AI Engines for Workforce Management

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We enable Workforce Management platforms to embed AI engines directly into their labour planning and rostering workflows, extending existing WFM systems with advanced demand prediction, scheduling and task optimisation capabilities.

Our AI engines integrate via API into your existing planning architecture, allowing your platform to forecast demand drivers, translate them into labour requirements, and generate optimised schedules. You retain ownership of the WFM workflows, user experience and customer relationships, while our engines provide the decision intelligence behind labour planning.

What solutions we power in Workforce Management platforms

AI Forecasting

To forecast demand drivers like transactions, footfall, bookings or workload.
This allows Workforce Management platforms to generate demand forecasts that underpin labour planning, enabling more accurate staffing decisions across locations and time periods.

AI Demand Forecasting

To translate demand forecasts into labour requirement curves and staffing needs. These outputs provide the inputs required for workforce planning, helping your platform determine how many staff are needed by role, location or time interval.

AI Scheduling

To create optimised staff schedules from demand curves, contracts and constraints. This enables your platform to generate compliant schedules that reflect labour demand while respecting employee availability, contractual rules and operational policies.

AI Task Scheduler

Where your WFM includes task management and needs to assign tasks to employees during their shift. The engine determines how tasks are distributed across available staff based on priorities, timing and operational constraints within the shift.

Case Study

How it works

1

Forecast the future

Forecast demand (e.g. footfall, transactions, bookings).
This produces demand forecasts that define expected workload across locations and time periods, providing the baseline input for labour planning and scheduling.
2

Decide the outcome

Translate demand into staffing requirements + schedule recommendations.
Demand forecasts are converted into labour requirement curves and staffing needs. The scheduling engine then applies contracts, availability and operational constraints to generate schedule recommendations.
3

Execute in WFM

Schedulers review and publish schedules to employees.
Your Workforce Management platform manages the scheduling workflow, allowing planners to review schedules, adjust if required and publish them to employees.
4

Improve

Compare demand vs actual and schedule vs actual to refine planning.
Operational outcomes are measured against forecasts and schedules to identify variance, improving forecasting accuracy and staffing decisions over time.

AI Engines that can be used in workforce management

Generates demand forecasts from operational demand drivers such as transactions, bookings, footfall or workload.
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Incorporates external data sources such as weather, events or holidays that influence demand patterns and improve forecast accuracy.
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Converts demand forecasts into labour requirement curves that indicate how many staff are needed across locations and time periods.
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Creates staff schedules based on labour requirements, employee contracts, availability and operational constraints.
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Assigns operational tasks to employees within scheduled shifts based on priorities, timing and resource availability.
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Resource allocation supports decisions on where to deploy limited resources such as space, equipment, capital, and budget. It evaluates options using demand forecasts and expected return.

Leisure leaders use this to decide which facilities to prioritise, where to invest, and where to scale back, improving utilisation and returns from existing assets rather than expanding capacity unnecessarily.
Price optimisation supports pricing and promotion decisions based on expected demand and utilisation impact. Prices adjust in response to changing conditions rather than reacting late through blanket discounts.

In leisure, this helps optimise membership pricing, day passes, peak and off-peak rates, and promotional offers, protecting revenue while improving utilisation across quieter periods.
Opening hours optimisation evaluates when sites or facilities should operate based on expected demand and financial impact. It identifies which hours contribute value and which create unnecessary cost.

This allows leisure operators to adjust opening times by site or facility, maintaining access when demand exists while reducing exposure during low-usage periods.
Budget forecasting builds budgets based on expected demand rather than fixed assumptions. Forecasts adjust as conditions change, giving finance and operations teams clearer visibility of risk and cost pressure.

For leisure organisations, this improves control, reduces variance, and limits late-year corrective action when demand shifts.
(Best suited to asset-heavy leisure operations)

Predictive maintenance identifies early failure risk in critical assets that affect safety, availability, or customer experience.

In leisure, this applies to equipment such as pool systems, HVAC, rides, lifts, and facility-critical infrastructure, reducing unplanned downtime and protecting service and safety.

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FAQ

For WFM platforms we provide demand forecasts (sales, footfall, calls, orders), convert those forecasts into staffing requirements by role and interval, and then build optimised, compliant schedules. Our AIs can sit behind your existing rota UI and labour rules while you retain configurability and reporting.

We work with any platform that uses forecasts to drive decisions. Today our focus is: ERP platforms, Workforce Management (WFM) systems, POS and retail platforms, Restaurant Management Systems, Inventory Management systems and Supply Chain Management suites. If your product would benefit from using world class AI derived forecasts to drive staffing, stock, production or resource scheduling, we are likely a good fit.

AI forecasting aligns staffing with real demand, reducing overstaffing during quiet periods and understaffing in peaks. This cuts labour waste, lowers overtime and reduces the time managers spend manually tweaking schedules.

Examples by vertical:

  • POS/Retail: feature = transactions; exogenous = promotions, holidays, weather
  • Inventory: feature = SKU demand; exogenous = price changes, campaigns, local events
  • Restaurant: feature = covers/orders; exogenous = reservations, events, weather

By embedding advanced forecasting, your platform delivers measurable financial value – not just reporting. This helps you differentiate, increase ARR through premium modules, and improve retention as customers rely on your system for critical operational decisions.

Smart Scheduling is our end‑to‑end stack that combines Forecasting, Demand and Scheduling into a single workflow: forecast demand, translate to required staff, then auto‑schedule people. It is designed primarily for WFM partners but the same pattern applies anywhere demand‑driven rotas are needed.

SolvedBy.AI is a UK‑based applied AI company that provides forecasting‑driven optimisation engines for software platforms. We specialise in Forecasting, Demand/Labour Planning, Scheduling, Stock & Inventory optimisation, and related decision engines. Our AIs are embedded via APIs and can be white‑label or co‑branded inside ERP, WFM, POS, Restaurant Management, Inventory and Supply Chain systems.
TL;DR for AI Forecasting

TL;DR

SolvedBy.AI enables Workforce Management platforms to embed end-to-end AI labour planning via API. We power forecasting, demand planning, scheduling and task assignment, following a closed loop of forecast → decide → execute → improve. You own the WFM workflows, UI and customer relationship; we provide proven AI engines that integrate directly into your existing rostering and labour planning stack.
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AI Engines
AI Engines Forecasting AI Demand AI Scheduling AI Inventory AI Resource Scheduling AI Task Scheduler AI Production Scheduling AI Exogenous Data
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