Choose a narrow use case
Explaining report changes, detecting unusual values and forecasting are different tasks. A language model can explain a pattern without being a validated demand model. Define an output that a team member can check.
Prepare consistent inputs
Align dates, inventory, revenue, segments and change history. A missing day is not zero. A change in accounting may look like a market spike. Label these events before assessing automated conclusions.
Ask for the basis
A useful recommendation identifies the period, metrics and assumptions. Rising ADR and falling occupancy may justify reviewing segments. An unavailable OTA rate does not establish that a competitor is full or justify a rate increase on its own.
Evaluate on historical data
Compare a forecast against a simple baseline, such as comparable previous dates. Use only information available at the forecast date. Future bookings leaking into the evaluation inflate apparent accuracy. Review errors separately for strong and weak stay dates.
Define HotelMatrix’s role
Market analysis and available forecasting capabilities can support hypothesis checks. The module and required data are agreed during onboarding. These examples do not imply every AI use case is included in every plan or that the platform automatically publishes rates.
Keep human oversight
Assign a decision approver. Use aggregated or anonymised information for learning exercises. Check access rules before passing information to external services. Keep recommendations alongside actual actions to measure usefulness rather than persuasive wording.
Choose one recurring task, define a quality check and compare its outcome with work done without AI.