Language
What it delivers
Forecasts of demand and staffing based on historical data, seasonal patterns and external factors. Transparent models: every forecast can be traced back to the underlying data, no black box.
How it works
Planning AI runs on the Ontology: the model of your organization capturing people, locations, skills and rules. For every schedule change or call-out, the algorithm recalculates the best-fitting deployment, weighing all constraints at once (working hours legislation, collective labor agreements, availability, travel distance, experience, expertise, etc.) instead of assessing them manually one by one.
Schedules for doctors, nurses and care staff, including matching care demand with available caregivers.
Deployment of technical and operational staff across multiple locations and projects.
Capacity planning for claims handlers and loss adjusters during demand peaks.
Schedules across hundreds of locations and clients, with varying shift hours and contract requirements.
Three examples of Planning AI in practice, by sector.
Insurers
The team leader of a claims department sees the influx of claims double after a summer storm, while existing staffing levels are based on an average week. Planning AI redistributes processing capacity across claim files based on urgency and complexity.
More about Planning AI for insurers →Facility services sector
The regional manager of a facility services provider with hundreds of locations currently plans shifts largely by hand, spending significant time on absences and same-day rescheduling. Planning AI creates a cross-location deployment plan, taking into account availability, travel time, and shift type.
More about Planning AI for facilities →Healthcare
The scheduler for an institution with 250 doctors struggles with evening and night shifts: too few people during peak times, too many during quiet periods. Planning AI balances demand and availability and proposes a schedule that accounts for contract hours, preferences, and collective labor agreement rules.
More about Planning AI for healthcare →Government
At the permit department of a medium-sized municipality, lead times increase as soon as the number of applications exceeds available capacity. Planning AI distributes available capacity across current and incoming cases based on priority and deadline.
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