A glossy interface is no longer proof of a serious product. That’s the warning TripWorks is putting to tour and activity operators worldwide, as new research exposes a widening gap between what AI-generated booking platforms promise and what they actually deliver when things get complicated.
The company has a name for what’s happening: an “AI copycat bubble.” Generative AI can now produce a convincing, polished platform in a matter of hours – something that looks, at first glance, indistinguishable from systems built over years of real-world iteration. The problem, TripWorks argues, is that looking the part and doing the job are very different things.
The research backs them up. Two major travel industry studies found that 91 per cent of travellers now turn to AI when planning a trip – yet that same figure, 91 per cent, say they don’t fully trust what it tells them. A separate survey found that only 8 per cent of travellers consider AI-generated answers sufficient on their own, with more than half clicking through to source websites to verify the information. Harvard Business Review has flagged the same underlying tension, noting that generative AI is enabling fast-moving new entrants to ship products that simply haven’t been stress-tested against the complexity of real operations.
That complexity has already caused visible damage. AI travel planners have sent tourists down routes blocked by road closures. Airlines have dealt with mispriced tickets traced back to AI-assisted errors. Mistranslations by AI systems have triggered emergency responses. These aren’t edge cases – they’re early signals.
For booking platforms specifically, the failure points are predictable: weather disruptions, last-minute cancellations, group logistics, refund disputes, OTA integration quirks. These are the moments that reveal whether a platform was engineered or assembled. TripWorks CEO Aaron Fessler is direct about the distinction.
“Generative AI can build a beautiful demo,” he said. “It can’t build operational maturity. A booking platform needs to be treated as infrastructure – not a pretty website.”
Fessler says the copycat platforms share a common profile: no real operations engine, logic that’s never been tested under load, and a fundamental confusion between interface and infrastructure. They perform well when nothing goes wrong. When something does, operators are on their own.
His message to anyone currently evaluating a new system is blunt: ask hard questions. What’s the real-world booking volume? How does it behave at peak load? What safeguards exist against AI-generated errors? A vendor that can’t answer those questions clearly probably hasn’t had to.
“A demo is not a platform,” Fessler said. “The companies that come out the other side of this bubble will be the ones that combine AI with real engineering, real testing, and real operational experience. AI is a powerful accelerator. It is not a substitute for any of those things.”
