Why Most AI Projects Will Fail — And How to Find the Companies That Won't
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What is the main topic discussed in this episode?
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 have proven they can build amazing models and lose extraordinary amounts of money.
That was 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 discuss 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.
How is the AI spending boom shifting to an ROI-first mindset?
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 Adobe. 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.
Thank you, Rachel. Happy to be here.
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?
Well, first of all, I think you're absolutely right. Over the past couple of 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.
Are boards and CEOs starting to cap AI budgets and demand returns?
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 wind.
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 determination yet?
Well, if you look at the four major hyperscalers in the U.S. 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 $410 billion, and this year it's over $700 billion. for 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, their spending on AI is way up. Their 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 checker.
I think that's a perfect phrase.
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Chapters
7 chapters
1
What is the main topic discussed in this episode?
0:03–1:00
2
How is the AI spending boom shifting to an ROI-first mindset?
1:00–3:07
3
Are boards and CEOs starting to cap AI budgets and demand returns?
3:07–7:32
4
How much does training frontier AI cost and who absorbs those expenses?
7:32–12:24
5
Why do so many enterprise AI projects fail — is it experimentation or missing business requirements?
12:24–17:13
6
Does lack of trust and poor data quality cause AI adoption to stall?
17:13–22:34
7
Which types of companies are actually profiting from AI today?
22:34–25:12