This concerns the automatic recording of accrued and taken leave hours, and showing an up-to-date balance per employee. No assessment of leave requests, no conversation with the employee: just the arithmetical processing that ensures the balance is correct.
This task scores high on structured criteria: structure and volume both receive a 5, judgment scope a 1. That is exactly the combination where automation is most obvious. Leave accrual follows fixed rules — a set number of hours per period worked, depending on contract type and employment arrangement — and leave taken is a simple deduction. No interpretation is needed, no exceptional case that calls for human judgment. It is calculation according to a fixed formula, thousands of times per month, for every employee anew.
Customer contact, physical work and creativity all also score a 5, which means these aspects form no obstacle whatsoever. No physical action is required, no customer needs to experience anything, no creative input is involved. Cost of errors and compliance sit at a moderate level (3): an incorrect balance is annoying and needs to be corrected, but it is generally not a task with direct legal consequences the way, for example, payroll processing can have.
The decisive axes are structure, volume and judgment scope. All three point in the same direction: this is a task that AI can take over today, in the form of rpa (robotic process automation). No learning system needed, no model that recognizes patterns — just software that applies the accrual rules and updates the balance as soon as leave is requested or approved.
Tracking leave balances is a calculation task without exceptions that require human work. Once the accrual rules per contract type have been correctly set up — full-time, part-time, on-call worker, each with its own accrual percentages — the system can independently keep track of what someone has accrued and taken. The HR employee no longer has to manually add or deduct hours; the leave system or HR system does that automatically with every change.
An example: an employee works 32 hours per week and, under the collective labor agreement, is entitled to a fixed number of leave hours per year, accrued per period worked. Each month the system automatically adds the correct number of hours to the balance. If the employee requests three days of leave and it is approved, the system automatically deducts those hours. At no point does anyone need to think about the correct amount — the formula is fixed.
The outcome does depend on one important condition: the accrual rules per contract type must be correctly configured. That is not a minor caveat. Companies with many different contract forms — full-time, part-time, on-call agreements, temporary contracts with deviating accrual — must have all these rules correctly programmed before the system can run reliably. If that configuration is wrong, the system calculates flawlessly and quickly, but with the wrong formula. The task itself is still structured and high-volume, but the outcome is unusable until the configuration is correct.
This differs greatly per organization. A company with exclusively permanent full-time contracts has a simple configuration and a low chance of errors. A staffing agency or a hospitality business with many varying contract forms — comparable to the situation that also comes up in what can AI take over in hospitality — has a much more complex set of accrual rules, and therefore more chance that something goes wrong in the setup. In retail too, where many part-time and flex contracts occur, the setup must be more precise than at an organization with uniform contracts; see also what can AI take over in retail for how varying employment arrangements affect the degree of automation.
This assessment only concerns keeping track of the balance, not the decision whether a leave request is approved or denied. That decision touches on personnel policy and falls outside this task description; separate legal requirements apply to it and an automation judgment is not appropriate there. This page also does not address any consequences for staffing levels — what an organization does with freed-up HR capacity is a choice that lies with the organization itself.
Anyone wanting to know more about the broader rules of play around AI use in personnel processes will find background in the AI Act and your employees. For organizations also looking at administrative tasks in the financial area, there are comparable analyses on can AI take over exchanging digital invoices via e-invoicing and can AI take over maintaining an audit log of financial changes, where similar trade-offs between structure and oversight play a role.
This page provides a general picture based on the task itself. How this plays out in your own organization depends on the number of contract forms, the quality of the current system setup, and the extent to which leave rules have already been established. For an initial indication, there is the free quickscan from ftetoai: twelve questions, no account required, with an indication of what portion of the hours in your profile can be taken over by AI today. A full work scan that goes deeper into your specific situation is still under construction.
Vraag maar. Ik ken de kennisbank van deze site; wat ik niet weet, zeg ik erbij.
Answers come from this site’s knowledge base. Not tailored advice, and not a scan of your company.