Gild Does for Hiring What Boeing Did for Airplanes
Airplane systems are incredibly complex, expensive, and directly affect performance. The same is true for the hiring process. Gild today announced their end-to-end “smart hiring success platform,” leveraging machine intelligence, automation, and collaboration. Just like aircraft “health” systems now save millions of dollars by collecting vast amounts of data and signaling to pilots and maintenance crews when to take action before there is a problem, Gild’s system offers a proactive on-line system using data and analytics to help the recruiting team find, recruit, and hire better candidates faster.
Recruiters Need Help
An entire field of psychology (and the 2002 Nobel prize in economics ) is devoted to helping us make good decisions — — because, left to our own devices, we often don’t. Research shows a variety of biases in hiring decisions and even in how people approach their own job search. Given that bad hiring decisions are incredibly costly, this is an area ripe for support by more data-centric approaches.
This isn’t to say that hiring decisions aren’t based on data now. They are, sometimes, it’s just that humans can only look at relatively small amounts of data. Machine systems, however, are tireless at sifting through applications, past results, and testing new approaches to learn the best way to do the task. I just received my copy of Cognitive Cooking with Chef Watson: Recipes for Innovation from IBM & the Institute of Culinary Education . It’s nothing for IBM’s Watson artificial intelligence to read and learn from 9,000 recipes in Bon Appétit magazine (or 23 million MedLine papers when supporting cancer research). We can’t say the same for our own abilities to process information.
In the case of the Gild platform, recruiting teams are supported in a hybrid approach to writing job postings, sourcing and communication with prospects, and even scheduling and interview support.
My first contact with Gild was when I read about their tools for identifying job prospects based on publicly available work and social media activity. Gild tools can reach into online sites like GitHub (a collaboration platform focused on building computer code) and StackOverflow (a popular question and answer site for programmers) to algorithmically identify the best coders. They’ve extended this ability (from their press release):
Smart sourcing: Gild’s hiring recommendation engine scours the web to compile a list of relevant prospects. Gild indicators tell recruiters which prospects are most likely to change jobs and the right time to reach out to them. Gild also applies patented technology to score tech prospects’ expertise (based on publicly available work) and demand (based on the current job market).
Adding Machine Learning to the Team
I got a glimpse of how current users are doing with Gild when I talked to co-founder and CEO, Sheeroy Desai last week. He said, “the more you use it, the smarter it gets.” The critical question for me was about how the recruiting teams adapt their work practice given Gild. Desai noted, “you need software that has the workflow [built in], but then you need the collaboration.” This isn’t about a machine taking over, it’s about evolving to better practice and outcomes. Recruiting teams learn the new tool, and the more they use it the more valuable it becomes, but I suspect they also need to reevaluate how they coordinate their work in the same way you would with any new team member.
We have a fairly mature understanding of how to support human project teams: Involve only people needed for the work (rather than for political reasons), allow the groups to stay together over multiple projects, provide feedback from the task itself, keep meetings focused on work rather than reporting, etc. However, we do not have a standard set of best practices for integrating hybrid systems into standing teams. We don’t have a clear process for helping people hand off tasks where the system will do a better job — and perhaps more importantly — helping people see the opportunities in their uniquely human capabilities. My book, The Plugged-In Manager: Get In Tune With Your People, Technology, and Organization To Thrive , is a start, but Watson didn’t win Jeopardy until 2011, so machine teammates didn’t make it into the discussion.
Perhaps it’s an easier transition in aviation where these systems are focused on other machines rather than people. For talent management, we may have to work harder to make the transition. We may, as I’ve had the chance to write about elsewhere, lead by letting go: We may accelerate our improvement by letting go of systems and rules built to run 20th century organizations (while still holding tight to our values, relationships, and performance standards).
How have you incorporated machine capabilities into your own workflow? What is your biggest challenge as you try? Let me know in the comments section here (or at TerriGriffith.com).
Originally published at terrigriffith.com.