Recreation businesses run on a combination that rarely comes together in other sectors in quite the same way: seasonal peaks, a large population of flexible and temporary staff, guest contact that is the product itself, and a back office that must keep running year-round while the front end operates at half capacity for half the year. A large share of the hours goes into front-office work — checking in, providing information, changing bookings, handling complaints — and into planning: rosters, capacity allocation across accommodations or attractions, and maintenance that peaks around the seasonal turnover. There is also administrative work that continues regardless of the season: invoicing, contract management, reporting to owners or franchisors.
The circumstance that most determines the outcome is the distribution of work between the permanent core and the flexible layer. An organisation with a small permanent core and a large seasonal layer has different bottlenecks than a business that operates year-round with a fixed team. That difference determines not only where the hours are, but also where repetition lies — and repetition is precisely where AI gains traction.
At some recreation businesses, a booking change, a standard question about opening hours, or an initial response to a complaint is now handled by a system, without an employee seeing it before it is sent. At other businesses, exactly the same task still runs entirely through an employee, with the same repetition, the same standard question, the same time pressure during the season. The difference does not lie in the nature of the work — that is comparable — but in whether the underlying data (booking systems, knowledge base, complaint history) is in order and connected. Where that is not the case, the task remains human work, not because the work requires it, but because the precondition for it is missing.
The same three categories run through the season. Part of the work can be taken over by a system: routine bookings, standard communication, initial triage of questions. Part is handled partly, with an employee approving or rejecting and giving a reason: a draft response to a complaint, a proposal for a roster adjustment due to illness, a planning scenario for a busy week. And part remains human work: the guest with a problem on the floor, the judgment call on a borderline case in a contract, the conversation with an owner about an investment. That division shifts with the season — during peak periods it often shifts more towards the system, simply because the volume demands it, while during quieter periods there is more room for human handling.
"AI ambition" is often filled in at different levels within recreation businesses without this being made explicit. One director means a chatbot for the website, another means a system that fully calculates the seasonal planning, a third is thinking of automated invoicing to owners. These are not gradations of the same ambition — they are different ambitions with different requirements. A chatbot requires a well-populated knowledge base; a planning system requires reliable historical data on occupancy and staffing; automated invoicing requires standardised contracts. Without that translation step, the pattern emerges that also occurs outside this sector: why people mean different things by the same words, and a decision that one person interprets as a "small project" while another sees it as "fundamental change".
This also clashes with existing departmental goals. A front-office team that is judged on guest satisfaction scores experiences an automated first response differently than a planning department that is driven by cost savings. What to do when those goals work against each other is a question that extends beyond recreation alone and is addressed separately on the page about clashing departmental goals.
Readiness for an AI ambition in recreation depends strongly on data chains that are often fragmented across booking systems, point-of-sale systems and staff planning, on how many seasonal staff are trained to recognise deviations from a system proposal, and on whether decision-making authority is clearly defined between location and head office. An ambition to automate roster planning is only realistic once it is clear who may override a system proposal and on what grounds. This does not touch on the question of whether someone keeps their job — separate statutory requirements apply there, independent of this assessment — but on the question of whether the organisation has already structured the work in a way that allows it to be shifted.
The recreation sector is not alone in this: similar bottlenecks around peak load and flexible staffing layers are also seen in the AI ambitions at play in the cleaning sector and, with a different peak structure but the same issue around planning and oversight, in the AI ambitions in healthcare.
The question of which work in your business can genuinely be taken over by AI is not answered in general terms but per task — that is precisely what the FTE TO AI work scan is designed for. For an initial picture, without waiting for that, there is the free readiness check: eight short questions, one per dimension, showing where your organisation is furthest along and where it lags furthest behind. The full ambition assessment, with the four layers and five confidence gates, is under development.
Vertel wat u wilt bereiken, dan kijken we samen wat daarvoor moet staan.
Answers come from this site’s knowledge base. Not tailored advice, and not a scan of your company.