The Old Software Moat Is Dead — How to Spot the Enterprise AI Companies That Are Actually Winning

**Adam Field** (0:01)
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 automatically overnight, change how we do business, I think is absolutely incorrect.

**Rachel Warren** (0:28)
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 percent 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, a true competitive advantage isn't just about renting a model. It's about automating the billions of complex workflows 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 percent 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** (2:10)
Rachel, thank you for having me. It's great to be here.

**Rachel Warren** (2:13)
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 kind of the fundamental difference between the old school modes of digital automation, what an AI-driven workflow engine can actually do today?

**Adam Field** (2:34)
So 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 automations, 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 output. 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** (3:22)
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** (3:42)
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 are playing in both spaces, but 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.