It’s not news that most businesses are facing a talent crunch.
Gartner, Inc.’s 2019 Emerging Risks Survey found that 63% of senior executives indicated that a talent shortage was a key concern for their organization. The Society of Human Resource Management, meanwhile, found that 68% of HR professionals are experiencing difficulty recruiting candidates for full-time positions in their organizations.
In the midst of this crisis, the gig economy has emerged as a strong source of potential solutions—hire talent with the exact combination of skills and experience you need for only as long as you need them, then rinse and repeat project by project.
And a lot of what makes the gig economy so appealing is the technology behind it—input your requirements, and an algorithm will find the best person to complete your task. Theoretically, such an approach requires the investment of fewer resources to generate a better outcome.
At the low-end of the gig economy, this does indeed play out very successfully in the real world. For the Ubers, AirBnBs and TaskRabbits of the world, the initial requirements are fairly simple. Are they qualified? Are they available? Are they nearby? Three simple checks and an algorithm has all the information it needs to quickly match you to the nearest delivery driver or vacation rental—far more efficiently than the traditional process.
Limitations of Gig Economy Technology
It’s tempting to believe this premise holds true at the high-end of the gig economy. After all, people analytics have been a hot HR trend for several years, and according to Bersin, a research arm of Deloitte, 69% of companies installed analytics-driven HR databases in 2017.
It would be a corporate dream to be able to punch a few buttons, input the degree, experience, and expertise requirements into a computer and have it find you the best consultant for any given project. That is, until the rubber hits the road and the algorithm finds a consultant who is perfect on paper but loathed by your entire staff.
That’s because, at the high-end of the gig economy, qualitative factors like work styles and personality matter just as much as the quantitative factors that an algorithm can sift through.
In our ten-years of matching high-end independent consultants with Fortune 500 companies, we have found that these the qualitative factors usually boil down to:
- Cultural fit: A senior executive who spent his career working at conservative firms may not thrive at a laid-back start-up. A consultant who’s used to working in a fast-paced culture may struggle when deployed to a firm that prefers process over action. These kinds of cultural mismatches can derail a project before it even begins.
- Judgment: A resume can only tell you so much. Does someone work better with a team or on their own? Are they great to building consensus or better at the technical aspects? These kinds of details can only be gleaned through conversations and interviews and not through a computer screen.
For both of these elements, it takes a human to sift through the nuances on the side of both the talent and the client and then make the best possible match.
That’s why, here at BTG, our skilled Talent Solutions team works hand-in-hand with all our clients to scope the work, identify and interview talent, and work through all aspects of contracting, compliance and project oversight. Technology supplements every step of the process, but at the end of the day, our Talent Solutions team verifies all decisions to guarantee a successful outcome for our clients.
And we are not alone in our approach. The Harvard Business Review found that the ability to pick up the phone or send an email and have it reach an actual person is invaluable to building relationships. They reported that in high-anxiety settings, funneling customers toward an automated self-service platform caused long-term damage to service relationships and that offering access to a human increased the customer’s trust in the firm.
Additionally, as the debate rages over whether artificial intelligence, robotics, and other upcoming technologies will replace humans, the strongest position emerging is that humans and technology will amplify and enhance each other—not unlike our own model.
Deloitte Insights recently wrote that “solutions created collaboratively by humans and machines are different from, and superior to, solutions created by either humans or machines individually,” and “that humans, so far from being replaceable, are essential partners in realizing AI’s optimal value.” These kinds of collaborative solutions could potentially include human explainers who help C-suite executives understand why an algorithm arrived at the conclusion it did and human trainers who can help chatbots better understand human behavior and psychology.
In the talent space, we look forward to seeing how this human-technology partnership plays out in the coming years and how our team can successfully leverage the latest gig economy technology to better serve our clients.
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