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TALENT AND WORKFORCE EFFECTS IN THE AGE OF AI

by Susanne Hupfer
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Article 19 minute read03 March 2020


TALENT AND WORKFORCE EFFECTS IN THE AGE OF AI INSIGHTS FROM DELOITTE’S STATE OF
AI IN THE ENTERPRISE, 2ND EDITION SURVEY

19 minute read 03 March 2020
 * Susanne Hupfer United States


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 * 
 * Introduction
 * The changing nature of work
 * Minding the AI skills gap
 * Filling the AI skills gap: Replace versus retrain
 * Redesigning jobs: Automation and augmentation
 * Considerations for AI leaders
 * Deloitte Services

Will AI-driven automation render most jobs obsolete, or is smart technology
ushering in an age of humans working in collaboration with artificial
intelligence? A new Deloitte survey suggests the direction organizations are
headed.


INTRODUCTION

Over the past few years, artificial intelligence has matured into a collection
of powerful technologies that are delivering competitive advantage to businesses
across industries. Global AI adoption and investment are soaring. By one
account, 37 percent of organizations have deployed AI solutions—up 270 percent
from four years ago.1 Analysts forecast global AI spending will more than double
over the next three years, topping US$79 billion by 2022.2


LEARN MORE

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Companies and countries around the globe increasingly view development of strong
AI capabilities as imperative to staying competitive. Deloitte’s State of AI in
the Enterprise, 2nd Edition offers a global perspective of AI early adopters,
based on surveying 1,900 IT and business executives from seven countries and a
variety of industries.3 These adopters are increasing their spending on AI
technologies and realizing positive returns. Almost two-thirds (65 percent)
report that AI technologies are enabling their organizations to move ahead of
the competition. Sixty-three percent of the leaders surveyed already view AI as
“very” or “critically” important to their business success, and that number is
expected to grow to 81 percent within two years.

These leaders see AI rapidly transforming their businesses and industries.
Fifty-seven percent predict that AI will “substantially transform” their company
within the next three years; two-thirds believe that their industry’s
transformation will happen within five years. As AI drives these
transformations, it is changing how work gets done in organizations by making
operations more efficient, supporting better decision-making, and freeing up
workers from certain tasks. The nature of job roles, and the skills that are
most needed, are evolving.

Indeed, the effect AI will ultimately have on jobs is uncertain: Are we staring
at a dim future in which AI-driven automation has made most jobs obsolete, or is
AI ushering in a new age characterized by humans working in collaboration with
the technologies—augmented by AI capabilities rather than displaced by them?4
Early indicators support the optimistic view: While AI adopters express concern
about automation as an ethical risk, they emphatically believe that human
workers and AI will augment each other, changing the nature of work for the
better.


THE CHANGING NATURE OF WORK

As AI adoption advances, the way organizations do their work is evolving.
Seventy-one percent of adopters report that AI technologies have already changed
their company’s job roles and necessary skills, and 82 percent believe AI will
lead to moderate or substantial changes to job roles and skills over the next
three years.

For AI adopters, improving internal business operations is a benefit on par with
enhancing products and services (figure 1). TiVo, for example, streamlines IT
operations by using a machine learning5 platform to automatically detect,
classify, aggregate, and route IT incidents.6 The AI-aided process has reduced
actionable events from about 2,500 to 150 daily, enabling the professionals in
TiVo’s network operations center to more easily manage highly complex
operations, 24/7.

The third AI benefit—making better decisions—also has implications for the
nature of work. For example, researchers from MIT have developed a machine
learning model designed to help ER physicians determine the optimal time to
switch patients suffering from sepsis from one treatment protocol to
another—often a challenging decision for clinicians.7 Trained on historic health
data from sepsis patients, the model predicts whether a patient will need
vasopressor medications within the next few hours. In a clinical setting, the
model could be integrated into a bedside monitor, alerting clinicians ahead of
time when a treatment change may be warranted—an example of human experts and AI
achieving better decisions together.

Another top benefit of AI involves automating tasks to free up workers to be
more creative. Salesforce's Einstein Voice Assistant—a voice-based AI assistant
for interacting with Salesforce CRM software—illustrates this benefit: Sales
reps and other field workers speak conversationally to the assistant, which
transcribes notes, automatically associates them with relevant accounts and
contacts, and makes recommendations for follow-up tasks.8 Workers are freed from
mundane data entry tasks and can instead concentrate their efforts on their
customer interactions.

> “Beyond automating tasks, the other more remarkable impact of AI on an
> enterprise will be on decision-making: Large organizations still struggle to
> make good decisions on time.”
> 
> —Jay Dwivedi, president, xInvest Consultants



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Changing how work gets done within the organization—by making operations more
efficient, supporting better decision-making, and freeing up workers from
repetitive tasks—is core to what companies want to achieve with AI. Few
anticipate it being easy, though: “Integrating AI into the company’s roles and
functions” is tied for first place as a challenge for AI initiatives—on par with
challenges related to building and deploying AI (figure 2). Moreover, only 38
percent of executives reported their organization has “high expertise” in
integrating AI into their business processes, and just 37 percent reported “high
expertise” in integrating AI into their IT environments.



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MINDING THE AI SKILLS GAP

To meet their AI aspirations, companies will likely need the right mix of talent
to translate business needs into solution requirements, build and deploy AI
systems, integrate AI into processes, and interpret results. However, most early
adopters face an AI skills gap and are looking for expertise to boost their
capabilities. In fact, 68 percent of executives surveyed report a
moderate-to-extreme skills gap, and more than a quarter (27 percent) rate their
skills gap as “major” or “extreme.” The gap is evident across all countries
surveyed, ranging from 51 percent reporting moderate-to-extreme gaps in China to
73 percent reporting the same in the United Kingdom.

What do leaders regard as the “most needed” roles to fill their company’s AI
skills gap? The top four most-needed roles are “AI builders,” who are
instrumental in creating AI solutions: researchers to invent new kinds of AI
algorithms and systems, software developers to architect and code AI systems,
data scientists to analyze and extract meaningful insights from data, and
project managers to ensure that AI projects are executed according to plan
(figure 3). Beyond these AI builders, adopters are seeking "AI translators” who
bridge the divide between the business and technical staff—both at the front and
back ends of building AI solutions:

 * Business leaders to translate business problems/needs into requirements that
   guide the building of a solution, and to interpret results from an AI system
   and make decisions
   
 * Change management experts to implement change strategies and help integrate
   AI into the organization’s processes
 * User experience designers to make AI systems easier to navigate
 * Subject-matter experts to infuse their domain expertise into AI systems



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As adopters gain experience building AI production systems, they amass and hone
AI skills. Yet companies with greater AI experience report a larger skills gap
(figure 4). Within organizations, the supply of AI skills appears unable to keep
up with growing demand.

As AI experience increases within an organization, the kinds of roles that
adopters seek undergo an interesting shift. For companies with relatively little
AI experience (they’ve built five or fewer production systems), AI researchers
are the most sought-after, with about a third of surveyed executives rating them
as a top-two needed role (figure 5). Business leaders rank near the bottom. By
the time adopters have become highly experienced at building AI solutions
(they’ve built 20 or more production systems), however, business leaders have
bubbled to the top, and AI researchers have sunk almost to the bottom.



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What to make of this curious flip? Many companies embarking on AI initiatives
may feel they need to hire AI superstars—researchers with advanced degrees who
can invent new AI algorithms and techniques—to spearhead their efforts.9 And by
the time organizations have accumulated substantial AI experience, they may have
filled their ranks with enough of these brilliant technology experts. At that
stage, companies have shifted to seeking business leaders who can play the
crucial “translator” role: figuring out what results from AI systems mean, and
how those results should factor into business decisions and actions.

> “I’m in favor of education of senior management before establishing technical
> centers of excellence. Business needs to lead the charge, and leaders need to
> believe in order to drive the organization forward expeditiously.”
> 
> —Jack C. Crawford, managing partner, Datalog.ai

Is it possible that the less-experienced AI adopters are placing too much
emphasis on finding AI researchers, who are scarce and in such high demand that
they command lavish salaries?10 These heavyweights are certainly called for when
one needs to invent new AI algorithms and techniques or create highly
customized, domain-specific solutions.11 But not all companies will need to push
these boundaries, and many can turn to an array of AI tools that can be used by
software developers without deep AI expertise, such as machine learning
application program interfaces (APIs), cloud-based AI services and AI
development platforms, pretrained machine learning models, and even automated
machine learning (AutoML).12 It’s worth noting (figure 5) that demand for
software developers, data scientists, and project managers—the crucial
professionals who can plan, architect, and build AI projects, and make use of
existing AI tools and techniques to bring a project from concept to
production—doesn’t wane as adopters gain more experience building AI solutions.

It’s also possible that less-experienced AI adopters may be focusing too little
on business leaders who are able to understand not only their organization’s
business strategy but the ways in which AI initiatives can support and
accelerate it. In an article headlined, “The AI roles some companies forget to
fill,” the authors underscore the importance of involving business leaders early
in the process: “Many companies rush into the AI race without clear objectives,
hope a brilliant AI researcher and a technology team can create something great
without guidance, and end up with little to show for it. Recruiting an AI
quarterback to provide the business input, and ensuring success with
well-defined metrics, is the most important job that most companies miss.”13


FILLING THE AI SKILLS GAP: REPLACE VERSUS RETRAIN



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How are AI adopters attempting to fill their skills gap? Executives revealed a
strong inclination to bring in new talent to plug the gap (figure 6). In fact,
leaders are 3.1 times more likely to prefer replacing employees with new
AI-ready talent, versus keeping and retraining their existing workforce.

Respondents in all countries surveyed lean toward bringing in new talent (figure
7). At one extreme, AI adopters in Canada are 6.2 times more likely to favor
replacing over retraining. At the other end, Germany is just 1.7 times more
likely to favor replacing employees—perhaps partially due to that country’s
labor laws, which place stringent requirements around employee dismissals.14
Notably, there appears to be no correlation between the size of the AI skills
gap in a particular country and the preferred approach for addressing it.



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The desire to replace workers with new, AI-ready talent is clear, but is it a
viable strategy at a time when there’s fierce competition for expertise? Reports
reveal a scarcity of AI talent around the world. Canadian firm Element AI
recently analyzed LinkedIn profiles to gauge the size of the worldwide top-tier
AI talent pool and counted 36,524 self-reported PhD-level AI experts (including
data scientists and machine learning researchers and engineers).15 We’ve already
noted that not all AI adopters need to hire AI researchers, but for those that
do, that’s a tiny global pool to fight over. A 2017 report from Chinese tech
titan Tencent cast a wider net with looser criteria and estimated that “AI
researchers and practitioners” number 300,000 worldwide (200,000 employed, plus
100,000 students in the pipeline).16 These two reports provide some useful
bookend estimates for the global AI talent pool.

At the same time, trends on job search sites indicate strong demand for AI
talent.17 A LinkedIn search for AI-based jobs yields more than 64,000 US
openings and over 230,000 worldwide openings.18 It’s hardly surprising, then,
that competition for AI-trained professionals is vigorous. Glassdoor chief
economist Andrew Chamberlain reports that “the supply of people moving into this
field is way below demand.”19 Employers report difficulty filling AI job
openings, and some say it’s impeding their growth.20 Articles abound about
talent wars for techies such as AI researchers and data scientists (aka
“America’s hottest job”).21

Companies may believe that seeking the best external talent will provide an
advantage, but they shouldn’t overlook the option of training their existing
employees. Indeed, notwithstanding their desire to replace workers, AI adopters
also report training their current workforces to strengthen expertise and narrow
their skills gap. The majority are training developers to create AI solutions,
IT staff to deploy those solutions, and employees to use AI in their jobs
(figure 8). Companies in Germany appear to be outpacing other countries with
their keen focus on training.



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REDESIGNING JOBS: AUTOMATION AND AUGMENTATION

There’s vigorous debate around the ultimate effect of AI on jobs. Pessimists
foresee workers being largely supplanted by robots and automation, and facing a
dim future with people competing for the few remaining jobs that require human
skills. Optimists believe that AI technologies—like other new technologies
before them—will produce more jobs than they eliminate and give rise to new
roles that call for new skills and different ways of working.22

According to a 2018 World Economic Forum report on the future of jobs, companies
expect work tasks to be increasingly performed by machines. In 2018, people
carried out an estimated 71 percent of task hours; by 2022, the human share is
expected to drop to 58 percent, with machines handling the remaining 42 percent.
Despite this sobering finding, the report presents a positive global forecast:
While technology advances are expected to displace as many as 75 million
existing jobs, emerging tasks and roles are projected to generate upward of 130
million jobs.23 The report cautions that achieving the predicted net job gains
will “entail difficult transitions for millions of workers and the need for
proactive investment in developing a new surge of agile learners and skilled
talent globally … [I]t is critical that businesses take an active role in
supporting their existing workforces through reskilling and upskilling, that
individuals take a proactive approach to their own lifelong learning and that
governments create an enabling environment, rapidly and creatively, to assist in
these efforts.”

AI-driven automation is already taking over routine, repetitive tasks in many
industries, and may even be used for complex, specialized efforts that were once
the bailiwick of highly trained humans, such as radiology and pathology.24 MIT
and CMU researchers—taking the perspective that occupations are collections of
tasks—have analyzed nearly 1,000 occupations and more than 18,000 work tasks and
assigned each a “suitability for machine learning” (SML) score.25 Across
industries, they concluded that most occupations have at least some tasks that
are SML but that there are few, if any, occupations for which all tasks are SML.
They propose shifting the debate away from a focus on full job automation and
“pervasive occupational replacement” and toward the “redesign of jobs and
reengineering of business processes.”

Deloitte researchers propose reimagining work not as a set of tasks arranged in
a predefined process but, rather, as a collaborative effort in which “humans
define the problems, machines help find the solutions, and humans verify the
acceptability of those solutions.”26 The concept of using computer intelligence
to augment human capabilities is hardly new: As early as 1960, the computer
scientist and psychologist J.C.R. Licklider envisioned symbiotic partnerships
between humans and computers in which humans “set the goals, formulate the
hypotheses, determine the criteria, and perform the evaluations” and computers
“do the routinizable work that must be done to prepare the way for insights and
decisions.”27

One dramatic example demonstrating Licklider’s vision comes from a “freestyle
chess” match held in 2005, eight years after IBM’s Deep Blue supercomputer
famously defeated world chess champion Garry Kasparov. Contestants could be any
combination of humans and computers, and the surprise victors were two amateurs
who “coached” three computers. Kasparov noted that “weak human + machine +
better process was superior to a strong computer alone and, more remarkably,
superior to a strong human + machine + inferior process. … Human strategic
guidance combined with the tactical acuity of a computer was overwhelming.”28

Where do AI adopters stand on automation and augmentation? At least in the short
term, cost-cutting through automation appears alluring: Nearly two-thirds of our
survey respondents agree (22 percent strongly agree) that their organization
would like to cut costs by automating as many jobs as possible. However, the
potential for job disruption is concerning, and 36 percent rank job cuts from
AI-driven automation as a top-three ethical risk. Despite these worries, they
resoundingly believe that AI has the potential to change the workforce
positively: Three-quarters agree that AI technologies already empower their
employees to make better decisions, and the same proportion foresee human
workers and AI augmenting each other, encouraging new ways of working. Seven in
10 believe AI will enhance employee job performance and satisfaction.

Companies are recognizing that automation is not synonymous with job
elimination. Notably, “reduce head count through automation” is the least
popular AI benefit reported by our survey respondents, and a greater proportion
of executives ranked “free up workers to be more creative by automating tasks”
as a top AI benefit (figure 1). While executives in Australia see AI automation
more as a way to reduce head count, adopters in the other countries
surveyed—especially China and the United Kingdom—show a distinct preference for
using AI automation to free up workers for higher-value tasks (figure 9). As
Licklider predicted, organizations can use AI to automate mundane tasks, freeing
up human workers to apply their uniquely human capabilities (such as
interpretation, communication, judgment, and empathy) to less-routine tasks, as
well as to explore new problems and opportunities.29 Deloitte researchers
believe that companies that use automation primarily to optimize processes and
reduce costs (for example, through job cuts) will likely struggle to
significantly expand value creation in the long term; they recommend that
companies create a strategy around “redefining work”—encouraging workers with
newly freed-up capacity to identify and create new sources of value for their
businesses.30

> “Focus on augmenting people, not replacing them. Despite concerns, AI is not
> all about reducing labor costs, and organizations that approach the technology
> in this manner stand to miss out on real gains. Instead, early AI projects
> should focus on enabling employees to pursue higher value activities.”
> 
> —Falguni Desai, global head of strategy and transformation, equities, Credit
> Suisse

Across industries, there are signs that organizations are reimagining some jobs
as teamwork between humans and AI (see sidebar, “AI and humans in
collaboration”). As human-machine collaborations emerge, Deloitte researchers
have cautioned that organizations should not outsource fairness, morality, and
societal standards to algorithms.31 Avoiding bias—in AI algorithms and the data
used to train them—is an important ethical consideration when building AI
solutions.32 Some experts predict the emergence of new oversight roles to
evaluate AI systems for adherence to laws, regulations, and ethical standards.33



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AI AND HUMANS IN COLLABORATION

DEEP LEARNING ASSISTS PATHOLOGISTS

For pathologists, recognizing cancer metastases in lymph node tissue is
time-consuming and error-prone. Studies indicate that about one-quarter of
metastatic cancer stagings would be reclassified upon a second pathologic review
and that small metastases can be underdetected when reviews are
time-constrained.34 Google AI has developed a deep learning program—LYmph Node
Assistant (LYNA)—to detect metastatic cancer, training it on high-resolution
pathology slides of lymph nodes from breast cancer patients. LYNA has been able
to detect 92.4 percent of tumors—compared with 73.2 percent recognized by human
pathologists—and has accurately identified suspicious areas of tissue that are
sometimes too small for human detection.35

LYNA could be used to alert pathologists to areas of concern for further human
review and diagnosis. In a test with simulated diagnostic tasks, six
pathologists saw their average time to review tissue slides reduced from about
two minutes to one minute per slide with LYNA’s aid.36 The researchers noted
that “pathologists with LYNA assistance were more accurate than either
unassisted pathologists or the LYNA algorithm itself, suggesting that people and
algorithms can work together effectively to perform better than either alone.”37

PROGRAMMERS GET A BOOST FROM AI

Game company Ubisoft has created Commit Assistant, an AI-based bug detector.38
When developers commit new code to a codebase, the tool can identify potential
bugs—based on what it has learned from past coding errors—and alert developers
to review and fix the code. Ubisoft reports the AI assistant can accurately
identify six in 10 software problems and expects it to eventually even suggest
potential code fixes.

Other tools can provide a time-saving boost to developers during the coding
process. Deep TabNine is a deep learning model that has been trained on 2
million GitHub code files.39 As programmers type code, Deep TabNine predictively
presents “code autocomplete” suggestions, not unlike phrase autocompletes on a
search engine page.

VIRTUAL AGENTS AND HUMANS COOPERATE ON CUSTOMER SERVICE

Companies across industries are employing AI-based virtual agents—chatbots—to
handle customer service and IT support calls. These agents can process thousands
of calls annually, learning and adapting as they go, leading to reduced time and
cost per call and improved customer experience.40 Some companies view chatbots
as a way to lessen the burden on their human support personnel, who are freed up
to work on higher-value tasks. In other cases, virtual agents assist human
agents by sifting through documents and delivering the right information exactly
when needed.

Having humans in the loop is still considered essential.41 When chatbots get
stuck because they can’t discern a caller’s intent or face a complex issue for
which they haven’t yet been trained—or when human empathy is needed to soothe
frustrated callers—calls typically get routed to humans. And in one survey, 93
percent of chatbot owners reported that having humans interact with bots, for
validation and curation, is important to improving chatbot performance.42 For
example, the software company LivePerson offers an AI-powered dashboard that
allows humans to serve as “bot managers,” monitoring and troubleshooting
chatbots.43 Using sentiment analysis, the dashboard displays real-time customer
satisfaction scores for calls, and if a score drops too low, a human bot manager
can seamlessly take over and tweak the conversation. Furthermore, LivePerson
employs deep learning to recommend “next actions” to human agents and to
continually improve chatbot interactions.44


CONSIDERATIONS FOR AI LEADERS

Companies in the AI game are feeling a sense of urgency as their businesses and
industries undergo AI-fueled transformation. At a time when competition for AI
skills is fierce, maintaining a competitive advantage may depend upon having a
strategy for dealing with AI talent shortages and the changing nature of work.

Early adopters should consider strengthening their AI foothold by:

Deciding what skills are needed. From the start, AI adopters should take a close
look at how specialized their AI needs are. Then they can consider whether they
really need AI research superstars to break new AI ground, or whether they can
achieve their goals with a skilled engineering team that can be trained to use
available AI tools.

Adopters should also consider involving business leaders early and throughout
the life cycle of AI initiatives. These leaders can connect the company’s
business models and strategy to the requirements for AI systems, as well as
establish metrics for project success. Given the challenge of integrating AI
into a company’s roles and functions, AI adopters should also consider how
change management experts might be utilized. These professionals, who work to
ensure that organizations actually use new systems or processes after developing
them, may be one key to overcoming AI integration hurdles.

Finding the right balance between hiring and reskilling. Given AI talent
shortages, replacing existing workers with AI-ready talent is no silver bullet
to fix AI skills gaps. In addition to hiring, leaders should consider
identifying and reskilling current developers, IT staff, and other employees to
help build up the company’s AI expertise. Consider establishing programs to
train developers to create AI solutions and IT staff to deploy those solutions.

Given the difficulties of integrating AI technologies into the company’s
operations, leaders should also consider structured programs to train employees
on how to use AI systems in the course of their jobs, and also develop
structured ways to integrate AI into roles and functions. For their own part,
employees should aim to embrace an attitude of lifelong learning and consider
how AI assistance may supercharge their work in the future.

Redesigning work for the age of AI. AI-driven automation will likely change the
nature of how many humans conduct their jobs. But automation has a role far
broader than reducing head count or optimizing processes: As we saw in the
pathology and IT incident management examples, organizations can use automation
to free workers from repetitive or error-prone tasks, allowing them to bring
their human skills of judgment, interpretation, and empathy to bear on more
complex decisions. Leaders should create a vision now for what their “augmented
workforce” looks like—and evolve it as their AI capabilities advance. They
should consider creating a strategy for “redefining work”—focused on how workers
with freed-up capacity can create new sources of business value.45

One area where human judgment is absolutely needed is ensuring that
organizations build and deploy AI systems in ethical ways. The Notre Dame
Deloitte Center for Ethical Leadership promotes the view that everyone involved
in advancing AI—from corporate boards and management, to researchers and
engineers—shares responsibility for applying ethical constructs throughout the
AI product life cycle.46




ACKNOWLEDGMENTS

The author would like to thank Jeff Loucks for astute insights and discussions
that helped shape this topic, and Sayantani Mazumder for her invaluable data
analysis efforts and support in creating this report. Thanks are also due to
Paul Sallomi, David Jarvis, Natasha Buckley, and Susan Hogan for contributing
thoughtful suggestions to our work, and Jeanette Watson for her valued guidance.

Cover art by: Sonya Vasilieff



ENDNOTES

 1.  Pooja Singh, “Enterprise use of AI has grown 270 percent globally over the
     past four years,” Entrepreneur Asia Pacific, January 22, 2019. View in
     article

 2.  International Data Corporation, “Worldwide spending on artificial
     intelligence systems will grow to nearly $35.8 billion in 2019, according
     to new IDC spending guide,” March 11, 2019. View in article

 3.  Jeff Loucks et al., Future in the balance? How countries are pursuing an AI
     advantage, Deloitte Insights, May 1, 2019. To obtain a global view of how
     organizations are adopting and benefiting from AI technologies, in Q3 2018
     Deloitte surveyed 1,900 IT and line-of-business executives from companies
     that are prototyping or implementing AI solutions. Seven countries were
     represented: Australia, Canada, China, Germany, France, the United Kingdom,
     and the United States. View in article

 4.  Deloitte, Unleashing talent in the Age of With™: Your people with augmented
     power, 2019. View in article

 5.  With machine learning technologies, computers can be taught to analyze
     data, identify hidden patterns, make classifications, and predict future
     outcomes. These systems are able to improve accuracy over time without
     being explicitly programmed. Most AI technologies, including advanced and
     specialized applications such as natural language processing and computer
     vision, are based on machine learning and its more complex progeny, deep
     learning. View in article

 6.  BigPanda, “TiVo embraces BigPanda,” April 4, 2019, YouTube video, 2:44.
     View in article

 7.  Rob Matheson, “Machine-learning system could aid critical decisions in
     sepsis care,” MIT News, November 7, 2018. View in article

 8.  Bill Detwiler, “How Salesforce is making Einstein Voice a customizable
     voice assistant for today’s mobile workers and data-hungry businesses,”
     TechRepublic, June 14, 2019. View in article

 9.  Megan Beck, Thomas H. Davenport, and Barry Libert, “The AI roles some
     companies forget to fill,” Harvard Business Review, March 14, 2019. View in
     article

 10. Cade Metz, “Tech giants are paying huge salaries for scarce A.I. talent,”
     New York Times, October 22, 2017. View in article

 11. George Seif, “Don’t make this big machine learning mistake: Research vs
     application,” Towards Data Science, August 10, 2018. View in article

 12. Google Cloud, “Cloud AI building blocks,” accessed February 3, 2020; Amazon
     Web Services, “Machine learning on AWS,” accessed February 3, 2020; IBM,
     “AI tools for business,” accessed February 3, 2020; Parul Pandey, “AutoML:
     The next wave of machine learning,” Heartbeat, April 18, 2019. View in
     article

 13. Beck, Davenport, and Libert, “The AI roles some companies forget to fill.”
     View in article

 14. Many employees in Germany are protected by the Protection against Dismissal
     Act (Kündigungsschutzgesetz), which sets out strict legal requirements
     around employee terminations, including those for reasons relating to
     company operations. See Sabine Feindura, “Hire and fire: Protection against
     unfair dismissal in Germany,” Labor Law Magazine, September 26, 2016. View
     in article

 15. Element AI, Global AI talent report 2019, 2019. This study searched
     LinkedIn for individuals with doctoral degrees who describe their work as
     “machine learning” and who have job titles of “data scientist,” “research
     scientist,” “machine learning engineer,” “machine learning researcher,” or
     “data analyst.” The researchers explain that a PhD is a “useful proxy for
     the highly technical skills required to qualify as a specialist.” Their
     LinkedIn queries indicated 36,524 people who qualified as self-reported AI
     specialists according to the criteria. The authors note some caveats,
     including that profiles contain self-reported information and that LinkedIn
     is not widely used in all countries. View in article

 16. James Vincent, “Tencent says there are only 300,000 AI engineers worldwide,
     but millions are needed,” Verge, December 5, 2017. View in article

 17. Indeed, “Top 10 AI jobs, salaries and cities,” June 28, 2019; Sarah
     Overmyer, “Jobs of the future: Emerging trends in artificial intelligence,”
     Indeed, August 23, 2018. Indeed reports that AI job postings on its site
     increased 136.3 percent from May 2016 to May 2017, 57.9 percent from May
     2017 to May 2018, and 29.1 percent from May 2018 to May 2019. Searches for
     AI-related jobs on Indeed rose 49.1 percent between May 2016 and May 2017,
     rose 32 percent between May 2017 and May 2018, and decreased by 14.5
     percent from May 2018 to May 2019. The decrease in searches may indicate
     there are more available AI jobs than qualified professionals to fill them.
     View in article

 18. Job searches were performed on LinkedIn.com on January 15, 2020, using the
     Boolean search query: “artificial intelligence” or “ai” or “machine
     learning” or “deep learning” or “natural language processing” or “computer
     vision.” It’s important to note that not all job openings are posted to
     LinkedIn, and some countries have higher usage of the site than others. We
     present the numbers as a rough barometer of demand for AI skills. View in
     article

 19. Ann Saphir, “As companies embrace AI, it's a job-seeker's market,” Reuters,
     October 15, 2018. View in article

 20. Ibid. View in article

 21. Michael Sasso, “This is America’s hottest job,” Bloomberg, May 18, 2018.
     View in article

 22. Wall Street Journal, “Will AI destroy more jobs than it creates over the
     next decade?”, April 1, 2019; Peter Evans-Greenwood, Harvey Lewis, and Jim
     Guszcza, “Reconstructing work: Automation, artificial intelligence, and the
     essential role of humans,” Deloitte Review 21, July 31, 2017. View in
     article

 23. World Economic Forum, The future of jobs report 2018, 2018. A promising
     early data point comes from a prominent online job board that analyzed
     proprietary data from more than 50 million job postings, as well as survey
     results from job seekers and employers, and concluded that AI created three
     times as many jobs as it destroyed in 2018; see Alison DeNisco Rayome, “AI
     created 3x as many jobs as it killed last year,” TechRepublic, June 27,
     2019. View in article

 24. Forbes Technology Council, “Tech experts predict 13 jobs that will be
     automated by 2030,” Forbes, March 1, 2019; James Guszcza, Harvey Lewis, and
     Peter Evans-Greenwood, “Cognitive collaboration: Why humans and computers
     think better together,” Deloitte Review 20, January 23, 2017. View in
     article

 25. Erik Brynjolfsson, Tom Mitchell, and Daniel Rock, “What can machines learn,
     and what does it mean for occupations and the economy?,” AEA Papers and
     Proceedings, 2018. View in article

 26. Guszcza, Lewis, and Evans-Greenwood, “Cognitive collaboration”;
     Evans-Greenwood, Lewis, and Guszcza, “Reconstructing work.” View in article

 27. J.C.R. Licklider, “Man-computer symbiosis,” IRE Transactions on Human
     Factors in Electronics, March 1960. View in article

 28. Garry Kasparov, “The chess master and the computer,” New York Review of
     Books, February 11, 2010. View in article

 29. Deloitte researchers assert that, while the skills needed to execute
     specific tasks are ever-changing and subject to automation and
     obsolescence, enduring human capabilities that help with understanding the
     context of a problem, exploring alternative solutions, and creatively
     applying new techniques will outlast technology advances and market shifts.
     They recommend that businesses embrace and cultivate these human
     capabilities—e.g., imagination, empathy, curiosity, resilience, creativity,
     social intelligence, teaming, and critical thinking—in order to increase
     their strategic advantage. See John Hagel, John Seely Brown, and Maggie
     Wooll, Skills change, but capabilities endure: Why fostering human
     capabilities first might be more important than reskilling in the future of
     work, Deloitte Insights, August 30, 2019. View in article

 30. John Hagel, Jeff Schwartz, and Maggie Wooll, “Redefining work for new
     value: The next opportunity,” MIT Sloan Management Review, December 3,
     2019. View in article

 31. Guszcza, Lewis, and Evans-Greenwood, “Cognitive collaboration.” View in
     article

 32. Jonathan Shaw, “Artificial intelligence and ethics: Ethics and the dawn of
     decision-making machines,” Harvard Magazine, January–February 2019; Lynda
     Spiegel, “The dangers of asking AI to evaluate a job candidate’s
     interview,” Wall Street Journal, October 16, 2019. View in article

 33. Rick Wartzman, “How AI anxiety is creating more jobs for humans,” Fast
     Company, April 25, 2018. View in article

 34. Martin Stumpe and Craig Mermel, “Applying deep learning to metastatic
     breast cancer detection,” Google AI Blog, October 12, 2018. View in article

 35. Yun Liu et al., “Detecting cancer metastases on gigapixel pathology
     images,” Google Research, 2017; Stumpe and Mermel, “Applying deep learning
     to metastatic breast cancer detection.” View in article

 36. Stumpe and Mermel, “Applying deep learning to metastatic breast cancer
     detection.” View in article

 37. Ibid. View in article

 38. Robert Lemos, “Will AI help dev and test teams—or replace them?,”
     TechBeacon, accessed February 3, 2020. View in article

 39. Reina Qi Wan, “Deep TabNine: A powerful AI code autocompleter for
     developers,” Medium, July 19, 2019. View in article

 40. P.V. Kannan and Josh Bernoff, “The future of customer service is AI-human
     collaboration,” MIT Sloan Management Review, May 29, 2019. View in article

 41. Jared Council, “When chatbots falter, humans steer them the right way,” WSJ
     Pro Artificial Intelligence, June 12, 2019. View in article

 42. Eileen Brown, “New research finds human validation is critical for chatbot
     owners,” ZDNet, May 1, 2018. View in article

 43. Alec Sears, “Chatbots for the retail industry—current applications,”
     Emerj—Artificial Intelligence Research and Insight, December 12, 2018. View
     in article

 44. LivePerson Knowledge Center, “LivePerson's conversational commerce
     platform,” accessed January 30, 2020. View in article

 45. Hagel, Schwartz, and Wooll, Redefining work for new value. View in article

 46. Deloitte, AI ethics: A new imperative for businesses, boards, and C-suites,
     2019. The Notre Dame Deloitte Center for Ethical Leadership is a
     collaboration between the University of Notre Dame and Deloitte. View in
     article

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TOPICS IN THIS ARTICLE

Artificial intelligence (AI) , Automation , Cognitive technologies , Talent ,
Technology Industry , Future of Work , Emerging technologies , Digital
Transformation


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YOUR PRIVACY

YOUR PRIVACY

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