How busy will Saturday be? The answer can shape staffing, food preparation and marketing. Yet the clues are spread across last year’s attendance, advance sales, the weather forecast, school calendars and events nearby. Putting them together can take longer than the decision itself.
In a September 2026 IAAPA webinar, accesso’s Angie Judge, Chief Data & Intelligence Officer, described the traditional approach to forecasting as “guess or grind”. Teams debate a number around a table or spend hours working through spreadsheets. She and Mike Evenson, Chief Commercial Officer, discussed how AI could change that work and help attraction teams make better use of the information they already have.
For leaders considering AI, the place to start is a decision their team already makes. These four questions help narrow the job.
1. What would a better forecast change?
A busy Saturday might call for more staff at admissions and food outlets; a quieter one might change the timing of a promotion or a supply order.
Last year’s attendance gives the team a starting point, but the calendar has shifted. School breaks fall on different dates, a campaign may be running and the weather forecast changes. Judge showed how AI-assisted forecasting can bring those factors together and refresh the forecast as new information arrives.
The team can then work at the level it needs: tomorrow’s staffing, next month’s plan or a longer budget cycle. People can add information the model does not yet know, such as a planned closure, and test what might happen if those plans change.
Finance, marketing and operations can work from the same forecast while making different choices from it.
2. What happens after a dashboard flags a problem?
Dashboards and reports are useful for keeping an eye on performance. They can show that food and beverage revenue fell on a day when attendance was strong, but they may not explain why.
The drop could reflect smaller purchases, short staffing during a rush or visitor concerns about price. Finding out may require point-of-sale records, visitor flow, staffing and comments that live in separate systems.
Decision intelligence helps a team bring those pieces together and investigate what needs attention. In the webinar, Judge also described reporting that runs in the background and alerts a person when it finds an issue. That leaves less time spent checking reports for problems that are not there.
3. Do we agree on what the numbers mean?
Before asking AI about “visitation,” a venue needs to decide how ticket scans, member entries and group admissions count toward it. Revenue brings its own rules across ticketing, retail and memberships. If those definitions are inconsistent, a fluent answer can still be wrong.
Judge described the work beneath the prompt: cleaning and connecting records, setting business definitions and adding context such as weather and local events. That foundation lets a question span the operation instead of stopping at one system.
Visitor feedback is a good test. A handful of comments about expensive food may warrant a closer look, but before changing a menu or price, the team should find out whether the concern is widespread, whether it is increasing and how it compares with relevant benchmarks.
4. Who owns the decision?
AI can flag a problem, explain a forecast or help test a scenario. Venue teams still decide which issue matters, which tradeoffs are acceptable and what action fits their visitors and mission.
Leaders need to see what informed a recommendation and challenge it when it conflicts with what they know. They also need clear rules for handling visitor information. An answer delivered quickly is of little value if nobody can check its basis.
Choose one recurring decision, such as staffing for a busy weekend or investigating an unexpected revenue gap. Map out:
- What information do we use today, and where does it live?
- Which external factors change the answer?
- How often does the decision need to be revisited?
- Who will review the recommendation and decide what happens next?
The answers will show where to begin. The venue may need cleaner records before it needs a model. It may already have a sound forecast but struggle to get it to the people planning the day.
accesso Intelligence is a decision intelligence platform for the experience economy. It connects operational data with industry context so attraction teams can investigate performance and plan for what is coming. The first step is to name the decision that needs a clearer answer.