Will artificial intelligence make you a better leader?
Agile
leadership and AI both depend on learning to let go.
Consider this real-life
scene: Reflecting on the
difficult moments of his week, the new CEO of a UK manufacturer felt angry. His
attention kept going back to the tension in several executive-team meetings. He
had an urge to shake the team and push several of its members, who were riven
by old conflicts, to stop fighting and start collaborating to solve the
company’s real problems. He also sensed, though, that a brute-force approach
was unlikely to get very far, or to yield the creative insights that the
company desperately needed to keep up with its fast-changing competitive
environment. Instead, he calmed himself, stopped blaming his team, and asked
himself whether he could break the logjam by pursuing truly new approaches to
the company’s problems. It was then that his mind turned to, of all things,
artificial intelligence.
Like many leaders, the
CEO was struggling to cope with the stress induced by uncertainty, rising
complexity, and rapid change. All of these are part and parcel of today’s
business environment, which is different enough from the one many of us grew up
with to challenge our well-grooved leadership approaches. In a recent article,
we described five practices that can help you step back from the tried and true
and become more inwardly agile.. Here, we want to describe the relationship between
some of those ideas and a technology that at first glance seems to add
complexity but in fact can be a healing balm: artificial intelligence (AI),
which we take to span the next generation of advanced data and analytics
applications. Inner agility and AI may sound like strange bedfellows, but when
you consider crucial facts about the latter, you can see its potential to help
you lead with clarity, specificity, and creativity.
The first crucial fact
about AI is that you don’t know ahead of time what the data will reveal. By its
very nature, AI is a leap of faith, just as embracing your ignorance and
radical reframing are. And like learning to let go, listening to AI can help
you find genuinely novel, disruptive insights in surprising and unexpected
places.
A second fact about AI
is that it creates space and time to think by filtering the signal from the
noise. You let the algorithms loose on a vast landscape of data, and they
report back only what you need to know and when you need to know it.
Let’s return to the CEO
above to see an example of these dynamics in action. The CEO knew that his
company’s key product would have to be developed more efficiently to compete
with hard-charging rivals from emerging markets. He urgently needed to take
both cost and time out of the product-development process. The standard
approach would have been to cut head count or invest in automation, but he
wasn’t sure either was right for his company, which was exhausted from other
recent cost-cutting measures.
All this was on the
CEO’s mind as he mused about the problematic executive dynamics he’d been
observing—which, frankly, made several of his leaders unreliable sources of
information. It was the need for objective, creative insight that stoked the
CEO’s interest in AI-fueled advanced data analytics. A few days later, he began
asking a team of data-analytics experts a couple broad and open-ended
questions: What are the causes of inefficiencies in our product design and
development workflow? What and where are the opportunities to improve
performance?
The AI team trained
their algorithms on a vast variety of data sources covering such things as
project life-cycle management, fine-grained design and manufacturing documents,
financial and HR data, suppliers and subcontractors, and communications data.
Hidden patterns in the communication networks led to a detailed analysis of the
interactions between two key departments: design and engineering. Using
aggregated data that didn’t identify individual communications, the team looked
at the number of emails sent after meetings or to other departments, the use of
enterprise chat groups and length of chats, texting volume, and response rates
to calendar invites, the algorithms surfaced an important, alarming discovery.
The two departments were barely collaborating at all. In reality, the process
was static: designers created a model, engineers evaluated and commented,
designers remodeled, and so on. Each cared solely about its domain. The
data-analytics team handed the CEO one other critical fact: by going back five
years and cross-referencing communications data and product releases, they
provided clear evidence that poor collaboration slowed time to market and
increased costs.
By liberating the AI
team to follow a direction and not a destination, the CEO’s original question,
“How do we improve productivity?” became a much more human, “How are we working
as a team, and why?” Based on this new empirical foundation, he enlisted the
engineering and design leaders to form a cross-disciplinary team to reimagine
collaboration. Working with the data scientists, the team was able to identify
and target a 10 percent reduction in time to market for new-product development
and an 11 percent reduction in costs. But the CEO didn’t stop there. He also
used the experience to ask his executive team to develop a new agility. The
previously fractured team worked hard to build a foundation of trust and true
listening. Regular check-ins helped them pause, formulate new questions, invite
healthy opposition, and ask themselves, “What are we really solving for?” The
team was growing more complex to address the company’s increasingly complex
challenges.
In our experience, AI
can be a huge help to the leader who’s trying to become more inwardly agile and
foster creative approaches to transformation. When a CEO puts AI to work on
the toughest and most complex strategic challenges, he or she must rely on the
same set of practices that build personal inner agility. Sending AI out into
the mass of complexity, without knowing in advance what it will come back with,
the CEO is embracing the discovery of original, unexpected, and breakthrough
ideas. This is a way to test and finally move on from long-held beliefs and
prejudices about their organization, and to radically reframe the questions in
order to find entirely new kinds of solutions. And the best thing about AI solutions is that they can
be tested. AI creates its own empirical feedback loop that allows you to think
of your company as an experimental science lab for transformation and
performance improvement. In other words, the hard science of AI can be just
what you need to ask the kind of broad questions that lay the foundation for
meaningful progress.
By Sam Bourton, Johanne Lavoie, and Tiffany
Vogel
https://www.mckinsey.com/business-functions/organization/our-insights/will-artificial-intelligence-make-you-a-better-leader?cid=other-eml-alt-mkq-mck-oth-1804&hlkid=44473fae4a50473393b6a18e8a51c512&hctky=1627601&hdpid=74224e97-9142-447a-88e1-e22190845962
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