AI And Machine Learning
For Success with AI, Bring Everyone On Board - Sun and Planets Spirituality AYINRIN
Chad Hagen.
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Summary.
AI is intimidating employees. As machines perform intellectually demanding tasks that were previously reserved for human workers, people feel more excluded and less necessary than ever. The problem is only getting worse. Eighty percent of organizations say their main technological goal is hyperautomation—or the complete end-to-end automation of as many business processes as possible. Executives often pursue that goal without feedback from employees—the people whose jobs, and lives, will feel the greatest impact from automation.
In this article the author examines what keeps leaders from involving rank-and-file employees in AI projects, how they should model inclusive behavior, and what organizations must do to develop employee-inclusive AI practices. Those practices will make companies more likely to improve long-term performance—and to keep their employees happy, productive, and engaged.
AI is intimidating your employees. As machines increasingly perform intellectually demanding tasks that were previously reserved for humans, your people feel more excluded and less necessary than ever. And the problem is getting worse. According to the market research company Vanson Bourne, 80% of organizations say that their main technological goal is hyperautomation—the end-to-end automation of as many business processes as possible. Executives have a tendency to pursue that goal without any feedback from their employees—the people whose jobs, and lives, will be most affected by achieving it. But my decades of research into the enterprise adoption of emerging technologies has proved one thing time and again: The savviest leaders prioritize participation by the rank and file throughout the adoption process.
When
employees are excluded from that process, they become averse to working
with AI, never develop trust in its capabilities, and resist even the
positive changes that come from using it. Nonetheless, done correctly,
human-AI collaborations represent the most promising way of working.
They may not always be the fastest, cheapest, or easiest way to
introduce and use artificial intelligence, but the alternative, which
excludes workers, is no alternative at all. Consider one example, from
researchers at New York University’s Center for Cybersecurity. The
research team used Copilot, a tool developed by GitHub to generate code
automatically, to produce 1,692 software programs with no input from
human coders. Forty percent of those programs had critical security
flaws.
In
this article I examine what keeps leaders from including rank-and-file
employees in AI projects, how they should model inclusive behavior, and
what your organization must do to develop employee-inclusive AI
practices. Those practices can make your long-term performance more
likely to improve and your employees more likely to be happy,
productive, and engaged.
Becoming Comfortable with AI
You
can’t bring everyone into the AI adoption process if you’re not heavily
involved yourself. But business leaders often ask me how they can guide
an AI-based transformation when they have no personal expertise with
the technology.
Business
leaders don’t have to be AI experts. They only need to be AI-savvy
enough to recognize the technology’s benefits for the organization and
its stakeholders. Once AI has been deployed, leaders must learn to
empower and drive human-AI collaborations. For example, they should be
able to identify opportunities for AI integration in everyday workflows
and to anticipate its potential advantages for teams and projects
associated with the technology. In short, learning must be part of their
ongoing AI leadership.
Some
executives in my advanced leadership classes have wondered aloud
whether they need to become professional coders to be effective leaders.
What they need is not coding expertise but a foundational understanding
of the technology.
The Basics of AI
Most
managers know that AI tools are computational systems that have
autonomous learning ability. They understand that AI can learn from
large datasets and engage in pattern recognition and problem-solving.
They’ve probably already seen it used in a variety of organizational
applications: scanning the résumés of job applicants, evaluating
employee performance, optimizing task scheduling, managing inventory,
and automating repetitive tasks so that employees can explore new ideas
and promote innovation rather than count widgets. It’s AI’s ability to
learn—using algorithms to process new data and change its computation of
information based on that data—that results in comparisons to human
intelligence. But too many business leaders implicitly assume that AI
can take over almost any position from humans.
The
reality is that AI cannot think like a human, and it isn’t all that
creative. First, it generates no novel ideas; its ideas exist in the
datasets that are fed into it. Not even the most sophisticated AI
systems can infer meaning
from learning, as humans do. They cannot draw analogies, and they
cannot appreciate cultural and contextual nuances. Whereas humans can
extract the deeper meanings and intricate nuances of business
conversations, AI cannot tell when what is said is contradictory to what
is meant. For example, it will interpret “You’re serious about this
offer?” as a simple request to confirm what is being offered. Most
humans will understand that the other party is unhappy with what is
being offered.
Business
leaders who are just AI-savvy enough recognize that the technology can
do much to improve work efficiency and the overall functioning of an
organization. They must also recognize that it cannot entirely replace
humans and, most important, it cannot do our thinking for us.
Three Ways AI Can Alienate Employees
Once
you’ve become comfortable with your ability to discuss and champion AI
adoption, you’ll need to generate enthusiasm throughout the rank and
file—not an easy process. To be an effective leader, you must understand
why AI causes a rift between your workers and management and find ways
to bridge the gap between what they’re feeling about it and what you’d
like them to feel. And you’ll need to prevent the territorialism and
tribalism that can occur when one group controls AI and another doesn’t
even understand it.
Here are three common reasons for workers’ alienation.
Employees lose autonomy and become cynical.
Not long ago a colleague of mine applied for a credit card at her bank.
The employee helping her entered all her information into a computer
program, which ran an algorithm to determine whether she qualified. My
colleague, who earns a good living and has good credit, was surprised
when the employee informed her that the algorithm had decided she did
not qualify for the card. When she asked for an explanation, he replied
that the decision was fact-based and automated, so he could not add much
to it. Eventually he mumbled that he was not a machine, so why should
she expect him to understand the algorithm’s decision? That comment
revealed that the employee did not feel in control of his job, was
clearly demotivated, and had no intention of trying to make the
algorithm’s decision comprehensible to my colleague. The result was poor
customer service and a missed business opportunity.
Not long ago a colleague of mine applied for a credit card at her bank.
The employee helping her entered all her information into a computer
program, which ran an algorithm to determine whether she qualified. My
colleague, who earns a good living and has good credit, was surprised
when the employee informed her that the algorithm had decided she did
not qualify for the card. When she asked for an explanation, he replied
that the decision was fact-based and automated, so he could not add much
to it. Eventually he mumbled that he was not a machine, so why should
she expect him to understand the algorithm’s decision? That comment
revealed that the employee did not feel in control of his job, was
clearly demotivated, and had no intention of trying to make the
algorithm’s decision comprehensible to my colleague. The result was poor
customer service and a missed business opportunity.
When
you automate easy tasks but leave difficult and emotionally demanding
ones to humans, you negatively affect the well-being of your workers. A
2021 study from Georgia State University revealed that the more
automation is introduced in the workplace, the worse employee health and
job satisfaction become.
Employees don’t understand AI and resist it.
People generally prefer to work with and receive advice from humans
rather than AI. You should be aware of this bias and recognize that
employees will respond emotionally rather than rationally to the
technology—even when it has proved to be superior to humans.
People generally prefer to work with and receive advice from humans
rather than AI. You should be aware of this bias and recognize that
employees will respond emotionally rather than rationally to the
technology—even when it has proved to be superior to humans.
If
you want to make AI adoption inclusive, you must position yourself as
both a mediator and a facilitator in human-AI interactions. You need to
ensure that your employees receive adequate support and training to
interact effectively with AI systems and to create opportunities for
them to turn to a human if those interactions go wrong. If they feel
truly included in how you plan to work with AI, they will be less averse
to it.
A
failure to be inclusive may even lead to active resistance. For
example, when workers at Amazon’s packing facilities were “supervised”
by AI algorithms, they became more injury-prone. They were forced to
meet high productivity targets, with few if any opportunities to take a
break, and could be indiscriminately fired for not hitting their
targets. Frustrated, they signed petitions and gathered outside their
warehouses, united by the rallying cry “We are not robots!”
Indeed, as one employee succinctly put it, “[Productivity is] all they
care about. They don’t care about their employees. They care more about
the robots than they care about the employees.”
If
you want to avoid resistance from your employees when introducing AI,
you must push them out of their comfort zone while ensuring that they
understand why you’re doing so. They should know how you plan to take
care of them during this transition. You’ll need to exercise patience,
because it will take time and effort for workers to become familiar with
AI and see how it can help them in their jobs.
AI creates business silos.
In addition to eliciting resistance, AI adoption can undermine
inclusiveness by entrenching silos in your organization in three ways.
First, because the deep expertise required to understand and operate AI
systems is often found only in tech teams, employees in other
departments (such as HR, operations, and marketing) may have difficulty
interacting with AI. But they need the know-how to make use of it in
ways that are meaningful to their own business goals. Second, data
ownership and access can be a contentious issue between departments. AI
systems rely heavily on data for training and decision-making, but
individual teams may have their own data repositories and be unwilling
or unable to share data with others. Third, the impact AI has will vary
across teams: Some may find it more useful than others do, and some may
see it being used to automate their tasks more than the tasks in other
departments. When different teams feel more or less threat (or benefit)
from the adoption of AI, they may turn to siloed behavior, avoiding
collaboration and information sharing to protect their own interests.
In addition to eliciting resistance, AI adoption can undermine
inclusiveness by entrenching silos in your organization in three ways.
First, because the deep expertise required to understand and operate AI
systems is often found only in tech teams, employees in other
departments (such as HR, operations, and marketing) may have difficulty
interacting with AI. But they need the know-how to make use of it in
ways that are meaningful to their own business goals. Second, data
ownership and access can be a contentious issue between departments. AI
systems rely heavily on data for training and decision-making, but
individual teams may have their own data repositories and be unwilling
or unable to share data with others. Third, the impact AI has will vary
across teams: Some may find it more useful than others do, and some may
see it being used to automate their tasks more than the tasks in other
departments. When different teams feel more or less threat (or benefit)
from the adoption of AI, they may turn to siloed behavior, avoiding
collaboration and information sharing to protect their own interests.
Employee
resistance often creates an organization in which experts in AI and
those in business work separately. People mentally shut down and live
within the realm of their own expertise. And when AI is adopted
differently across silos, resources may be duplicated or underutilized,
limiting leaders’ ability to scale up the technology across the
organization. Teams may collect, store, and manage data independently,
resulting in inconsistencies, redundancy, or incomplete datasets. That
can hinder your ability to leverage the full potential of your data.
When departments operate in isolation, cross-functional collaboration
and interdisciplinary problem-solving become impossible. It will be your
job as an inclusive leader to stress the importance of collaboration
and push for the implementation of technological and organizational
solutions, such as centralizing data for analysis in cloud-based tools.
To address all these challenges, you need to adjust your organization’s culture.
A More Effective and Inclusive Model for AI
As
a business leader, you have to make people feel like full-fledged
members of your organization—empowered to work like human beings while
collaborating with AI in every automated process. AI can quickly produce
code for new programs, for example, but human employees are needed to
fix any security flaws and other glitches.
An
inclusive approach will make employees feel in control of the adoption
process, reduce aversion to the technology, and increase trust in it.
Those outcomes will help integrate it more efficiently in your
employees’ workflow and will enhance the likelihood of creating value
across the organization (rather than establishing only siloed, and thus
minor, effects). To achieve them, the organization must consistently
follow four practices.
Create space and time for social connection.
When working with AI, people have to spend a lot of time in front of
computer screens communicating with machines. That limits their
interaction with other humans. A 2022 poll by the Pew Research Center
revealed that a major concern people have about the presence of
artificial intelligence in their lives is that it isolates them from
other humans. As a leader, you have an important responsibility to
foster the social connections of your employees, which you can do
through events and online communities within and outside the
organization. Digital underwriters, for example, often issue insurance
policies without even meeting applicants. They could be asked to have
weekly meetings with other underwriters and with the people who built
the AI system they use to discuss possible improvements. Uber now allows
its drivers, who are under constant algorithmic supervision and feel
dehumanized as a result, to telephone other people in the organization
when they need help or have a question.
When working with AI, people have to spend a lot of time in front of
computer screens communicating with machines. That limits their
interaction with other humans. A 2022 poll by the Pew Research Center
revealed that a major concern people have about the presence of
artificial intelligence in their lives is that it isolates them from
other humans. As a leader, you have an important responsibility to
foster the social connections of your employees, which you can do
through events and online communities within and outside the
organization. Digital underwriters, for example, often issue insurance
policies without even meeting applicants. They could be asked to have
weekly meetings with other underwriters and with the people who built
the AI system they use to discuss possible improvements. Uber now allows
its drivers, who are under constant algorithmic supervision and feel
dehumanized as a result, to telephone other people in the organization
when they need help or have a question.
The Fortune
500 dairy company Land O’Lakes provides an excellent example of how to
free employees from the solitude of working with AI. It began its AI
transformation in 2017, when it sought to partially automate commodity
forecasting and propensity modeling. Company leaders prioritized
speaking with the rank and file about the expected challenges, helping
establish a common understanding of the project’s possibilities and
limits and assuring people that the company wasn’t pursuing tech for the
sake of tech. Teams coordinated across departments, but company leaders
also conducted weekly people-to-people check-ins with every business
unit to address any challenges, emotional or procedural, that may have
arisen. That approach was crucial to the success of Land O’Lakes’ AI
transformation. Employees were given opportunities to voice concern, to
question tactics, and to raise anything else that might be on their
minds.
Make tech and nontech teams collaborate.
As an AI-savvy leader, you know that successful human-AI collaborations
cross disciplines. Your tech and business experts should not retreat to
their separate corners, literal and virtual. So build diverse teams
that work together to adopt AI. For example, business experts can
explain to tech experts what goals must be achieved, and tech experts
can make suggestions regarding which AI systems will be needed.
Meanwhile, HR can familiarize employees with the AI system they’ll be
using and the skills they’ll require, and operational staffers can focus
on integrating the entire human-AI workflow into the organizational
setup.
As an AI-savvy leader, you know that successful human-AI collaborations
cross disciplines. Your tech and business experts should not retreat to
their separate corners, literal and virtual. So build diverse teams
that work together to adopt AI. For example, business experts can
explain to tech experts what goals must be achieved, and tech experts
can make suggestions regarding which AI systems will be needed.
Meanwhile, HR can familiarize employees with the AI system they’ll be
using and the skills they’ll require, and operational staffers can focus
on integrating the entire human-AI workflow into the organizational
setup.
To
lead such diverse teams and bring them together, you must communicate
in ways that unite rather than divide people, allowing for and
integrating multiple perspectives and identifying roadblocks that may
complicate or prevent collaboration. As a business leader, you can start
by explaining the organization’s needs to your tech and business teams
and then outline how the tech experts will become part of the business
process to achieve the desired results. Try to establish a common
language and understanding for both groups regarding how to approach
challenges, recognize patterns, break big problems down into smaller
ones, and find a shared work method. Without that common language, your
teams may fail to cohere, and the inclusive culture you’ve tried to
develop may dissipate.
In
one of my consulting projects I watched the chief technology officer of
a global financial institution present the company’s new tech strategy.
Just a few minutes in, the CEO interrupted. He said he didn’t
understand anything the CTO was saying and pressured him to present his
message in three simple bullet points. It was embarrassing for the CTO.
The tech team retrenched. IT departments stopped trying to talk to top
executives. The CEO lost credibility with senior executives, who
realized he wouldn’t be capable of guiding the bank through its
AI-adoption project. He hadn’t become AI-savvy, didn’t connect AI to the
purpose of the company, and, worst of all, had not developed the
inclusive mindset needed to translate from the CTO to the business and
back. Needless to say, the project failed. The CEO left the company the
following year.
Successful
human-AI collaborations cross disciplines. Your tech and business
experts should not retreat to their separate corners, literal and
virtual.
When
done properly, mixing teams can fundamentally improve not only a
company’s technology but also its overall culture. In 2017 the
agricultural equipment maker CNH Industrial’s leadership team decided it
wanted to create a host of AI-powered automation capabilities. It also
wanted to connect customers with internal and external partners and
promote CNH as a service-oriented business.
The
executives began the transformation process by speaking with employees
from its commercial vehicle unit, industry-specific vehicle units, IT,
and operations. Digital advisers and a new digital team were created
within CNH’s existing IT organization to support ongoing strategy,
implementation, and execution. By establishing cross-disciplinary teams
and keeping them involved throughout the process, CNH was able to
quickly adopt (or retire) experimental approaches. It lowered the
barriers between developers and business owners, and it allowed for
real-time feedback on scheduled work.
Constantly develop your own leadership skills.
Making your employees feel included in your AI adoption project
requires that you account for their uncertainty and discomfort when
dealing with AI. As an AI-savvy leader, you should be seen as open to
listening to their concerns. My research indicates that employees are
indeed more willing to trust and engage with AI if their leaders are
humble and demonstrate that openness.
Making your employees feel included in your AI adoption project
requires that you account for their uncertainty and discomfort when
dealing with AI. As an AI-savvy leader, you should be seen as open to
listening to their concerns. My research indicates that employees are
indeed more willing to trust and engage with AI if their leaders are
humble and demonstrate that openness.
Consider
Satya Nadella, the CEO of Microsoft, who is a master at using empathy
to foster inclusion. One of the first things he did when he was
appointed CEO, in 2014, was to persuade his employees that no matter how
successful Microsoft had been in the past, they should stay open to new
ideas and other ways of working. Asking them to think differently
required courage, but it also showed the importance of being
humble—unafraid of receiving feedback from others. A humble attitude in a
leader encourages employees to interact regularly with experts in
different departments to understand and relate to the diverse
perspectives at work in the organization.
Reward workers for being human.
Employees want you to tell them how you see their role in the human-AI
collaborative process. They also want to know how they will be rewarded
for the value that collaboration creates. For humans and AI to work
together successfully, you need to establish clear guidelines for who is
credited with what. Otherwise your employees may feel that you’ve
downplayed their contribution and attributed the project’s success
largely to the AI.
Employees want you to tell them how you see their role in the human-AI
collaborative process. They also want to know how they will be rewarded
for the value that collaboration creates. For humans and AI to work
together successfully, you need to establish clear guidelines for who is
credited with what. Otherwise your employees may feel that you’ve
downplayed their contribution and attributed the project’s success
largely to the AI.
To
ensure that employees feel included, let them share in the rewards that
come with the value that AI creates. Emphasize that in your view,
humans are crucial to the performance of AI and therefore deserve
appropriate acknowledgment. Even just a companywide email recognizing
and celebrating someone’s accomplishments can go a long way toward
boosting morale.
. . .
AI
adoption is a complex process that requires everyone involved to learn,
question, and collaborate. How your company approaches it will depend
on the level of your employees’ technological acumen, your budget, and
many other critical factors. But the approach I recommend is one that
any company can take to optimize the process.
It
should begin with managers’ learning just enough about AI to feel
confident communicating its importance to their teams. Then you need
constant human-to-human connection among cross-disciplinary business
units as well as meetings at which everyone feels free to speak openly.
Such gatherings provide excellent opportunities for managers to show
vulnerability, communicate their own questions, or even just listen to
venting among colleagues. When your transformation is underway, and your
business is focused on optimizing AI rather than simply implementing
it, you should reward your employees for their uniquely human
contributions. If they don’t feel valued and respected, your
transformation attempt will certainly fail.
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