At our ReimagineHR summit in London on Thursday, CEB (now Gartner) Principal Executive Advisor Clare Moncrieff led a session on creating a common vision of digitalization for the business and HR. After examining hundreds of trends, our research councils serving chief HR officers and chief information officers have identified six deep shifts in the business environment that will result from digitalization. These shifts should act as the framework for heads of HR to:
- Ensure talent conversations with the line are grounded in business context
- Identify the current talent implications of these shifts, project future implications, and partner with the line and C-suite peers to prioritize and respond to each
- Improve their teams’ business acumen (to underscore the importance of this, 58 percent of HR business partners indicated in one of our surveys that building business acumen was their top development goal in 2017)
(The case studies we link to below are available exclusively to CEB Corporate Leadership Council members)
1) Demand Grows More Personal
As customers seek personalized products that align with their preferences and values as individuals (rather than as segments), companies will rely on digital channels and digital innovations in logistics and customer service to achieve personalization at scale. Customers will continue to expect lower-effort, nonintrusive service.
This could, for example, affect how HR functions look for new talent. Attraction of critical talent now requires differentiated, customized branding and career coaching. Candidates will demand a more effortless, personalized application experience. AT&T approached this shift by creating a more personalized “Experience Weekend” to show the innovation of its brand to campus candidates and make top talent more likely to accept job offers.
With the rapid advance of AI and machine learning technology, employers need talent with skills and knowledge in this field at a greater rate than university programs are able to churn them out. The imbalance of supply and demand has led to large, rich companies buying up all the AI startups and luring AI professors away from universities with much larger salaries. To address this issue and expand access to AI education, Stanford University professor and Coursera co-founder Andrew Ng has launched a new website, Deeplearning.ai, whose courses “offer coders without an AI background training in how to use deep learning, the technique behind the current frenzy of investment in AI,” Wired’s Tom Simonite reports:
“This sounds naive, but I want us to build a new AI-powered society,” Ng tells WIRED. “The only way to build this is if there are hundreds of thousands of people with the skills to do things like improve the water supply for your city or help resource allocation in developing economies.” Ng’s new courses cost $49 a month and are offered through online-education startup Coursera, which he cofounded in 2012 and where he still sits on the board. …
With his new courses, Ng is offering a solution to a problem he helped create. His prominence comes from work on deep learning at Stanford and Google’s X Labs that helped prove machine learning could do transformational things for businesses. In a 2012 paper, he and coauthors described a system that learned to recognize cats in still images from YouTube without human help. Now there aren’t enough people with machine learning skills to go around.
Ng’s motivation to launch Deeplearning also stems from his sense that people who don’t understand how AI works are afraid of it, which he thinks we shouldn’t be, Daniel Terdiman adds at Fast Company: Read more
With AI and machine learning taking on an increasingly major role in the workplace, prognosticators are divided on whether these technologies will result in the mass displacement of human workers or create so many new jobs that their net impact on employment is ultimately positive. In his latest piece for the Wall Street Journal, Greg Ip, a member of the techno-optimist camp, uses an example from the past to illustrate why he’s not worried about AI taking everyone’s jobs:
Until the 1980s, manipulating large quantities of data—for example, calculating how higher interest rates changed a company’s future profits—was time-consuming and error-prone. Then along came personal computers and spreadsheet programs … The new technology pummeled demand for bookkeepers: their ranks have shrunk 44% from two million in 1985, according to the Bureau of Labor Statistics. Yet people who could run numbers on the new software became hot commodities. Since 1985, the ranks of accountants and auditors have grown 41%, to 1.8 million, while financial managers and management analysts, which the BLS didn’t even track before 1983, have nearly quadrupled to 2.1 million.
Just as spreadsheets drove costs down and demand up for calculations, machine learning—the application of AI to large data sets—will do the same for predictions, argue Ajay Agrawal, Joshua Gans and Avi Goldfarb, who teach at the University of Toronto’s Rotman School of Management.
Accordingly, Ip posits that AI and machine learning will make certain skills obsolete but open up new opportunities for more valuable and productive work that uses these technologies as tools to improve human decision-making. Deloitte US CEO Cathy Engelbert and managing director Scott Corwin recently advanced the same argument about self-driving cars and trucks at Quartz, dismissing fears that these technologies will kill jobs:
Google.org, the tech giant’s philanthropic arm, is investing $50 million in a multinational project to better understand and prepare for the coming changes in the way we work, its president Jacquelline Fuller announced in a blog post last week:
This two-year commitment will fund nonprofits focused on this issue, with our first grantees in the U.S. and Europe; we have plans to expand to other regions soon. These organizations will also be able to draw on Googlers’ volunteer time for technical advice. Combined with our $50 million effort to help close the global education gap, Google.org has now committed $100 million to supporting education and economic opportunity—our largest giving initiative to date.
The initiative’s grants will focus on three key areas, Fuller explains: Better connecting job seekers with jobs, helping ensure training is effective and wide-reaching, and improving the quality of low-wage workers’ jobs. Regarding the first of these goals, Fuller takes the opportunity to plug Google for Jobs, the machine learning-enhanced job search function Google launched in June, and Google Hire, its applicant tracking system that launched for small US businesses last month.
In a separate blog post, Google.org adds that $2 million of this fund will be devoted to funding research “to better anticipate and understand what the world’s fast-changing workforce will need in the years to come and how technology can help produce positive outcomes.”
On Sunday, Microsoft released plans for its second-generation HoloLens headset, announcing that the next design of the augmented-reality glasses will incorporate a powerful AI coprocessor. That AI will allow the device to independently analyze sensory data, including what a user sees and hears, without needing to send that data off to the cloud. This will save processing time, making the device faster and more powerful while still preserving the user’s mobility. (For a deeper look at this type of device-native AI technology, read this Bloomberg piece.)
Microsoft’s news also came just a few days after Google’s announcement of a new Glass Enterprise offering. Lisa Eadicicco goes over the differences between the devices for Time:
While the basic concepts behind HoloLens and Google Glass overlap, in execution they couldn’t be less alike. Google Glass is meant to be physically insubstantial like a pair of literal glasses, only noticeable when someone needs it for a specific task. It displays a small virtual screen above the wearer’s eye, which can be glanced at without disrupting other visual tasks. The new version is even friendlier, able to clip onto existing eyeglasses and rendering the technology more accessible for those who need prescription glasses or protective eyewear in their jobs (though it must remain in wireless range of a smartphone to work properly).
HoloLens, by contrast, is much more immersive, since it can display larger graphics that fall within the wearer’s field of view. And unlike Glass, it’s also a functionally holistic device, unconstrained by reliance on smartphone or virtual-reality-style computer tethers to operate. All of HoloLens’s necessary computing components are baked into the headset.
Google Glass, originally developed from a passion project of company cofounder Sergey Brin, was supposed to unlock the next frontier in digital connectivity. While the smartphone has made technology and information omnipresent in our lives, Glass promised to remove the cumbersome barrier of a handheld device and allow for hands-free computing using voice and optical commands. Apps would seamlessly integrate with reality and life would never be the same. But that grandiose vision fell infamously short, as Glass failed to take off as a mass consumer product and the company stopped offering the product in 2015. Development went on in semi-secret, however, and the product has now found a second life as a business solution, which Alphabet, Google’s parent company, is calling Glass Enterprise Edition. In a fascinating profile of the surprisingly resurgent product, Wired‘s Steven Levy catches us up on recent events:
For about two years, Glass EE has been quietly in use in dozens of workplaces, slipping under the radar of gadget bloggers, analysts, and self-appointed futurists. Yes, the population of those using the vaunted consumer version of Glass has dwindled, tired of being driven out of lounges by cocktail-fork-wielding patrons fearing unwelcome YouTube cameos. Meanwhile, Alphabet has been selling hundreds of units of EE, an improved version of the product that originally shipped in a so-called Explorer Edition in 2013. Companies testing EE—including giants like GE, Boeing, DHL, and Volkswagen—have measured huge gains in productivity and noticeable improvements in quality. What started as pilot projects are now morphing into plans for widespread adoption in these corporations.
The new version has also undergone design advancements and now has more processing power, networking capability, and battery life, but that’s the least interesting part of the product’s evolution.
Most prognosticators of the future of work believe that as more and more rote mechanical and basic knowledge work is automated, many of the jobs that will remain for people will involve interacting with and caring for other human beings. As the US population gets older on average, the Bureau of Labor Statistics projects that the health care industry will add more jobs than any other sector of the economy in the coming decade (other developed countries with aging populations are looking at something similar). The expansion of the health care workforce is the main reason why some economists see women having an advantage over men in the job market of the near future, and health care is also seen as a viable second career for many blue-collar men whose jobs in manufacturing have been eaten up by automation or outsourcing.
Lost in this conversation, however, is the question of whether the health care jobs of the future will provide as decent a living as the manufacturing jobs of the past. While highly skilled and trained professionals like nurses may enjoy good pay and job security, the majority of health care jobs are in direct care, comprising home health aides, nursing assistants, and direct support professionals for people with disabilities or special needs. Direct care workers make up a large and growing segment of the health sector, Soo Oh writes at Vox, and face little risk of being replaced by machines anytime soon:
One of the fastest-growing fields is direct care: There are at least 3.6 million direct care workers in the US, not including an estimated 800,000 unreported workers, according to researchers. The Bureau of Labor Statistics projects an increase of more than 1 million new direct care workers — personal care workers, home health aides, and nursing assistants — between 2014 and 2024. Unlike food service or retail jobs, which round out the top five growing jobs, direct care workers are not in immediate danger of being edged out by automation or internet commerce.
Unfortunately, these jobs offer low pay, few or no benefits, and taxing work conditions: