LEMA® Collective
Research / Data & AI Data & AI

Data layovers: the hidden cost of systems that do not talk

3 min read · 17 July 2026

A traveller books on your app, changes a seat on your site, calls about a change, then checks in at a kiosk. Four touchpoints, and your systems still cannot see all four.

Summer winds down, travellers squeeze in the last escapes, and the last thing they want is another layover, least of all with their data.

Picture it. A traveller books on your app, changes a seat on your website, then calls in about a last-minute change. By the time they reach your check-in kiosk they have had four separate touchpoints with you, yet the agent in front of them can see almost none of it, because the underlying systems are still trying to connect the dots. That is a data layover.

More than 80% of travel executives point to fragmented data, not the technology itself, as the thing holding the experience back. The result is billions invested in digital transformation, and millions of travellers still explaining their booking to four different systems.

Integration beats rip-and-replace

The technology is rarely the problem. The fragmentation is. The systems do not talk, and frontline teams are left flying blind. This is why forward-looking operators are dropping the rip-and-replace instinct in favour of integration layers: connecting the systems they already run to create real-time, 360-degree visibility of the customer.

Those companies see shorter resolution times, more personalised offers, higher guest satisfaction, and fewer costly service failures. Unified data turns disruption into opportunity. A delayed flight triggers an instant rebooking. A missed check-in syncs with the hotel and the car hire. The journey becomes choreographed rather than chaotic, and the business moves in step with its customers, aiding rather than annoying. Better margins, more resilient operations, and stronger loyalty follow.

Where to start

The fastest way to know whether your data is AI-ready is to look, honestly. That is what our Data & AI Readiness Audit does: it shows you where your architecture is leaking value and what to fix first.

Start with an audit