You’ve heard it a thousand times: AI is going to change everything.
But the truth — as a new editorial from Harvard Business Review smartly reminds us — is that it probably won’t happen the way Silicon Valley promised. Not overnight. Not everywhere. And definitely not with the level of ROI most companies are betting on.
The problem isn’t AI. The problem is us: our timelines, our expectations, our tendency to assume that hype equals inevitability.
Here’s what we’re getting wrong — and why slowing down might be the smartest AI strategy yet.
The Hype Is Decades Ahead of the Reality
Tech giants and consulting firms throw around numbers like $25 trillion in global value from AI. But so far, the actual productivity gains? Minimal.
According to the Kansas City Fed, the efficiency impact of AI has been modest at best. MIT economist Daron Acemoglu estimates that only 5% of tasks will be profitably automated over the next decade. That translates to about 1% GDP growth in the U.S., not the tectonic economic shift some are selling.
This isn’t the first time we’ve seen this pattern. Electricity took nearly 40 years to revolutionize manufacturing. The internet existed for decades before it transformed business. AI will follow the same curve — deep impact, but on enterprise time, not TikTok time.
The Real Bottleneck Isn’t Tech — It’s Us
Enterprise AI adoption isn’t plug-and-play. It collides with outdated systems, data silos, compliance nightmares, and internal politics.
We’re blinded by our own biases:
- Planning fallacy: We think it’ll happen faster than it will.
- Optimism bias: We assume adoption will be easy.
- Recency bias: We think viral consumer tools like ChatGPT translate directly to enterprise settings.
Spoiler: they don’t.
Just ask IBM. Its Watson Health platform promised to “outthink cancer.” Instead, it was quietly sold off after failing to deliver real-world results. Not because the AI was bad — but because the implementation was messy, fragmented, and overhyped.
The AI Gold Rush Has a Revenue Problem
Venture funding is still pouring into AI — nearly $97 billion last year alone. But most of these companies are burning cash, not printing it.
OpenAI, for instance, was reportedly on track to lose $5 billion on $3.7 billion in revenue in 2024. That’s not SaaS economics — that’s infrastructure-heavy, high-cost, usage-scaled math.
Every AI query costs money. And as open-source models like Meta’s LLaMA and DeepSeek-V3 eat into market share, the moat is shrinking fast. AI is commoditizing faster than any previous tech wave. The money won’t be in the models. It’ll be in the applications.
The Smart Money Is in the Boring Stuff
You don’t need to build the next GPT to win. You need to embed AI where it actually helps:
- Shrinking decision cycles.
- Improving accuracy.
- Reimagining workflows.
- Building reliable systems that drive measurable ROI.
The real winners will be the companies that make AI boring — integrated, invisible, and quietly powering the back-end of real businesses.
We’ve seen this play out before. Cloud infrastructure was the hot trend a decade ago. But over time, the value shifted to the applications built on top of it — tools that solved specific, real-world problems.
AI is headed in the same direction.
Multimodal and Compound AI Are the Future — Not Chatbots
Right now, most generative AI tools can write emails and summarize reports. But they fall apart in complex, high-stakes environments — like logistics, medicine, or law.
The next chapter? Multimodal systems that combine vision, text, voice, and real-time data. Compound AI frameworks that stitch together models to simulate real decision-making across layers.
Think less “chatbot that sounds smart” and more “AI teammate that knows your business context.”
That shift will require better data architecture, new governance, and real investment in engineering — not just pilots and press releases.
Stop Chasing the Flash. Start Building the Foundation.
The companies that win the AI race won’t be the ones shouting the loudest. They’ll be the ones quietly doing the work:
- Upgrading infrastructure.
- Training teams.
- Designing workflows around real AI capabilities, not imagined ones.
As the HBR piece puts it, we need to stop asking if machines can think — and start asking if we can think smartly about machines.
Because when the dust settles, AI’s impact won’t come from moonshots. It’ll come from the companies that made the technology useful, practical, and painfully well-integrated into the systems that run the world.
