The next generation of jobs — no, it is not AI.
The next generation of jobs — no, it is not AI.
A business can be seen fundamentally as an aggregator of capital, demand, capacity, supply and talent. Much has been made of the technology’s disruptive effects on business, and its ability to reshape what a business is and how it works. In many areas, such disruption has come to pass — for example, fundamental technological innovations have, in the last decade, upended existing models to transform capital (crowdfunding), demand (social media), capacity (the “sharing economy”), and supply (analytics).
Talent, however, has yet to be subject to a fundamentally different business model. Employment as we know it is largely a creation of the Industrial Revolution; though much has changed since, with labor laws, education, increasing automation, and the rise of a larger middle class all playing their part, the fundamental model — in which most employees in this economy enter into a contract with their employers for defined work at a set hourly or annual rate of pay — has not fundamentally changed since.
Since then, except for incremental variations such as the shifting role of a contingent workforce (whose impact on employees, and on general economic stability of workers, varies greatly, with lower-skill and older workers bearing the brunt of significant negative effects), there have been few fundamental changes to the model. While in the last few decades, a larger, more professionalized contingent workforce (enabled in large part by technology) gives businesses access to a larger base of talent and greater financial flexibility than in decades past, it doesn’t fundamentally change the “work for hire” model.
Especially in the tech economy, skilled talent is increasingly moving away from traditional models, exacerbating a “talent gap” in the sciences and technology. Worker satisfaction is another issue. According to Gallup, only 13% of employees are truly engaged with their employers, 63% of Millennials are constantly on the move looking for new learning opportunities, and 90% of all developers are self-taught (compared to 41% who have on-the-job training).
Extra-curriculars, passion, and underused skills
“Staff” in most companies tend to be put in a box defined by their roles and responsibilities. They are trained to get incrementally better at those roles. It isn’t uncommon for employees to end their careers optimizing within a narrow frame.
But humans as we know are good at multiple things at the same time — though often the most interesting skills, or those the bearer are most passionate about, are relegated to the last line of a resume. How many times have we heard that extracurricular activities are one of the best ways to judge the well-roundedness of an individual? But, the moment such well-rounded humans join a company, they’re put in a box defined by job title and responsibilities — an approach that significantly reduces both larger human potential and the value of the worker to the employer.
Gilberto Titericz is the top-ranked data scientist at Kaggle, a data science competition platform and community. While he was an automation engineer at an oil refinery for Petrobras Brasil, he couldn’t use his interest in data science on his job. He self-taught to be the best data scientist among hundreds of thousands of data scientists by competing and winning challenges for companies like Otto Group (product category predictive modeling), Caterpillar group (tube assembly pricing). Eventually, Titericz was able to make a career shift to a job that recognized both his passion and his “extracurricular” achievements. Now a data scientist at Airbnb. Could Gilberto’s oil refining company retain talent like him if they had thought of him broader than an automation engineer?
Accessing talent like Gilberto to solve newer problems requires nontraditional approaches. Companies hoping to get the most from “talent” with a similar depth and range of skills and skilled talent (I hate using the word “talent” here, given the air of corporate ownership it imparts) are not at all well served by traditional employment models. In fact, I’d argue that those very models are holding businesses back from realizing the value of the full potential of their employees, or those they hope to hire. Which employment constructs would have made Gilberto stay with Petrobras and benefit the company while allowing Gilberto to learn new skills?
Maximizing human potential, not the potential of a human in a box, needs an unshackling of people from the current construct. Humans should not be losing jobs because a bot/ AI took over a process. Technology should be used to create looser constructs of employment, less boxy employment that allows humans to contribute their entire being to the needs of a company that they believe in.
And, that would probably allow humans to compete with the accelerating trend of robotic automation.
