Internal mobility and contractor redeployment are core strategies used by most companies to increase productivity at work. However, there is one core problem that is a significant inhibitor to utilizing technology to optimize both of these activities – lack of updated information.

Let’s look at the 3 most common reasons employees and contractors don’t keep their information updated and how AI can help.

 

1. A Worker’s Expected Level of Effort vs. Perceived Value Is Low

Engagement on internal mobility, talent management, and online marketplace platforms decrease as the noise and number of solutions increase every year. The effort put into keeping these platforms updated doesn’t provide enough value to keep employees or contractors engaged. The misalignment of expectations creates distrust in the platforms as a whole and is why engagement within them is always a struggle. We have to stop expecting people to put so much work into technology without trusting that what they get out of it will be time well spent. Recommended job titles and job descriptions aligned to a hierarchical work structure aren’t a big enough draw. In a recent survey by Deloitte, “more than 50 percent of respondents told us that it was easier for employees to find a job outside their organization than inside.” Updated data is a critical component of how most of these systems work, but it is extremely cumbersome for any worker to maintain. By the time an employee or contractor has written their resume you have lost them.

 

How do we build an experience that increases engagement and focuses on delivering what employees and contractors value most? Job titles and job descriptions are not as relevant as we think. How do we understand a person’s identity, passion, and purpose and align that to a specific body of work? In a recent job search on Paths.io, when searching for jobs in the US with the keyword “LGBTQ,” the top companies in the results were Aramark, Sunrun, Mayo Clinic, and Oregon State University. Companies need to start aligning their identity – mission, vision & values – to those they want to attract and engage within their organizations. Match on passion and purpose first. The neural networks within AI can bridge the gap in supporting employees and contractors by identifying the transferable skills they have, then recommend ways of upskilling or pivoting to paths or projects that align to their passion and purpose. 

 

2. Workers Trust Relationships With Humans More Than Technology

Humans have an innate ability to build relationships and depend on one another to build trust. Employees use connections to network internally through management and leadership to develop their careers. Contractors depend on the relationships they build in project-based work to network and find their next gig. People instinctively trust other people they like and work well with which probably contributes to much of the bias we see today. We all depend on a human’s ability to use judgments built over time to aid in decision making. We trust people we know. 

 

To bring AI into any process, you have to understand what mental model you are breaking. “People are more likely to have unachievable expectations for products that they assume have human-like capabilities,” as Google states in their people + AI guidebook. Unfounded expectations are a significant hurdle to overcome in enabling any technology system to break a behavior as intimate as human relationships. We have to take a step back and use AI to enable this behavior and not break it. How can we use data to understand the underlying connectivity between people and recommend new people to build relationships with versus jobs? 

 

3. Lack of insight into Strategic Initiatives and Project Objectives.

Gone are the days of defining oneself by job title, skills, or company. We are all humans who are motivated by different things. There is a common lack of awareness of the overarching project or strategic objective a company is trying to accomplish, which aligns with a group of workers they think they need. We have made work too much about skills and job titles and not enough about the result and impact a worker will have. Humans are capable of accomplishing more when motivations are aligned. It’s incredible what skills one can learn when motivated enough – everyone has a different learning curve. 

 

How do we give talent a better view of the overarching projects or initiatives companies focus on achieving? AI can understand and dissect projects and recommend the right type of workers within an organization that can contribute to making or completing them. Why are we matching workers to job titles, when we could match them to a body of work? Today many recommendation engines support job to candidate or candidate to candidate matching. Why not project to candidates? What is the right mix of people you need vs. have to accomplish this project?

 

In conclusion, AI can be incredibly helpful in supporting Talent Mobility, but the process needs to be turned on its head, and these three pain points need to be solved collectively. Companies need to pull back the curtain and expect to give the same vulnerability we ask people to do in the job search process. Take the time to align passion and purpose to bodies of work. Provide valuable experiences driven by AI that enable productive relationships and ways of utilizing and progressing skills to achieve more.