The Accelerated Adoption of Optimizers and Self-Scheduling in Workforce Scheduling

Workforce management is rapidly becoming a priority in boardrooms worldwide. Persistent staff shortages and growing concerns about employee wellbeing are putting pressure on existing teams while raising expectations for organizations. At the same time, employees are demanding more engagement and flexibility—especially in sectors such as healthcare, where demand continues to rise due to an aging global population. These dynamics are reshaping organizations across industries.

Fortunately, the potential for improvement is significant. Advanced mathematics, AI, and self-scheduling are emerging as essential tools in workforce scheduling, boosting both productivity and employee satisfaction. Among our clients, we see strong growth in the adoption of optimization tools and self-scheduling. For example, more than 100 organizations now leverage self-scheduling, with employee participation increasing by over 30% in just six months.

Workforce scheduling has become a critical process in response to the growing challenges of workforce management. Until recently, schedules were created manually by a team leader or central planner. Given the complexity of building an effective schedule, planners were often satisfied simply if all shifts were covered. This approach relied heavily on the planner’s experience and knowledge of employee preferences and restrictions.

However, manual planning often led to unnecessary hiring, unfulfilled contracts, or overlooked preferences. Some planners were also skeptical about workforce optimization tools, viewing them either as a threat to their role or as producing results they found unfamiliar.

The benefits of mathematics and AI in workforce planning are substantial. With the right input on requirements and preferences, these technologies can generate rosters that outperform manual schedules on every front. “Better” can mean many things: strictly adhering to labor and skill requirements, honoring personal requests, supporting ergonomic considerations, or minimizing additional hiring. These improvements can be tracked through KPIs, measured before and after the introduction of optimization tools. Across numerous implementations, optimizers consistently improve all KPIs, enhancing both efficiency and quality.

Naturally, schedules generated by AI-powered optimizers will differ from manually created ones, requiring careful review. Occasionally, adjustments are needed to reflect overlooked preferences. The purpose of an optimizer is to support planners in making complex decisions—similar to how a calculator assists with mathematical work—while the final decision always rests with the planner, who can override or fine-tune the proposed schedules. In practice, using an optimizer reduces planning effort by more than 50%, allowing planners to focus on tactical issues and customer-facing tasks. Adoption is accelerating, with organizations increasingly using optimizers to build shift schedules or assign daily tasks and workstations.

In recent years, self-scheduling has gained momentum, typically following a three-step process. First, each employee creates their own draft schedule. Next, everyone gains visibility into the full schedule, including unfilled or overstaffed shifts, and can adjust their availability to resolve conflicts. Finally, a central planner addresses any remaining issues to ensure every shift is covered without over- or understaffing.

This approach maximizes employee engagement and works best when only minimal changes are required after the second and third rounds. Employees value having greater influence over their schedules, feeling more involved, and experiencing planning as a collaborative process. Surveys show higher satisfaction, stronger engagement, and a greater willingness to swap shifts with colleagues.

The adoption of self-scheduling has soared: usage has increased by more than 30% in just six months, with over a hundred organizations now using it—including large international companies and many healthcare providers. Last year alone, ten hospitals each had more than 1,000 employees fully engaged in self-scheduling.

Self-scheduling is most effective when employee preferences align closely with organizational needs, making it essential to continually improve this balance. In the third round, it can be challenging for planners to resolve outstanding conflicts without reworking the entire schedule—an area where optimizers provide valuable support. Typically, initial schedules are built around required skills and shift coverage, with specific assignments made closer to the actual workday. These final decisions can be handled manually, or preferably, with optimization tools that respect skill requirements, labor regulations, and personal preferences.

AI in workforce scheduling can also predict which shifts are likely to be most popular in upcoming scheduling rounds, helping employees make more informed choices when building their schedules. Last year, we published a paper with practical tips and strategies to help organizations maximize the impact of these tools.

Self-scheduling may also need to be adapted to each department’s needs. Some departments might prefer two or four rounds instead of the standard three—either starting directly with group visibility (round two) or adding a priority round where certain staff can choose shifts before opening them to all employees. Reviewing daily shift requirements against actual needs is equally important, and leveraging historical data and forecasting ensures the right number of employees are scheduled at the right time and place. Finally, optimizers continue to evolve, improving not only through advances in mathematics but also by learning from the differences between planned and actual schedules.

Mathematics and AI are accelerating the adoption of optimizers and self-scheduling in workforce planning. Customer success stories across industries clearly demonstrate the measurable benefits of AI-driven workforce scheduling. Within the coming years, we expect most employees to be scheduled in this way.

Beyond improving employee satisfaction, engagement, and productivity, AI in workforce management will also free up valuable planner time, reduce lead times, and ultimately enhance both service and quality.