Last November, Amazon announced that it was bringing its voice-controlled assistant Alexa into the workplace, launching Alexa for Business at its its annual AWS re:Invent conference. This week, the company revealed how far the enterprise version of Alexa has come, who is using it, and how the product is being applied in business settings. Amazon Chief Technology Officer Werner Vogels expanded on these points in a post on his blog, All Things Distributed:
Voice interfaces are a paradigm shift, and we’ve worked to remove the heavy lifting associated with integrating Alexa voice capabilities into more devices. For example, Alexa Voice Service (AVS), a cloud-based service that provides APIs to interface with Alexa, enables products built using AVS to have access to Alexa capabilities and skills.
We’re also making it easy to build skills for the things you want to do. This is where the Alexa Skills Kit and the Alexa Skills Store can help both companies and developers. Some organizations may want to control who has access to the skills that they build. In those cases, Alexa for Business allows people to create a private skill that can only be accessed by employees in your organization. In just a few months, our customers have built hundreds of private skills that help voice-enabled employees do everything from getting internal news briefings to asking what time their help desk closes.
Alexa for Business is now capable of interfacing with common enterprise applications like Salesforce, Concur, and ServiceNow, Vogels added, while IT developers can use the Alexa Skills Kit to enable custom apps as well. WeWork, one early adopter of the service, has “built private skills for Alexa that employees can use to reserve conference rooms, file help tickets for their community management team, and get important information on the status of meeting rooms.”
Last year, the Montreal-based startup Element AI estimated that there were fewer than 10,000 people worldwide with the necessary skills to design artificial intelligence/machine learning systems, but the Chinese internet conglomerate Tencent Holdings later estimated the total number of AI researchers and practitioners at between 200,000 and 300,000 people.
Element AI came out with a new estimate on Wednesday, Jeremy Kahn reports at Bloomberg, putting the number of AI specialists with recently-earned PhDs at 22,000, of whom 3,000 are looking for work. With less restrictive parameters, however, the total number of AI experts could be four times greater:
Element AI said it scoured LinkedIn for people who earned PhDs since 2015 and whose profiles also mentioned technical terms such as deep learning, artificial neural networks, computer vision, natural language processing or robotics. In addition, to make the cut, people needed coding skills in programming languages such as Python, TensorFlow or Theano.
Gartner is projecting worldwide IT spending to reach $3.7 trillion this year, a 4.5 percent increase from 2017, with enterprise software expected to be the fastest-growing component of IT spend, growing by 9.5 percent from $355 billion last year to $389 billion in 2018. HR technologies are among the leading drivers of innovation in this space, with significant spending forecast on software-as-a-service solutions in financial management systems (FMS), human capital management (HCM), and analytic applications. Big data, algorithms, machine learning, and AI are among the technologies expected to drive growth in IT investments in the coming years.
(For readers who want to hear more about our IT spending forecast, Gartner analysts discuss these findings in detail in a complimentary webinar, available on demand here.)
For talent management leaders, this information carries significant implications. In the coming years, technology will inevitably be more embedded into the HR function: The only choice for leaders is whether they want to be on the front or back end of the adoption curve. Technology in the HR realm is advancing at a rapid rate, but the function seems consistently hesitant to take advantage of the opportunities and efficiencies it offers. A wide range of tools are newly available or in development that can help solve perennial HR challenges such as candidate vetting, employee wellness, space management, analytics strategy, recruiting and retaining diverse employees, understanding drivers of high performance, making learning more accessible, or offering digital assistants for all employees.
Using its vast trove of user data, LinkedIn compared the US talent landscape in 2012 and 2017 to see what roles had grown the most in demand in that time. At the top of the professional networking site’s list of the top 20 fastest-growing jobs is “machine learning engineer,” the ranks of which have expanded nearly tenfold in the past five years, followed by “data scientist,” “sales development representative,” “customer success manager,” “big data developer,” and “full stack engineer.”
The proliferation of digital roles such as data scientist is unsurprising, given that these jobs are no longer limited to “tech companies” but are now needed in all sorts of organizations. However, Maria Ignatova notes at LinkedIn’s Talent Blog, there are two other key takeaways from the list that employers can learn from:
Hiring for outstanding soft skills is a high priority: Many of the roles on the list are customer-facing and underscore the importance of being able to screen candidates for soft skills. Traditionally, that has been one of the most challenging parts of the hiring process, with standard interviews just not cutting it. Many companies now are starting to use soft skills assessments or job auditions to see candidates in a more authentic light.
Some roles are so new, that the current talent pool is minimal: A few of the jobs on this list didn’t even exist five years ago, or if they did, they were niche with very few professionals in these roles. This means that you have to get creative when it comes to sourcing talent and be willing to approach people from different fields and consider non-standard skillsets. Reskilling the workforce due to shortage of talent is one of the top trends that will impact you if you are hiring for these roles.
LinkedIn’s findings also point to something we’ve found in our research at CEB, now Gartner: The convergence of demand around a smaller number of critical roles. Among S&P 100 companies, we found, 39 percent of job postings last year were for just 29 roles.
When it comes to the threat or promise of automation, experts are divided as to whether AI and robotics will eliminate jobs en masse or merely automate rote tasks and free up more of workers’ time for innovation and creativity. McKinsey has put out some interesting research throughout the year in which they attempt to forecast the impact of these new technologies on the workforce. In January, they released the attention-grabbing headline finding that half of the work currently performed by humans could be automated with already-existing technology. Though fewer than 5 percent of jobs can be automated entirely, their research found, most jobs could have at least one third of their component tasks automated today.
In an update to that work published this week, McKinsey takes a closer look at the various factors that will drive automation in the coming decades—such as technical feasibility, cost of deployment, and labor market considerations—and concludes that “between almost zero and 30 percent of the hours worked globally could be automated by 2030, depending on the speed of adoption.” The effects will not, however, be evenly distributed among occupations:
Activities most susceptible to automation include physical ones in predictable environments, such as operating machinery and preparing fast food. Collecting and processing data are two other categories of activities that increasingly can be done better and faster with machines. This could displace large amounts of labor—for instance, in mortgage origination, paralegal work, accounting, and back-office transaction processing. … Automation will have a lesser effect on jobs that involve managing people, applying expertise, and social interactions, where machines are unable to match human performance for now.
The recent news of Microsoft’s massive increase in headcount for its AI division indicates the company’s dedication to the technology that it believes will shape the future. The business unit was launched by CEO Satya Nadella a year ago to position the company for what he saw as a “paradigm shift in computing” of which he wanted Microsoft to be on the forefront.
“Microsoft is dedicated to democratizing AI for every person and organization, making it more accessible and valuable to everyone and ultimately enabling new ways to solve some of society’s toughest challenges,” the company wrote in an announcement at the time.
Having grown from 5,000 to over 8,000 employees, the AI and Research group now accounts for roughly 7 percent of the Seattle-based giant’s total workforce. So what is all that talent working on? What does Microsoft have to show for all this investment?
For starters, the investment goes far beyond human capital. Microsoft’s $26 billion acquisition of LinkedIn last year undoubtedly played a big role in the company building out its AI capabilities. An initial run of joint projects is underway, making it clear this merger aims to significantly reshape the way technology is used in the workplace, GeekWire’s Nat Levy reports:
Office 365 will include a new “profile card” that can display LinkedIn information. For example, interviewers using Outlook would be able to easily access LinkedIn profiles of job seekers. This integration, the first between Office 365 and LinkedIn since the acquisition, is designed to make it easier for people to search for others inside their organizations.
Amazon Mechanical Turk
Amazon’s Mechanical Turk platform allows individuals and organizations to outsource minute “human intelligence tasks” for very small payments, and is commonly used to fulfill the mundane task of feeding data into machine-learning algorithms. A study of US-based “Turkers” last year found that the typical user was young, well-educated, and using Mechanical Turk for work daily or regularly. Most use the platform to supplement income from other sources, though about a quarter said they earned most or all of their income there. Also, more than half of Turkers reported earning less than $5 an hour, well below the US federal minimum wage of $7.25.
Like other workers earning a precarious living in the gig economy, Turkers run a high risk of being exploited, but this highly distributed workforce has begun to fight for its rights. In a Wired’s Miranda Katz takes a look at how Turkers are pushing for changes at Amazon to protect them from being underpaid (or not paid at all):
[T]he Turker workforce has proven particularly difficult to organize: MTurk magnifies the challenges of the gig economy, with its isolated workers spread across the globe and hidden behind usernames, performing minute tasks on a platform operated by a massive, wealthy corporation. MTurk is also one of the least consumer-facing corners of the gig economy—so while ethically minded customers have taken Uber, Handy, and the like to task for their treatment of workers, Amazon’s gig-work platform has largely managed to evade public scrutiny for its low pay and reported lack of transparency. …