Gartner’s latest research paints a striking picture: 40% of enterprise applications will have task-specific AI agents by 2026. Right now, we’re at 5%. That’s not gradual adoption. That’s a landslide. And yet McKinsey found that while 88% of enterprises have AI running somewhere in their operations, only 6% are seeing real financial returns across the business. Everyone’s adopting. Almost no one’s scaling. The bottleneck isn’t technology anymore. It’s figuring out whether what you’re building actually works for the people who have to use it.
The Validation Gap Nobody Talks About
The pitch sounds great: AI that joins your meetings, transcribes everything, writes up the recap, and flags who owes what to whom. Some of these tools even jump in when the conversation stalls. Technically, it’s remarkable work. But here’s what gets glossed over in product demos: this isn’t software that behaves the way software usually behaves. You can ask the same thing twice and get different answers both times. That’s not a bug. That’s how language models function.