Could AI predict when someone is likely to quit in professional services?
What if you could predict flight risks before they arose? With the latest advanced technologies, it may just be possible.
A degree of foresight would always be helpful with all resource planning. It’s a delicate balancing act matching skills, grade, and availability to projects.
As a chief operations officer, you need to know exactly how your programs and projects are performing. And that includes the ability to monitor realized, planned, and expected attrition concerning projects in the backlog.
The cost of attrition
According to the 2020 Professional Services Maturity Benchmark (PSMB), the cost of replacing a valuable employee can be more than $150k, and the time it takes to hire and ramp up a new consultant averages 121 days.
And, as attrition rises, it says, “most other aspects of performance suffer,” from employee engagement and productivity, to client satisfaction and lost revenue, not to mention the firm’s ability to recruit new talent.
But, while resource and project planning tools should provide a forecast for involuntary attrition (layoffs) in relation to short-term pipeline projects, what about resignations? Can you predict those?
With the right system, which features analytics engines powered by artificial intelligence (AI), it’s possible. Almost.
Predicting a flight risk
With a flexible, people-focused cloud ERP integrated with a people and talent management application, which delivers real-time, actionable visibility and insight into utilization levels — and the ability to manage this between acceptable boundaries — you can predict and even prevent voluntary attrition.
By scanning time and attendance for billable hours, and understanding how people spend non-billable time, trends may emerge. By looking at non-productive hours of individuals, you could unearth an indication of an impending resignation.
By forecasting under- and over-utilization, and monitoring unrealized assigned work, you can tell when burnout or low morale may create a flight risk. Then you can combine this with AI scans of records of performance, plus participation in HR initiatives, to identify patterns.
This analysis would all be in real-time, too, so you could pre-empt any moves away from the company; you wouldn’t need to wait for the bi-annual appraisal to discover the bad news.
Disengagement provides a warning
You can also measure engagement by combining AI scans of historical data with predictive analytics.
The PSMB report says: “HCM vendors are adding rich predictive analytics, providing visibility into levels of employee engagement to provide early warning for employees who are likely to quit.”
Understanding engagement is crucial to the success of any people-focused organization. According to the Employee Engagement Trends report by Intuo and Unit4, “By measuring engagement correctly, managers can take actions at the right time and predict outcomes such as employee turnover or absenteeism.”
With your people being such a high cost, and possibly the only revenue generator, predicting voluntary attrition would be a gamechanger. If you could predict voluntary attrition (and somehow prevent it), you could keep recruitment costs low while maintaining high margins by minimizing your use of contractors.
If you could predict when someone is likely to quit — assuming you couldn’t do anything about it — you’d at least have a better foresight over resource availability. This would mean you could balance available skills with what’s required by projects both in the short and medium term.
Put your people first
While some of these capabilities to predict voluntary attrition are theoretically possible, many are yet to be fully baked into systems. But it’s not far off. While you wait, there’s value in improving how you measure engagement and balance of people utilization.
Learn more about Putting Your People First.