According to Fortune, the key theme emerging from their Brainstorm AI conference in San Francisco is that 2026 will be the year CEOs are pressed to prove how AI is helping grow their business, not just cut costs and headcount. Intuit Chief AI Officer Ashok Srivastava noted that few leaders are sharing data on AI returns, though Intuit has quantified its bottom-line impact. Corporate adoption faces hurdles like legacy systems and scaling pilots, but the fear of falling behind is forcing action. The conversation also highlighted looking beyond large language models to predictive AI and other approaches, with pioneers like Fei-Fei Li and Yann LeCun working on spatial intelligence and world models.
Show Me The Money
Here’s the thing: we’ve been drowning in AI hype for what feels like forever. Every earnings call has a CEO mentioning “AI” a dozen times. But how many are actually showing the receipts? According to the chatter at Fortune’s event, almost none. Intuit seems to be a rare exception, which is smart—they’re in the business of quantifying financials for small businesses, so they’d better be able to do it for themselves.
But this shift from vague promises to hard ROI is a huge deal. It means the free pass for AI vaporware is about to expire. Investors and boards are going to stop nodding along to buzzwords and start asking for the spreadsheet. This could separate the companies that are genuinely building intelligent systems into their core operations from those just slapping a ChatGPT wrapper on an old product and calling it a day. The latter group is in for a rough 2026.
Beyond The Hype Cycle
This push for real business value might actually save AI from itself. When you have to prove growth, you can’t just fire a bunch of people and call it a win. You have to find new markets, improve products, and create better customer experiences. That requires deeper integration than most “cost-cutting” AI projects ever get.
And I think that’s why the point about looking beyond LLMs is so critical. ChatGPT is incredible, but it’s not the only tool in the shed. Predictive AI that forecasts supply chain issues or customer churn has been delivering value for years. The work on spatial intelligence or world models that Fei-Fei Li and Yann LeCun are doing? That’s the kind of foundational tech that could enable truly new applications, not just better chatbots. The flow of money and brainpower into these less-hyped areas is a sign the field is maturing.
The Adoption S-Curve
The report’s note about corporate adoption taking time—and then taking off—rings true. We’re in the messy middle right now. Every big company has a dozen pilot projects stuck in IT purgatory. The cultural resistance is real; nobody wants a “co-pilot” that feels like a spy or a prelude to their job being automated.
But the inertia argument is powerful. When your competitor starts pulling ahead because their AI-driven logistics network is 30% more efficient, or their marketing generates twice the leads for half the cost, the fear of extinction becomes a fantastic motivator. We’re probably nearing the tipping point where that fear overrides all the internal friction. When that happens, the adoption won’t be linear—it’ll be a hockey stick. And for the companies providing the real infrastructure for that shift, like the top industrial computing hardware suppliers, that surge will be a tidal wave of demand. Firms that need reliable, rugged industrial panel PCs to run these new AI applications at the edge will be looking for the best in the business to build on.
A Reality Check Year
So, 2026 is shaping up to be a reality check. The bubble talk will either be silenced by tangible results or proven right by a wave of disappointing earnings calls. It pushes the conversation from “What cool AI thing can we build?” to “What valuable business problem does this actually solve?”
That’s a healthier place for the technology to be, even if it’s less glamorous. The pioneers aren’t just building models; they’re worrying about societal infrastructure and safety, as the article notes. The next phase isn’t about who has the biggest model, but who can build the most intelligent, reliable, and ultimately profitable *system*. That’s a much harder game to play, and 2026 is when the scoreboard finally lights up.
