The State of Global AI Adoption in 2023

The State of Global
AI Adoption in 2023

2
The State of Global AI Adoption in 2023
Contents
Introduction
The current state of AI
Generative AI: the new frontier of automation
Generative AI use cases across Industries
AI adoption by industry
AI application matrix in healthcare
AI application matrix in banking and finance
AI application matrix in manufacturing
AI application matrix in retail
How to make AI work for your business
Estimating AI Readiness: questions to ask for your company
Conclusion
3
4
6
7
8
9
10
12
13
14
15
18

3
STORM IS GATHERING,
AND AI GATHERS STRENGTH
The last two years have been challenging for the tech
industry due to economic headwinds and recessionary
budget pressures. The economic uncertainty on the
horizon is going to require boards to become more
selective and nuanced about technology decisions.
But despite geopolitical and economic turbulence,
the adoption of AI remains the silver lining in the tech
landscape thanks to its immense potential in supporting
business continuity and sustainable growth.
To survive and thrive, companies all over the world
invest in improving supply operations, modernizing
infrastructure, and leveraging growth opportunities. As
a result, full-scale AI adoption is going strong across all
industries, with high-performing organizations reporting
results — such as cost reduction and performance
gains — linking those gains to artificial intelligence and
its transformational effect.
Generative AI also deserves much of the credit for
renewing enthusiasm for artificial intelligence. Having
taken the market by storm, it is poised to solve business-
specific challenges and unlock more automation
opportunities for global organizations.
Artificial intelligence is a dynamic force behind each
high-performing organization. From manufacturing
to hospitality to retail, global companies adopt AI
by default, as new, AI-powered features are added
to the software they already use.
Those companies who want to claim leadership
in the market opt for distinctive AI features and
software tailored to their unique business case.
In this paper, we’ll take a look at the current status
of AI adoption by industry and the main blockers
that hamper implementations. We’ll help you
estimate the readiness of your organization for AI
adoption and zoom in on generative AI and why it’s
the next frontier for natural language processing.
automation opportunities for global organizations.
The Time is Now
AI has reached a tipping point
69%
the percentage of companies
that rank artificial intelligence
and machine learning as a high
priority for their organizations.
Rackspace
$4.4 trillion
an analysis of the 63 use cases for generative AI
and its annual value.
McKinsey
the percentage of AI adopters
that reported cost savings
and efficiencies from artificial
intelligence.
IBM
But although the statistics demonstrate that the global
turbulence hasn’t taken a toll on AI investment, there are
still critical AI adoption challenges that may discourage
AI innovation and growth in 2023 and beyond. Is your
business prepared?
54%

4
CONVERTED BY RECESSION,
NORMALIZED BY VALUE:
The current state of AI
Despite plummeting tech investment, AI-driven advancements continue to permeate all industries - and this
shows no sign of changing. More than ever before companies feel the need to optimize and pivot, pinning their
hopes on machine intelligence.
What specifically has changed in the technology trends of AI:
As AI is becoming the table stakes for companies, the market for smart technologies is consistently
growing. From startups to incumbents, companies of all sizes make artificial intelligence and its
offshoots a crucial part of their innovation journeys.
The recent convergence of cloud-based architectures and open-source AI toolkits has ushered in
the democratization of AI technologies.
Generative AI and foundation models have entered the landscape to make place for new automation
capabilities and improve existing ones across a broad range of modalities.
In 2023, artificial intelligence has finally reached
a tipping point, moving from being a speculative
technology to a commonly used tool for organizations.
According to AI adoption statistics, over 80% of
enterprises now believe that artificial intelligence and
machine learning are the key technologies to achieving
business goals centered around growing revenue,
increasing operational efficiency, and boosting
customer experience.
80%
the percentage of enterprises that
prioritize AI-based technologies
on their way to higher revenues,
operational efficiency, and customer
excellence.
37.3%
an annual growth rate of
artificial intelligence from
2023 to 2030.
41%
the increase in quarterly
funding at the beginning of
2023 that signals a rebound
in AI investment.
CBInsights
ResearchAndMarkets Grand View Research

5
The application matrix of smart systems hasn’t
experienced any major transformations. The
overwhelming majority of adopters employ machine
intelligence to optimize services and business
processes along with improving customer experience.
AI techniques have also become a part of the new
product development cycle. Digital champions not
only imply algorithms for analysis but also focus on the
underlying data models.
41%
the percentage of companies that use
data analytics and artificial intelligence
for at least part of the digital product
development process.
64%
the percentage of
businesses that expect AI
to increase productivity
within the organization.
79%
the percentage of customer
service professionals who
consider AI/automation tools to
be key to their overall strategy.
Hubspot
PwC Forbes Advisor
Leading AI applications by year
Most commonly adopted AI use cases, by function, % of respondent1
Service operations optimization
Creation of new AI-based products
Customer service analytics
Customer segmentation
New AI-based enhancements of products
Customer acquisition and lead generation
Contact-center automation
Product feature optimization
Risk modeling and analytics
Predictive service and intervention
24
20
19
19
19
17
16
16
15
14
Service operations2
Product and/or service development
Mckinsey
Marketing and sales Risk
1
Out of 39 use cases. Question was asked only of respondents who said their organizations have adopted AI in at least one function.
2
Eg, field services, customer care. back office.

6
GENERATIVE AI:
the new frontier of automation
The year 2023 has marked the increasing adoption of
generative AI models, also known as large language
models or LLMs. This year, we’ve seen SaaS LLMs
increasing in popularity, along with the game-changing
launch of ChatGPT. The number of companies
using SaaS LLM APIs has grown by 1310% between
November 2022 and May 2023.
Thanks to the exponential growth of Generative
AI, executives were able to establish a more clear
image of how generative AI can be deployed for their
use cases. Information security, customer service,
and marketing as well as innovation and product
development are now seen as the strategic areas for
the adoption of LLMs and Gen AI.
SECURITY
Information security
and IT
64%
INNOVATE
Research and innovation,
and product development
63%
ENGAGE
Customer service, marketing
and sales
IBM
Execs have identified three priorities for generative AI adoption
50%
In all these areas, Generative AI is poised to transform
business operations, augment the capabilities of
individual workers, and automate time-consuming
manual tasks. Approximately 60% to 70% of work
activities can be automated using the technology,
according to McKinsey. Implementing LLMs is
expected to have a significant impact on customer
operations, marketing, sales, software engineering,
and research and development.
$404 billion
potential productivity lift from adopting
Gen AI in customer operations.
$463 billion
potential productivity lift from
adopting Gen AI in marketing.
$414 billion
potential productivity lift from adopting
Gen AI in product development.
$328 billion
potential productivity lift from
adopting Gen AI in R&D.
McKinsey

7
GENERATIVE AI USE CASES
ACROSS INDUSTRIES
The effect from adopting generative AI technologies will differ based on the business function and industry.
Generative AI
productivity
impact,
in billion
USD
BANKING
• Offering personalized finance
management advice
• Transforming customer service
and sales
• Predicting credit risk and
advancing сredit scoring
TRAVEL AND LOGISTICS
• Augmenting travel planning tools
• Providing recommendations for
travel destinations and itineraries
• Optimizing traffic management
systems
PHARMACEUTICALS AND MEDICAL PRODUCTS
• Automating drug development enhancing
drug discovery
• Streamlining clinical trials and enrollment
• Optimizing the design and execution of clinical
trials for medical devices
RETAIL
• Conversational AI adoption
• Providing assistance during product
search and offering personalized
recommendations
• Generating content for marketing and sales
EDUCATION
• Providing aid in learning
• Automating grading
assignments
• Creating personalized teaching
materials and lesson plans
ENERGY
• Optimize the use of
renewable energy
sources
• Automating and
controlling energy
systems
HEALTHCARE
• Automating administrative tasks
• Providing recommendations for
follow-up and lifestyle advice
• Supporting medical research and
diagnosis
AGRICULTURE
• Enhancing crop
management
• Assisting in personalized
training in agriculture
• Predicting demand and
supply
INSURANCE
• Analyzing claim data to prevent
fraud
• Improving risk assessments
• Providing personalized product
and service offerings
MEDIA AND ENTERTAINMENT
• Creating animation and
visual effects
• Supporting interactive
storytelling
• Producing content at-scale
ADVANCED MANUFACTURING
• Producing design for blueprints
and instructions
• Analyze data from sensors and
machinery
• Providing insights and decision
support
$110 $390
$340 $70
$230 $110
$300 $70
$260 $290
$240

8
AI ADOPTION BY INDUSTRY,
ACCELERATED
A few years ago, there was a wide AI gap among
industries. Industries like tech were traditionally far ahead
of other verticals, while finance and healthcare trailed
behind due to stringent regulations and AI stigma. In
2023, the gap has tightened, making artificial intelligence
a priority for healthcare, banking, and tech alike.
Loosening restrictions on the use of artificial intelligence
technology also make its adoption possible for industries
that used to be left out of smart transformation. However,
there is still enormous room for growth in AI invention
across all industries and an enormous opportunity for
those companies that can see it.
Levels of AI maturity by industry, 2021 and 2024
2021 2024
The median AI Maturity Index in 2021 and 2024 by industry
Median AI Maturity (0-100)
Arthmetric average of Foundation index and Differentiation index
Accenture

9
As of today, technology, automotive, and aerospace
stand to professionalize and formalize their approach
to AI faster than others - with the average maturity*
index to approach 60 by 2024. Other innovation
leaders such as retail and manufacturing are
also expected to make a quantum leap to mature
foundational AI capabilities. Regulation-heavy players
are still cautious about going full-on with smart
automation, yet are advancing fast into the field.
In terms of AI investment, the focus areas with the
most investment include medical and healthcare ($6.1
billion). It is followed by data management, processing,
and cloud ($5.9 billion) and Fintech ($5.5 billion).
$6.1 billion
the amount of AI investment in medical
and healthcare.
$5.9 billion
the amount of AI investment in
Fintech.
AI Index Report
AI application matrix in healthcare
Three areas with the biggest AI potential:
• Supporting diagnosis and treatment decisions
• Clinical trials
• Imaging diagnostics (radiology, pathology)
Consumer benefits:
Smart algorithms can help enhance the accuracy
and speed of diagnosis by monitoring and analyzing
patient data and providing treatment. This, in turn, can
lead to better patient outcomes, improved quality of
life, and reduced healthcare costs. Generative AI can
streamline administrative tasks, assist researchers in
clinical trial planning, and offer more engagement to
patients.
Industry gains:
Automation of time-consuming administrative tasks
allows healthcare professionals to cut time spent
on paperwork. More effective analysis and disease
prevention help reduce the risk of illness and
hospitalization, thus cutting the costs of healthcare.
Market drivers:
• Increase in investments by pharma and MedTech
companies into artificial intelligence systems
• Rising costs of healthcare and the need to
optimize workflows
• Rising requirement for remote patient monitoring
systems and data analysis

10
Barriers to overcome:
• Lack of skilled AI workforce
• Ambiguous and evolving industry regulations
• Data privacy and security
• Lack of technological expertise
Ready-to-go applications:
Tools to improve and streamline administration for
insurers, payers, and providers
Longer-term potential:
AI and robotics in healthcare (robot-assisted surgeries,
robot doctors)
High-potential use case: Clinical trials
AI-supported patient recruitment allows researchers to
find and enroll patients who meet the specific criteria
for a trial. By analyzing large amounts of patient data
and medical records, AI algorithms significantly speed
up the recruitment process and ensure that the right
patients are enrolled. Smart algorithms also support
at-scale data analysis during clinical trials to identify
patterns or correlations. This can help researchers
better understand the effects of a new treatment.
$14.6 billion
the state of the AI in healthcare market in 2023.
$102.7 billion
the state of the AI in healthcare market by 2028.
the growth rate of the market
with the forecast period.
MarketsAndMarkets
47.6%
AI application matrix in banking and finance
• Chatbots and virtual assistants
• Risk management compliance and security
• Personalized offers and customer retention
Consumer benefits:
Chatbots and virtual assistants powered by artificial
intelligence provide instant answers and tailored
advice to customers round-the-clock. This empowers
consumers to make more informed financial decisions
and get their issues resolved faster. Moreover, AI
algorithms ensure higher security by detecting
anomalies in transaction data.

11
Industry gains:
By implementing AI-enabled tools into their workflows,
banks shorten support wait times, ease the strain on
human workers, and scale up-selling and cross-selling
activities. Using a smart decision management system
helps financial services companies to prevent fraud
and ensure compliance with relevant regulations. The
speed of AI-supported analysis also allows banks to
improve the accuracy and efficiency of KYC processes.
Market drivers:
• Rising demand for personalized financial services
• Growing adoption of smart technologies among
leading financial institutions
• The growing availability and volume of data
• Skill gap and workforce adaptation
• Security standards and regulatory requirements
• A weak core technology and data backbone
Tools to detect and prevent fraudulent transactions
Super apps with built-in digital identity, instant
payment, and data-driven capabilities
High-potential use case: Chatbots and
virtual assistants
Virtual assistants and chatbots offer 24/7 assistance to
customers, guiding them through simple transactions
and helping them resolve basic issues. By automating
these routine tasks, banks can free up their customer
service representatives to focus on more complex
inquiries, effectively reducing customer wait times.
Also, by analyzing historical customer data, a virtual
assistant offers personalized budgeting or savings
advice to a customer. This helps banks and finance
service companies build stronger relationships with
their customers.
$1 trillion
the potential annual value of AI and analytics for
global banking.
$64 billion
the value of AI in banking and finance by 2030.
86%
the number of financial services AI adopters
that think of artificial intelligence as a core
success factor for their businesses.
Deloitte
Allied Market Research
McKinsey

12
AI application matrix in manufacturing
• Predictive maintenance based on sensor data
analysis
• Inventory management and forecasting
• Process optimization based on smart automation
and analytics
Consumer benefits:
Through intelligent inventory management and order
processing systems, manufacturers can calculate with
near-100% certainty when orders can be shipped and
when they will arrive at their customers’ warehouses.
Real-time visibility into equipment performance
allows manufacturers to improve product quality and
minimize the number of faulty products.
Industry gains:
By identifying and addressing issues early on,
manufacturers reduce the number of defects in
products, thus saving costs associated with recalls and
returns. Through predictive maintenance, companies
can increase production line availability, reduce
maintenance costs, and prevent unplanned downtime.
Market drivers:
• More complex decision-making processes due to
the surge in digital information
• The need to optimize sustainability efforts in
manufacturing
• Disruption in supply chains
• Inability to pivot legacy applications and
technology infrastructure
• Lack of interoperability
• Lack of universal industrial data
Quality control with artificial intelligence
Product conceptualization assisted by generative AI
High-potential use case: Predictive
maintenance based on sensor data analysis
Equipped with IoT, data analytics, and machine
learning, companies can squeeze maximum
intelligence from their sensor data to make data-driven
decisions and optimize their maintenance strategies.
Predictive maintenance aims to identify early warning
signs or patterns in the data that indicate a potential
issue with the equipment. By detecting these patterns,
companies can schedule maintenance or repairs
before a breakdown occurs, minimizing downtime and
reducing costs associated with emergency repairs.
$16.3 billion
the value of the AI in manufacturing market by
2027. Market and Markets
improvement in industrial forecasting,
driven by AI implementation
McKinsey
85%
the percentage of industrial
manufacturing business leaders that
made AI fully functional at scale within
their organization.
KPMG
49%

13
AI application matrix in retail
• Supply chain planning
• Customer support (chatbots, AI shopping
assistants)
• Personalized shopping experience based on
generative AI
Consumer benefits:
For customers, AI-based improvements result in
reduced shopping time and higher satisfaction thanks
to personalized offerings tailored to their preferences.
Also, customers can enjoy round-the-clock services
as chatbots and shopping assistants can address their
queries 24/7. Through accurate demand prediction,
retailers can provide instant or same-day delivery.
Industry gains:
Smart algorithms can identify patterns and trends,
enabling retailers to make data-driven decisions and
tailor their offerings to meet customer demands. This
can lead to more granular offering, better inventory
management, and improved supply chain efficiency.
Market drivers:
• Evolving customer demands resulting from the
availability of personalized and/or higher-quality
AI-enhanced products and services.
• A growing number of distribution channels
• The need for supply chain optimization
• Insufficient quality, volume, and accuracy of retail
data and lack of tracking or data analytics
• Concerns about customer data
• Lack of skilled specialists
Product and service recommendations for customers
based on their purchase behavior
Avatar-based online shopping experience
High-potential use case: Personalized
shopping experience based on generative AI
Generative AI steps up personalization, making it
more proactive, and allows companies to anticipate
future customer behaviors and preferences. Through
generative AI applications, retailers can generate
personalized emails at scale, create smarter marketing
journeys, and provide more personalized shopping
experiences for customers.
$100 billion
the value of the AI in retail market by 2032.
GMI Insights
the percentage of retail executives
who saw increased revenue
streams after adopting AI.
Statista
73%
$404 billion
the potential productivity lift from bringing
generative AI into customer operations.
McKinsey

14
REALIZING THE POTENTIAL:
how to make AI work for your business
The impact of enterprise AI adoption can vary,
depending on how well companies assess their AI
readiness before investing in the project. To evaluate
the degree of a company's readiness, decision-makers
should calculate their AI Readiness Index that depends
on the organizational structure, business strategy, IT
infrastructure, and data.
Moreover, AIRI rests on nine dimensions, as shown
in the infographic below. Leveraging their enterprise
data, infrastructure, and in-house AI talent, companies
can build a strong case for value and make the most
out of their AI investment.
AI Readiness Index (AIRI):
InData Labs framework for evaluating the adoption of AI in businesses
Organizational readiness – suitable management
and governance mechanisms that will ensure the
sustainability and long-term value of AI solutions.
Business value readiness - alignment between business
and technology that maximizes the value one gets from AI.
Data readiness - availability of accurate, complete,
and uniform data within the organization; the ability to
extract and unify data from different resources.
Infrastructure readiness – a prerequisite for AI is
appropriate infrastructure and interfaces.

15
ESTIMATING AI READINESS:
questions to ask for your company
To understand where they are on an AI journey,
organizations need to see whether they have the
right elements in place across skills and resources,
infrastructure and technology, processes, and models.
While short-term gains depend on infrastructure
readiness, the overall success of AI adoption hinges
on how well the company can adapt to the technology
and how receptive it is to AI-driven transformations.
Organizational Readiness
QUESTIONS TO ASK:
✓ Does your C-suite have clear accountability for
data and AI strategy and execution?
✓ How do your organizational processes align with
the new technology?
✓ Has your organization invested in upskilling
current resources/hiring skilled resources?
✓ Does your security strategy take into account AI-
based applications?
CHALLENGES:
• Lack of in-house skills and AI expertise
• Outdated delivery frameworks that aren’t cut out
for automation
• Data governance, compliance, and risk
BEST PRACTICES:
• Bringing outside experts to implement AI-based
projects
• Adopting Agile and DevOps delivery practices to
ensure continuous development and delivery and
respond to unclear requirements and outcomes
• Developing standardized data management
practices
• Developing a comprehensive AI adoption strategy
or turning to AI providers to get it worked out

16
Business Value Readiness
QUESTIONS TO ASK:
✓ How does your company see the potential value
of AI projects for your business?
✓ Have you defined and prioritized business cases
for AI adoption?
✓ Have you identified clear, cost criteria for what
constitutes the success of smart application
adoption?
CHALLENGES:
• Inability to define AI business use cases with
measurable value
• Inability to calculate TCO, performance, and ROI
for the project
BEST PRACTICES:
• Coming with a particular scenario, problem
statement, or use case that employs AI methods
and techniques
• Calculating the impact of artificial intelligence
according to the AI maturity within a company
(TCO - for early adopters, AI performance - for
developed projects, ROI - for high performers)
• Turning to a technology partner to validate your
business case for AI and the feasibility of your
solution
Data Readiness
QUESTIONS TO ASK:
✓ Does your organization have a company-wide
data platform that consolidates your data?
✓ Does the company practice strong data
management and governance practices?
CHALLENGES:
• Inability to integrate data from diverse sources
due to siloed infrastructure
• Inability to prepare and clean data for AI
development
• Lack of self-service access to data
• Lack of the right talent and expertise to manage
the data value chain
BEST PRACTICES:
• Assessing the current data landscape
• Getting a clear understanding of the current data
platform architecture, data security, and privacy
policies in place
• Establishing consistent data management
practices to ensure quality, free-flowing data
• Transforming isolated data platforms into a single
source of truth
• Engaging data experts in building a robust data
core, ready for artificial intelligence

17
Infrastructure Readiness
QUESTIONS TO ASK:
✓ Do you have a cloud platform and technology
strategy that support your AI initiatives?
✓ Do you have the resources, processes, and tooling
needed to develop, train, and operate machine
learning models?
CHALLENGES:
• Lack of interoperability between AI technologies
and a legacy infrastructure
• On-premise, bulky systems
• Lack of the right talent and expertise to transform
an organization’s IT infrastructure
BEST PRACTICES:
• Migrating to the cloud to build a flexible, scalable,
and cost-effective infrastructure ready for artificial
intelligence
• Adopting the MLOps approach to automate
and gain visibility into all steps of ML system
development, including integration, testing,
releasing, deployment, and infrastructure
management.

18
Organizations continue to gain competency in AI as
the market matures rapidly. Full-scale deployment
of AI technologies is increasing across the board,
with high-outcome organizations reporting revenue-
generating results, such as new market entries and
product innovations.
To maximize the potential of artificial intelligence and
enable AI-driven intelligence across organizations,
companies must invest in organizational, foundational,
and technological aspects of AI adoption. Equipped
with business value-driven use cases, talents and
expertise, and the right IT enablers, companies can
shift to adaptive technology and operating models
that promote the long-term value of AI investment and
innovation agility.
THE RECOVERY WILL BE AI-DRIVEN
All over the world, business leaders believe AI is
critical to success over the next five years.
Economic headwinds seem to be gathering for global
companies in general and for technology investment
specifically. However, artificial intelligence seems to
be one of the technology trends that didn’t drop the
adoption pace. And with multiple regulatory incentives,
AI innovation is poised to grow in 2023 and beyond.

indatalabs.com
Since 2014, InData Labs has been helping global
companies leverage the power of AI and Data Analytics
to achieve business outcomes. As a leading AI
technology partner, InData Labs handles the full-cycle
process of digital transformation, including consulting,
design, implementation, and maintenance.

With its proficiency in artificial intelligence, generative
AI, cloud development, and analytics, InData Labs has
helped over 150 clients from the USA, UK, EU, and
other countries bring their projects across the goal line
and make sense of the trending technologies. As a
recognized leader, InData Labs is listed among the top
Data Science and Machine Learning partners and AI
service providers.
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The State of Global AI Adoption in 2023

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