How to Spot the Enterprise AI Companies That Are Actually Winning

In this episode of Motley Fool Hidden Gems Investing, Motley Fool contributor Rachel Warren talks with Adam Field, Chief AI Officer at Tungsten Automation — a company serving 25,000 organizations, including 40% of the Fortune 100 — about what separates real AI transformation from expensive spin. They discuss:

• Why most enterprise AI pilots quietly die before they scale
• What "boring AI" actually means, and why it's the most important signal investors aren't paying attention to
• Why the competitive moat that once made legacy software giants unassailable has effectively disappeared overnight.

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A full transcript is below.

This podcast was recorded on July 19, 2026.

Adam Field: It's like handing someone the best camera and calling them a photographer. We would never do that. 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 automatically overnight, change how we do business, I think, is absolutely incorrect.

Rachel Warren: 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 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, 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 DAI strategy for a company that has been a giant in 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 the ones you might be watching. Adam, welcome to the show.

Adam Field: Rachel, thank you for having me. It's great to be here.

Rachel Warren: I really want to lay the foundation for our conversation today. You've spent decades leading product vision and software automation. For investors that might be trying to understand this space, what is the fundamental difference between the old school modes of digital automation and what an AI-driven workflow engine can actually do today?

Adam Field: When you think about 30 years plus of software development, we like to talk about it as being very deterministic, meaning you built software 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'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, everything’s called an agent nowadays, is give it a task, give it an output, give it a goal, and it will use the tools and information at its disposal to go and get something done. That's really the fundamental shift. There's obviously a lot of nuance under all that, but that's the basic shift.

Rachel Warren: 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 a tendency to ignore a lot of that infrastructure beneath. I'm wondering from where you sit, why is that automation layer where some of that real value is being created?

Adam Field: Well, you've got 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. Some of those like Nvidia playing in both spaces, but really wear 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. 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. The model itself becomes a bit of a commodity. 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.

Rachel Warren: I think another 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. It's current iteration obviously has changed a lot. Maybe you could walk our audience through that journey a bit.

Adam Field: Rachel, I always find it funny when people ask me— analysts, customers, investors—and they ask, are you doing AI in your product? I said, yeah, for 40 years, we've been doing AI in our product. What does that really mean? Well, here’s what’s unfortunate: Unfortunately, I think a lot of that traditional AI has gotten kicked aside, and everyone’s focused on the latest and greatest and 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. We think bringing together traditional machine learning methods and the latest generative AI and large language models, to use the right AI to solve the right problem is where organizations are going to get motion value. 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.

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Rachel Warren: I've seen you use this phrase to describe the information trapped inside an enterprise system, dark data, and maybe, 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?

Adam Field: 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. What does that mean? In our world, it means most of that, 80% of that 80% 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. 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 somewhere or scanned and stored somewhere for reference later. But no machine or human are making use of it. That's the difference now.

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? We've been able to take advantage of that information for a very long time. Just recently now have we 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 decision that's what these models are capable of doing. That's what's really exciting in new.

Rachel Warren: I think it's fascinating, 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. Real-world applications where we're seeing the value now, it's interesting. Obviously, you can look at metrics like a company's cash conversion cycle as well as others to really see, 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? What capital efficiency does it bring about for these big corporate entities?

Adam Field: 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. 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, leading to obviously better customer acquisition and better customer retention. Quite often, these organizations can't make these decisions because they spend time reviewing things quite often that are still in documents.

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.

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 things, pull the relevant information out, look at the risk, see if there's any anomaly, 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.

Rachel Warren: I want to talk a bit about the mechanics of why enterprise AI implementations tend to stall and where they succeed. There's this widening gap in the corporate world between running a successful AI pilot and deploying AI in actual scaled production. I wonder, if I could hear your thoughts, why do so many enterprise AI projects fail to make that leap past the pilot phase?

Adam Field: Because I think quite often, these pilots will focus on a specific scenario. We use the 80-20 rule a lot. They'll focus on the 80% in our world, look how quickly I can extract information from a document. We get this a lot. We talk to 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 people are taking photos of their documents and IDs in a darkly lit basement, that are sideways. How do you handle that scenario? How do you fix that? How do you handle the exceptions? 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. 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. 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. 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, 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. 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.

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Rachel Warren: One of the other 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 it's easier to cheaper to build in some cases. I think there's been this concern that Code itself is becoming commoditized, and some of the generic software functionality, could be spun up in days rather than quarters, so what happens to the competitive modes of a lot of these traditional SaaS businesses? What are your thoughts on that?

Adam Field: Well, you're referring to the SaaS apocalypse. I looked at Google Trends reports starting about 18-24 months ago, that term started to increasingly get Googled, and then 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? I mean, it's an honest question. They see the big behemoths, publicly traded companies and their valuations going down. Here's what I think. I think the old mode of I'm your system of record, therefore, you can't replace me, that mode 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. 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. Moving the data, that's easy. That's not a moat anymore. But do I think like these big SaaS companies are going to go away in these reduced valuations? Do I think that there's, a reason for it versus just, 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 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. We take that risk away from organizations. I think those SaaS applications and those companies will survive.

Rachel Warren: I think another interesting question that comes to mind, as well, 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 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. I'm just 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.

Adam Field: It's so interesting and such an important conversation right now. For fun, a few months ago, 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, six months prior were called agents, six months later, even though I'm very convinced nothing at all had changed in their product or what they were doing. It is really difficult to cut through that hyper but I coined a term, some months ago that I called boring AI. What I meant by that was, I think you’ve got to look under the covers in the organizations that are doing the foundational work.

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 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. 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.

Those things take time. 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. What outcomes are they actually 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 cliques 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, those are finite. But what I think you'll find in companies that I want 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. They're 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 I think they're doing it right, and they're using the technology to grow, not just to, squeeze more out of that stone.

Rachel Warren: Well, and one final question, looking ahead in the coming years, what makes you the most cautious about AI and its various iterations, and what do you find the most exciting? What areas are you most excited about?

Adam Field: I'll start with maybe the negative side. The collision first, and then we'll end on something positive. The caution side comes around to security. What are these foundational model companies doing with my data? What happens when we're starting to see a lot more even the open source world is suffering from bad actors, putting 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, Anthropic and the U.S. government and that being put on hold because it was deemed maybe a cyber weapon. Those are the things you worry about. How are countries investing? What does this mean for the future of national security? Those are the things that are obviously majorly concerning.

On the positive side, I was listening to a speaker recently who is an expert and had decades of expertise in education. He was talking about people in impoverished parts of the world who lack access to really good education now being able to get it with the power of ChatGPT, 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 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. That's what keeps me going, and I think that potentials inspiring.

Rachel Warren: Fascinating. I think there is a lot to be excited about as the world of AI progresses. Thank you so much, Adam, for your time today and for joining me.

Adam Field: Rachel, it's been my pleasure, and thank you to all the viewers for signing in and having to listen.

Rachel Warren: As always, people on the program may have interests in the stocks they talk about. 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.

Rachel Warren has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Advanced Micro Devices and Nvidia. The Motley Fool has a disclosure policy.