their headcount. If you see a company that's making huge AI investments and hiring people, those are the companies that I want to hook my wagon to because I think they're doing it right. And they're using the technology to grow, not just to, you know, squeeze more out of that stone. Well, and one final question, you know, looking ahead in the coming years, what makes you

Starting point is 00:22:33 the most cautious about AI and its various iterations? And what do you find the most exciting? What areas are you most excited about? So I'll start with the, maybe the negative side, the collision first, and then we'll end on something positive. So the caution side comes around to security. you know, what are these foundational model companies doing with my data? What happens when someone was starting to see a lot more, even the open source world is suffering

Starting point is 00:23:02 from bad actors putting, you know, injecting nefarious code into tools that all of us use every day at a much more rapid rate than we've ever seen before. I don't need to tell probably all the viewers about what happened with Mythos and Fable and Thropic and the U.S. government and that being put on hold because it was deemed maybe a cyber weapon. So those are the things you worry about, like, you know, how are countries investing and what does this mean for the future of national security? You know, those are the things that are obviously majorly concerning. On the positive side, I was listening to a speaker recently who was an expert and had decades of expertise in education. And he was talking about people in impoverished parts of the world who

Starting point is 00:23:49 lack access to really good education now being able to get it with the power of chat gpt claude and some of these other tools because what you could do is you can take a teacher and multiply him or her exponentially with these tools to allow them to educate people who lack proper education that's totally inspiring to me learning new ways to do things and learn i learn something new every single day with these tools that may have taken 10 times as long and I would have had to go find the right book or website or something. So that's what keeps me going. And I think that potential is inspiring. Yeah, fascinating. I think there is a lot to be excited about as the world of AI progresses. And thank you so much, Adam, for your time today and for joining

Starting point is 00:24:36 me. Oh, Rachel, it's been my pleasure. And thank you to all the viewers for signing in and having to listen. As always, people on the program may have interests in the stocks they talk about, and The Motley Fool may have formal recommendations for or against, so don't buy or sell stocks based solely on what you hear. All personal finance content follows Motley Fool editorial standards and is not approved by advertisers. Advertisements are sponsored content and provided for informational purposes only. To see our full advertising disclosure, please check out our show notes. For The Motley Fool Hidden Gems Investing Team, I'm Rachel Warren. Thanks for listening. We'll see you next time.

Starting point is 00:25:10 We'll see you next time.

Motley Fool Hidden Gems Investing - The Old Software Moat Is Dead — How to Spot the Enterprise AI Companies That Are Actually Winning

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