The staffing challenge most workflow systems don't solve

How users interact with recommendations

The interaction model is intentionally simple and closely aligned to your existing process. When assigning staff, you continue to work within the control sheet, navigating to the staffing section and selecting from the dropdown just as you do today.

The difference is that recommended staff are now surfaced automatically, along with their scores. If you want to better understand why someone is being suggested, you can open the justification pop-up, which provides additional detail about their experience, historical involvement, and current workload.

This allows you to quickly validate recommendations, compare options, and make a confident selection without needing to reference multiple systems or consult with others.

Maintaining control and flexibility

A key aspect of this capability is that it does not replace human decision-making.

The recommendations are advisory, meaning you retain full control over who is ultimately assigned. You can select any staff member from the list, regardless of whether they are recommended, and you can override suggestions whenever needed.

This ensures that the system supports real-world scenarios, where factors such as client relationships, staff development goals, or unique circumstances may influence the final decision.

No disruption to existing workflows

AI-powered staff recommendations fit seamlessly into your current process. The way you assign staff does not change, and the way you create and manage bookings on the calendar also remains the same. There are no new steps, no additional systems to learn, and no restructuring required. 

Instead, the enhancement focuses on improving the quality of a single, critical decision point. By embedding intelligence directly into that moment, the system delivers value immediately without introducing friction.

Configuring recommendation behavior

While the system provides strong default behavior, firms also have the ability to tailor how recommendations are generated.

Through the Staffing Recommendation Preferences utility, authorized users can adjust the relative importance of the criteria used in the recommendation model. For example, a firm may choose to place greater emphasis on client familiarity during certain periods, or prioritize workload balancing during peak season.

These adjustments allow the system to reflect how your firm actually operates, rather than applying a one-size-fits-all model. If needed, settings can be modified over time or reset to their default configuration.

A single step with impacts throughout the workflow

When staff are assigned more effectively at the outset, teams benefit from better alignment between people and work. Workloads become more balanced, reducing the likelihood of bottlenecks and last-minute reassignment. Managers spend less time evaluating staffing options, and assignments are made with greater consistency.

Over time, these improvements result in more predictable delivery, reduced rework, and more efficient use of available capacity, all without requiring additional headcount.