Tourism

AI-ready data in tourism

AI is only as good as the data it can reach. Most tourism businesses have the data. Very few have it in a state that AI can actually use.

By Zakir Hoosen, Director, Fuzzelogic Solutions. Board-level guidance in plain English.

Every tourism business thinks it has data. Bookings, guest preferences, reviews, revenue, occupancy, maintenance logs, supplier records. The list is long. The problem is not whether the data exists. The problem is whether an AI system can actually use it.

I have seen hotels with ten years of guest data that cannot answer a simple question: what does a returning guest actually want? The data is there. It is in three different systems, in five different formats, and nobody has checked whether it is accurate in two years. That is a data problem, not a technology problem.

What AI-ready data means

AI-ready is not about having more data. It is about having data that is reachable, trustworthy, explainable, changeable, and governed. Fuzzelogic uses these five tests for a reason. They tell you whether your data is in a state that AI can work with, or whether you are about to spend money on a system that will fail.

1. Reachable. Can the data the AI needs actually be found when it needs it?
2. Trustworthy. Do you know the data is accurate, current, and complete?
3. Explainable. Can someone explain why the system made a particular decision?
4. Changeable. Can the system be changed when the business changes?
5. Governed. Has someone decided what the system may and may not do?

In tourism, the data problem is usually in the first two. The booking system does not talk to the CRM. The review platform is a separate silo. The maintenance system is on a spreadsheet in the engineering office. Each piece is useful on its own. Together, they could tell a story. But only if they can be reached and only if they are accurate.

The cost of bad data

Two thirds of AI transformations did not deliver. In my view, a significant portion of those failures trace back to data. The system was fed inaccurate or incomplete information. It made recommendations that did not match reality. The business lost confidence and pulled the plug.

For a tourism business, bad data has a direct cost. A pricing model fed with incomplete competitor data will set the wrong rates. A personalisation engine fed with old guest preferences will send the wrong offers. A demand forecast fed with incomplete booking data will overstaff or understaff. Each of those costs money, and the board does not always see the connection.

Where tourism data goes wrong

The most common pattern I see is the system that was never connected. The hotel has a property management system, a revenue management tool, a review aggregator, and a loyalty platform. Each holds valuable data. None of them share it. The result is four incomplete pictures when the business needs one.

The second most common pattern is the data that was never checked. Guest email addresses are wrong. Booking dates are duplicated. Room types are inconsistent across systems. None of this matters for a monthly report. It matters enormously for an AI system that is making decisions based on patterns in the data.

In my opinion, most tourism businesses underestimate how much cleaning is needed. It is not unusual for a data readiness exercise to reveal that a quarter of the records in a key system are incomplete, duplicated, or out of date. That is not a failure. It is the normal state of business data that has never been examined with AI in mind.

What the board should ask

Three questions cut through quickly. Where does our guest data live, and can it be accessed in one place? When was the last time someone checked whether that data is accurate? If we fed it into an AI system tomorrow, would the system be working with good information or garbage?

If the answers are vague, the business is not ready for AI. It is ready for a data readiness exercise.

How Fuzzelogic approaches it

Fuzzelogic works with tourism businesses to understand what data they have, where it lives, and what state it is in. The AI readiness assessment takes two to four weeks, is fixed price, and gives you a clear verdict on whether your data is ready for AI and what needs to change.

The 10-20-70 rule applies here too.

The data piece is in the 20%. It is not the biggest part, but it is the foundation. Without it, the 70% has nothing to work with.

The bottom line

AI-ready data in tourism is not about collecting more information. It is about making the information you already have accessible and accurate. Most tourism businesses have enough data. They do not have it in a state that AI can use. The board's job is to understand that gap before signing off on any AI investment.

Start with the assessment. Two to four weeks, fixed price, and you own the verdict and the roadmap whether or not we build any of it. When you are ready to talk AI, call Fuzzelogic Solutions and ask for Zak. www.FuzzelogicSolutions.com | info@FuzzelogicSolutions.com | +44 (0)1624 618950