Ibm's global ai adoption index 2021 executive summary

IBM Watson Global AI Adoption Index 2021 1
Global AI Adoption
Index 2021
New research commissioned by IBM in partnership
with Morning Consult

Introduction Artificial Intelligence (AI) today is changing the way businesses
operate in fundamental ways, from how they communicate with their
customers through virtual assistants, to automating key workflows and
even managing network security.
Almost a third of the IT professionals surveyed in IBM’s Global AI
Adoption Index 2021, conducted by Morning Consult, say their
business is using AI, similar to IBM’s 2020 findings. 43 percent of
businesses reported that their company accelerated its rollout of AI
as a result of the COVID-19 pandemic. However, lack of AI skills and
increasing data complexity were mentioned as top challenges.
The past year amplified a host of new strategic priorities for businesses
as they had to work to meet the needs of their customers while still
finding ways to be more cost efficient, more responsive and make
faster, more informed decisions. Companies that can overcome
adoption and deployment barriers and tap AI and automation tools to
tackle these challenges will be able to deliver value from AI in 2021.
The data sheds new light on the deployment of AI across 5,501
businesses in China (500), France (500), Germany (500), India (500),
Italy (500), Latin America (1,000 across Brazil, Mexico, Colombia,
Argentina, Chile, Peru), Singapore (500), Spain (500), the United
Kingdom (500), and United States (501). The polling was conducted
online through Morning Consult’s proprietary network of online
providers in April 2021. All respondents were required to have
significant insight or input into their firm’s IT decision-making. See full
details on the methodology at the end of the summary.

Global AI Adoption
Today, almost one-third of IT professionals say their firm is using AI
technology and almost half say their companies are exploring AI,
similar to findings in IBM’s report From Roadblock to Scale: The Global
Sprint Towards AI. The adoption of AI is being driven by the continuing
repercussions of the COVID-19 crisis, general business needs, and the
technology being more accessible.
Larger companies are almost 70% more likely (a difference of 18
percentage points) than smaller companies to have actively deployed
AI as part of their business operations.
Over one-third (34%) of global IT professionals reported that their
company has not deployed any AI projects.
Key Findings
80%
60%
40%
20%
0%
Global AI Adoption
Deployed AI Exploring AI
France Germany India Italy Singapore Spain United
Kingdom
United
States
China Latin
America
AI adoption rates around the world

The top drivers of AI adoption in organizations are:
1. Advances in AI that make it more accessible (46%)
2. Business needs (46%)
3. Changing business needs due to COVID-19 (44%)
Half of global IT professionals report that compared to 2-3 years ago,
AI solutions are now better designed to fit the needs of businesses and
almost half say AI solutions are now more accessible and easier
to deploy.
The top three factors a company considers when evaluating
AI providers are:
1. Automates processes to empower higher value work (47%)
2. Provides trust in business outcomes (40%)
3. Ability to deploy anywhere—on any public cloud, private cloud
or on-premises (40%)
Where are businesses on their AI journeys?
IT professionals have taken the following steps to explore or deploy AI
in their business operations:
• 34% - My company is analyzing data to build and scale AI, but has
not rolled out any AI projects
• 31% - My company is currently using pre-built AI applications such
as chatbots
• 27% - My company is developing proofs of concept for specific AI-
based or AI-assisted projects
• 24% - My company is exploring AI solutions, but we have not
purchased any tools or apps
• 21% - My company is deploying AI across the business
are exploring AI
43%
are exploring AI have deployed AI
43% 31%
31%
are exploring AI
have deployed AI
74% of
companies are
exploring or
deploying AI
43%

Barriers to AI Adoption
Similar to our 2020 findings, global business leaders worry most about
lack of AI skills and expertise as barriers to adoption. Increasing data
complexity and data silos is are concerns for one-third of companies,
but these barriers are noted significantly more often at larger
organizations.
What are the top three barriers to AI adoption?
1. Limited AI expertise or knowledge (39%)
2. Increasing data complexity and data silos (32%)
3. Lack of tools or platforms for developing AI models (28%)
Increasing data complexity and data silos comprise the largest barrier
to AI adoption at larger companies, 11% higher than at smaller ones.
Limited AI expertise is the largest barrier at smaller organizations.
More than one in three businesses cite difficulties in steps along
their organization’s journey to AI:
• Analyzing data to build and scale trusted AI (39%)
• Infusing AI throughout their business (37%)
• Organizing data to create a business-ready analytics foundation
(37%)
• Collecting data to make it simple and accessible (37%)
IT professionals at larger companies were more likely to report that their
biggest difficulties involve data analysis and infusing AI throughout their
organization. IT professionals at smaller companies were more likely to
report that their biggest difficulty is data collection.
AI Investments and Use Case Trends
Companies around the world have accelerated their rollout of AI
as a result of the COVID-19 pandemic, a trend that was especially
pronounced at larger companies. In the next 12 months, businesses
plan to invest in all areas of AI, from skills and workforce development
to buying AI tools and embedding those into their business processes.
43% of global IT professionals reported that their company has
accelerated their rollout of AI as a result of the COVID-19 pandemic.
Larger companies were 31% more likely than smaller companies (a
difference of 12 percentage points) to report that their company had
accelerated their rollout of AI as a result of the COVID-19 pandemic.
Over a third of global IT professionals report that making employees
more productive (38%) and needing a better way to interact with
customers (36%) influenced their decision to use automation software
or tools as a result of the COVID-19 pandemic.
One-third of global IT professionals report their company plans to
invest in:
• Embedding AI into current AI applications and processes (34%)
• Reskilling and workforce development (34%)
• Off-the-shelf AI applications (34%)
• Proprietary AI solutions (33%)
• Off-the-shelf tools to build their own applications and models (33%)
IT professionals in China and India were more likely to report their
company plans to invest in each area of AI, especially in proprietary AI
solutions, embedding AI into current applications and processes, and
off-the-shelf tools to build their own applications and models.

31%
25%
25%
20%
19%
16%
15%
14%
14%
11%
11%
10%
9%
8%
7%
7%
6%
6%
Data security
Automation of processes
Customer care
Virtual assistants/smart chatbots
Business process optimization
Fraud detection
Sensor data analysis (Internet of Things)
AI monitoring and governance
Marketing
Supply chain
Personal security
Predictive decision making
Image recognition
Financial trading
Natural language processing (NLP)
Search
Recommendations
Healthcare diagnostics
Where companies are allocating AI investment
in the next 12 months

Approaches to Trustworthy AI
A majority of respondents say that trusted, explainable AI is crucial
to widespread adoption of the technology and to the success of their
business, including maintaining brand integrity and meeting regulatory
compliance. Trust is now clearly top of mind for businesses as they
think about their consumers, with a majority of businesses believing
that consumers are more likely to choose services of a company that
offers transparency and an ethical framework on how its data and AI
models are built, managed, and used. But while global businesses are
now acutely aware of the importance of trustworthy AI, more than half of
survey respondents cite significant barriers in getting there.
84% of IT professionals report that the ability to explain how their AI
arrived at a decision is important to their business. The issue is 14%
more critical for those using AI compared to those exploring AI, with
over 90% of businesses using AI today saying their ability to explain
how it arrived at a decision is critical.
Over three-quarters of global IT professionals report that it is critical
to their business that they can trust the AI’s output is fair, safe and
reliable.
IT professionals at larger companies were almost 32% more likely (a
difference of 10 percentage points) to say it’s critical that they can trust
the AI’s output is fair, safe and reliable.
IT professionals in India (95%), China (85%), Latin America (82%),
and US (80%) were more likely to report that it is important to their
business that they can trust the AI’s output is fair, safe and reliable.
86% of global IT professionals strongly or somewhat agree that
consumers are more likely to choose services of a company that offers
transparency and an ethical framework on how its data and AI models
are built, managed, and used. 90%
Do you trust
your AI?
More than 90%
of companies
using AI say their
ability to explain
how it arrived at
a decision is
critical

90%
89%
89%
88%
87%
65%
62%
58%
58%
58%
58%
66%
64%
63%
63%
62%
62%
62%
61%
61%
60%
Maintaining brand integrity and customer trust
Meeting external regulatory and compliance obligations
Meeting internal reporting obligations
Ability to monitor and govern data and AI across its lifecycle
Ensuring applications and services minimize bias
Lack of skills or training to develop and manage trustworthy AI
AI governance and management tools that don’t work across all data environments
AI outcomes that are not explainable
Lack of regulatory guidance from governments or industry
Lack of company guidelines for developing trustworthy, ethical AI
Building models on data that has inherent bias (social, economic, and so on)
Lack of clarity on provenance of training data
Lack of collaboration across roles involved in AI model development and deployment
Lack of AI policies
Monitoring AI across cloud and AI environments
Unexpected performance variations or model drift
Speed to value
Ability to capture metadata from models/compliance reporting
Tracking changes in data and model versions
Unintended bias
Ability to explain AI-powered decisions
Most important aspects of AI trust and explainability1
Biggest barriers to developing trusted AI2
Biggest AI modeling and management issues businesses are mitigating3
1
Cited as very or somewhat important by 50% of respondents
2
Cited as large or medium barriers by 50% of respondents
3
Cited as very or somewhat concerning by ~65% of respondents

AI Understands the Language of Business
One of the foundational technologies for AI models, natural language
processing (NLP), has steadily become one of the most important and
commonplace tools for organizations to communicate with customers
and empower their employees. Over the last year, a large number of
organizations, from small ventures to massive enterprises, were either
motivated to adopt this technology to create more efficient, personalized
experiences for their customers during the pandemic, or recognized the
value it could bring to their organization in the future.
Almost half of respondents report that their company is currently using
NLP and one-quarter plan to use NLP in the next 12 months.
Over half of IT professionals in India or China report their companies
are currently using NLP applications.
Increasing the level of adoption for NLP technology in the future will
hinge upon how businesses make use of new tools that automate many
of the common barriers to entry, for instance, lowering the requisite
skillset for training and deploying language models. Professionals at
companies considering the use of NLP report the top five barriers to
entry for adopting this technology as:
• Technology is too expensive (29%)
• Requiring too much training to be relevant (26%)
• Difficult to keep up-to-date (24%)
• Technology is too complex to use (22%)
• Lacking requisite skillset in organization (22%)
Cost is the greatest barrier to adopting NLP technologies in the US,
Latin America and Europe, however, training requirements are a greater
barrier in India, while complexities and lack of customization are
barriers in China.
Over half (52%) of global IT professionals report that their company
is using or considering using NLP solutions to improve customer
experience, with 43% using NLP to increase cost efficiency.
Regardless of industry, half of businesses deploying AI are using NLP to
improve customer experience.
42%
35%
34%
34%
33%
31%
29%
27%
25%
24%
23%
18%
Email or text classification
Machine translation
Virtual agents for customer service
Call center automation
Survey analysis
Targeted advertising
Automate analysis of complex documents
Text summarization
Complex document search
Virtual assistants for employee engagement
Sentiment analysis
Most popular uses for NLP
42% 26%
are currently
using NLP
plan to use
NLP in the next
12 months

As businesses become more familiar with the potential of AI, automation
technologies are becoming more deeply embedded into day-to-day
operations in order to drive greater efficiencies, save costs, and more.
Automation is also being utilized by businesses today for increasingly
complex use cases, such as automating the response and resolution to
IT incidents.
80% of companies are already using automation software and tools, or
plan to use this technology in the next 12 months.
The top three reasons a majority of businesses are currently using
or considering using automation tools are:
1. Driving greater efficiencies (58%)
2. Saving costs (58%)
3. Giving valuable time back to employees (42%)
Over a third of global IT professionals report that making employees
more productive (38%) and needing a better way to interact with
customers (36%) influenced their decision to use automation software
or tools as a result of the COVID-19 pandemic.
During the COVID-19 pandemic, only 18% of companies were
influenced by the demand for products to adopt automation
technologies, while the majority (38%) cited the need to make
employees more productive.
The UK has the lowest adoption of automation tools (32% not using,
49% already using, 19% in next 12 months), while China has the
highest adoption (8% not using, 71% already using, 21% in next
12 months).
Only 8% of companies in China have no plans to use automation
technologies. 92% are already using, or plan to in the next 12 months.
Intensifying and Expanding the Use of Automation 80% of
companies
are using
automation
software
and tools or
planning to use
them in the
next 12 months
of companies used
automation to make
employees more
productive during
the COVID-19
pandemic
38%
1
61%
8
19%
61%
61%
19%
using
planning to use

56%
54%
45%
40%
38%
37%
33%
32%
35%
40%
43%
43%
43%
44%
88%
89%
85%
83%
81%
80%
77%
Network performance
Integration of apps and data
Business process management (BPM)
Application performance management (APM)
Observability
Process and task mining
Robotic process automation (RPA)
Most popular uses for automation software
and tools
Using
Interested
in using Total
A majority of IT professionals across all demographics report that their
company is currently using automation software or tools.
39% of global businesses are currently using automation to preempt
potential downtime or technical issues.
Of those companies using automation tools, respondents in China were
the most likely of any country to focus on driving greater efficiencies
(75%) while the European countries all rank among the lowest
(France, 45%; Germany, 41%; Italy, 51%; Spain, 58%; UK, 49%).
Companies who have already deployed AI technologies are more than
twice as likely to be using RPA as those companies who are exploring AI
(exploring, 25%; deployed, 54%).
Companies in China are more likely to be using or considering using AI
to personalize customer experiences and automate business workflow,
while those in the US and India are most likely to be using AI to automate
IT operations.
For smaller companies, activity monitoring is the largest use case for
automation technologies (36%) while larger companies are placing a
greater focus on automating IT operations (48%).
Automating IT operations is the top use case for automation cited by
respondents, whether in use or in exploration.

As remote work practices become more widely adopted, and new
applications, IoT, and edge computing become more common,
businesses are increasingly overwhelmed by the enormous volume
of data generated each day—the “data sprawl.” Organizations are
also burdened by the prospect of making sure this information is
accessible, secure, accurately informing their business intelligence,
and complying with emerging privacy regulations. This vast amount of
data is often spread across extensive IT estates, including traditional
data centers, as well as multiple clouds in many locations, with
multiple vendors.
87% of global IT professionals report it is very or somewhat important
to their company that they can build and run their AI projects
wherever the data resides.
A majority of IT professionals in Latin America (60%), India (78%),
Spain (55%), and the US (52%) report it is very important to their
company that they can build and run AI projects wherever the
data resides.
Though 83% of IT professionals feel confident they have the right
tools to find data across their business wherever it resides, only
51% report that their platform offers a single, unified view of their
organization’s data.
72% of IT professionals in India, compared with only 23% in China,
are very confident that their company has the right tools in place to
find data across their business, no matter where it resides, so it can
be organized, analyzed and turned into useful insights. In the US, this
figure is 44%.
Over two-thirds (67%) of global IT professionals report their company
is drawing from over 20 different data sources to inform their AI, BI,
and analytics systems.
Improving Access to Data Anywhere in an
Organization
22%
21%
19%
14%
9%
1%
15%
8%
15%
15%
14%
14%
17%
18%
Less than 20
20-50
51-100
101-500
501-1000
More than 1000
Don’t know/Not sure
75% of larger companies report drawing from
over 20 different data sources in inform their,
AI, BI and analytics systems
Company ≤ 1000 Company 1000
67%
38%
of companies
draw from
more than 20
data sources
for their AI

Methodology
The polling was conducted online through Morning Consult’s proprietary
network of online providers in April 2021. All respondents were required
to have significant insight or input into their firm’s IT decision-making.
Representative Sample of Business
Decision-makers in 15 Markets
• 501 in United States
• 500 in China
• 500 in India
• 500 in Singapore
• 2,500 in European Union countries
(UK, Italy, Spain, France, Germany)
• 1,000 in Latin America
(Brazil, Mexico, Colombia, Argentina, Chile, Peru)
• Conducted online through Morning Consult’s proprietary network
of online providers
Respondents Represented a Mix of Small
and Large Firms
• 28% of respondents came from firms with more than
1,000 employees
• 29% of respondents came from firms with between 251
and 1,000 employees
• 18% came from firms with 51-250 employees
• 25% came from smaller businesses
(50 employees or less)
• Sole proprietorships were not sampled
Respondents Represented a Mix of Seniority
• All respondents were required to have significant insight or input
into their firm’s IT decision-making
• One-quarter of the sample was at a VP level or above
(including C-suite executives)
• The remainder of the sample represented a mix of directors and
senior manager-level employees with close knowledge or authority
in their firm’s IT/AI practices

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