The Old Software Moat Is Dead — How to Spot the Enterprise AI Companies That Are Actually Winning
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What is the main topic discussed in this episode?
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 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 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 and transactions and documents that keep those businesses running. Joining us today is Adam Field, Chief AI Officer at Tungsten Automation.
How is AI different from traditional deterministic 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 in digital workflow transformation for four decades. Tungsten Automation serves over 25,000 global organizations, including 40 percent of the Fortune 100. 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.
Rachel, thank you for having me. It's great to be here.
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 and 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 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 goal, 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.
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.
Why does the automation layer (not just models or chips) matter for enterprise value?
And then you have the application layer. 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.
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Chapters
6 chapters
1
What is the main topic discussed in this episode?
0:01–1:35
2
How is AI different from traditional deterministic automation?
1:35–3:53
3
Why does the automation layer (not just models or chips) matter for enterprise value?
3:53–6:33
4
What is 'dark data' and how can companies turn it into revenue?
6:33–14:02
5
How much can automating document workflows improve a company's cash conversion cycle?
14:02–20:20
6
Why do many enterprise AI pilots fail to scale into production?
20:20–24:08