2024-State-of-AI-Change-Readiness-eBook.pdf

The state of AI
change readiness
Accelerating AI transformation through the employee experience
eBook

1 Microsoft WorkLab, Work Trend Index Annual Report. May 2024
Readiness for AI
transformation atwork
People throughout history have always been innovators,
but we often create tools faster than we can adapt our
behaviors and shift our practices. With the exponential
growth of AI capabilities in the past few years, we are at a
point where the shift to an AI-powered workplace is not
just about the tools available, but also how people are
shifting their work habits and behaviors.
While excitement for AI isevident in the rapid—and
growing1 —use of AI tools at work,many organizations
have not fully integrated these tools in
formal,organization-sponsoredrollouts.Asleaders
considerenterprise investments in AI, a new question
emerges: are people ready for this large-scale
AItransformation? And more importantly, how can we
best prepare for this shift in how we work to get the most
value from the promises of AI?
This eBook outlines findings fromaMicrosoft Viva
PeopleSciencestudy on AI readiness, discusses
implications, and provides practical guidance for
leadersand HR on how they can best support people
through change related to AI at work.
Leading through a transformation that is rapidly
redefining the way we work can be simultaneously
daunting for the effort and exciting for the reward. By
doubling down on change basics, leaning into
experimentation, and not losing sight of the importance
of the people experience, we shift from passive
participants to proactive co-creators of the vision forAI
transformation at our organizations. Use this as your
guide for approaching the AI transformation journey
intentionally and effectively.
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  02
How can leaders support their people

he vision forAI
transformation at our organizations. Use this as your
guide for approaching the AI transformation journey
intentionally and effectively.
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How can leaders support their people
through AI transformation and gain
ultimate organizational value?

Key terms
Concepts used throughout the eBook:
High Performing Organization:
High Performing Organizations (HPOs)
are organizations that continuously
exceed expectations in areas ranging
from financial performance to employee
engagement.
Artificial Intelligence:
Artificial Intelligence (AI), as referred to in
this report, is defined in the study
as 'generative AI, specifically, software that
can perform tasks that normally require
human intelligence’.
Transformation:
A complex, long-lasting change which
impacts organizational culture, structure,
competitive landscape, and customer
expectations.
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  03
Table of contents
Study Introduction
Readiness for AI transformation
at work 02
The current study 04
Ready or not AI is at work 05
Key Findings
AI transformation and the employee
experience cannot be decoupled 07
Organizations can drive Realized Value
of AI (RIVA) through AI readiness 10
Quality of change experience differs
across levels 11
High Performing Organizations in the
era of AI 12
HPOs take a more people-centric
approach to change 13
HPOs connect dots from AI vision
to AI value 14
HPOs lead with access-driven
experimentation 15
People Science Guidance
Role-specific experimentation
and change agility 17
Microsoft case study 19
Meet people where they are
in their AI journey 20
Guiding principles 23

17%
EMEA
17%
APAC
61%
NAMER
5%
LATAM
The current study
1.8K full time employees
This study by the Microsoft Viva People
Science team consisted of a total of 1,800
full-time, global employees2 with
representation across levels and types of
industries, representing four distinct
regions and nine countries. Data were
collected using an online panel vendor.
17%
High Performing Organizations (HPOs) are respondents that, when asked to
evaluate their organization’s performance, reported their organization is “always”
delivering on at least 6/10 (more than half) of the performance indicators presented.
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Organization size
Respondentswere limited to
organizations of 1K employees or larger.
42.3% From orgs of 1,000 - 4,999
27.4% From orgs of 5,000 - 14,999
30.3% From orgs of 15,000 or more
Levels
Within this report, ’leaders' consist
of directors and above.
13.9% C-level executive
17.1% Vice president or director
We additionally split by:
38.0% Managers
31.1% Individual contributors
Industries
Top five industries with
highestrepresentation include:
12.1% Healthcare
11.9% Technology
10.4% Retail
9.3% Financial services
9.0% Manufacturing
2 Microsoft Viva People Science, AI Transformation Readiness Research, April 2024

This BYOAI trend, coined by the WTI, is seen in
our study as well, with 67% of those using AI at
work reporting use of at least some AI tools not
provided by their organization. BYOAI indicates
excitement for AI but can also pose risk. This risk
being that organizations miss out on benefits of
strategic AI adoption at scale¹.
With this level of employee demand and use of
AI at work, why the organizational delay? What
we can tell from our study, is this lack of
organization-sponsored AI investment isn’t due to
a lack of value placed on how AI can transform
organizations.
Most senior-level leaders have high support for AI
transformation. In fact, 78% of executives
believeAI is critical for their organization to
succeed and worth the investment of money,
time, and effort. This indicates potential for
organizational investments in AI tools and
integrations in the future.
Despite support, leaders indicate concern with
the time it takes for transformation. About two
thirds of executives believe adopting AI will take
more time and effort than other
technologies.While we agree that full AI
transformation won’t happen overnight,
organization-sponsored rollouts and change
efforts can take advantage of the employee
energy for AI use to get started on their AI
transformation.
Most tools
are provided
Some tools
are provided
None
provided
All tools are
provided
Only about 1/3 of employees
are using solely organization-
provided AI tools
How many AI tools that you use
are provided by your organization?
30%
30%
29%
8%
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80% of employees say
they are currently using
AI at their workplace
Employees, not organizations, are leading the

I tools that you use
are provided by your organization?
30%
30%
29%
8%
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80% of employees say
they are currently using
AI at their workplace
Employees, not organizations, are leading the
charge on AI integration at work. In our study, a
staggering 80% of employees say theyalready
use AI tools at work. The 2024 Microsoft Work
Trend Index (WTI), reports similar rates of use,
with 75% of knowledge indicating they use AI at
work.
Mounting evidence also points to employees not
waiting for their companies to offer AI tools
before finding solutions to enhance their own
work. This creates a 'bring your own AI’ (BYOAI)
scenario¹.
Ready or not,
AI is at work
80%
1 Microsoft WorkLab, Work Trend Index Annual Report. May 2024

In most organizational change, the first hurdle is
building excitement and buy-in.When it comes
to AI, people are already using the technology,
reducing the need to build energy. It’s to
organizations’ advantage to harness this
existing energy and guide it toward AI use-
cases and strategic applications that will be
beneficial to individuals and the organization.
In Microsoft HR, a key step in AI transformation
has been capturing the energy and
momentum and channeling them in a way
that would continue to stokethe creative
spark that was ignited across the organization.
As a result, we harnessed ideas while driving for
measurable results and encouraging cross-group
collaboration in the adoption of technology."
Christopher J.Fernandez
Corporate Vice President, Human Resources
Microsoft
AI transformation undoubtedlypresents unique
challenges but having an existing group
ofusers excited aboutusing AI, already
experimenting with AI in their work,
andwanting to share learnings with othersis a
leg up thatmany change initiatives do not have
the benefit of. Use thisexcitement as
aspringboard into AI transformation.
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disconnection from colleagues, unclear
responsibilities), we found those who have
experienced three or more burnout risk factors
in the previous month had substantially higher
stress and strain and substantially lower
confidence they could keep up during change.
For AI transformation specifically, while AI
tooling can be part of the solution (e.g., helping
with faster task completion and productivity), we
first need to create the time and environment
that allows employees to shift their behavior and
begin to utilize AI in this way. Consider the
change saturation, time demands, and
experience of employees during change, not just
the technical elements of rollout.
When organizations approach change
thoughtfully in terms of supporting their people
withupskilling and learning, proactively
addressing concerns, and collecting ongoing
feedback, theyset up betterconditions for
sustainable change. This becomes even more
critical with longer-term transformation versus
smaller change initiatives.
Work transformations of any kind don’t happen
in a vacuum. AI transformation will consist of
many changes in the sea of other organizational
change that your people are going through.
People already having a negative employee
experience are likely to struggle even more
throughout change.
In this study, we found that although most
employees find the pace of change at their
organization to be just right, stress and strain are
still present. Meaning even in the best of
conditions, change is hard. This stress and strain
related to change can be an indicator of
burnout. Through self-report on seven risk
factors of burnout (e.g.,overwhelming workload,
AI transformation
and the employee
experience cannot
be decoupled

tions, change is hard. This stress and strain
related to change can be an indicator of
burnout. Through self-report on seven risk
factors of burnout (e.g.,overwhelming workload,
AI transformation
and the employee
experience cannot
be decoupled
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As we consider ability to keep up with
changes, we want agreement to remain
high, while stress and strain caused by
change to stay low.
This is the case for employees with low
burnout, but as burnout risk factors
increase, we see the desired pattern
reverse.
Those with 3+ burnout factors are
reporting high stress and strain paired
with low ability to keep up with
changes.
Employees experiencing burnout struggle to keep up with changes
Percent favorable
31%
49%
64%
40% 46%
60%
70%
87%
75% 74%
52%
No burnout risk
factors
1 burnout risk
factor
2 burnout risk
factors
3+ burnout risk
factors
Ability to
keep up
Stress
Strain
Ability: I can keep up with the changes at my org
Strain: Adapting to the changes at my org is a strain on me
Stress: Changes at my org stress me out

50%
61%
71%
84%
78%
52%
73% 75%
86%
80%
70%
85% 84%
91% 87%84%
89% 85%
95% 93%
Engagement Recommend Retention Individual
productivity
Team
productivity
Differences in EX by AI use frequency
Never Low frequency Moderate frequency High frequency
EX can support stronger AI adoption
Engagement
How happy are you working
at your current company?
Recommend
I would recommend my company
as a great place to work.
Retention
I plan to be working at my
company two years from now.
Individual productivity
I feel like I am productive at work.
Team productivity
I feel like my team is productive
at work.
Your AI strategy
is also a people
strategy
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(A few times a
month or less)
(At least once a week) (At least once a day)
Percent favorable
Encouragingly, this study finds early indication
that AI use does have a positive relationship with
key employee experiences. Favorability on
engagement, among other outcomes, increases
with frequency of AI use. High frequency (at least
once a day) users score highest across the
employee outcomes.
Self-report measures of productivity—which we
often think of as a key benefit of AI use—also
increase with more frequent use. Interestingly,
the largest experience increase across use
frequency is engagement. Engagement jumps 18
percentage points from the low to moderate AI
use frequency groups.
While these findings are correlational in nature
(not causal), it’s worth taking note that AI use
and engagement are not unrelated. Building an
AI transformation strategy without considering
EX would miss a potential propellant of adoption
success.
Transformation, of any kind, is all about
behavioral change. The use of AI is often overlaid

and engagement are not unrelated. Building an
AI transformation strategy without considering
EX would miss a potential propellant of adoption
success.
Transformation, of any kind, is all about
behavioral change. The use of AI is often overlaid
on top of products or processes that people are
already using in their day-to-day. This means
with AI transformation we’re both asking people
to create new habits and break old ones.
We talk about employee engagement as the
degree to which employees invest their
cognitive, emotional, and behavioral energies
toward positive organizational outcomes.
Successful AI transformation strategies will
consider how a strong employee experience (EX)
and an engaged workforce can serve as a
foundation for and accelerant of AI adoption.

87% 85% 83% 82% 80% 80%
66% 63% 60% 56% 55% 54%
AI allows me to
complete tasks
faster
AI helps me be
more productive at
work
AI simplifies my
complex tasks
AI helps me
improve the quality
of my work/output
AI helps me make
better decisions
AI helps reduce my
work-related stress
Engaged AI users All other AI users
The connection between engaged employees
and better business performance3 iswell-
established. In this study, we see engagement is
not only related to use frequency, but also
support for and value derived from AI.
Ultimately, engaged employees are more
supportive of AI integration in their workplace
and are eager to contribute to the success of
the transformation. Engaged employees are
also reporting more positive outcomes of AI
adoption. We call this set of outcomes RIVA,
or Realized Individual Value of AI.RIVA
encapsulates a myriad of ways that an employee
might see a direct impact of AI use on their day-
to-day, such as completing tasks faster or
reducing work-related stress.
Realized Individual Value of AI (RIVA) is higher for engaged employees
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Engagement as a
foundation for AI
transformation
3 Microsoft WorkLab, The New Performance Equation in the Age of AI. April 2023
Percent favorable
Engaged employees are 2.6x as
likely to say they fully support
AI integration and are eager
to contribute to AI
transformation success
When we think of all the ways AI can support an
employee, those who are already engaged are
findings ways to capitalize on that value. Engaged
employees have higher RIVA, with scores
averaging 19 percentage points higher, even
when controlling for high frequency use.
When an employee’s RIVA is high, it indicates

are already engaged are
findings ways to capitalize on that value. Engaged
employees have higher RIVA, with scores
averaging 19 percentage points higher, even
when controlling for high frequency use.
When an employee’s RIVA is high, it indicates
success in translating their AI use into tangible
benefits, which may be spurring even more use of
the tool. Thirty-four percent of engaged
employees are high frequency users versus only
12% of non-engaged employees. We hypothesize
that the more benefits users see, the more likely
they are to be motivated and inspired to continue
leveraging AI, leading to a virtuous cycle between
ongoing use and realized value of AI.

Organizations can
drive RIVA through
AI readiness
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Provide examples:
Understanding how AI could be
integrated into work is key for AI
readiness. Make it real by
providing role-specific
examples of how to apply AI into
workflows. Not sure where to start?
Consider the use-case scenarios already
identified in the Microsoft Copilot
Scenario Library.
Build AI skills together:
Ensure people have the basic
skills and information necessary
to use AI tools. Use time in
already scheduled meetings to
review and share tips.
Make time to experiment:
Lack of time and access to tools
is a barrier to AI use. Create
space to use new AI skills and
test which use-cases are most
valuable.
Get feedback on value:
As experimentation and use is
underway, get feedback on what
people like most. Use this
feedback to inform ongoing
skilling and AI use-cases.
Share success stories:
Amplify stories from across the
organization of where AI has had
the most positive impact—and
how teams have overcome
barriers.
*Methodology: Regression Analyses was performed to model the
relationship between AI Readiness and RIVA for AI users (n = 1443),
controlling for job level and current AI use frequency.
Tips for supporting AI readiness
Employees can be primed for higher Realized
Individual Value of AI (RIVA) in their work
through AI readiness. ​AI readiness consists of
several factors: awareness, desire, knowledge,
and the opportunity to integrate, and see the
value of integrating AI. For current AI users,
these combined AI readiness factors drive up
to 62% of RIVA, even when controlling for job
level and use frequency*.
This means those with higher AI readiness get

portunity to integrate, and see the
value of integrating AI. For current AI users,
these combined AI readiness factors drive up
to 62% of RIVA, even when controlling for job
level and use frequency*.
This means those with higher AI readiness get
more value from the AI tools they are using,
regardless of how often they use the AI tools.
Feeling primed and motivated sets employees
up for success to experience better
productivity, wellbeing, and output quality
once they are using the AI tools at work.
What does an AI ready employee look like?
✓ Understands how AI could be integrated
✓ Is motivated to integrate AI
✓ Has the skills to integrate AI
✓ Has the opportunity tointegrate AI
✓ Sees the value in integrating AI
AI readiness can be supported by leaders in a
variety of ways. Use the best practices
checklist outlined in the tips for supporting AI
readiness. Supporting AI readiness for your
people helps your organization to realize the
value of your AI investments. Much of what
builds AI readiness is also part of change
management best practices. Build these habits
with all organizational change.

Even if you are not yet embarking on an AI
transformation, now is the time to build strong
change habits. As we look at AI readiness, up to
43% of how AI-ready an individual is can be
explained by their previous experience with
change*. This strong relationship means the
positive change experience you build today will
set you up for success in the future.
In looking at current change experience, we see
an opportunity for better support at the
individual contributor level. Across a variety
offactors, leaders are having a substantially
Critical pillars for successful change
Quality of change
experience differs
across levels
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  11
Communications
What this means: If leaders don't
feel they are over-communicating,
they areprobably not doing
enough.
What to do:Consider campaigns,
omnichannel communication
approaches, and usingmanagersto
reinforcemessaging.
85%
85% of leaders
believe their
organization
communicates
consistently
during change,
while only 55%
of individual
contributors
agree.
55%
What this means: People across all
role levels need the skills necessary
for change and the opportunities to
do so.
What to do: Provide everyone
access to upskilling material and
subject matter expertise through
expert communities.
86%
86% of leaders
have good
opportunities to
improve skills,
learn, and grow
during change,
while only 64%
of individual
contributors have
this experience.
Skilling
64%
What this means: Leaders need to
intentionally integrate employee
feedback as they roll out changes.
What to do: Implement
mechanisms to collect employee
sentiment and other relevant data
before, during, and after change.
83%
83% of leaders
feel included in
decisions about

intentionally integrate employee
feedback as they roll out changes.
What to do: Implement
mechanisms to collect employee
sentiment and other relevant data
before, during, and after change.
83%
83% of leaders
feel included in
decisions about
change that
affects their job,
while only 44%
of individual
contributors
agree.
Measurement
44%
different experience compared to individual
contributors. Leaders who’ve had positive
change experience in the past, may be at risk of
underestimating the support their people need
during change in the future. Leaders concluding
people are more change-ready than they are can
result in the skipping of the critical change steps
of communications, skilling, and
measurement.This risk of assumed alignment is
amplified when there is no mechanism for
employee feedback.
Take stock of your current change management
capabilities and opportunities for improvement.
Build, buy, or borrow where you have gaps to
ensure quality change experiences that can
directly improve how employees feel as you
begin to integrate AI. Focusing on change best
practices will pay dividends when it comes to
participation, adoption, and ultimately the
success of your AI rollout.
* Methodology: Regression Analyses was performed to model the relationship between AI Readiness and Change Experiences (n = 1389) controlling for job level.

High Performing Organizations (HPOs)4
continuously exceed expectations in areas
ranging fromfinancial performance to employee
engagement. There are three pillars that work
together to characterize high performance at
these organizations: engaged employees,
productive teams, and resilient business.
Respondents at HPOs were identified based on
how often they reported their organization
demonstrated a set of key performance
indicators. The HPO category includes those
who indicated that a majority of the
performance indicators were 'always'
demonstrated (17% of respondents).
Compared to those at typical organizations,
people at HPOs see more value in bringing AI to
their organization. They are substantially more
likely to say they fully support the integration of
AI at their workplace and are eager to contribute
to the success of the transformation.
What do HPOs do differently? We see key
differences about how they approach change,
bring people along with the AI vision, and
provide access to AI tools. We unpackthese
areas and what this means for leaders as we go
into the era of AI.
Employees at HPOs see the value
AI can bring to their organizations.
High Performing
Organizations in the
era of AI
Those at HPOs are 90%
more likely to say they fully
support AI being integrated
in their workplace.
70% more likely to say AI is
critical for their organization to
be successful
80% more likely to say AI will
distinguish their organization as
an employer of choice
70% more likely to say AI will
boost their organization’s
revenue and financial success
70%
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  12
80%
70%
4 Microsoft Viva People Science, Redefining High

rganization as
an employer of choice
70% more likely to say AI will
boost their organization’s
revenue and financial success
70%
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  12
80%
70%
4 Microsoft Viva People Science, Redefining High
Performance in the New Era of Work. October 2023. More likely compared to employees at typical organizations

Knowing great change experience is critical to AI
readiness, it is not surprising that we also see
HPOs scoring higher on RIVA.
What we can learn from HPOs is that moving from
a 'good' to 'great' change experience is all about
how we prioritize the individuals going through
the change and support them during the process.
Leaning into fundamental people-centric change
experiences can set an organization up for
success when tackling an AI transformation. Due
to the positive experiences that HPO employees
have already had with change –they are primed
and ready to go for an AI-driven future.
HPOs excel at ensuring people are at the center of change
Employees at HPOs report more positive change
experiences than those at typical organizations
across the board. When we look at which
experiences have the largest differences
between HPOs and typical organizations, we see
common themes that center around the human
experience. HPOs approach change with a more
people-centric focus, ensuring support, respect,
and inclusion.
Employees atHPOs more often felt considered
and respected during negative change
moments, felt included in decisions, and felt that
their management wasreceptive to feedback.
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HPOs take a more
people-centric
approach to change
Employees at HPOs are 60% more
likely to feel included in decisions
about change that will impact
their jobs.
66%
66%
64%
66%
60%
59%
98%
98%
96%
98%
94%
94%
Received consistent communication
Encouraged to celebrate milestones
Felt cared for and supported
Able to give feedback
Felt included in decisions
Felt considered and respected
Percent favorable
Experiences during change at HPOs versus typical organizations

stent communication
Encouraged to celebrate milestones
Felt cared for and supported
Able to give feedback
Felt included in decisions
Felt considered and respected
Percent favorable
Experiences during change at HPOs versus typical organizations
uncover a people-centric approach as a key differentiator
HPOTypical org

HPOs are more effective at cascading their vision to all employees
Just as we saw differences across levels in
change experience, a gap is also seen when we
look at the perceived value that AI can provide.
We know that 60%of leaders worry their
organization’s leadership lacks a plan and vision
to implement AI¹. In this study, about half
ofindividual contributors say they see the value
of integrating AI in their own work, but only 28%
see AI as critical to their organization's success.
Individual contributors more naturally see how
AI can benefit their own work but have a
disconnect in seeing how these benefits ladder
up to organizational goals. Individual
contributors can see the ‘what’s in it for me’ but
are struggling with the ‘what’s in it for us’—
which could be due to a lack of clear vision.
Gaps between senior level leaders and individual
contributors are not necessarily uncommon, but
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  14
Generative artificial intelligence (AI) in our workplace is critical for my organization to be successful.
differences in perceived AI value poses a risk to
successful adoption and a potential for the gap
to continue growing if leaders do not
intentionally define and clarify their vision for AI.
If leaders do not help their people understand
both how the investment in AI is helping their
own work anddriving organizational success, we
may see a lack of motivation to support
organization-level adoption initiatives. Managers
also score substantially lower than leaders,
uncovering an opportunity for better cascading
of the AI vision.
At HPOs we see less of a gap. Not only are
employees at HPOs more ready for AI
transformation, but they have a greater sense

Managers
also score substantially lower than leaders,
uncovering an opportunity for better cascading
of the AI vision.
At HPOs we see less of a gap. Not only are
employees at HPOs more ready for AI
transformation, but they have a greater sense
that their organizations will benefit from AI.
Individual contributors at HPOs have an average
of +35 percentage points higher agreement on
the organizational value of AI compared to
individual contributors at typical organizations.
What does this mean? Typical organizations are
struggling to help individual contributors
connect the dots between micro-value (how AI
helps me individually) to macro-value (how AI
helps my organization). HPOs are further along.
91%
63%
54%
65%
51%
24%
Leader Manager Individual Contributor
HPO Typical organization
Individual contributors at HPOs have higher
agreement that AI has organizational value
compared to managers at typical
organizations.
HPOs connect
dots from AI
vision to AI value
1 Microsoft WorkLab, Work Trend Index Annual Report. May 2024

HPOs are providing access to organization-
sponsored AI tools at a substantially higher rate
than typical organizations and in turn are seeing
more frequent use of AI. HPO employees are
more likely to report that they understand where
AI could be integrated in their work (87% at
HPOs versus 67% at typical organizations) and
more likely to have opportunities to integrate AI
tools at work (83% at HPOs versus 56% at typical
organizations).
This experimentation is likely fueling a virtuous
loop from use to realized value, in that more
experimentation leads to greater realized value
and vice versa. We see that 87% of those at
HPOs agree there is value in integrating AI to
their workplace versus 64% of employees at
typical organizations.
Despite the prevalence of AI tools at work, the
majority of people are using at least some tools
that have not been provided directly by their
organization. This may cause employee
hesitance to share with their peers and leaders
how they are using AI in their day-to-day.
Encouraging AI experimentation—the testing
and sharing of what worked and what didn’t—
provides highly useful information, uncovering
valuable AI use-cases across roles.
Understanding the most impactful way to use AI
helps move from an aspirational AI vision to one
that is tangible, already tested, and rooted in the
real experiences of your employees. Providing
access to AI tools creates an atmosphere that
will encourage more experimentation, frequent
use, and the sharing of experiences.
HPOs lead with
access-driven
experimentation
9%
32%
31%
26%
6%
19%
27%
48%
None
Some
Most
All
Amount of AI tools provided by their organization
HPO Typical organization
Extent of organization-sponsored AI tools much higher at HPOs, which

lead with
access-driven
experimentation
9%
32%
31%
26%
6%
19%
27%
48%
None
Some
Most
All
Amount of AI tools provided by their organization
HPO Typical organization
Extent of organization-sponsored AI tools much higher at HPOs, which
could be driving high use frequency for HPO employees
20%
26%
30%
22%
10%
13%
29%
47%
Never
Low
Mod
High
Frequency of AI tool use
HPO Typical organization
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  15
83% of employees at HPOs say they
have opportunities to integrate AI into
their work. Only 56% of employees at
typical organizations say the same.
Low frequency = A few times a month or less
Moderate frequency = At least once a week
High frequency = At least once a day

HPOs celebrate milestones and achievements
recognizing employees' efforts and
contributions to the change. Sharing success
stories is a great way to do this. Showcase both
smooth paths to success, as well as stories
about teams who ran into barriers and how they
met their challenges with resilience.
HPOs also provide empathy and support
during change. Recognizing that change can be
stressful, HPOs do not dismiss the emotions of
their people, but rather anticipate concerns and
provide relevant support.
In AI transformation: Help employees see their
part in AI transformation and clarify how their
role may shift (and stay the same) in the future.
Reduce uncertainty and acknowledge where
there are unknowns. As you go through your
transformation, continue to ask, “How can we
bring our people along during this change?”
HPOs invest in employees’ development
during change, provide proactive upskilling
and reskillingand opportunities to grow.
In AI transformation: Use peer-to-peer
learning and sharing of AI tips and tricks
through communities. Encourage the building
of AI skills through training and
experimentation.
During change, High Performing Organizations
excel in communications, skilling, and
measurement. As you go into your own AI
transformation take a page out of the HPO
handbook:
HPOs leverage transparent two-way
communication to openly share reasons for
change, its impact, and the expected benefits.
In AI transformation: Use channels that allow
people to be part of the AI conversation, avoid
tools limited to one-way sharing of
information in favor of tools that dynamically
capture employee responses and input.
HPOs engage employees early and often
seeking and acting on feedback throughout

ple to be part of the AI conversation, avoid
tools limited to one-way sharing of
information in favor of tools that dynamically
capture employee responses and input.
HPOs engage employees early and often
seeking and acting on feedback throughout
the process. They integrate this feedback into
decision making.
In AI transformation: Deploy ways to gather
input and ensure action is taken on the
feedback gathered. Share feedback with those
who are in the best position to make
meaningful adjustments throughout change.
Want to learn more about HPOs?
See our report with research on what matters
most for organizational performance.
Take an HPO
approach to AI
transformation
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  16
Com
m
unications
Skilling
M
easurem
ent

roles find differing value across the variety of AI
capabilities available.
Use the Microsoft Copilot Scenario Libraryto
start your experimentation journey. Explore
different AI use-cases, key performance indictors,
and potential benefits of AI use by role. Consider
this a jump-start to thoughtful implementation
and continue to iterate and co-create additional
scenarios with your people. Have employees test,
provide feedback, and generate new ideas that
surface as they use AI in their work.
Experimentation can take many forms, varying on
level of structure (informal versus organized) and
relatedness to a particular role (general versus
specific). Use a mix of experimentation methods,
consider what methods have been successful in
the past and how multiple methods may work
together. Informal, general testing can be useful
as people are getting used to AI at work.
Organized and specific testing is necessary as
substantial AI investments and systemic workflow
integrations are made.
Uncovering key use-cases for AI at your organization can range from
highly informal and general to very organized and role-specific
Formal upskilling
Organization-led training
programs or academies that
cover AI basics and best
practices for use
HPOs provide far more access to AI tools than
typical organizations, with 48% from HPOs
versus 26% from typical organizations saying all
AI tools they use are provided by their
organization. This creates space for more use
and organization-supported experimentation.
Experimentation can be highly valuable in
uncovering AI use-cases and scenarios that drive
value for individuals, teams, and the organization.
As you consider the most impactful AI use-cases
for your organization, remember AI is not a one-

tion.
Experimentation can be highly valuable in
uncovering AI use-cases and scenarios that drive
value for individuals, teams, and the organization.
As you consider the most impactful AI use-cases
for your organization, remember AI is not a one-
size-fits all solution and can be tailored to unique
needs. Often, this means different functions and
Elevate impact
through
role-specific
experimentation
General Specific
Informal
Organized
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  17
AI community building
Creation of  communities for
connecting people to experts,
peer support, resources, and
organic upskilling
Functional AI academies
Training of pre-identified,
basic set of function-specific
AI uses, particularly where
workflows can be enhanced
Prompt libraries
Resources providing pre-
engineered prompts for a
variety of tasks that people
can pull into their own use
AI survey
Ask for explicit feedback in a
survey to understand where
AI had or could have the
biggest impact
Departmental hack-a-thon
Designate a time for self-
appointed teams to focus on
ways to integrate AI into
current workflows
Independent
experimentation
Leaders encourage individuals
to test AI tools they have
access to in their day to day
Crowd sourcing
Establish open forums for
people to share success
stories, solutions to problems,
and AI use-case ideation
Workflow needs mapping
Look at current workflows and
current needs, match these
with known AI capabilities to
uncover low-hanging fruit

How can your approach to AI transformation be more agile?
Take small, directionally
correct changes
Break down the bigger vision
into small, tangible shifts in
the right direction to help
make the change less
daunting. Experiment and
provide room to re-route or
change the vision, as
needed.
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  18
Focus on flexibility and
iterative progress
Stay flexible and avoid being
too beholden to the path
originally outlined. Provide
space for shifts based on
feedback and the changing
context in which the change
is being implemented.
Co-build with your
employees
Call on the collective
wisdom of those going
through the change to
determine the most sensible
path. Gather ongoing
feedback to determine what
can yield the most value and
adoption.
AI transformation
Iterative
adjustments
Iterative
adjustments
7 Harvard Business Review, Is Your Mindset About Generative
AI Limiting Your Professional Growth? May 17, 2024
⁵ Gartner. 3 Bold and Actionable Predictions for the Future of GenAI. April 12, 2024
⁶ Microsoft. AI Data Drop: New research shows who has an early AI advantage. 2024.
Change agility
for AI
transformation
integrating an agile, experimental, and
personalized approach to your organizational
change process, allows for your AI transformation
vision to mature over time and flex to meet the
unique needs of users. This means shorter,
cyclical, function-specific change rollouts
focusing on iterative change informed by
feedback and shifting context.
It may be tempting to wait until AI tools and
models are in a more final form, allowing for a
more traditional change rollout, but there is risk in
waiting indefinitely. Early adoption has its

ve change informed by
feedback and shifting context.
It may be tempting to wait until AI tools and
models are in a more final form, allowing for a
more traditional change rollout, but there is risk in
waiting indefinitely. Early adoption has its
advantages and requires a mindset shift—seeing
past the uncertainty to the opportunities and
creative uses for AI⁷.​ Agile change can help
navigate ambiguity and move ahead even without
knowing its exact end-state. Focus on forward
momentum, allowing room to adjust.
With new AI models offering increased
capabilities, even a perfect change plan for
the AI of today quickly becomes outdated.
By 2027, it's predicted that more than 50% of
AI models used at work will be specific to
industry or a particular business function—a
rise from about 1% in 2023⁵.
Value derived from current AI models is
already influenced by the functional role and
industry of the individual using it⁶. As such,

Embarking on large-scale transformation
can feel like standing at the starting line of
a marathon, not knowing where the finish
line is. At Microsoft, we recognize our AI
transformation could indeed be a
marathon, and while we may not have a
clear map to the end-point, the race has
started, and it’s time to get moving.
“We focused on setting short-term goals
around AI experimentation to get us
moving in the right direction,” recalls Chris
Owen of the HR AI Orchestration,
Adoption, & Impact Team at Microsoft, “It
may not be the end of the transformation
‘race,’ but if we can say ‘hey, there’s a
water stop at mile two’ that’s a way we can
bring people along on this journey.”
Microsoft built an AI Champion
Community to support AI experimentation
and deploy thoseAI journey 'water stops'.
These activities included an AI Basics
learning path, group co-innovation
activities, and local team AI use-case
ideation led by AI Champs.
AI Champs become SMEs, serving asgo-
to resources for tips, tricks, and innovative
best practices from across the global AI
Champ Community. The real value in
having AI Champs lies in theirability to 1)
scale, amplify, and build upon the impact
of adoption efforts, 2) adapt efforts in
ways that are relevant to their colleagues,
and 3) build colleagues' excitement forthe
future of an AI-powered organization and
being part of creating this direction. "We
want this to be a co-created,
transformation experience,” says Owen,
“Our north star continues to be
‘community is agility’.”
Identify your champs: Use both a top-down and
bottom-up approach to selecting Champs (both
nominated and volunteered). Seek innovators and
influencers who embrace change, love to tinker, and are
motivated toexplore ways to use AI.

is agility’.”
Identify your champs: Use both a top-down and
bottom-up approach to selecting Champs (both
nominated and volunteered). Seek innovators and
influencers who embrace change, love to tinker, and are
motivated toexplore ways to use AI.
Encourage ownership:Community organizerscan be
aresource more than an authority, empoweringChamps
to makethe program and training resources theirown.
Be agile and iterative:Your program is a living thing
which evolves with theneeds of your people and
theconstantly evolving capabilities of yourAItools.
Make it fun:Consider ways toinject fun viacompetitions
or gamification to help avoid change fatigue. Try an
innovation jam or AI skilling in the style of an escape room.
Startsmall, practical, and personalized:Have AI
Champs focus onapplications that resonate best with the
needs and work oftheir cohorts.
Build community platforms:Use communication
platforms to buildcommunity and share success
stories,best practices, and lessons learned. Make it easy
for people to connect, help each other, and keep the
momentum going between more formal sessions.
Building an AI Champion Community
Only 61% of individual contributors have SMEs on
their team to build competence during change,
compared to 83% of leaders and 74% of managers².
AI Champs can extend support needed and close
change experience gaps.
Experimentation in
Action: Microsoft
AI community
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  19
2 Microsoft Viva People Science, AI Transformation Readiness Research, April 2024

Multipliers
Very Engaged / Very High Change Exp /
Very High AI Sentiment
Multipliers are already experimenting with
AI and sharing with their peers. These are
your early adopter, AI champions!
Leverage them as SMEs and role models.
Needs: Opportunities to experiment and
play with new tools and features
Advocates
Engaged / High Change Exp / High AI Sentiment
Advocates support change and while they
may not be proactively experimenting,
they quickly adopt. Advocates can be fast
followers of Multipliers –a winning
combination for AI transformation.
Needs: Clear expectations and direction, a
Multiplier change buddy
Persuadables
Slightly Engaged / Medium Change Exp /
Medium AI Sentiment
With average experiences, Persuadables
are in the prime spot to be swayed.
Focusing on their needs for improved EX
and change experience can boost
sentiment and create more AI excitement.
Needs: Feedback outlets, clarified vision
AI Skeptics
Engaged / High Change Exp / Low AI Sentiment
Despite an overall good EX, AI Skeptics are
unsure of AI integration at work. These
individuals need some convincing to help
them move toward adoption of AI tools.
Needs: Info on benefits of AI to their work
and how the organization is creating
safeguards to minimize negative impact
Change Pessimists
Very Disengaged / Low Change Exp / Low
AI Sentiment
Change Pessimists have struggled with
previous change experiences and are
disengaged at work. Coupled with lower-
than-average AI sentiment, these
individuals will need very targeted
support.
Needs: Significant change support
28% of sample
44% of sample
18% of sample
6% of sample
4% of sample
Leaders and HR need to
understandwhere theirpeople are,
not only in their own AI journey, but

individuals will need very targeted
support.
Needs: Significant change support
28% of sample
44% of sample
18% of sample
6% of sample
4% of sample
Leaders and HR need to
understandwhere theirpeople are,
not only in their own AI journey, but
also in their work experience, to
empower their own AI
experimentation.In our analysis, we
found five keyemployee profiles that
organizations are likely to encounter*.
These profiles were created by
examining participants’
1) current employee engagement
levels,
2) experiences with past change
initiatives at their organization, and
3) expressed optimism and readiness
for AI integration at work.
Knowing wherepeople are intheir AI
journey helpsto identify their key
needs during change and AI
transformation. Some profiles may be
eager to experiment, where others
may need support and
encouragement to dive in. Encourage
experimentation from employees
representing all profiles to get the
most well-rounded feedback on what
is working and where additional
support is needed.
AI transformation profiles
Meet people
where they
are in their
AI journey
*Methodology: Latent profile analysis was used to
group respondents by response patterns to questions
on engagement, previous change experience, and AI
optimism (n = 1389). This method facilitates the
grouping of respondents based on similar sentiments.
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  20

Bringing people
along requires
surfacing concerns
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  21
When asked specifically aboutconcerns, across
levels, there isrelative agreement that using AIat
work can have risk (57% of leaders, 54% of
managers, and 52% of individual contributors
agree).Yet individual contributors have far more
skepticism:
• Only 56% of individual contributorssay
theytrust the output of AI versus 82% of
leaders and 71% of managers.
• Only47% of individual contributors agree
that AI will transform work for the better
versus 84% of leaders and 69% of managers.
Top concernsacross all levels and profiles include:
1. Security and data privacy.
2. Over-reliance on AI for automatic decision
making, particularly those decisions that have
impact on employees (e.g., hiring).
3. Potential job loss or deskilling due to AI taking
over tasks traditionally done by humans.
Addressing the concerns for your people is an
important part of managing potential change
resistance. To do this, uncover what the top
concerns are for your organization through
ongoing feedback mechanisms. Communicate
what security efforts are in place to ensure the
safety and accuracy of AI use. Provide skilling where
AI has been integrated, both reskilling where there
may be automation, and upskilling where AI may
shift a workflow. As you begin to rollout AI, share
success stories and quick wins.
Fear around AI may be driven by the unknown.
Exposure can be helpful in reducing apprehension
even for AI skepticsor people who do not feel tech
savvy. Don't discount the value of surfacing
concerns, this builds trust that key risks are not
being overlooked and helps refine and improve
your AI strategy.
AI usealone does not indicate full support for

skepticsor people who do not feel tech
savvy. Don't discount the value of surfacing
concerns, this builds trust that key risks are not
being overlooked and helps refine and improve
your AI strategy.
AI usealone does not indicate full support for
AI at work, nor a lack of concern. When asked
to what level they support AI use at their
workplace, the majority are split between full
support and support with some skepticism.
Security
People want security in AI processing
and storage of their personal data and
organization’s sensitive information.
Looking for confirmation that data is
protected and not misused.
Over-reliance
How and why AI models make
decisions is not always easy to
interpret, creating a ‘black-box’. People
note blindly following decisions without
human validation could cause
unintended harm.
Job loss
People worry AI could negatively
impact certain industries and
occupations, especially roles with
repetitive tasks, or deskilling, with roles
being reduced in scope or skill
necessary to complete.
Top concerns include:
As you think about generative AI being
integrated into the way we work, what are
you most concerned about?

While time will ultimately uncover how AI
transformation is different than other change seen
in the past, here are some early guiding principles
to keep top of mind and build upon in your own AI
journey:
Recognize the scope
Impact beyond the workplace
AI has the potential toredefine how we interact
with technology, shaping expectations in and
outside of our work lives.
More than a softwarerollout
AI transformation isa continuousevolution that
requires cultural and behavioral shifts, not just
technical changes.
Lean into the change
Go with the energy of your people and consider the
risk of delay. The earlier your employees have
exposure to AI tools, the sooner they can build the
capabilities necessary for the future.
Take an agile approach
Build yourvision as you experiment
Getting to yourAI vision will be an ongoing process
that requires experimentation and adaptation.Be
agileand flexible to avoid being left behind.
Co-create with your people
Get feedback from your people on the most
impactful uses of AI at your organization. AI
transformation will not be a one-size-fits all, so
creating room for personalization can be a key
impact factor.
Address the fear factor
Lead with empathy
With the excitement, there is also fear—particularly
a fear that taps into our own sense of future
relevance and control in the work we do. Viewing AI
transformation as just a process change misses the
critical, human element and the need to bring
people along and include them in the shift.
Prioritizesecurity
A top concern across employee levels is the
securityof theirdata with the introducing of new
technology.IT and HR can work together to ensure
thatinfrastructure anduse-casesof AI
toolsemphasize security oforganizational
andpersonal data.

curity
A top concern across employee levels is the
securityof theirdata with the introducing of new
technology.IT and HR can work together to ensure
thatinfrastructure anduse-casesof AI
toolsemphasize security oforganizational
andpersonal data.
Better together
More than ever, cross-disciplinary teams can be working
together to ensure the success of this change. The
combined expertise across a variety of backgrounds
allows organizations to take a holistic perspective on the
pros and cons of any given approach and supports a
more well-rounded deployment. Not only is this an
exercise in risk-management, but also a best practice in
the identification of opportunities to apply AI in the most
impactful ways across your organization.
Guiding
principles for AI
transformation
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  22

Viva can support agile change management.
We are all going throughthis AI
transformation journey together. At
Microsoft, Responsible AIis at the core of
how we build and deploy AI.As we think
about the criticality of the employee
experience in AI transformation, Viva is here
to help through apps that enable
communications, measurement, and skilling.
Visit our website to learn more
about the Microsoft Vivasuite
Use Microsoft Viva
in your agile
change
management
Accelerate your AI workforce transformation
Microsoft Viva
Copilot in Microsoft Viva      •      In the flow of work      •      Trusted platform
Communications
SkillingMeasurement
Microsoft Viva
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  23

1 Microsoft WorkLab, Work Trend Index Annual Report: AI at Work is Here. Now Comes the Hard
Part. May 2024.
2 The AI Transformation Readiness Study was conducted by the Microsoft Viva People Science
team utilizing an Online Panel Vendor, commissioned by Microsoft, with 1,800 full-time
employees across nine markets between March 29, 2024 and April 9, 2024. This survey was 10
minutes in length and conducted online. Global results have been aggregated across all
responses to provide a total or average. Each sample was representative of business leaders
across regions, ages, and industries (i.e., Education, Financial Services, Healthcare,
Manufacturing, Professional Services, Retail, Technology). Each sample included specific
parameters on company size (i.e., organizations with 1,000+ employees) and job level (i.e.,
business leaders/business decision makers, those in mid- to upper job levels such as, C-level
executive, VP or Director, Manager). The overall sampling error rate is 2.31 percent at the 95
percent level of confidence. Markets surveyed include: Brazil, China, France, Germany, India,
Japan, Mexico, United Kingdom, and the United States.
3 Microsoft WorkLab, The New Performance Equation in the Age of AI. April 2023.
4 Microsoft Viva People Science, Redefining High Performance in the New Era of Work. October
2023.
5 Gartner, 3 Bold and Actionable Predictions for the Future of Gen AI. April 12, 2024.
6 Microsoft WorkLab, AI Data Drop: Which Jobs Have an AI Advantage? 2024.
7 Harvard Business Review, Is Your Mindset About Generative AI Limiting Your Professional
Growth? May 17, 2024.
© 2024 Microsoft Corporation. All rights reserved. This document is provided “as-is.” Information

hich Jobs Have an AI Advantage? 2024.
7 Harvard Business Review, Is Your Mindset About Generative AI Limiting Your Professional
Growth? May 17, 2024.
© 2024 Microsoft Corporation. All rights reserved. This document is provided “as-is.” Information
and views expressed in this document, including URL and other internet website references, may
change without notice. You bear the risk of using it. This document does not provide you with any
legal rights to any intellectual property in any Microsoft product. You may copy and use this
document for your internal, reference purposes
References
AUGUST 2O24  /  THE STATE OF AI CHANGE READINESS  /  24