Flex time meets flux time: More US workers identify as independent

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Share this email Share this email on LinkedIn   Share this email on Twitter   Share this email on Facebook   OUR BEST IDEAS, QUICK AND CURATED | SEPTEMBER 30, 2022 View in browser   Edited by Barbara Tierney   Senior Editor, New York This week, we look at what a growing number of independent workers means for US companies. Plus, an interview with Erika James, the dean of Wharton, and Tech for Execs explains how businesses can put the technology behind "deep fakes" to good use. The holy grail of employment has long been a full-time job with one employer. But a big chunk of the US workforce has shifted away from that pursuit. In a recent American Opportunity Survey, a remarkable 36 percent of employed respondents identified themselves as independent workers—a category that includes contract, freelance, temporary, and gig workers. That is a hefty increase since 2016, when McKinsey estimated the US independent workforce at 27 percent of the employed population. Independents' day. Independent work in the US cuts across all ages, education levels, and incomes, though it tends to skew toward younger workers and those with lower incomes. The survey found that just over a quarter of respondents say they do independent work because they must, citing "to support basic family needs" as their motivation. Another quarter of these workers report that they do independent work because they enjoy the autonomy and flexibility it offers—reflecting research that shows Americans increasingly embracing flexible work. A third of employed respondents who earn more than $150,000 a year also say they work independently. An intriguing twist. Senior partner Kweilin Ellingrud and her colleagues found that these independent workers are also far more optimistic than the average American worker about their own futures and the economic outlook. This optimism—buoyed in part by the positivity of first-generation immigrants—is notable considering the hardships that independent workers face, including a lack of access to healthcare. A majority report being concerned about the stability of their employment, compared with 35 percent of permanent workers. Flux time. With the economy entering a period of uncertainty, what does the growing number of independent workers mean for companies and employment overall? Throughout the Great Attrition era, McKinsey research has shown that most organizations haven't pivoted fast enough to address the fact that the workforce has indeed changed. Too many still rely on the idea that traditional workers will fill traditional jobs. Looking to those who want flexibility, and understanding why people are attracted to this type of work, will help companies fill vacancies they've had a hard time filling. This goes beyond hybrid work models to considering how best to support this growing slice of workers and help them flourish. US manufacturing as a test case. Although the sector represents just 11 percent of US GDP, manufacturing makes a disproportionate economic contribution, including 20 percent of the nation's capital investment, 35 percent of productivity growth, and 70 percent of business R&D spending. Manufacturers are in the process of modernizing their operations by pursuing automation, digitization, and greater sustainability to change the way they produce—and where. For these efforts to be effective, companies will need to focus on building the skills of their employees, both existing workforces and contract workers. Independent workers may now be in a better position to claim a slice of these high-skill, high-wage jobs as the industry becomes more innovative and competitive. Off the charts Life sciences: Seeking healthier returns Progress in the life sciences industry helps companies better predict, prevent, treat, and cure a wide range of diseases. At the same time, many companies are grappling with falling or stagnant productivity—with the exception of those in the fast-growing biotech subsector. The need to improve productivity is urgent, not only to meet shareholder expectations but also to maximize the returns that can be invested to improve patient outcomes. Check out our chart of the day here. INTERVIEW 'Don't let fear stop you from forging ahead' Erika H. James is the first woman and the first person of color to be the Wharton School's dean since the institution was founded in 1881. With a background in organizational psychology, she is an expert on crisis leadership, workplace diversity, and management strategy. She recently sat down with McKinsey partner Monne Williams to talk about the nuances of mentorship, the gains and setbacks she sees in the pursuit of diversity, and how confidence builds a record of success. More on McKinsey.com Who's looking after the pediatricians? | A recent survey found that more than 60 percent of pediatricians reported experiencing exhaustion from excessive and prolonged emotional, physical, and mental stress. Here's what hospitals and other health systems can do to help. Airline retailing: How payment innovation can improve the bottom line | Airline retailing could be worth $40 billion by 2030. A new report delves into how improvements in this area can help airlines brighten customer experience, increase revenue, and decrease costs. Overcoming the fear factor in hiring tech talent | McKinsey research shows that unconventional tech hires are not so unconventional after all. But the willingness to hire them and the commitment to help them expand their capabilities require a shift in thinking. TecH for execs GANs: Putting the technology behind deep fakes to use for business good Our experts serve up a periodic look at the technology concepts leaders need to understand to help their organizations grow and thrive in the digital age. What they are. Generative adversarial networks (GANs) are a type of AI in which two algorithms—a generator and a discriminator—compete against each other to perform a task, which eventually results in better performance of said task. Recall that data is the food of algorithms. The generator works to produce a synthetic version of the data it's fed that is good enough to "fool" the discriminator. The discriminator works to distinguish between data that is genuine and synthetic. At the start, the generator creates random patterns that the discriminator can easily distinguish from "real" data, but produces more representative data with each attempt. Over time, the generator will get very good at generating synthetic data that can fool the discriminator. Why it matters. You might recognize immediately how GANs can be used nefariously: for example, to create videos of people that look and sound very real but are completely fabricated. These have come to be known as "deep fakes." Such uses of the technology are just one of the reasons consumers are rightfully getting squeamish about putting personal data into the hands of others, including businesses. But businesses can use the technology for good. First off, GANs can be used to detect fakes (cue image of Evil Superman versus Good Superman). Organizations can also use GANs to generate cyberattacks that criminals have not yet deployed and devise ways to counteract them. Of course, criminals can use GANs to create new attacks, so it's up to businesses to stay one step ahead. GANs have plenty of industry-specific uses as well. Some healthcare providers use GANs to analyze X-ray images to accelerate and improve physicians' diagnoses. Pharma companies are turning to GANs to learn which of millions of potential compounds are best suited for new treatments. Financial markets are leveraging these algorithms to help predict stock price movements. Retailers are using them to boost personalization by developing a very granular understanding of what customers are most likely to respond to, including customized product descriptions. Where to start with GANs. Across industries, GANs arguably are getting the most traction as a means of generating synthetic data to train AI models when they have insufficient natural data. Just over a quarter of organizations we surveyed last year say they're generating synthetic data. Have a valuable business use case but lack the data? Consider turning to GANs. What technology concepts would you like us to help explain next? Let us know. Share this Tech for Execs BACKTALK Have feedback or other ideas? We'd love to hear from you. Tell us what you think Follow our thinking Share these insights Did you enjoy this newsletter? Forward it to colleagues and friends so they can subscribe too. Was this issue forwarded to you? Sign up for it and sample our 40+ other free email subscriptions here. This email contains information about McKinsey's research, insights, services, or events. By opening our emails or clicking on links, you agree to our use of cookies and web tracking technology. For more information on how we use and protect your information, please review our privacy policy. You received this email because you subscribed to The Shortlist newsletter. 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