The State of Organizations 2023 - Flipbook - Page 15
may already be facing with digital deployment.
Companies may lack necessary data sets and
in-house technical talent (and may incur increased
costs as a result). They may also face resistance to
change. Some employees may still be wary of a class
of technology that can be abused or exploited if
proper guardrails and privacy protections aren’t set.
Cybersecurity is a key issue to address. More
than half the respondents in the 2022 McKinsey
Global Survey on Digital Trust reported that their
organizations had suffered at least one material
data breach in the previous three years. And 55
percent of responding executives in that survey
reported incidents in which active AI produced
biased, incorrect, or otherwise problematic results
that reduced employees’ confidence in AI or created
financial losses. Another big challenges to applying
AI is attracting and retaining the talent needed
to drive technological change, such as digital,
analytics, software development, customer insights,
and data science (Exhibit 8).
users of it, it can help them address key issues.
Every discussion, whether about strategy, finance,
marketing, talent, operations, or governance, needs
to consider how the organization is capturing and
mining data, training algorithms, and launching
new initiatives based on insights being generated.
Such AI-first organizations focus not just on the
technologies behind AI but also on the operating
models, culture, talent, leadership, and capabilities
required to get the most from their AI investments.
Embedding AI in corporate culture
AI-first organizations are focused on building a
perpetual-learning culture and teaching AI to the
whole workforce. It’s the only surefire way to drive
widespread acceptance of AI. The organizations
that report the highest returns from AI are nearly
three times more likely than others to report using
a variety of capability-building programs, such as
experiential learning, self-directed online courses,
and certification programs, to develop technical
employees. They are also nearly twice as likely as
others to offer programs for nontechnical employees.
Finding the right formula
Technologies such as ChatGPT could change an
organization, so it’s important for a company to
have them on their radar. If organizations redefine
themselves as entities driven by AI, not just power
Leaders in AI-first organizations also must be
change agents who can advocate for the inclusion
of technology in every strategy and process
discussion. They can help bridge the divide
between technology and business teams. Above
all, they should actively embrace their role as talent
scouts—attracting, retaining, and cultivating highend software engineers, machine-learning experts,
data engineers, data scientists, and other top talent
Hiring or developing AI-savvy leaders
At a system level, leaders in AI-first organizations
will likely need to establish a new management
layer, with new technology roles—for instance,
All respondents were asked to select the top 3 trends for their organizations. For these data, an additional question was posed to respondents: For which roles
are you about to face or already facing capability gaps? The response options displayed are the top options selected, have the largest gaps compared with
other roles, and represent a sufficient number of responses to be meaningful.
Source: McKinsey State of Organizations Survey, >2,500 leaders in organizations with ≥1,000 employees across industries in Canada, China, France, Germany,
India, Spain, UK, and US, May–June 2022
AI-first
organizations
are focused
on building
a perpetuallearning culture
and teaching
AI to the whole
workforce.
The State of Organizations 2023
March 2023
Exhibit 8
Survey respondents report existing or impending capability gaps in
technology-oriented roles.
Digital and analytics capability gaps, by role, % of respondents (n = 489)1
40
Digital and analytics, overall
Software development
32
Customer insights
26
Data science
25
Product management
Digital marketing
1
18
chief analytics officer, chief data officer, and chief
privacy officer. These leaders will have the requisite
technology skills and background but will also need
to demonstrate business acumen and understand
how the organizations and the ecosystems they
operate in create value and drive growth. They will
also understand that corporate conduct counts, so
they will show a keen interest in and understanding of
data ethics and hygiene, for example, making it clear
that some suggestions about data-oriented projects
and practices simply aren’t up for negotiation. In this
way, they can help ensure that organizations avoid
the “garbage in, garbage out” phenomenon and steer
clear of potential privacy issues.
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for AI-related roles. McKinsey research shows
that companies seeing the biggest bottom-line
returns from applied AI are more likely than others
to have well-defined capability building programs to
cultivate AI talent.
Being thoughtful about AI-related risks and
ethical concerns
AI-first organizations contribute to and adopt the
requirements for responsible, trustworthy AI use
that various bodies for standards are setting. They
consider possible unintended effects on and
consequences for societal and environmental wellbeing, technical robustness and safety, data privacy
and governance, transparency, diversity, and fairness,
among other factors. These are many of the same
concerns that already affect people analytics.
McKinsey research shows that the companies
seeing the biggest bottom-line returns from applied
AI—those that attribute at least 20 percent of EBIT
to their use of AI—are more likely than others to
follow best practices that enable explainable AI.
That is, they can always express why an AI system
reached a particular decision, which is especially
important in organizations in which AI models may
be used to automate hiring searches, loan approvals,
or other sensitive processes. We have seen steady
increases in the focus on mitigating AI risks related
to equity and fairness.
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