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[00:03]
A
The one company that is unequivocally making money from AI is Nvidia. That's the one company that seems to be making a ton of money. There's a lot of other companies that are, that have proven they can build amazing models and lose extraordinary amounts of money.
[00:22]
B
Start with Steve Lucas, chairman and CEO of Boomi, explaining who is actually making money in AI right now and who isn't. Steve is a 30 year enterprise software veteran who previously turned Marketo into a $4.75 billion acquisition. I'm motley fool analyst Rachel Warren. Steve has sat across the table from hundreds of CEOs navigating the AI moment and what he's hearing might surprise you. We discussed the ROI reckoning that's coming, what separates real AI winners from expensive experiments, and why the next wave of big beneficiaries probably isn't who you think. We hope you enjoy. Welcome back to Motley Fool Conversations. I'm Motley fool analyst Rachel Warren. Today we're looking past the AI hype cycle to focus on execution, data infrastructure and true return on investment. Joining us is Steve Lucas, Chairman and CEO of Boomi. Steve is a multi time CEO with nearly 30 years of enterprise software leadership, including senior roles at Salesforce and Adobe. And previously as CEO of Marketo, he drove a massive turnaround resulting in a $4.75 billion acquisition by ADO. Now at the helm of Boomi, a data activation powerhouse serving over 30,000 global customers, Steve is here to talk about the current state of AI, where corporate tech budgets are actually moving and how investors can spot the real winners. Steve, welcome to the show.
[01:42]
A
Thank you, Rachel. Happy to be here.
[01:45]
B
So for the last few years, it seems as though investors have largely rewarded companies for simply having an AI strategy using the right AI buzzwords and earnings calls. But it seems we're entering something of the next phase in that journey where Wall street demands understandably measurable business outcomes and roi. So I'm curious, what do companies need to do and or keep top of mind to actually deliver to that end?
[02:10]
A
Well, first of all, I think you're absolutely right. Over the past couple years, we've gone from we didn't have AI, now it exists to boards pressuring executive teams, CEOs and leaders at companies to put AI into their company build an AI strategy. And in the two years that we've seen that pressure kind of mount, we've seen the birth of agentic AI inside of businesses and all those things, I think that the pressure has now started to subside. And as you pointed out, now it's about returns. And I've been quoted a few times as saying that ROI supersedes AI and there is no doubt that that is the case today. I just think it's the enormity of the pressure on that left hand side coupled with rushing into a lot of AI projects. We're not seeing the high rates of return that you'd expect from businesses. Now that's going to change as AI matures and how organizations manage AI matures as well. But we're definitely seeing a change in the winds.
[03:19]
B
Do you think we've reached a point where AI spending could become a drag on earnings for companies that fail to demonstrate meaningful returns on those investments, or do you think it's just too early to really make that, make that determination yet?
[03:33]
A
Well, if you look at the four major hyperscalers in the US alone and the amount of CapEx that they put into AI last year versus this year, this is kind of the canary in the coal mine. Last year it was around 400 billion, 410 billion, and this year it's over 700 billion. Four companies. That's an extraordinary increase in spending. And obviously that is not reflective of the broader market, but it's an indicator of the broader market. The broader market organizations, they're spending on AI is way up, they're spending on software applications is down and spending on infrastructure is up as well. So AI and infrastructure seem to be the two big investment priorities for large organizations. But I think we are, you said the blank check era. I think that's a perfect phrase. And we are at a place where organizations, I walk into board meeting after board meeting, CEO after CEO, and what I hear continuously is help me show return after these projects that we initially pursued. They're not providing roi. That is, it is real, it's in the market. That's being discussed. And you're starting to hear other executives call it out as well, which is let's stop trying to scare all the executives with these scare tactics into investing in AI and in business. Let's help them show real rates of return.
[05:00]
B
You mentioned earlier that you'll go in these meetings and there's C suite executives basically saying, you know, help us show that we are making this profitable or on the path to making it profitable. Are we at a time where boards are aggressively holding C suite execs accountable for these investments yet? Is there still a bit of a grace period that we're seeing grace period for now?
[05:21]
A
Not for long. You look at what's happening, I think if you look at the. You talk about this hidden cost. I published a white paper recently that talks about the cost of training GPT2, which I think all of us are largely familiar with. It was the first time we encountered this bewildering technology called, you know, large language models, or GPT. The cost to train GPT2, and this is public data, was around just shy of $50,000. Manageable, affordable. The cost to train the frontier models that we're seeing in 2026, over a billion dollars. It's extraordinary. So we've gone from like used car to aircraft carrier. Now that cost is heavily subsidized by investors, right? I mean, OpenAI is burning $3 billion a month. That's a reported reliable number. You can't lose $3 billion a month into perpetuity. You just can't. No, no organization can sustain those kinds of negative economics, no matter what how transformative the technology. So those costs will be borne by someone. It'll be the consumer, it'll be the enterprise, the, the business itself. So we haven't seen the full cost of AI yet. But every CEO I talk to, they say the same thing, which is, wow, my spend on AI from last year to this year went up 10x20x. You read in the news, people are saying, hey, we got to put a halt or, you know, put a stop to this AI spending. Even Elon Musk, who loves to spend money, put a cap on what his employees can spend at Tesla and SpaceX. That was a recently reported news item as well. The point being is that while there's a grace period for now, most CEOs in the very short term, the next six months will start putting caps on the investment within AI internally. And then there's going to be this heightened demand to see real ROI at a board level before any company spends tens, hundreds of millions, billions of dollars on AI.
[07:33]
B
Now, I believe you suggested that as many as 40% of enterprise AI projects could ultimately be abandoned in the end. And that's a very interesting figure. And it ties into what you've been talking about. I wonder if you could kind of dive into that mindset. But also what are the characteristics of projects that fail? What separates them from the ones that are actually creating lasting value?
[07:53]
A
Well, we're in a, in a heavy era of experimentation with AI. To a certain degree. You have to expect AI projects to fail partly because they're really easy to start, right? It takes five minutes, you crack your knuckles and you're asking Claude or OpenAI to do things with your business data so you can start very easily, but what happens is you ignore business requirements, strategic outcomes, because you can just start iterating with AI. So I do think to a certain degree, kind of, because coding has become so easy now, accessing or starting to build outcomes with AI, good or bad, has become easy. So we rush into these things and we forget the basics of outcome, roi, productive results. So we, we kind of rush in. That's part of it. Part of it is just experimentation, seeing what works, what doesn't, where there's roi. But even Gartner is saying that a number of these, what we call agentic projects, that's just AI with agents working inside of businesses, that these things are either going to be, they're either going to fail or fail to just return results and they'll be shut off by the end of 2027. So you've got very credible analyst organizations calling this out. I absolutely believe that as well. And we see it every day. So I think right now we're still on the the edge of that grace period. I don't want to say blank check, but I think very quickly these costs are going to get reined in. And especially when funding starts to dry up for some of these frontier model organizations, they're going to pass those costs onto the consumer. Trading at Schwab is now powered by Ameritrade, bringing you an expanding library of education with even more ways to sharpen your trading skills. Access new online courses, insightful webcasts, articles, engaging videos and more, all curated just for traders. Plus guided learning paths with content designed to fit your unique interests. No sifting to find exactly what you need so you can spend your time learning to trade brilliantly. Learn more@schwab.com trading looking back at your
[10:08]
B
experience in the tech space and enterprise software. I mean, do companies tend to fail in this space because the technology doesn't work or because customers don't trust or adopt the tools? And I'm curious how that can translate to the current AI revolution.
[10:24]
A
After 30 years in software, I know one thing and that is if humans don't trust something, it will never be used. And forget AI. The reality is I've seen thousands of business intelligence or analytics or data projects that fail because the data wasn't accurate, no one trusted it, and all it takes is one time and one person sitting in a room. And you've been in one of these meetings too, I guarantee. And anybody listening to this where they say this is not accurate, this is wrong, and the moment someone asserts it's inaccurate or it's wrong, it degrades the entire system, the effort, all the, you know, that everyone put into it and suddenly mistrust begins. So change only happens at the speed of trust. And here we are with AI, which it's not about data that humans rely on. This is potentially about AI that could do the job of a human. No, I don't think that, that, I think all that, that, you know, hype about AI taking people's jobs is just nonsense and fud. And you know, it's, it's more, you know, trying to scare people into buying a product than it is reality right now and certainly for the foreseeable future. That being said, I think that it's trust that is the number one thing is do you view Rachel? Do I, Steve? Do we trust AI? And if you're watching this right now, do you trust it? And if the answer is no, except to build a good PowerPoint, then we're not there yet.
[12:06]
B
Many of the biggest gains from AI so far have gone to chip makers, infrastructure providers. Obviously those are entities that are really key to this continued build out. But I wonder where you maybe see the next wave of AI winners emerging across the enterprise technology stack just from your vantage point.
[12:24]
A
Yeah, well, the one company that is unequivocally making money from AI is Nvidia. That's the one company that seems to be making a ton of money. There's a lot of other companies that are, that have proven they can build amazing models and lose extraordinary amounts of money. Now I am an AI protagonist, I'm a big believer in it and I think the potential for AI is extraordinary and I think companies should be experimenting right now. I think trying and failing is a critical part of success. So I believe all those things. But the next wave of companies that will be, I think, AI beneficiaries. I mean certainly I believe our company's one simply because we are critical AI infrastructure. We enable organizations to connect to their data, radically improve the quality of it and deliver it securely to models and AI agents. That matters. But take a look at organizations like Snowflake Databricks, Datadog. These organizations, they all center around the notion of data and they're showing massive benefit. Their market caps are up, their growth is up, and a lot of it has to do with, they provide critical AI infrastructure and, and for business in particular. I, I think we've seen the, the vast majority of financial impact right now at the consumer level. And I think this next wave is going to be enterprise AI for B2B. But I think within that statement it's going to be a whole lot of data, a whole lot of infrastructure. Those are the companies that we'll see accelerate. We're seeing that in our business.
[14:06]
B
Yeah. And I was going to say, I mean how should investors evaluate opportunities in areas like data infrastructure integration, automation and governance? Those are less visible than some of the flashy AI models, but certainly really critical to successful deployment.
[14:20]
A
Well, whether you're an electric car or a petrol car believer, neither one of those goes without energy and the data is the energy. So AI without data is meaningless. That's the plot that I think investors sometimes miss. And I think what's critical is understanding which businesses have data, data and graph moats. And I'm talking about a knowledge graph. Which businesses have unique data they're either handling for their clients or they generate themselves. Data that can't be automatically generated by AI or easily replicated. Those are golden nuggets for investors. And by the way, I think, you know, on the investment front, like even I think about this is, you know, you see a lot of organizations that are rush, rushing into more I characterize as like unique models where they're thinking about, well, how do I provide some type of unique, like either a hardware plus AI, a chip plus software, unique services like forward deployed engineers and I'll use the word ontology to along with AI, people are looking for that. I'm not just software as a service type of message. And you'll see that from every large organization messaging to investors is. I'm not just software that's critical for software CEOs because they want to deliver the message that I'm not easily disrupted by AI. So investors need to watch out for that. But I think data, I do think unique combinations of AI plus matter. But I'm still a believer in infrastructure. That's why I'm here.
[16:01]
B
Well, and I think that's one of the bigger questions. I mean obviously some of the most well known AI companies have not yet entered the public markets. There's rumors they will if you think of anthropic or open AI obviously as a couple examples. But I think one of the biggest questions that a lot of investors have who are watching these companies right now is how do these companies monetize long term, how do they retain durable profits over the long run? And it sounds like you're saying these are the models, the business models that they're developing in order to ensure that they're able to retain that financial growth and flexibility.
[16:35]
A
Well, yeah, I mean and again, I think for most investors, if you just step back and think about, and let's not, you know, kind of target or talk about any one particular company, but a frontier model company that's losing billions of dollars a month. So the first narrative that we heard in kind of like AI narrative 1.0 was, hey, AI is going to take all these human jobs and you need to be ready for that. Well, that didn't happen, at least not yet. But to a certain degree, I think a number of these public or frontier model AI CEOs, they were counting on AI taking these human jobs. They need it because there's just not enough software revenue to cover what it costs to train and build these models into perpetuity. So they, they needed AI to be successful and consume some of the labor market. But here we are, and that hasn't happened. So what next? Well, for AI to be successful long term, as I said earlier, these organizations, these big frontier models, they're going to have to convince enterprise organizations to use their model privately. That's a big step. This isn't just about consumer growth and what you or I use AI for at a small business level or at home. Well, you know, I'll pay my 200 bucks a month to, to Anthropic or OpenAI, but they've demonstrated that they need more revenue. So the enterprise shift is going to happen. And I think there's so much more that needs to happen. I don't know how these organizations turn a profit without significant penetration into labor markets. And again, that's what they're counting on.
[18:22]
B
Very interesting. The other thing that this discussion brings up is, you know, we have heard a lot of companies, not just in the tech space, but certainly in the tech space, that have announced, you know, layoffs over the last, say, six to 12 months in some cases, alleging AI efficiency being a driving factor. And I know you've talked a bit about today why you don't view, you know, AI as a replacement for human labor. And obviously I think there are a lot of people that share that view. So when we see companies that will announce layoffs citing AI as a driving factor, is that sort of trying to hide a hemorrhaging business, so to speak? I mean, is that something that we should be perhaps reading between the lines a bit?
[19:05]
A
I think that there's a whole lot of spin going on right now. That's what I think. I think you're absolutely right. These layoffs, you know, where's the data behind it? Simply saying we've achieved so much productivity. Therefore, we need to lay off 9,000 people as a matter of convenience. There's no fact in it, or if there is, it's very little and certainly uncommunicated. What I would want to see as an investor is show me where the rate of return. Efficient. You're, you're more efficient in your finance team, more efficient in your, in your engineering team. We're producing 10 times the amount of code in a third of the time. Show me the numbers and show me the money. And if you do that, then I'm going to start to buy into that. So absolutely, this is, this is a matter of convenience. I don't want to say that AI is the scapegoat. I think it's just a convenient foil right now.
[20:03]
C
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[22:00]
B
If we're investors, we're trying to look beyond the AI headlines. What are metrics, signals, company characteristics even that you would focus on as we're trying to identify the longer term winners in enterprise AI?
[22:13]
A
Well, it's going to go way beyond market cap. I think market cap is kind of semi fickle right now. It changes with winds and you can have Anthropic make a statement about some new feature in their product and then suddenly, you know, dozens of publicly traded organizations tank market capitalization wise because of a perceived feature or detriment to a market based on AI. I think signals that you can look out for. We talked about this briefly, but I think it's going to be organizations that are showing acceleration number one in their new customer acquisition. If you truly have transformative technology, the indicator for that, the success of that technology is not forcing a product down your existing customers throats. It's our new organizations coming to you, seeking that innovation, that transformative technology and you obviously as a complement to the frontier model businesses, the unbelievable growth going from zero to a billion to 20 billion and beyond in a matter of a few short years, that's unprecedented. That came through entirely new logo or new client acquisition, that's consumer growth and new business growth. Now that will change over time as Anthropic and OpenAI and Mistral and others run out of new logos. They're going to start to mature and think about new feature selling and customer expansion and all those gnarly words that happen with mature businesses. But the number one for me is new logo acquisition. That's always an indicator of a sufficiently transformative technology.
[24:01]
B
One final question. I'm sort of a two parter. What excites you the most about AI right now and what are you most cautious about?
[24:10]
A
That is a really good question and I'll try not to get too philosophical on you, Rachel. Be philosophical, here we go. I've been a type one diabetic for almost 30 years. And you know, we all play the hand that we're dealt in life. And so I wear a sensor on this arm and I have an insulin pump on that arm. And I've had a small army of amazing humans, doctors, clinicians, nurses that have had helped me live a full and healthy life, which I love. But as I sit here, there's data streaming between my phone and my glucose sensor and my insulin pump 24, 7 non stop and my life depends on it. The reason I offer that that background is because AI has not just the potential or promise, but it will transform lives. It will not only make my own personal life and the ability to manage type 1 diabetes profoundly easier, which I welcome. I, I look forward to that day. But it will cure it, along with the vast majority of the things that we think about as challenges to human life today. I think within the next two decades, and I genuinely mean this, and this is why I'm an AI optimist. The things that we, that we treat, that we are challenged with, health wise, that we have to overcome, will largely be managed and, or solved by AI. I think we will live longer, healthier lives and that I love. That's what I look forward to. And for the, the billions of people that struggle with health challenges out there, I think that there's an exciting future to look forward to. That being said, what I don't look forward to is AI being aware of all of that data, which I know it has to be and it's used to market products and services to. And that is the fine line that we walk every day is how do we benefit, radically benefit humankind while not trying to sell you a cup of coffee.
[26:30]
B
Well, I think that's the perfect note to end on. And you've given us all, I think, a lot to think about. Thank you so much, Steve, for your time today.
[26:37]
A
Thank you, Rachel. I really enjoyed it, as always.
[26:40]
B
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.

Summary6 min read

Motley Fool Hidden Gems Investing

Episode: Why Most AI Projects Will Fail — And How to Find the Companies That Won't

Date: July 12, 2026

Host: Rachel Warren (Motley Fool Analyst)

Guest: Steve Lucas (Chairman & CEO of Boomi)

Episode Overview

This episode moves beyond the current AI hype to focus on what truly separates profitable, sustainable enterprise AI ventures from expensive, flashy experiments likely to fail. Motely Fool analyst Rachel Warren sits down with Steve Lucas, a veteran leader in enterprise software (past CEO roles at Marketo, now CEO of Boomi), to discuss the looming "ROI reckoning," where real business results and trustworthy data infrastructure will determine the next generation of winners and losers. Listeners learn the signals to spot durable AI opportunities and company types most likely to generate lasting value.

Key Discussion Points & Insights

1. The ROI Reckoning for AI Investments

Pressure Has Shifted: The pendulum has swung from “every company needs an AI strategy” to boards demanding measurable business returns.
- Quote (Steve Lucas, 02:09):
  “I’ve been quoted a few times as saying that ROI supersedes AI and there is no doubt that that is the case today.”
Spending Trends:
- Enterprise AI and infrastructure spending are soaring, while traditional software app budgets are down.
- There’s been a dramatic increase in capital expenditure among hyperscalers (from ~$410B to over $700B year-over-year) ([03:33]).
Grace Period Ending:
- Boards have given tech leaders some leeway, but that "blank check" era is closing fast.
- Most companies will start capping AI investment and demand proof of ROI in the next 6 months ([05:21]).

2. Why Most AI Projects Will Fail

High Failure Rate:
- Steve Lucas estimates up to 40% of enterprise AI projects may ultimately be abandoned ([07:32]).
Reasons for Failure:
- Projects are easy to start and often lack clear business objectives or strategic outcomes.
- The ease of experimentation leads to “rushing in” and neglecting basics like ROI and productive results.
- Trust is pivotal: Without user trust, technology adoption fails, regardless of how technically impressive it may be ([10:24]).
  - Quote:
    “Change only happens at the speed of trust. ... If humans don’t trust something, it will never be used.”

3. Who’s Making Money in AI?

Today’s Standouts:
- Nvidia is underscored as the only company unequivocally generating large profits from AI right now ([12:24]).
Many AI Models Burn Cash:
- OpenAI reportedly burns $3 billion per month, a figure that cannot be sustained indefinitely ([05:21], [16:34]).
- Steve highlights the need for these companies to reach massive scale or enterprise adoption to become profitable.

4. Identifying the Next Wave of Winners

Critical Sectors:
- Data infrastructure and integration companies (Boomi, Snowflake, Databricks, Datadog) are gaining momentum and are central to enterprise AI ([12:24], [14:05]).
  - Quote (14:19):
    “AI without data is meaningless ... Which businesses have unique data they're either handling for their clients or they generate themselves. Data that can’t be automatically generated by AI or easily replicated. Those are golden nuggets for investors.”
- The next big impact will come from enterprise/B2B AI, not just consumer apps ([12:24]).
Investor Focus Areas:
- Look for businesses with strong “data moats” and unique, hard-to-replicate data assets or knowledge graphs.
- Integration of hardware + software + specialized services offers differentiation.

5. AI Economics and Business Models

Challenge of Monetization:
- Massive model training costs ($1B+ for latest models) mean that most model providers need either huge revenue from enterprise or broad labor disruption for sustainable profits ([05:21], [16:34]).
- AI’s path to profitability may require deep enterprise market penetration, not just consumer subscriptions.

6. AI Layoffs – Signal or Spin?

AI as a Layoff Excuse:
- Many companies are citing “AI efficiency” as the reason for layoffs, but without showing evidence of productivity gains ([19:04]).
  - Quote (19:04):
    “I think there’s a whole lot of spin going on right now. ... There’s no fact in it, or if there is, it’s very little and certainly uncommunicated. ... Show me the numbers and show me the money.”
- Steve views this as mostly “convenient foil” rather than real cause.

7. How Can Investors Spot Real AI Winners?

Beyond the Hype:
- Market cap can move wildly based on headlines, so deeper signals are needed.
- The #1 sign: Rapid, organic new customer acquisition rather than just upselling existing customers ([22:13]).
  - Quote:
    “If you truly have transformative technology, the indicator for that ... is: are new organizations coming to you, seeking that innovation, that transformative technology?”
As markets mature, growth patterns shift:
- Early-stage: New logo growth.
- Mature: Customer expansion and new feature selling.

8. Philosophical Outlook: Promise and Peril

Steve’s Personal Connection:
- As a type 1 diabetic, Steve relies on medical data streaming constantly to his devices ([24:10]).
- He believes AI will soon deliver profound healthcare breakthroughs, even a cure for type 1 diabetes, and improve quality and length of human life in the next two decades.
  - Quote (24:10):
    “AI has not just the potential or promise, but it will transform lives. ... It will cure [diseases], along with the vast majority of the things that we think about as challenges to human life today.”
Major Caution:
- Worries about AI’s access to personal health and behavioral data for marketing and commercialization:
  - Quote:
    “...how do we benefit, radically benefit humankind while not trying to sell you a cup of coffee.”

Notable Quotes & Memorable Moments

On ROI Superseding AI:
“ROI supersedes AI and there is no doubt that that is the case today.” — Steve Lucas ([02:09])
On Trust:
“Change only happens at the speed of trust. ... If humans don’t trust something, it will never be used.” — Steve Lucas ([10:24])
On Unique Data as a Moat:
“Data that can’t be automatically generated by AI or easily replicated. Those are golden nuggets for investors.” — Steve Lucas ([14:19])
On AI Layoff Excuses:
“There’s a whole lot of spin going on right now. ... Show me the numbers and show me the money.” — Steve Lucas ([19:04])
On the True Test for Transformative AI:
“The indicator for success of that technology is: are new organizations coming to you, seeking that innovation, that transformative technology?” — Steve Lucas ([22:13])
On AI’s Human Impact:
“AI has not just the potential or promise, but it will transform lives. ... It will cure [diseases]. ... That’s why I’m an AI optimist.” — Steve Lucas ([24:10])

Timestamps for Important Segments

AI hype to ROI focus — 02:09
CapEx surge and "blank check" era fading — 03:33 to 05:21
Cost escalation, unsustainable burns (OpenAI, etc.) — 05:21
Why 40%+ projects fail, importance of outcomes — 07:32
Trust as prerequisite for adoption — 10:24
Current money makers: Nvidia, infrastructure — 12:24
Data moats and enterprise opportunity — 14:19
Challenges for frontier model profitability — 16:34
Skepticism on AI-powered layoffs — 19:04
Real investor signals: new customer acquisition — 22:13
Personal stake: AI in healthcare and its promise/cautions — 24:10

Takeaways for Investors

Obsess over hard ROI, not just AI buzzwords.
Seek businesses with critical data–unique, hard-to-replicate data assets and infrastructure.
Look past market cap shifts; focus on organic customer adoption and new logo acquisition.
Remain vigilant—AI's costs may be understated and its economic model is still evolving.
The flashiest models might not win; look for "plumbing" and "energy" (data) plays.
The true transformation may arrive in health and enterprise sectors—but with it comes a responsibility to safeguard personal data.

Loading summary

Transcript29 lines

[00:03]
A
The one company that is unequivocally making money from AI is Nvidia. That's the one company that seems to be making a ton of money. There's a lot of other companies that are, that have proven they can build amazing models and lose extraordinary amounts of money.
[00:22]
B
Start with Steve Lucas, chairman and CEO of Boomi, explaining who is actually making money in AI right now and who isn't. Steve is a 30 year enterprise software veteran who previously turned Marketo into a $4.75 billion acquisition. I'm motley fool analyst Rachel Warren. Steve has sat across the table from hundreds of CEOs navigating the AI moment and what he's hearing might surprise you. We discussed the ROI reckoning that's coming, what separates real AI winners from expensive experiments, and why the next wave of big beneficiaries probably isn't who you think. We hope you enjoy. Welcome back to Motley Fool Conversations. I'm Motley fool analyst Rachel Warren. Today we're looking past the AI hype cycle to focus on execution, data infrastructure and true return on investment. Joining us is Steve Lucas, Chairman and CEO of Boomi. Steve is a multi time CEO with nearly 30 years of enterprise software leadership, including senior roles at Salesforce and Adobe. And previously as CEO of Marketo, he drove a massive turnaround resulting in a $4.75 billion acquisition by ADO. Now at the helm of Boomi, a data activation powerhouse serving over 30,000 global customers, Steve is here to talk about the current state of AI, where corporate tech budgets are actually moving and how investors can spot the real winners. Steve, welcome to the show.
[01:42]
A
Thank you, Rachel. Happy to be here.
[01:45]
B
So for the last few years, it seems as though investors have largely rewarded companies for simply having an AI strategy using the right AI buzzwords and earnings calls. But it seems we're entering something of the next phase in that journey where Wall street demands understandably measurable business outcomes and roi. So I'm curious, what do companies need to do and or keep top of mind to actually deliver to that end?
[02:10]
A
Well, first of all, I think you're absolutely right. Over the past couple years, we've gone from we didn't have AI, now it exists to boards pressuring executive teams, CEOs and leaders at companies to put AI into their company build an AI strategy. And in the two years that we've seen that pressure kind of mount, we've seen the birth of agentic AI inside of businesses and all those things, I think that the pressure has now started to subside. And as you pointed out, now it's about returns. And I've been quoted a few times as saying that ROI supersedes AI and there is no doubt that that is the case today. I just think it's the enormity of the pressure on that left hand side coupled with rushing into a lot of AI projects. We're not seeing the high rates of return that you'd expect from businesses. Now that's going to change as AI matures and how organizations manage AI matures as well. But we're definitely seeing a change in the winds.
[03:19]
B
Do you think we've reached a point where AI spending could become a drag on earnings for companies that fail to demonstrate meaningful returns on those investments, or do you think it's just too early to really make that, make that determination yet?
[03:33]
A
Well, if you look at the four major hyperscalers in the US alone and the amount of CapEx that they put into AI last year versus this year, this is kind of the canary in the coal mine. Last year it was around 400 billion, 410 billion, and this year it's over 700 billion. Four companies. That's an extraordinary increase in spending. And obviously that is not reflective of the broader market, but it's an indicator of the broader market. The broader market organizations, they're spending on AI is way up, they're spending on software applications is down and spending on infrastructure is up as well. So AI and infrastructure seem to be the two big investment priorities for large organizations. But I think we are, you said the blank check era. I think that's a perfect phrase. And we are at a place where organizations, I walk into board meeting after board meeting, CEO after CEO, and what I hear continuously is help me show return after these projects that we initially pursued. They're not providing roi. That is, it is real, it's in the market. That's being discussed. And you're starting to hear other executives call it out as well, which is let's stop trying to scare all the executives with these scare tactics into investing in AI and in business. Let's help them show real rates of return.
[05:00]
B
You mentioned earlier that you'll go in these meetings and there's C suite executives basically saying, you know, help us show that we are making this profitable or on the path to making it profitable. Are we at a time where boards are aggressively holding C suite execs accountable for these investments yet? Is there still a bit of a grace period that we're seeing grace period for now?
[05:21]
A
Not for long. You look at what's happening, I think if you look at the. You talk about this hidden cost. I published a white paper recently that talks about the cost of training GPT2, which I think all of us are largely familiar with. It was the first time we encountered this bewildering technology called, you know, large language models, or GPT. The cost to train GPT2, and this is public data, was around just shy of $50,000. Manageable, affordable. The cost to train the frontier models that we're seeing in 2026, over a billion dollars. It's extraordinary. So we've gone from like used car to aircraft carrier. Now that cost is heavily subsidized by investors, right? I mean, OpenAI is burning $3 billion a month. That's a reported reliable number. You can't lose $3 billion a month into perpetuity. You just can't. No, no organization can sustain those kinds of negative economics, no matter what how transformative the technology. So those costs will be borne by someone. It'll be the consumer, it'll be the enterprise, the, the business itself. So we haven't seen the full cost of AI yet. But every CEO I talk to, they say the same thing, which is, wow, my spend on AI from last year to this year went up 10x20x. You read in the news, people are saying, hey, we got to put a halt or, you know, put a stop to this AI spending. Even Elon Musk, who loves to spend money, put a cap on what his employees can spend at Tesla and SpaceX. That was a recently reported news item as well. The point being is that while there's a grace period for now, most CEOs in the very short term, the next six months will start putting caps on the investment within AI internally. And then there's going to be this heightened demand to see real ROI at a board level before any company spends tens, hundreds of millions, billions of dollars on AI.
[07:33]
B
Now, I believe you suggested that as many as 40% of enterprise AI projects could ultimately be abandoned in the end. And that's a very interesting figure. And it ties into what you've been talking about. I wonder if you could kind of dive into that mindset. But also what are the characteristics of projects that fail? What separates them from the ones that are actually creating lasting value?
[07:53]
A
Well, we're in a, in a heavy era of experimentation with AI. To a certain degree. You have to expect AI projects to fail partly because they're really easy to start, right? It takes five minutes, you crack your knuckles and you're asking Claude or OpenAI to do things with your business data so you can start very easily, but what happens is you ignore business requirements, strategic outcomes, because you can just start iterating with AI. So I do think to a certain degree, kind of, because coding has become so easy now, accessing or starting to build outcomes with AI, good or bad, has become easy. So we rush into these things and we forget the basics of outcome, roi, productive results. So we, we kind of rush in. That's part of it. Part of it is just experimentation, seeing what works, what doesn't, where there's roi. But even Gartner is saying that a number of these, what we call agentic projects, that's just AI with agents working inside of businesses, that these things are either going to be, they're either going to fail or fail to just return results and they'll be shut off by the end of 2027. So you've got very credible analyst organizations calling this out. I absolutely believe that as well. And we see it every day. So I think right now we're still on the the edge of that grace period. I don't want to say blank check, but I think very quickly these costs are going to get reined in. And especially when funding starts to dry up for some of these frontier model organizations, they're going to pass those costs onto the consumer. Trading at Schwab is now powered by Ameritrade, bringing you an expanding library of education with even more ways to sharpen your trading skills. Access new online courses, insightful webcasts, articles, engaging videos and more, all curated just for traders. Plus guided learning paths with content designed to fit your unique interests. No sifting to find exactly what you need so you can spend your time learning to trade brilliantly. Learn more@schwab.com trading looking back at your
[10:08]
B
experience in the tech space and enterprise software. I mean, do companies tend to fail in this space because the technology doesn't work or because customers don't trust or adopt the tools? And I'm curious how that can translate to the current AI revolution.
[10:24]
A
After 30 years in software, I know one thing and that is if humans don't trust something, it will never be used. And forget AI. The reality is I've seen thousands of business intelligence or analytics or data projects that fail because the data wasn't accurate, no one trusted it, and all it takes is one time and one person sitting in a room. And you've been in one of these meetings too, I guarantee. And anybody listening to this where they say this is not accurate, this is wrong, and the moment someone asserts it's inaccurate or it's wrong, it degrades the entire system, the effort, all the, you know, that everyone put into it and suddenly mistrust begins. So change only happens at the speed of trust. And here we are with AI, which it's not about data that humans rely on. This is potentially about AI that could do the job of a human. No, I don't think that, that, I think all that, that, you know, hype about AI taking people's jobs is just nonsense and fud. And you know, it's, it's more, you know, trying to scare people into buying a product than it is reality right now and certainly for the foreseeable future. That being said, I think that it's trust that is the number one thing is do you view Rachel? Do I, Steve? Do we trust AI? And if you're watching this right now, do you trust it? And if the answer is no, except to build a good PowerPoint, then we're not there yet.
[12:06]
B
Many of the biggest gains from AI so far have gone to chip makers, infrastructure providers. Obviously those are entities that are really key to this continued build out. But I wonder where you maybe see the next wave of AI winners emerging across the enterprise technology stack just from your vantage point.
[12:24]
A
Yeah, well, the one company that is unequivocally making money from AI is Nvidia. That's the one company that seems to be making a ton of money. There's a lot of other companies that are, that have proven they can build amazing models and lose extraordinary amounts of money. Now I am an AI protagonist, I'm a big believer in it and I think the potential for AI is extraordinary and I think companies should be experimenting right now. I think trying and failing is a critical part of success. So I believe all those things. But the next wave of companies that will be, I think, AI beneficiaries. I mean certainly I believe our company's one simply because we are critical AI infrastructure. We enable organizations to connect to their data, radically improve the quality of it and deliver it securely to models and AI agents. That matters. But take a look at organizations like Snowflake Databricks, Datadog. These organizations, they all center around the notion of data and they're showing massive benefit. Their market caps are up, their growth is up, and a lot of it has to do with, they provide critical AI infrastructure and, and for business in particular. I, I think we've seen the, the vast majority of financial impact right now at the consumer level. And I think this next wave is going to be enterprise AI for B2B. But I think within that statement it's going to be a whole lot of data, a whole lot of infrastructure. Those are the companies that we'll see accelerate. We're seeing that in our business.
[14:06]
B
Yeah. And I was going to say, I mean how should investors evaluate opportunities in areas like data infrastructure integration, automation and governance? Those are less visible than some of the flashy AI models, but certainly really critical to successful deployment.
[14:20]
A
Well, whether you're an electric car or a petrol car believer, neither one of those goes without energy and the data is the energy. So AI without data is meaningless. That's the plot that I think investors sometimes miss. And I think what's critical is understanding which businesses have data, data and graph moats. And I'm talking about a knowledge graph. Which businesses have unique data they're either handling for their clients or they generate themselves. Data that can't be automatically generated by AI or easily replicated. Those are golden nuggets for investors. And by the way, I think, you know, on the investment front, like even I think about this is, you know, you see a lot of organizations that are rush, rushing into more I characterize as like unique models where they're thinking about, well, how do I provide some type of unique, like either a hardware plus AI, a chip plus software, unique services like forward deployed engineers and I'll use the word ontology to along with AI, people are looking for that. I'm not just software as a service type of message. And you'll see that from every large organization messaging to investors is. I'm not just software that's critical for software CEOs because they want to deliver the message that I'm not easily disrupted by AI. So investors need to watch out for that. But I think data, I do think unique combinations of AI plus matter. But I'm still a believer in infrastructure. That's why I'm here.
[16:01]
B
Well, and I think that's one of the bigger questions. I mean obviously some of the most well known AI companies have not yet entered the public markets. There's rumors they will if you think of anthropic or open AI obviously as a couple examples. But I think one of the biggest questions that a lot of investors have who are watching these companies right now is how do these companies monetize long term, how do they retain durable profits over the long run? And it sounds like you're saying these are the models, the business models that they're developing in order to ensure that they're able to retain that financial growth and flexibility.
[16:35]
A
Well, yeah, I mean and again, I think for most investors, if you just step back and think about, and let's not, you know, kind of target or talk about any one particular company, but a frontier model company that's losing billions of dollars a month. So the first narrative that we heard in kind of like AI narrative 1.0 was, hey, AI is going to take all these human jobs and you need to be ready for that. Well, that didn't happen, at least not yet. But to a certain degree, I think a number of these public or frontier model AI CEOs, they were counting on AI taking these human jobs. They need it because there's just not enough software revenue to cover what it costs to train and build these models into perpetuity. So they, they needed AI to be successful and consume some of the labor market. But here we are, and that hasn't happened. So what next? Well, for AI to be successful long term, as I said earlier, these organizations, these big frontier models, they're going to have to convince enterprise organizations to use their model privately. That's a big step. This isn't just about consumer growth and what you or I use AI for at a small business level or at home. Well, you know, I'll pay my 200 bucks a month to, to Anthropic or OpenAI, but they've demonstrated that they need more revenue. So the enterprise shift is going to happen. And I think there's so much more that needs to happen. I don't know how these organizations turn a profit without significant penetration into labor markets. And again, that's what they're counting on.
[18:22]
B
Very interesting. The other thing that this discussion brings up is, you know, we have heard a lot of companies, not just in the tech space, but certainly in the tech space, that have announced, you know, layoffs over the last, say, six to 12 months in some cases, alleging AI efficiency being a driving factor. And I know you've talked a bit about today why you don't view, you know, AI as a replacement for human labor. And obviously I think there are a lot of people that share that view. So when we see companies that will announce layoffs citing AI as a driving factor, is that sort of trying to hide a hemorrhaging business, so to speak? I mean, is that something that we should be perhaps reading between the lines a bit?
[19:05]
A
I think that there's a whole lot of spin going on right now. That's what I think. I think you're absolutely right. These layoffs, you know, where's the data behind it? Simply saying we've achieved so much productivity. Therefore, we need to lay off 9,000 people as a matter of convenience. There's no fact in it, or if there is, it's very little and certainly uncommunicated. What I would want to see as an investor is show me where the rate of return. Efficient. You're, you're more efficient in your finance team, more efficient in your, in your engineering team. We're producing 10 times the amount of code in a third of the time. Show me the numbers and show me the money. And if you do that, then I'm going to start to buy into that. So absolutely, this is, this is a matter of convenience. I don't want to say that AI is the scapegoat. I think it's just a convenient foil right now.
[20:03]
C
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[22:00]
B
If we're investors, we're trying to look beyond the AI headlines. What are metrics, signals, company characteristics even that you would focus on as we're trying to identify the longer term winners in enterprise AI?
[22:13]
A
Well, it's going to go way beyond market cap. I think market cap is kind of semi fickle right now. It changes with winds and you can have Anthropic make a statement about some new feature in their product and then suddenly, you know, dozens of publicly traded organizations tank market capitalization wise because of a perceived feature or detriment to a market based on AI. I think signals that you can look out for. We talked about this briefly, but I think it's going to be organizations that are showing acceleration number one in their new customer acquisition. If you truly have transformative technology, the indicator for that, the success of that technology is not forcing a product down your existing customers throats. It's our new organizations coming to you, seeking that innovation, that transformative technology and you obviously as a complement to the frontier model businesses, the unbelievable growth going from zero to a billion to 20 billion and beyond in a matter of a few short years, that's unprecedented. That came through entirely new logo or new client acquisition, that's consumer growth and new business growth. Now that will change over time as Anthropic and OpenAI and Mistral and others run out of new logos. They're going to start to mature and think about new feature selling and customer expansion and all those gnarly words that happen with mature businesses. But the number one for me is new logo acquisition. That's always an indicator of a sufficiently transformative technology.
[24:01]
B
One final question. I'm sort of a two parter. What excites you the most about AI right now and what are you most cautious about?
[24:10]
A
That is a really good question and I'll try not to get too philosophical on you, Rachel. Be philosophical, here we go. I've been a type one diabetic for almost 30 years. And you know, we all play the hand that we're dealt in life. And so I wear a sensor on this arm and I have an insulin pump on that arm. And I've had a small army of amazing humans, doctors, clinicians, nurses that have had helped me live a full and healthy life, which I love. But as I sit here, there's data streaming between my phone and my glucose sensor and my insulin pump 24, 7 non stop and my life depends on it. The reason I offer that that background is because AI has not just the potential or promise, but it will transform lives. It will not only make my own personal life and the ability to manage type 1 diabetes profoundly easier, which I welcome. I, I look forward to that day. But it will cure it, along with the vast majority of the things that we think about as challenges to human life today. I think within the next two decades, and I genuinely mean this, and this is why I'm an AI optimist. The things that we, that we treat, that we are challenged with, health wise, that we have to overcome, will largely be managed and, or solved by AI. I think we will live longer, healthier lives and that I love. That's what I look forward to. And for the, the billions of people that struggle with health challenges out there, I think that there's an exciting future to look forward to. That being said, what I don't look forward to is AI being aware of all of that data, which I know it has to be and it's used to market products and services to. And that is the fine line that we walk every day is how do we benefit, radically benefit humankind while not trying to sell you a cup of coffee.
[26:30]
B
Well, I think that's the perfect note to end on. And you've given us all, I think, a lot to think about. Thank you so much, Steve, for your time today.
[26:37]
A
Thank you, Rachel. I really enjoyed it, as always.
[26:40]
B
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.