beyond-the-hype-capturing-the-potential-of-ai-and-gen-ai-in-tmt.pdf

February 2024
Beyond the hype:
Capturing the
potential of AI and
gen AI in TMT

Beyond the hype: Capturing
the potential of AI and gen
AI in TMT
February 2024

Contents
Introduction: The promise and the challenge of generative AI 2
State of the Art 4
The economic potential of generative AI 5
Making the most of the generative AI opportunity: Six questions for CEOs 33
Sector View: Telecom Operators 38
The AI-native telco: Radical transformation to thrive in turbulent times 39
How generative AI could revitalize profitability for telcos 48
Generative AI use cases: A guide to developing the telco of the future 60
Tech talent in transition: Seven technology trends reshaping telcos 70
Deploying Gen AI 81
The organization of the future: Enabled by gen AI, driven by people 82
The data dividend: Fueling generative AI 91
Technology’s generational moment with generative AI: A CIO and CTO guide 101
As gen AI advances, regulators—and risk functions—rush to keep pace 113
What the Future Holds 119
Six major gen AI trends that will shape 2024’s agenda 120
Appendix: Generative AI solutions in action 125
Glossary 127
Beyond the hype: Capturing the potential of AI and gen AI in TMT 1

Introduction: The promise and
the challenge of generative AI
The emergence of generative AI (gen AI) presents both a challenge and a significant opportunity for leaders looking
to steer their organizations into the future. How big is the opportunity? McKinsey research estimates that gen AI
could add to the economy between $2.6 trillion and $4.4 trillion annually while increasing the impact of all artificial
intelligence by 15 to 40 percent. In the technology, media, and telecommunications (TMT) space, new gen AI use
cases are expected to unleash between $380 billion and $690 billion in impact—$60 billion to $100 billion in
telecommunications, $80 billion to $130 billion in media, and about $240 billion to $460 billion in high tech. In
fact, it seems possible that within the next three years, anything not connected to AI will be considered obsolete or
ineffective.
Some leaders are moving to seize the moment and implement gen AI in their organizations at scale, but others remain
in the pilot stage, and some have yet to decide what to do. If companies are to remain competitive and relevant in the
coming years, it is essential that executives understand the potential impact of gen AI and develop the strategies
necessary to incorporate it into their operations. Such strategies would involve an AI-native transformation, focused
on building and managing the adoption of gen AI. McKinsey has conducted extensive research into how to embed
gen AI to ensure that the technology delivers meaningful value. We’ve also spent much of the past year working with
clients to create and then implement gen AI road maps. That combination of research and hands-on experience has
allowed us to identify more than 100 gen AI use cases in TMT across seven business domains.1
Our experience working with clients already indicates the potential for telcos to achieve significant impact with
gen AI across all key functions. The largest share of total impact will likely be in customer care and sales, which
together would account for approximately 70 percent of total impact; network operations, IT, and support functions
would round out the rest. The technology already is showing meaningful impact in enhancing interactions between
employees and customers: the personalization of products and campaigns, improvements in sales effectiveness, and
a reduction in time to market can spark a potential revenue increase of 3 to 5 percent. Customer care interactions—
where as much as 50 percent of activity could be automated—have potential for a 30 to 45 percent increase in
productivity while improving the customer experience and customer satisfaction scores. On the labor side, up to 70
percent of repetitive work activities could be automated via gen AI to improve productivity. There is also potential for
new efficiencies in knowledge search, validation, and synthesis, where some 60 percent of activity has the potential
for automation. And gen AI tools could boost developer productivity by 20 to 45 percent.
These areas provide rich soil for use cases. More challenging will be to go from sketching a road map to building
proofs of concept to scaling successfully and capturing impact. Years of experience in designing and implementing
digital transformations have taught us a lot, but gen AI’s nature and speed of disruption are creating a new layer of
uncertainty.
Becoming an AI-native organization at scale involves making the most of technology, data, and governance. Success
follows when leaders embrace an operating model that leverages the strengths of both humans and machines; is
rooted in agility, flexibility, and continuous learning; and is supported by strong data and analytics talent. Another
condition of success is to invest in data quality and quantity, focusing on the data life cycle to ensure high-quality
information for training the gen AI model. Building capabilities into the data architecture, such as vector databases
and data pre- and post-processing pipelines, will enable the development of use cases. Talent, data, technology,
governance—none of these can be an afterthought.
¹ Marketing and digital, sales and channels, customer care, customer strategy, support, additional areas, and new businesses.

Successful implementations share a clear vision and decisive approach. We advise that financial plans maintain or
increase gen AI budgets over the next year. These budgets should include resources dedicated to gen AI for the shaping
and crafting of bespoke solutions (for example, training large language models with telco-specific data, rather than
implementing off-the-shelf ones) or partnerships with IT vendors to accelerate the timeline for implementation.
The AI journey has been shown to contain many challenges and learning opportunities, such as preparing and shifting
an organization’s culture, finding data sets of significant size, and addressing the interpretability of the outputs provided
by models. Leaders should expect such daunting challenges as a shortage of talent, lack of organizational commitment
and prioritization (including among C-level executives), and difficulties in justifying ROI for certain business cases, all
amid a changing regulatory and ethics landscape that creates further uncertainty. But daunting does not have to mean
impossible. Developing a system of protocols and guardrails (such as building “moderation” models to check outputs
for different risks and ensure users receive consistent responses) will be a crucial step toward mitigating the new risks
introduced by gen AI. Another key will be change management—involving end users in the model development process
and deeply embedding technology into their operations.
This collection presents McKinsey’s top insights on gen AI, providing a detailed examination of this technology’s
transformative potential for organizations. It offers top management guidance on how to prepare for the implementation
of gen AI and explores the implications of gen AI’s use by the TMT industries, especially telecommunications. The
collection covers the essential requirements for deploying gen AI, including organizational readiness, data management,
and technological considerations. It also emphasizes the importance of effectively managing risks associated with gen
AI implementation. Furthermore, this compilation offers an overview of the future developments and advancements
expected in the field of generative AI.
Gen AI will continue to evolve. New capabilities, such as the ability to analyze and comprehend images or audio, and an
expanding ecosystem with marketplaces for GPT (generative pretrained transformers), are constantly emerging. For
leaders, the stakes are high. But so are the opportunities. The next move from TMT players will define how they move
from isolated cases to implementations at scale, from hype to impact.
Alex Singla
Senior Partner
Managing Partner
QuantumBlack
AI by McKinsey
Alexander Sukharevsky
Senior Partner
Managing Partner
QuantumBlack
AI by McKinsey
Brendan Gaffey
Senior Partner
Global Leader
TMT Practice
Noshir Kaka
Senior Partner
Global Leader
TMT Practice
Peter Dahlström
Senior Partner
Europe Leader
TMT Practice
Andrea Travasoni
Senior Partner
Global Leader
Telecom Operators
TMT Practice
Venkat Atluri
Senior Partner
Global Leader
Telecom Operators
TMT Practice
Tomás Lajous
Senior Partner
AI and Gen AI Leader
TMT Practice
Benjamim Vieira
Senior Partner
Digital and Analytics Leader
TMT Practice
Víctor García de la Torre
Associate Partner
TMT Practice

State of
the art
1

June 2023
The economic
potential of
generative AI
The next productivity frontier
Authors
Michael Chui
Eric Hazan
Roger Roberts
Alex Singla
Kate Smaje
Alexander Sukharevsky
Lareina Yee
Rodney Zemmel

Generative AI as a
technology catalyst
To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise
of generative AI, which were decades in the making. ChatGPT, GitHub Copilot, Stable Diffusion, and
other generative AI tools that have captured current public attention are the result of significant levels
of investment in recent years that have helped advance machine learning and deep learning. This
investment undergirds the AI applications embedded in many of the products and services we use
every day.
But because AI has permeated our lives incrementally—through everything from the tech powering
our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and
delight consumers—its progress was almost imperceptible. Clear milestones, such as when AlphaGo,
an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were
celebrated but then quickly faded from the public’s consciousness.
ChatGPT and its competitors have captured the imagination of people around the world in a way
AlphaGo did not, thanks to their broad utility—almost anyone can use them to communicate and
create—and preternatural ability to have a conversation with a user. The latest generative AI
applications can perform a range of routine tasks, such as the reorganization and classification
1
The economic potential of generative AI: The next productivity frontier 6
This article is excerpted from the full McKinsey report, The economic potential of generative AI: The
next productivity frontier. To read the full report, including details about the research, appendix, and
acknowledgements, visit mck.co/genai.

of data. But it is their ability to write text, compose music, and create digital art that has garnered
headlines and persuaded consumers and households to experiment on their own. As a result, a
broader set of stakeholders are grappling with generative AI’s impact on business and society but
without much context to help them make sense of it.
How did we get here? Gradually, then all of a sudden
For the purposes of this report, we define generative AI as applications typically built using foundation
models. These models contain expansive artificial neural networks inspired by the billions of neurons
connected in the human brain. Foundation models are part of what is called deep learning, a term
that alludes to the many deep layers within neural networks. Deep learning has powered many of
the recent advances in AI, but the foundation models powering generative AI applications are a step
change evolution within deep learning. Unlike previous deep learning models, they can process
extremely large and varied sets of unstructured data and perform more than one task.
Foundation models have enabled new capabilities and vastly improved existing ones across a broad
range of modalities, including images, video, audio, and computer code. AI trained on these models
can perform several functions; it can classify, edit, summarize, answer questions, and draft new
content, among other tasks.
Continued innovation will also bring new challenges. For example, the computational power required
to train generative AI with hundreds of billions of parameters threatens to become a bottleneck in
development.¹ Further, there’s a significant move—spearheaded by the open-source community and
spreading to the leaders of generative AI companies themselves—to make AI more responsible, which
could increase its costs.
Nonetheless, funding for generative AI, though still a fraction of total investments in artificial
intelligence, is significant and growing rapidly—reaching a total of $12 billion in the first five months
of 2023 alone. Venture capital and other private external investments in generative AI increased by
an average compound growth rate of 74 percent annually from 2017 to 2022. During the same period,
investments in artificial intelligence overall rose annually by 29 percent, albeit from a higher base.
The rush to throw money at all things generative AI reflects how quickly its capabilities have
developed. ChatGPT was released in November 2022. Four months later, OpenAI released a new
large language model, or LLM, called GPT-4 with markedly improved capabilities.² Similarly, by May
2023, Anthropic’s generative AI, Claude, was able to process 100,000 tokens of text, equal to about
75,000 words in a minute—the length of the average novel—compared with roughly 9,000 tokens
when it was introduced in March 2023.³ And in May 2023, Google announced several new features
powered by generative AI, including Search Generative Experience and a new LLM called PaLM 2 that
will power its Bard chatbot, among other Google products.⁴
From a geographic perspective, external private investment in generative AI, mostly from tech
giants and venture capital firms, is largely concentrated in North America, reflecting the continent’s
current domination of the overall AI investment landscape. Generative AI–related companies based
in the United States raised about $8 billion from 2020 to 2022, accounting for 75 percent of total
investments in such companies during that period.⁵
Generative AI has stunned and excited the world with its potential for reshaping how knowledge work
gets done in industries and business functions across the entire economy. Across functions such
as sales and marketing, customer operations, and software development, it is poised to transform
roles and boost performance. In the process, it could unlock trillions of dollars in value across sectors
from banking to life sciences. We have used two overlapping lenses in this report to understand

the potential for generative AI to create value for companies and alter the workforce. The
following sections share our initial findings.
Generative AI use
cases across functions
and industries
2

Generative AI is a step change in the evolution of artificial intelligence. As companies
rush to adapt and implement it, understanding the technology’s potential to deliver value
to the economy and society at large will help shape critical decisions. We have used two
complementary lenses to determine where generative AI with its current capabilities could
deliver the biggest value and how big that value could be (Exhibit 1).
The first lens scans use cases for generative AI that organizations could adopt. We define
a “use case” as a targeted application of generative AI to a specific business challenge,
resulting in one or more measurable outcomes. For example, a use case in marketing is the
application of generative AI to generate creative content such as personalized emails, the
measurable outcomes of which potentially include reductions in the cost of generating such
content and increases in revenue from the enhanced effectiveness of higher-quality content
at scale. We identified 63 generative AI use cases spanning 16 business functions that could
deliver total value in the range of $2.6 trillion to $4.4 trillion in economic benefits annually
when applied across industries.
That would add 15 to 40 percent to the $11.0 trillion to $17.7 trillion of economic value that we
now estimate nongenerative artificial intelligence and analytics could unlock. (Our previous
estimate from 2017 was that AI could deliver $9.5 trillion to $15.4 trillion in economic value.)
Our second lens complements the first by analyzing generative AI’s potential impact on
the work activities required in some 850 occupations. We modeled scenarios to estimate
when generative AI could perform each of more than 2,100 “detailed work activities”—
Exhibit 1
The potential impact of generative AI can be evaluated through two lenses.
McKinsey & Company
Lens 1
Total economic
potential of 60-plus
organizational use
cases1
1
For quantitative analysis, revenue impacts were recast as productivity increases on the corresponding spend in order to maintain comparability with cost
impacts and not to assume additional growth in any particular market.
Revenue
impacts of
use cases1
Cost impacts
of use cases
Lens 2
Labor productivity potential
across ~2,100 detailed work
activities performed by
global workforce

such as “communicating with others about operational plans or activities”—that make up
those occupations across the world economy. This enables us to estimate how the current
capabilities of generative AI could affect labor productivity across all work currently done by
the global workforce.
Some of this impact will overlap with cost reductions in the use case analysis described
above, which we assume are the result of improved labor productivity. Netting out this
Exhibit 2
Generative AI could create additional value potential above what
could be unlocked by other AI and analytics.
McKinsey & Company
AI’s potential impact on the global economy, $ trillion
1
Updated use case estimates from "Notes from the AI frontier: Applications and value of deep learning,” McKinsey Global Institute, April 17, 2018.
Advanced analytics,
traditional machine
learning, and deep
learning1
New generative
AI use cases
Total use
case-driven
potential
All worker productivity
enabled by generative
AI, including in use
cases
Total AI
economic
potential
11.0–17.7
13.6–22.1
17.1–25.6
2.6–4.4
6.1–7.9
~15–40%
incremental
economic impact
~35–70%
incremental
economic impact

overlap, the total economic benefits of generative AI—including the major use cases we
explored and the myriad increases in productivity that are likely to materialize when the
technology is applied across knowledge workers’ activities—amounts to $6.1 trillion to
$7.9 trillion annually (Exhibit 2).
While generative AI is an exciting and rapidly advancing technology, the other applications of
AI discussed in our previous report continue to account for the majority of the overall potential
value of AI. Traditional advanced-analytics and machine learning algorithms are highly
effective at performing numerical and optimization tasks such as predictive modeling, and
they continue to find new applications in a wide range of industries. However, as generative AI
continues to develop and mature, it has the potential to open wholly new frontiers in creativity
and innovation. It has already expanded the possibilities of what AI overall can achieve (see
Box 1, “How we estimated the value potential of generative AI use cases”).
In this chapter, we highlight the value potential of generative AI across two dimensions:
business function and modality.
Box 1
1
“Notes from the AI frontier: Applications and value of deep learning,” McKinsey Global Institute, April 17, 2018.
How we estimated the value potential of generative AI use cases
To assess the potential value of generative AI,
we updated a proprietary McKinsey database of
potential AI use cases and drew on the experience
of more than 100 experts in industries and their
business functions.1
Our updates examined
use cases of generative AI—specifically, how
generative AI techniques (primarily transformer-
based neural networks) can be used to solve
problems not well addressed by previous
technologies.
We analyzed only use cases for which generative
AI could deliver a significant improvement in the
outputs that drive key value. In particular, our
estimates of the primary value the technology
could unlock do not include use cases for which
the sole benefit would be its ability to use natural
language. For example, natural-language
capabilities would be the key driver of value in
a customer service use case but not in a use
case optimizing a logistics network, where value
primarily arises from quantitative analysis.
We then estimated the potential annual value
of these generative AI use cases if they were
adopted across the entire economy. For use
cases aimed at increasing revenue, such as some
of those in sales and marketing, we estimated
the economy-wide value generative AI could
deliver by increasing the productivity of sales and
marketing expenditures.
Our estimates are based on the structure of the
global economy in 2022 and do not consider the
value generative AI could create if it produced
entirely new product or service categories.

Value potential by function
While generative AI could have an impact on most business functions, a few stand out when
measured by the technology’s impact as a share of functional cost (Exhibit 3). Our analysis
of 16 business functions identified just four—customer operations, marketing and sales,
software engineering, and research and development—that could account for approximately
75 percent of the total annual value from generative AI use cases.
Notably, the potential value of using generative AI for several functions that were prominent
in our previous sizing of AI use cases, including manufacturing and supply chain functions,
is now much lower.⁶ This is largely explained by the nature of generative AI use cases, which
exclude most of the numerical and optimization applications that were the main value drivers
for previous applications of AI.
Exhibit 3
Web <2023>
<Vivatech full report>
Exhibit <3> of <16>
Using generative AI in just a few functions could drive most of the technology’s
impact across potential corporate use cases.
McKinsey & Company
Note: Impact is averaged.
¹Excluding software engineering.
Source: Comparative Industry Service (CIS), IHS Markit; Oxford Economics; McKinsey Corporate and Business Functions database; McKinsey Manufacturing
and Supply Chain 360; McKinsey Sales Navigator; Ignite, a McKinsey database; McKinsey analysis
Impact as a percentage of functional spend, %
Impact, $ billion
Marketing
Sales
Pricing
Customer operations
Corporate IT1
Product R&D1
Software engineering
(for corporate IT)
(for product development)
Supply chain
Procurement management
Manufacturing
Legal
Risk and compliance
Strategy
Finance
Talent and organization (incl HR)
0 10 20 30 40
0
100
200
300
400
500
Represent ~75% of total annual impact of generative AI

Generative AI as a virtual expert
In addition to the potential value generative AI can deliver in function-specific use cases,
the technology could drive value across an entire organization by revolutionizing internal
knowledge management systems. Generative AI’s impressive command of natural-language
processing can help employees retrieve stored internal knowledge by formulating queries
in the same way they might ask a human a question and engage in continuing dialogue. This
could empower teams to quickly access relevant information, enabling them to rapidly make
better-informed decisions and develop effective strategies.
In 2012, the McKinsey Global Institute (MGI) estimated that knowledge workers spent about
a fifth of their time, or one day each workweek, searching for and gathering information. If
generative AI could take on such tasks, increasing the efficiency and effectiveness of the
workers doing them, the benefits would be huge. Such virtual expertise could rapidly “read”
vast libraries of corporate information stored in natural language and quickly scan source
material in dialogue with a human who helps fine-tune and tailor its research, a more scalable
solution than hiring a team of human experts for the task.
Following are examples of how generative AI could produce operational benefits as a virtual
expert in a handful of use cases.
In addition to the potential
value generative AI can
deliver in function-specific
use cases, the technology
could drive value across
an entire organization
by revolutionizing
internal knowledge
management systems.

Customer operations
Generative AI has the potential to revolutionize the entire customer operations function,
improving the customer experience and agent productivity through digital self-service
and enhancing and augmenting agent skills. The technology has already gained traction
in customer service because of its ability to automate interactions with customers using
natural language. Research found that at one company with 5,000 customer service
agents, the application of generative AI increased issue resolution by 14 percent an hour and
reduced the time spent handling an issue by 9 percent.⁷ It also reduced agent attrition and
requests to speak to a manager by 25 percent. Crucially, productivity and quality of service
improved most among less-experienced agents, while the AI assistant did not increase—
and sometimes decreased—the productivity and quality metrics of more highly skilled
agents. This is because AI assistance helped less-experienced agents communicate using
techniques similar to those of their higher-skilled counterparts.
The following are examples of the operational improvements generative AI can have for
specific use cases:
— Customer self-service. Generative AI–fueled chatbots can give immediate and
personalized responses to complex customer inquiries regardless of the language or
location of the customer. By improving the quality and effectiveness of interactions via
automated channels, generative AI could automate responses to a higher percentage of
customer inquiries, enabling customer care teams to take on inquiries that can only be
resolved by a human agent. Our research found that roughly half of customer contacts
made by banking, telecommunications, and utilities companies in North America are
already handled by machines, including but not exclusively AI. We estimate that generative
AI could further reduce the volume of human-serviced contacts by up to 50 percent,
depending on a company’s existing level of automation.
— Resolution during initial contact. Generative AI can instantly retrieve data a company
has on a specific customer, which can help a human customer service representative more
successfully answer questions and resolve issues during an initial interaction.
— Reduced response time. Generative AI can cut the time a human sales representative
spends responding to a customer by providing assistance in real time and recommending
next steps.
— Increased sales. Because of its ability to rapidly process data on customers and their
browsing histories, the technology can identify product suggestions and deals tailored
to customer preferences. Additionally, generative AI can enhance quality assurance and
coaching by gathering insights from customer conversations, determining what could be
done better, and coaching agents.
We estimate that applying generative AI to customer care functions could increase
productivity at a value ranging from 30 to 45 percent of current function costs.
Our analysis captures only the direct impact generative AI might have on the productivity of
customer operations. It does not account for potential knock-on effects the technology may
have on customer satisfaction and retention arising from an improved experience, including
better understanding of the customer’s context that can assist human agents in providing
more personalized help and recommendations.

Marketing and sales
Generative AI has taken hold rapidly in marketing and sales functions, in which text-based
communications and personalization at scale are driving forces. The technology can create
personalized messages tailored to individual customer interests, preferences, and behaviors,
as well as do tasks such as producing first drafts of brand advertising, headlines, slogans,
social media posts, and product descriptions.
However, introducing generative AI to marketing functions requires careful consideration.
For one thing, using mathematical models trained on publicly available data without
sufficient safeguards against plagiarism, copyright violations, and branding recognition risks
infringing on intellectual property rights. A virtual try-on application may produce biased
representations of certain demographics because of limited or biased training data. Thus,
significant human oversight is required for conceptual and strategic thinking specific to each
company’s needs.
Potential operational benefits from using generative AI for marketing include the following:
— Efficient and effective content creation. Generative AI could significantly reduce the
time required for ideation and content drafting, saving valuable time and effort. It can also
facilitate consistency across different pieces of content, ensuring a uniform brand voice,
writing style, and format. Team members can collaborate via generative AI, which can
integrate their ideas into a single cohesive piece. This would allow teams to significantly
enhance personalization of marketing messages aimed at different customer segments,
geographies, and demographics. Mass email campaigns can be instantly translated into
as many languages as needed, with different imagery and messaging depending on the
audience. Generative AI’s ability to produce content with varying specifications could
increase customer value, attraction, conversion, and retention over a lifetime and at a
scale beyond what is currently possible through traditional techniques.
— Enhanced use of data. Generative AI could help marketing functions overcome the
challenges of unstructured, inconsistent, and disconnected data—for example, from
different databases—by interpreting abstract data sources such as text, image, and
varying structures. It can help marketers better use data such as territory performance,
synthesized customer feedback, and customer behavior to generate data-informed
marketing strategies such as targeted customer profiles and channel recommendations.
Such tools could identify and synthesize trends, key drivers, and market and product
opportunities from unstructured data such as social media, news, academic research, and
customer feedback.
— SEO optimization. Generative AI can help marketers achieve higher conversion and
lower cost through search engine optimization (SEO) for marketing and sales technical
components such as page titles, image tags, and URLs. It can synthesize key SEO tokens,
support specialists in SEO digital content creation, and distribute targeted content to
customers.
— Product discovery and search personalization. With generative AI, product discovery
and search can be personalized with multimodal inputs from text, images and speech, and
deep understanding of customer profiles. For example, technology can leverage individual
user preferences, behavior, and purchase history to help customers discover the most

Generative AI could also change the way both B2B and B2C companies approach sales. The
following are two use cases for sales:
— Increase probability of sale. Generative AI could identify and prioritize sales leads
by creating comprehensive consumer profiles from structured and unstructured data
and suggesting actions to staff to improve client engagement at every point of contact.
For example, generative AI could provide better information about client preferences,
potentially improving close rates.
— Improve lead development. Generative AI could help sales representatives nurture leads
by synthesizing relevant product sales information and customer profiles and creating
discussion scripts to facilitate customer conversation, including up- and cross-selling
talking points. It could also automate sales follow-ups and passively nurture leads until
clients are ready for direct interaction with a human sales agent.
Our analysis suggests that implementing generative AI could increase sales productivity by
approximately 3 to 5 percent of current global sales expenditures.
This analysis may not fully account for additional revenue that generative AI could bring
to sales functions. For instance, generative AI’s ability to identify leads and follow-up
capabilities could uncover new leads and facilitate more effective outreach that would bring
in additional revenue. Also, the time saved by sales representatives due to generative AI’s
capabilities could be invested in higher-quality customer interactions, resulting in increased
sales success.
Generative AI as a virtual collaborator
In other cases, generative AI can drive value by working in partnership with workers,
augmenting their work in ways that accelerate their productivity. Its ability to rapidly digest
mountains of data and draw conclusions from it enables the technology to offer insights and
options that can dramatically enhance knowledge work. This can significantly speed up the
process of developing a product and allow employees to devote more time to higher-impact
tasks.
relevant products and generate personalized product descriptions. This would allow
CPG, travel, and retail companies to improve their e-commerce sales by achieving higher
website conversion rates.
We estimate that generative AI could increase the productivity of the marketing function with
a value between 5 and 15 percent of total marketing spending.
Our analysis of the potential use of generative AI in marketing doesn’t account for knock-on
effects beyond the direct impacts on productivity. Generative AI–enabled synthesis could
provide higher-quality data insights, leading to new ideas for marketing campaigns and
better-targeted customer segments. Marketing functions could shift resources to producing
higher-quality content for owned channels, potentially reducing spending on external
channels and agencies.

Generative AI could increase
sales productivity by 3 to
5 percent of current global
sales expenditures.
Treating computer languages as just another language opens new possibilities for software
engineering. Software engineers can use generative AI in pair programming and to do
augmented coding and train LLMs to develop applications that generate code when given a
natural-language prompt describing what that code should do.
Software engineering is a significant function in most companies, and it continues to grow
as all large companies, not just tech titans, embed software in a wide array of products and
services. For example, much of the value of new vehicles comes from digital features such as
adaptive cruise control, parking assistance, and IoT connectivity.
According to our analysis, the direct impact of AI on the productivity of software engineering
could range from 20 to 45 percent of current annual spending on the function. This value
would arise primarily from reducing time spent on certain activities, such as generating initial
code drafts, code correction and refactoring, root-cause analysis, and generating new system
designs. By accelerating the coding process, generative AI could push the skill sets and
capabilities needed in software engineering toward code and architecture design. One study
found that software developers using Microsoft’s GitHub Copilot completed tasks 56 percent
faster than those not using the tool.⁸ An internal McKinsey empirical study of software
engineering teams found those who were trained to use generative AI tools rapidly reduced
the time needed to generate and refactor code—and engineers also reported a better work
experience, citing improvements in happiness, flow, and fulfillment.
Our analysis did not account for the increase in application quality and the resulting boost in
productivity that generative AI could bring by improving code or enhancing IT architecture—
which can improve productivity across the IT value chain. However, the quality of IT
architecture still largely depends on software architects, rather than on initial drafts that
generative AI’s current capabilities allow it to produce.
Large technology companies are already selling generative AI for software engineering,
including GitHub Copilot, which is now integrated with OpenAI’s GPT-4, and Replit, used by
more than 20 million coders.⁹

While other generative design techniques have already unlocked some of the potential to apply AI
in R&D, their cost and data requirements, such as the use of “traditional” machine learning, can limit
their application. Pretrained foundation models that underpin generative AI, or models that have
been enhanced with fine-tuning, have much broader areas of application than models optimized for
a single task. They can therefore accelerate time to market and broaden the types of products to
which generative design can be applied. For now, however, foundation models lack the capabilities
to help design products across all industries.
In addition to the productivity gains that result from being able to quickly produce candidate
designs, generative design can also enable improvements in the designs themselves, as in the
following examples of the operational improvements generative AI could bring:
— Enhanced design. Generative AI can help product designers reduce costs by selecting and
using materials more efficiently. It can also optimize designs for manufacturing, which can lead to
cost reductions in logistics and production.
— Improved product testing and quality. Using generative AI in generative design can produce
a higher-quality product, resulting in increased attractiveness and market appeal. Generative
AI can help to reduce testing time of complex systems and accelerate trial phases involving
customer testing through its ability to draft scenarios and profile testing candidates.
We also identified a new R&D use case for nongenerative AI: deep learning surrogates, the use
of which has grown since our earlier research, can be paired with generative AI to produce even
greater benefits (see Box 2, “Deep learning surrogates”). To be sure, integration will require the
development of specific solutions, but the value could be significant because deep learning
surrogates have the potential to accelerate the testing of designs proposed by generative AI.
While we have estimated the potential direct impacts of generative AI on the R&D function, we
did not attempt to estimate the technology’s potential to create entirely novel product categories.
These are the types of innovations that can produce step changes not only in the performance of
individual companies but in economic growth overall.
Value potential by modality
Technology has revolutionized the way we conduct business, and text-based AI is on the frontier
of this change. Indeed, text-based data is plentiful, accessible, and easily processed and analyzed
at large scale by LLMs, which has prompted a strong emphasis on them in the initial stages of
generative AI development. The current investment landscape in generative AI is also heavily
focused on text-based applications such as chatbots, virtual assistants, and language translation.
However, we estimate that almost one-fifth of the value that generative AI can unlock across our use
cases would take advantage of multimodal capabilities beyond text to text.
Product R&D
Generative AI’s potential in R&D is perhaps less well recognized than its potential in other business
functions. Still, our research indicates the technology could deliver productivity with a value ranging
from 10 to 15 percent of overall R&D costs.
For example, the life sciences and chemical industries have begun using generative AI foundation
models in their R&D for what is known as generative design. Foundation models can generate
candidate molecules, accelerating the process of developing new drugs and materials. Entos, a
biotech pharmaceutical company, has paired generative AI with automated synthetic development
tools to design small-molecule therapeutics. But the same principles can be applied to the design of
many other products, including larger-scale physical products and electrical circuits, among others.

Box 2
Deep learning surrogates
Product design in industries producing
physical products often involves physics-
based virtual simulations such as
computational fluid dynamics (CFD) and
finite element analysis (FEA). Although
they are faster than actual physical
testing, these techniques can be time-
and resource-intensive, especially for
designing complex parts—running CFD
simulations on graphics processing units
can take hours. And these techniques
are even more complex and compute-
intensive when they involve simulations
coupled across multiple disciplines (for
example, physical stress and temperature
distribution), which is sometimes called
multiphysics.
Deep learning applications are now
revolutionizing the virtual testing phase of
the R&D process by using deep learning
models to emulate (multi)physics-
based simulations at higher speeds and
lower costs. Instead of taking hours
to run physics-based models, these
deep learning surrogates can produce
the results of simulations in just a few
seconds, allowing researchers to test
many more designs and enabling faster
decision making on products and designs.
While most of generative AI’s initial traction has been in text-based use cases, recent advances in
generative AI have also led to breakthroughs in image generation, as OpenAI’s DALL·E and Stable
Diffusion have so amply illustrated, and much progress is being made in audio, including voice
and music, and video. These capabilities have obvious applications in marketing for generating
advertising materials and other marketing content, and these technologies are already being applied
in media industries, including game design. Indeed, some of these examples challenge existing
business models around talent, monetization, and intellectual property.10
The multimodal capabilities of generative AI could also be used effectively in R&D. Generative AI
systems could create first drafts of circuit designs, architectural drawings, structural engineering
designs, and thermal designs based on prompts that describe requirements for a product.
Achieving this will require training foundation models in these domains (think of LLMs trained on
“design languages”). Once trained, such foundation models could increase productivity on a similar
magnitude to software development.
Value potential by industry
Across the 63 use cases we analyzed, generative AI has the potential to generate $2.6 trillion to $4.4
trillion in value across industries. Its precise impact will depend on a variety of factors, such as the
mix and importance of different functions, as well as the scale of an industry’s revenue (Exhibit 4).

Exhibit 4
Generative AI use cases will have different impacts on business functions
across industries.
McKinsey & Company
Administrative and
professional services
Advanced electronics
and semiconductors
Advanced manufacturing3
Agriculture
Banking
Basic materials
Chemical
Construction
Consumer packaged goods
Education
Energy
Healthcare
High tech
Insurance
Media and entertainment
Pharmaceuticals and
medical products
Public and social sector
Real estate
Retail4
Telecommunications
Travel, transport, and logistics
Total,
$ billion
150–250
100–170
170–290
40–70
200–340
120–200
80–140
90–150
160–270
120–230
150–240
150–260
240–460
50–70
80–130
60–110
70–110
110–180
240–390
60–100
180–300
Total, % of
industry
revenue
0.9–1.4
1.3–2.3
1.4–2.4
0.6–1.0
2.8–4.7
0.7–1.2
0.8–1.3
0.7–1.2
1.4–2.3
2.2–4.0
1.0–1.6
1.8–3.2
4.8–9.3
1.8–2.8
1.8–3.1
2.6–4.5
0.5–0.9
1.0–1.7
1.2–1.9
2.3–3.7
1.2–2.0
Generative AI productivity
impact by business functions¹
M
a
r
k
e
t
i
n
g
a
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r
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r
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r
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c
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R
&
D
S
o
f
t
w
a
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i
n
g
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p
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y
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a
i
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a
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o
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e
r
a
t
i
o
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s
k
a
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l
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r
a
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g
y
a
n
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fi
n
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c
e
C
o
r
p
o
r
a
t
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2
T
a
l
e
n
t
a
n
d
o
r
g
a
n
i
z
a
t
i
o
n
2,600–4,400
Note: Figures may not sum to 100%, because of rounding.
1
Excludes implementation costs (eg, training, licenses).
2
Excluding software engineering.
3
Includes aerospace, defense, and auto manufacturing.
4
Including auto retail.
Source: Comparative Industry Service (CIS), IHS Markit; Oxford Economics; McKinsey Corporate and Business Functions database; McKinsey Manufacturing
and Supply Chain 360; McKinsey Sales Navigator; Ignite, a McKinsey database; McKinsey analysis
760–
1,200
340–
470
230–
420
580–
1,200
290–
550
180–
260
120–
260
40–
50
60–
90
Low impact High impact

For example, our analysis estimates generative AI could contribute roughly $310 billion in
additional value for the retail industry (including auto dealerships) by boosting performance in
functions such as marketing and customer interactions. By comparison, the bulk of potential
value in high tech comes from generative AI’s ability to increase the speed and efficiency of
software development.

The generative AI future
of work: Impacts on
work activities, economic
growth, and productivity
3
Technology has been changing the anatomy of work for decades. Over the years, machines
have given human workers various “superpowers”; for instance, industrial-age machines
enabled workers to accomplish physical tasks beyond the capabilities of their own bodies.
More recently, computers have enabled knowledge workers to perform calculations that
would have taken years to do manually.
These examples illustrate how technology can augment work through the automation of
individual activities that workers would have otherwise had to do themselves. At a conceptual
level, the application of generative AI may follow the same pattern in the modern workplace,
although as we show later in this chapter, the types of activities that generative AI could
affect, and the types of occupations with activities that could change, will likely be different as
a result of this technology than for older technologies.

The McKinsey Global Institute began analyzing the impact of technological automation of
work activities and modeling scenarios of adoption in 2017. At that time, we estimated that
workers spent half of their time on activities that had the potential to be automated by adapting
technology that existed at that time, or what we call technical automation potential. We also
modeled a range of potential scenarios for the pace at which these technologies could be
adopted and affect work activities throughout the global economy.
Technology adoption at scale does not occur overnight. The potential of technological
capabilities in a lab does not necessarily mean they can be immediately integrated into a
solution that automates a specific work activity—developing such solutions takes time. Even
when such a solution is developed, it might not be economically feasible to use if its costs
exceed those of human labor. Additionally, even if economic incentives for deployment exist, it
takes time for adoption to spread across the global economy. Hence, our adoption scenarios,
which consider these factors together with the technical automation potential, provide a sense
of the pace and scale at which workers’ activities could shift over time.
Large-scale shifts in the mix of work activities and occupations are not unprecedented.
Consider the work of a farmer today compared with what a farmer did just a few short years
ago. Many farmers now access market information on mobile phones to determine when and
where to sell their crops or download sophisticated modeling of weather patterns. From a more
macro perspective, agricultural employment in China went from an 82 percent share of all
workers in 1962 to 13 percent in 2013. Labor markets are also dynamic: millions of people leave
their jobs every month in the United States.11
But this does not minimize the challenges faced
by individual workers whose lives are upended by these shifts, or the organizational or societal
challenges of ensuring that workers have the skills to take on the work that will be in demand
and that their incomes are sufficient to grow their standards of living.
Also, demographics have made such shifts in activities a necessity from a macroeconomic
perspective. An economic growth gap has opened as a result of the slowing growth of the
world’s workforce. In some major countries, workforces have shrunk because populations are
aging. Labor productivity will have to accelerate to achieve economic growth and enhance
prosperity.
The analyses in this paper incorporate the potential impact of generative AI on today’s work
activities. The new capabilities of generative AI, combined with previous technologies and
integrated into corporate operations around the world, could accelerate the potential for
technical automation of individual activities and the adoption of technologies that augment the
capabilities of the workforce. They could also have an impact on knowledge workers whose
activities were not expected to shift as a result of these technologies until later in the future.

Accelerating the technical potential to transform knowledge work
Based on developments in generative AI, technology performance is now expected to
match median human performance and reach top-quartile human performance earlier
than previously estimated across a wide range of capabilities (Exhibit 5). For example, MGI
previously identified 2027 as the earliest year when median human performance for natural-
language understanding might be achieved in technology, but in this new analysis, the
corresponding point is 2023.
Exhibit 5
As a result of generative AI, experts assess that technology could achieve human-
level performance in some technical capabilities sooner than previously thought.
McKinsey & Company
Technical capabilities, level of human performance achievable by technology
¹Comparison made on the business-related tasks required from human workers. Please refer to technical appendix for detailed view of performance
rating methodology.
Source: McKinsey Global Institute occupation database; McKinsey analysis
Coordination with multiple agents
Creativity
Logical reasoning and problem solving
Natural-language generation
Natural-language understanding
Output articulation and presentation
Generating novel patterns and categories
Sensory perception
Social and emotional output
Social and emotional reasoning
Social and emotional sensing
Estimates post-recent
generative AI developments (2023)¹
Estimates pre-generative AI (2017)¹ Median Top quartile
Median Top quartile Line represents range
of expert estimates

As a result of these reassessments of technology capabilities due to generative AI, the total
percentage of hours that could theoretically be automated by integrating technologies that exist
today has increased from about 50 percent to 60 to 70 percent. The technical potential curve is
quite steep because of the acceleration in generative AI’s natural-language capabilities (Exhibit 6).
Interestingly, the range of times between the early and late scenarios has compressed compared
with the expert assessments in 2017, reflecting a greater confidence that higher levels of
technological capabilities will arrive by certain time periods.
Generative AI could propel higher productivity growth
Global economic growth was slower from 2012 to 2022 than in the two preceding decades.12
Although the COVID-19 pandemic was a significant factor, long-term structural challenges—
including declining birth rates and aging populations—are ongoing obstacles to growth.
Declining employment is among those obstacles. Compound annual growth in the total number
of workers worldwide slowed from 2.5 percent in 1972–82 to just 0.8 percent in 2012–22,
largely because of aging. In many large countries, the size of the workforce is already declining.13
Exhibit 6
The advent of generative AI has pulled forward the potential for
technical automation.
McKinsey & Company
Technical automation potentials by scenario, %
Time spent on
current work
activities1
1
Includes data from 47 countries, representing about 80% of employment across the world. 2017 estimates are based on the activity and occupation mix from
2016. Scenarios including generative AI are based on the 2021 activity and occupation mix.
2
Early and late scenarios reflect the ranges provided by experts (see Exhibit 6).
Source: McKinsey Global Institute analysis
2020 2030 2040 2050 2060
50
60
70
80
90
100
Updated early scenario
including generative AI2
Updated late scenario
including generative AI2
2017 early scenario2
2017 late scenario2
2023

Productivity, which measures output relative to input, or the value of goods and services
produced divided by the amount of labor, capital, and other resources required to produce
them, was the main engine of economic growth in the three decades from 1992 to 2022
(Exhibit 7). However, since then, productivity growth has slowed in tandem with slowing
employment growth, confounding economists and policy makers.14
The deployment of generative AI and other technologies could help accelerate productivity
growth, partially compensating for declining employment growth and enabling overall
economic growth. Based on our estimates, the automation of individual work activities
enabled by these technologies could provide the global economy with an annual productivity
boost of 0.5 to 3.4 percent from 2023 to 2040 depending on the rate of automation
adoption—with generative AI contributing to 0.1 to 0.6 percentage points of that growth—but
only if individuals affected by the technology were to shift to other work activities that at
least match their 2022 productivity levels (Exhibit 8). In some cases, workers will stay in the
same occupations, but their mix of activities will shift; in others, workers will need to shift
occupations.
Exhibit 7
Productivity growth, the main engine of GDP growth over the past 30 years,
slowed down in the past decade.
McKinsey & Company
Real GDP growth contribution of employment
and productivity growth, 1972–2022,
global GDP growth, CAGR, %
Source: Conference Board Total Economy database; McKinsey Global Institute analysis
Productivity growth bigger contributor
to GDP growth
1972–82 1982–92 1992–2002 2002–12 2012–22
Employment growth
Productivity growth 0.7 0.8
1.7
2.5
2.1
2.5 2.0
1.4
1.3
0.8
3.1 3.1
2.9
3.8
2.8

Exhibit 8
Generative AI could contribute to productivity growth if labor hours can
be redeployed effectively.
McKinsey & Company
Productivity impact from automation by scenario, 2022–40, CAGR,¹ %
Note: Figures may not sum, because of rounding.
1
Based on the assumption that automated work hours are reintegrated in work at productivity level of today.
2
Previous assessment of work automation before the rise of generative AI.
3
Based on 47 countries, representing about 80% of world employment.
Source: Conference Board Total Economy Database; Oxford Economics; McKinsey Global Institute analysis
Without generative AI² Additional with generative AI
China India
Mexico South Africa
United States
Japan Germany France
Global³ Developed economies
Emerging economies
Early Late
2.8
0.3
0.6
0.1
3.4
0.5
Early Late
3.1
0.6
0.6
0.2
3.7
0.8
Early Late
3.2
0.8
0.6
0.2
3.8
1.1
Early Late
3.0
0.6
0.7
0.2
3.7
0.8
Early Late
3.1
0.7
0.7
0.3
3.8
1.0
Early Late
2.4
0.1
0.5
2.9
0.1
Early Late
3.0
0.4
0.6
0.1
3.6
0.5
Early Late
2.8
0.3
0.5
0.0
3.4
0.3
Early Late
2.7
0.1
0.4
3.1
0.1
The capabilities of generative AI vastly expand the pool of work activities with the potential for
technical automation. That in turn has sped up the pace at which automation may be deployed and
expanded the types of workers who will experience its impact. Like other technologies, its ability
to take on routine tasks and work can increase human productivity, which has grown at a below-
average rate for almost 20 years.15
It can also offset the impact of aging, which is beginning to put a
dent in workforce growth for many of the world’s major economies. But to achieve these benefits,
a significant number of workers will need to substantially change the work they do, either in their
existing occupations or in new ones. They will also need support in making transitions to new
activities.

History has shown that new technologies have the potential to reshape societies. Artificial
intelligence has already changed the way we live and work—for example, it can help our phones
(mostly) understand what we say, or draft emails. Mostly, however, AI has remained behind the
scenes, optimizing business processes or making recommendations about the next product to buy.
The rapid development of generative AI is likely to significantly augment the impact of AI overall,
generating trillions of dollars of additional value each year and transforming the nature of work.
But the technology could also deliver new and significant challenges. Stakeholders must act—and
quickly, given the pace at which generative AI could be adopted—to prepare to address both the
opportunities and the risks. Risks have already surfaced, including concerns about the content that
generative AI systems produce: Will they infringe upon intellectual property due to “plagiarism”
in the training data used to create foundation models? Will the answers that LLMs produce when
questioned be accurate, and can they be explained? Will the content that generative AI creates
be fair or biased in ways that users do not want by, say, producing content that reflects harmful
stereotypes?
There are economic challenges too: the scale and the scope of the workforce transitions described
in this report are considerable. In the midpoint adoption scenario, about a quarter to a third of
work activities could change in the coming decade. The task before us is to manage the potential
Considerations for
businesses and society
4

positives and negatives of the technology simultaneously (for more about the potential risks of
generative AI, see Box 3, “Using generative AI responsibly”). Here are some of the critical questions
we will need to address while balancing our enthusiasm for the potential benefits of the technology
with the new challenges it can introduce.
Companies and business leaders
How can companies move quickly to capture the potential value at stake highlighted in this report,
while managing the risks that generative AI presents?
How will the mix of occupations and skills needed across a company’s workforce be transformed
by generative AI and other artificial intelligence over the coming years? How will a company enable
these transitions in its hiring plans, retraining programs, and other aspects of human resources?
Do companies have a role to play in ensuring the technology is not deployed in “negative use cases”
that could harm society?
How can businesses transparently share their experiences with scaling the use of generative AI
within and across industries—and also with governments and society?
Box 3
1
Ananya Ganesh, Andrew McCallum, and Emma Strubell, “Energy and policy considerations for deep learning in NLP,” Proceedings of the 57th Annual Meeting of the
Association for Computational Linguistics, June 5, 2019.
Using generative AI responsibly
Generative AI poses a variety of risks.
Stakeholders will want to address these
risks from the start.
Fairness: Models may generate
algorithmic bias due to imperfect training
data or decisions made by the engineers
developing the models.
Intellectual property (IP): Training
data and model outputs can generate
significant IP risks, including infringing
on copyrighted, trademarked, patented,
or otherwise legally protected materials.
Even when using a provider’s generative
AI tool, organizations will need to
understand what data went into training
and how it’s used in tool outputs.
Privacy: Privacy concerns could arise if
users input information that later ends
up in model outputs in a form that makes
individuals identifiable. Generative
AI could also be used to create and
disseminate malicious content such as
disinformation, deepfakes, and hate
speech.
Security: Generative AI may be
used by bad actors to accelerate the
sophistication and speed of cyberattacks.
It also can be manipulated to provide
malicious outputs. For example, through a
technique called prompt injection, a third
party gives a model new instructions that
trick the model into delivering an output
unintended by the model producer and
end user.
Explainability: Generative AI relies
on neural networks with billions of
parameters, challenging our ability
to explain how any given answer is
produced.
Reliability: Models can produce different
answers to the same prompts, impeding
the user’s ability to assess the accuracy
and reliability of outputs.
Organizational impact: Generative AI
may significantly affect the workforce,
and the impact on specific groups
and local communities could be
disproportionately negative.
Social and environmental impact: The
development and training of foundation
models may lead to detrimental social and
environmental consequences, including
an increase in carbon emissions (for
example, training one large language
model can emit about 315 tons of carbon
dioxide).1

Policy makers
What will the future of work look like at the level of an economy in terms of occupations and skills?
What does this mean for workforce planning?
How can workers be supported as their activities shift over time? What retraining programs can be put
in place? What incentives are needed to support private companies as they invest in human capital?
Are there earn-while-you-learn programs such as apprenticeships that could enable people to retrain
while continuing to support themselves and their families?
What steps can policy makers take to prevent generative AI from being used in ways that harm society
or vulnerable populations?
Can new policies be developed and existing policies amended to ensure human-centric AI
development and deployment that includes human oversight and diverse perspectives and accounts
for societal values?
Individuals as workers, consumers, and citizens
How concerned should individuals be about the advent of generative AI? While companies can assess
how the technology will affect their bottom lines, where can citizens turn for accurate, unbiased
information about how it will affect their lives and livelihoods?
How can individuals as workers and consumers balance the conveniences generative AI delivers with
its impact in their workplaces?
Can citizens have a voice in the decisions that will shape the deployment and integration of generative
AI into the fabric of their lives?
Technological innovation can inspire equal parts awe and concern. When that innovation seems
to materialize fully formed and becomes widespread seemingly overnight, both responses can
be amplified. The arrival of generative AI in the fall of 2022 was the most recent example of this
phenomenon, due to its unexpectedly rapid adoption as well as the ensuing scramble among
companies and consumers to deploy, integrate, and play with it.
All of us are at the beginning of a journey to understand this technology’s power, reach, and
capabilities. If the past eight months are any guide, the next several years will take us on a roller-
coaster ride featuring fast-paced innovation and technological breakthroughs that force us to
recalibrate our understanding of AI’s impact on our work and our lives. It is important to properly
understand this phenomenon and anticipate its impact. Given the speed of generative AI’s deployment
so far, the need to accelerate digital transformation and reskill labor forces is great.
These tools have the potential to create enormous value for the global economy at a time when it is
pondering the huge costs of adapting to and mitigating climate change. At the same time, they also
have the potential to be more destabilizing than previous generations of artificial intelligence. They are
capable of that most human of abilities, language, which is a fundamental requirement of most work
activities linked to expertise and knowledge as well as a skill that can be used to hurt feelings, create
misunderstandings, obscure truth, and incite violence and even wars.
We hope this research has contributed to a better understanding of generative AI’s capacity to
add value to company operations and fuel economic growth and prosperity as well as its potential
to dramatically transform how we work and our purpose in society. Companies, policy makers,
consumers, and citizens can work together to ensure that generative AI delivers on its promise to
create significant value while limiting its potential to upset lives and livelihoods. The time to act is now.16

Endnotes
1 Ryan Morrison, “Compute power is becoming
a bottleneck for developing AI. Here’s how
you clear it.,” Tech Monitor, updated March 17,
2023.
2 “Introducing ChatGPT,” OpenAI, November
30, 2022; “GPT-4 is OpenAI’s most advanced
system, producing safer and more useful
responses,” OpenAI, accessed June 1, 2023.
3 “Introducing Claude,” Anthropic PBC,
March 14, 2023; “Introducing 100K Context
Windows,” Anthropic PBC, May 11, 2023.
4 Emma Roth, “The nine biggest announcements
from Google I/O 2023,” The Verge, May 10,
2023.
5 Pitchbook.
6 Ibid.
7 Erik Brynjolfsson, Danielle Li, and Lindsey
R. Raymond, Generative AI at work, National
Bureau of Economic Research working paper
number 31161, April 2023.
8 Peter Cihon et al., The impact of AI on
developer productivity: Evidence from GitHub
Copilot, Cornell University arXiv software
engineering working paper, arXiv:2302.06590,
February 13, 2023.
9 Michael Nuñez, “Google and Replit join forces
to challenge Microsoft in coding tools,”
VentureBeat, March 28, 2023.
10 Joe Coscarelli, “An A.I. hit of fake ‘Drake’ and
‘The Weeknd’ rattles the music world,” New
York Times, updated April 24, 2023.
11 “Job openings and labor turnover survey,” US
Bureau of Labor Statistics, accessed June 6,
2023.
12 Global economic prospects, World Bank,
January 2023.
13 Yaron Shamir, “Three factors contributing to
fewer people in the workforce,” Forbes, April 7,
2022.
14 “The U.S. productivity slowdown: an economy-
wide and industry-level analysis,” Monthly
Labor Review, US Bureau of Labor Statistics,
April 2021; Kweilin Ellingrud, “Turning around
the productivity slowdown,” McKinsey Global
Institute, September 13, 2022.
15 “Rekindling US productivity for a new era,”
McKinsey Global Institute, February 16, 2023.
16 The research, analysis, and writing in this
report was entirely done by humans.
The research underpinning this report was led by Michael Chui, an MGI partner in McKinsey’s Bay Area office; Eric Hazan, a
senior partner in the Paris office; Roger Roberts, a partner in the Bay Area office; Alex Singla, a senior partner in the Chicago
office; Kate Smaje and Alexander Sukharevsky, senior partners in the London office; Lareina Yee, a senior partner in the
Bay Area office; and Rodney Zemmel, a senior partner in the New York office.

Making the most of the
generative AI opportunity:
Six questions for CEOs
As corporate leaders navigate the new gen AI era, they can begin to lay out
their road map and strategy by pondering a series of fundamental questions.
February 2024
© Getty Images
This article is a collaborative effort by Ben Ellencweig, Dana Maor, Alex Singla, Alexander Sukharevsky,
Lareina Yee, and Rodney Zemmel, representing views from QuantumBlack, AI by McKinsey.
33

Generative AI (gen AI) has taken the world
by storm, altering our understanding of the
possible. Creating next-era fashion collections
in a few clicks, engaging customers with hyper-
personalized offerings, and collapsing years of
tedious drug discovery work into a few months—
suddenly, all that and more seems within reach.
As in the early days of breakthroughs like
blockchain and the Internet itself, gen AI has
sparked a debate between those who believe
the technology will reshape the way we work and
live and those who see gen AI as the next NFT
moment, soaring briefly and failing to deliver on
its promise, as nonfungible tokens did earlier in
this decade.
So how much of today’s excitement about gen AI
reflects reality, and how much is myth? McKinsey
estimates that the technology will open a new era
of productivity and growth that could create $2.6
billion to $4.4 trillion of additional value.¹ In the
telecom space alone, the impact of new gen AI
use cases is expected to be in the range of $60
billion to $100 billion.
For CEOs seeking to unlock this upside, the key
is to understand how this value will materialize
and over what period, as well as where to invest
their resources. There are no right answers, at
least not yet. We are still in the technology’s
post-awareness, pre-deployment phase, with
most software engineers having only recently
gained access to gen AI tools. But based on our
experience working with clients over the past 15
months, we find that CEOs can better formulate
a strategy if they consider six essential questions
about gen AI:
1. Is the opportunity significantly larger than AI?
2. Are we ambitious enough with gen AI?
3. Where is the money in the value chain?
4. Do we have the right talent in place?
5. What does it take to cross the “Death Valley”
of scaling AI?
6. Are we thinking about risk in the right way?
Is the opportunity significantly
larger than AI?
Over the past year, many of our client
conversations and technology deployments have
focused on gen AI. Despite its novelty, however,
gen AI does not exist in a silo. Instead, it is simply
the newest, if most powerful, iteration in the
unfolding story of how artificial intelligence can
boost productivity and innovation. We estimate
that gen AI accounts for only 20 to 40 percent
of AI’s total value creation potential, with the
remainder coming from traditional, or “analytical,”
AI applications, which have heretofore been less
than fully deployed.
What’s more, other important technology trends,
such as Web 3.0 and augmented reality and
virtual reality (AR/VR), are continuing to make
progress in the shadow of gen AI. They will
eventually get a strong footing over the next
decade, with clear value creation potential for
organizations. Hence, executives rethinking
industries and business models should view the
opportunity more broadly than gen AI or even
all AI. A more effective approach is to consider
how their organizations can capitalize on the
confluence of emerging technology trends—a
truly watershed moment akin to the simultaneous
emergence of the first cloud, social network, and
smartphone applications in 2017.
Are we ambitious enough with
gen AI?
Gen AI has fascinated the world with jaw-
dropping applications like ChatGPT and Pi,
highlighting AI’s transformative potential.
Never before has technology pushed the art
1
“The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023.

of the possible so far ahead and so fast for non-
technologists. As companies rush toward this
technology, they are likelier to succeed if they solve
for value creation versus simply checking the box.
This is particularly concerning in the telco space,
where players have often expressed interest in
exploring incremental productivity applications and
less frequently turn their attention to reimagining
their businesses through the lens of AI.
Beyond simply avoiding a rehashed discussion
of tech versus telco and scaling use cases, senior
executives can benefit from asking a weightier
question: How do we reinvent our industry and
business model by leveraging the disintermediation,
radical cost curve shifts, and organic consumer
acquisition opportunities that gen AI can provide?
Moreover, the age of new platforms opens new
opportunities, including the creation from scratch of
hyperscalers or unicorn super apps. One only needs
to consider the opportunity associated with natural-
language virtual assistants and the disruption this
could have on the current business context, from
consumption to business model. Gen AI will reward
the bold. Already, some 80 percent of today’s
most popular gen AI products come from new
entrants,² with incumbents forced to play catch-up
or otherwise find their edge to lead.
Where is the money in the value chain?
Gen AI is creating a frenzy among founders and
investors, with a seemingly endless number of
players entering the field. A closer look at leading
gen AI players reveals a couple of winning plays that
CEOs might use to separate their organization from
the pack³ :
— Differentiate à la the fine-dining chef. The
ingredients of gen AI applications are not in
and of themselves a source of competitive
differentiation. Anyone can license the most
powerful closed-source models, which
contribute only about 15 percent of the value of
gen AI applications. This suggests that the real
value will be realized by those able to combine
the best available technology with proprietary
data. Telco leaders should reexamine their data
asset portfolios with an eye toward designing
features like unique consumer and distribution
journeys, such as an always-on customer care
assistant fine-tuned to each user profile and
embedded across user channels. Indeed, this
is exactly what people seem to be asking
for: among the top 50 gen AI applications,
consumers are paying for 90 percent of them,
revenue per user is three times higher than that
of other apps, and customer acquisition is mostly
organic.
— Find underserved segments of the value
chain. Gen AI models and applications get most
of the attention from investors and organizations,
but other critical segments of the gen AI value
chain remain surprisingly underrated. From
commercializing access to graphical processing
units (GPUs) to providing data cleaning,
augmentation, or risk management solutions,
the opportunities are plentiful. We see this play
already taking place in the data center space,
with investors exploring acquisitions to supply an
accelerating demand for workloads. Fortunately,
many opportunities still exist for organizations to
gain first-mover advantages in the gen AI market.
In fact, in most product categories, the gap
between the top two players is only two times,
making it easier for new entrants to establish
themselves as leaders in the field.
Do we have the right talent in place?
Our research shows that gen AI is expected to
supercharge automation, affecting up to 60 percent
of work activities over the next 20 years. This
impact should not be surprising; a gen AI model can
analyze in an hour more data than a human can in
ten lifetimes. But will AI replace us all or turn us into
automatons?
2
Olivia Moore, “How are consumers using generative AI?” Andreessen Horowitz, Sept. 13, 2023; By some estimates, gen AI start-ups alone have
already generated more than $1 billion of software-as-a-service revenue.
3
Ninety percent of these companies are already monetizing their offerings with more than three times the average revenue per user than
incumbents.

Those concerns seem overwrought, at least for
now. The fact is, gen AI can deliver only if it is
combined with exceptional human capital. Despite
the power of gen AI, middling employees will
produce middling results. Organizations must
recruit, retain, and develop truly outstanding talent
in both the technical and nontechnical spheres.
With the right people in place, organizations truly
could be on the verge of a new age of innovation.
What does it take to cross the ‘Death
Valley’ of scaling AI?
Only one in ten AI use cases have been deployed
in production,⁴ so gen AI has arrived at a time
when many leaders are disillusioned with the
yet-unfulfilled promises of artificial intelligence.
But AI does not have a technology problem; it
has a design problem. To be effective, AI models
require top-down focus, the right tech and
people capabilities, proper data access, modular
architecture, and effective change management.
Only then can disparate AI-driven solutions work
together continuously to create great customer
and employee experiences, lower unit costs,
and allow the organization to move faster than
ever. Without external intervention or guidance,
only about 3 percent of gen AI proofs of concept
eventually scale.
Creating a digitally capable organization involves
rewiring the way companies operate. This effort
should be broad, covering six dimensions:
1. business-led digital road map that aligns the
senior leadership team on the transformation
vision, value, and strategy, which is focused on
business reinvention
2. talent with the right skills and capabilities to
execute and innovate in both the technical and
business sides of the organization, including
upskilling
3. operating model that increases the
organization’s metabolic rate by bringing
together business and technology
4. technology that allows the organization to
innovate faster and more easily—in particular,
an IT architecture with a flexible orchestration
layer
5. data that is continuously enriched and easy
to consume across the organization to
improve customer experiences and business
performance
6. adoption and scaling of digital and AI solutions
to optimize value capture by building new skills
and leadership characteristics and by tightly
managing the transformation progress and
risks
Are we thinking about risk in the
right way?
Discussions of gen AI risks are plentiful, but
experience shows that most of these conversations
need calibrating to ensure that organizations
approach risk holistically and pragmatically.
Current conversations about risk tend to focus
on either short-term considerations (for example,
customer experience and protection of intellectual
property) or long-term, existential ones (whether
artificial general intelligence will rule the world).
Not enough focus is placed on intermediate risk,
such as how companies can maintain the trust
of their stakeholders in an AI-generated reality
where seeing is no longer believing. Also, other
categories of risks are simply not getting the
attention they deserve. Little is said, for example,
about organizations’ environmental, social, and
governance (ESG) risk, even though training a gen
AI model consumes about a million liters of water
for cooling.
4
“The state of AI in 2022—and a half decade in review,” McKinsey, December 6, 2022.

A more pragmatic perspective would be for CEOs
to steer their organizations toward accepting risk
as the reality of doing business with AI (for example,
hallucination is just a feature of gen AI). Fortunately,
risks can be managed. Plenty of banks, after all,
deal with customer credit and other difficult types
of risk daily and still manage to thrive. To navigate
these uncharted waters, organizations should set up
cross-functional teams to cover their specific risk
concerns (for example, regulatory, ethical, cyber, IP,
and societal risks), establish ethical principles and
guidelines for gen AI use, and establish continuous
monitoring for gen AI systems to address risk
dynamically.
An honest and thorough examination of these six
questions can lay the foundation of a comprehensive
gen AI strategy—one that truly focuses on how
the technology can transform an organization or
an entire industry. These conversations will not
necessarily be easy, which makes it essential
that they be led by CEOs. Perhaps most of all, it is
advantageous to think big. A road map, after all, can
lead to different destinations. Where do you want
your company to land?
Copyright © 2024 McKinsey & Company. All rights reserved.
Ben Ellencweig is a senior partner in McKinsey’s Stamford office, Dana Maor is a senior partner in the Tel Aviv office, and
Lareina Yee, a senior partner and chair of the McKinsey Technology Council, is based in the Bay Area–San Francisco office.
Alex Singla, a senior partner in the Chicago office, and Alexander Sukharevsky, a senior partner in the London office,
are managing partners of QuantumBlack, AI by McKinsey. Rodney Zemmel, a senior partner and managing partner of
McKinsey Digital, is based in the New York office.

Sector view:
Telecom
operators
2

Technology, Media & Telecommunications Practice
The AI-native telco: Radical
transformation to thrive in
turbulent times
Artificial intelligence, when deployed at scale, can help telcos protect
core revenues and drive margin growth. But capturing this opportunity
will require a wholly different approach.
February 2023
© Getty Images
This article is a collaborative effort by Joshan Abraham, Jorge Amar, Yuval Atsmon, Miguel Frade, and
Tomás Lajous, representing views from McKinsey’s Technology, Media & Telecommunications Practice.
39

Artificial intelligence (AI) is unlocking use cases
that are transforming industries across a wide
swath of the world’s economy. From infrastructure
that “self-heals” to radically reimagined (and
touchless) customer service and experience; from
large scale hyperpersonalization to automatically
created marketing messages and images leveraging
Generative AI tools like ChatGPT—it is all a reality
today. These AI solutions can powerfully augment
and sometimes radically outperform most traditional
business roles.
The impact from these solutions is becoming
evident. AI leaders—the top quintile of companies
that have taken the McKinsey Analytics Quotient
assessment—have experienced a five-year revenue
CAGR that is 2.1 times higher than that of peers and
a total return to shareholders that is 2.5 times larger.
Given the numerous challenges the telecom industry
has faced in recent years, such as flagging revenues
and ROIC, one might expect the industry would have
already adopted a full transition to this technology.
Yet, based on our experience with operators
across the world, telcos have yet to fully embrace
AI and an AI-focused mindset. Instead, models are
developed once and not enhanced as the business
context evolves. Machine learning (ML) is in name
only, limiting the ability of the system to improve
from experience. Most regrettably, AI investments
are often not aligned with top-level management
priorities; lacking that sponsorship, AI deployments
stall, investment in technical talent withers, and the
technology remains immature.
Contrast this disjointed state of affairs with an
AI-native organization. Here, AI is viewed as a
core competency that powers decision making
across all departments and organization layers.
AI investments are required to enable most
C-level priorities such as more personalized
recommendations for customers and faster speed
of answer in call centers. Top executives serve
as champions of critical AI initiatives. Data and AI
capabilities are managed as products, built for
scalability and reusability. AI product managers,
even those working on foundational products, are
celebrated for the benefits they generate for the
organization.
Reaching this state of AI maturity is no easy task,
but it is certainly within the reach of telcos. Indeed,
with all the pressures they face, embracing large-
scale deployment of AI and transitioning to being
AI-native organizations could be key to driving
growth and renewal. Telcos that are starting to
recognize this is nonnegotiable are scaling AI
investments as the business impact generated by
the technology materializes.
While isolated applications of the technology
can help individual departments improve, it’s AI
connected holistically at all levels and departments
that will be key to protecting core revenue and
driving margin growth in even the most difficult
of environments. Imagine the following not-so-
distant scenarios:
— Customer focused: Sarah, a New Yorker, is
a high average revenue per user (ARPU)
customer. Aware that Sarah spends half of
her phone usage time on fitness apps, the AI
creates an enticing customized upgrade offer
that includes a six-month credit applicable
to her favorite fitness subscription and NYC-
specific perks, such as a ticket to an upcoming
concert sponsored by the operator. Knowing
Sarah’s high digital propensity¹, the AI makes
the offer available to her as a digital-only
promotion.
— Employee focused: When Trevor, an associate
in a telco mall store, logs in at the start of his
shift, he receives a celebratory notification
congratulating him on his high-quality
interactions with customers the previous
day. And because the AI detected that Trevor
is underperforming peers in accessory and
device protection attach rates, he receives a
notification pointing him to coaching resources
specifically created to enhance performance in
those metrics.
1
Preference to transact and engage in digital channels, such as websites and mobile apps.

— Infrastructure focused: Lucile, director of a
capital planning team, uses AI to inform highly
targeted network investment decisions based
on a granular understanding of customer-level
network experience scores strongly correlated to
commercial outcomes (for example, churn). The
AI provides tactical recommendations of what
and where to build based on where customers
use the network and on automatically computed
thresholds after which new investments have
marginal impact on experience and commercial
outcomes for the operator.
How these possibilities could become reality is critical
to consider, especially given that most telcos currently
deploy AI in limited ways that will not drive sustainable,
at-scale success.
Why now? The case for becoming
AI native
Factors supporting this move for telcos include the
following:
— Increasing accessibility of leading AI technology:
AI-native organizations like Meta continue to
grow the open-source ecosystem by making new
programming languages, datasets, and algorithms
widely available. In parallel, cloud providers have
developed multiple quick-to-deploy machine-
learning APIs like Google Cloud’s Natural
Language API. Generative AI solutions, such as
ChatGPT, that are capable of creating engaging
responses to human queries are also accessible
through API. These two factors, coupled with
dropping costs of data processing and storage,
make AI increasingly easier for organizations to
leverage.
— Rapid explosion of usable data: Operators can
collect, structure, and use significantly more
data directly than ever before. This information
includes data flows from individualized app usage
patterns, site-specific customer experience
scores, and what can be purchased or shared
from partners or third parties. To answer privacy
fears raised by consumers and regulators, telcos
must also invest in building digital trust, including
actively managing data privacy and having a
robust cybersecurity strategy and a framework
to guide ethical deployment of AI.
— Proven use cases and outcomes: AI-native
organizations across industries have deployed AI
to achieve four critical outcomes highly relevant
to operators across the world: 1) drive revenue
protection and growth through personalization,
2) transform the cost structure, 3) enable a
frictionless customer experience, and 4) meet
new workplace demands. Operators can learn
from all of them. Streaming players, for example,
have long been known for providing highly
curated personalized content recommendations
based on past user behavior. To optimize cost
and deliver a seamless customer experience,
one of the leading US insurance companies
leverages AI assistants to reduce and even
eliminate human interactions for users to obtain
coverage or cancel policies with other carriers.
In turn, some of the leading tech companies in
the world are known for using AI to highlight the
traits of great managers and high-performing
teams and use those insights to train company
leaders.
— Technology investments recognized as a
business driver: In a postpandemic world,
there is broad consensus among investors and
executives that technology investments are not
a mere cost center but a fundamental business
driver with profound impacts on the bottom
line. Despite prospects of economic turmoil and
recessionary fears, IT spending is expected to
increase by more than 5 percent in 2023, with
technology leaders under growing pressure to
demonstrate impact on company financials.²
— Operator bets need hypercharging: As networks
and products converge, operators are making
bets on becoming cost and efficiency focused,
experience-centric, or ecosystem players. AI use
cases that are more relevant for each bet can
give them a better chance to hypercharge and
leapfrog competition.
For the greatest payoff, this shift requires
telcos to embrace the concept of the AI-native
organization—a structure where the technology
2
“2023 CIO and Technology Executive Survey,” Gartner, October 18, 2022.

is deeply embedded across the fabric of the
entire enterprise.
Using AI to reimagine the core business
Telcos have been under relentless pressure
over the past decade as traditional growth
drivers eroded and economic value increasingly
shifted to tech companies. By using AI to its
fullest extent, operators can protect their core
business from further erosion while improving
margins.
As the industry looks to leverage the power
of AI, we see six themes gaining prevalence in
strategic agendas based on our experience
working with telcos across the world.
Hyperpersonalize and architect sales and
engagement
Leveraging the breadth and depth of user-
level data at their disposal, operators have
been increasingly investing in AI-enabled
personalization and channel steering.
For example, a hyperpersonalized plan and
device recommendation for each line holder
could leverage granular behavioral data—such
as number of and engagement with apps
installed and device feature usage—to create
individualized plan recommendations (superior
network speed or streaming service add-ons),
promos (“Receive unlimited prepaid data to
be used for a music streaming service for only
$5 per month”), and messaging for specific
devices, locations, and events (“Upgrade
to the latest device featuring built-in VR”).
Subsequently, using audience segmentation
tools, customers can be guided to channels
that offer an engaging experience while driving
the most profitable sales outcome for the telco.
A subscriber, for example, with low-digital
propensity³, high ARPU, and high churn risk who
is living within a few miles of a store, might be a
good candidate to nudge to a device upgrade
in-store, leading to better customer experience
and potentially stronger loyalty for the operator.
Or consider a different scenario: this subscriber
uses an advanced 5G network in New York
City and is a regular user of fitness apps who
travels frequently outside the country. As a
result, her telco offers a personalized plan
recommendation with superior network access,
top fitness app subscription perks, and an
attractive international data plan.
Case study: An Asia–Pacific operator that
launched a comprehensive customer value
management transformation powered by AI
(with personalization at the core) achieved a
more than 10 percent reduction in customer
churn and a 20 percent uptake in cross-sell.
Reimagine proactive service
Earlier investments in digital infrastructure
combined with predictive and prescriptive
AI capabilities enable operators to develop
a personalized service experience based on
autonomous resolution and proactive outreach.
With fully autonomous resolution, for example,
the system can predict and resolve potential
sources of customer dissatisfaction before
they are even encountered. After noticing a
customer is accruing roaming charges while
traveling abroad, the AI system automatically
applies the optimal roaming package to her
monthly bill to minimize charges. It then follows
up with a personalized bill explanation detailing
the package optimization and resulting savings
for the customer, leading to a surprising and
positive CX moment.
Operators are also exploring the redesign
of digital service journeys with the help of
AI assistants serving as digital concierges.
Generative AI technologies, including
tools such as ChatGPT, have the potential
to enhance existing bots through better
understanding of more complex customer
intents, more empathetic conversations, and
better summarization capabilities (for example,
when a bot needs to handover a customer
interaction to a human rep). A single unified
AI assistant will likely also represent a step
change in speed, accuracy, and engagement
compared to the interactive voice response
systems of today.
3
Someone who prefers to transact in, and use, assisted channels, such as retail stores and call centers.

An AI-powered service organization is a key
ingredient to releasing the full capacity of
specialized reps for high-value interactions while
improving overall customer experience—one of the
key battlegrounds for telcos around the world.
Case study: A leading telco is expected to achieve
an approximately 10 percent decrease in device
troubleshooting calls, powered by a proactive AI
engine that considers the customer’s likelihood
of calling and issue severity to decide whether to
push the most effective resolution via SMS. This
proactive engine is also a key element of the
operator’s ambition to have the highest customer
satisfaction scores among competitors.
Build the store of the future
In retail, AI is leading a revolution in the design and
running of stores by streamlining operations and
elevating the consumer experience.
Some telcos already use virtual retail assistants
displayed on floor screens to conduct multiple
transactions with customers, including adding
balance to a prepaid account and selling prepaid
cards and TV subscriptions. A leading European
telco leverages AI tools for delivering more-
accurate device grading and trade-ins in the store.
The store of the near future includes the following
components:
— Front of house: Aisle layout and product
placement are optimized based on browsing
patterns analyzed by machine vision. Digital
signage is made relevant to individual customers
who are in-store and identified through
biometric or geofencing technology. Interactive
kiosks serve up personalized promos, service
assistance, and wait-time forecasts. Customers
are matched with reps who are given nudges
with personalized info likely to spark the
best interaction and lead to a truly seamless
customer experience.
— Back of house: Device SKUs are automatically
managed to optimize inventory and sales. Stores
stock curated assortments based on local
preferences surfaced in sales analytics. AI tools
such as computer-vision-based grading allows
for immediate price guarantees on devices that
are traded in.
— Outside: Consumers walking near the store
receive text or push notifications with a
personalized promotion and an invitation to
check the product in-store.
Case study: An Asian telco launched a 5G virtual
retail assistant in unmanned pop-up stores. The
digital human communicates with customers
in a personal and friendly way with engaging
facial expressions and body language. She
supports customers across multiple transactions,
from buying prepaid cards to getting SIM card
replacements.
Deploy a self-healing, self-optimizing network
The AI-native telco will leverage technology to
optimize decision making across the network
life cycle stages, from planning and building
to running and operating. In the planning and
building stages, for example, AI can be used to
prioritize site-level capacity investments based
on granular data, such as customer-level network
experience scores.
In the running and operating phases, AI can
prioritize the dispatching of emergency crews
based on potential revenue loss or impact on
customer experience. AI can also enable a self-
healing network, which automatically fixes faults—
for example, auto-switching customers from one
carrier frequency to another because the former
was expected to become clogged. This frees up
engineering resources for higher-value-added
activities.
Case study: A telecom operator developed an
AI-based customer network experience “score”
to improve its understanding of how customers
perceive their network and to inform network
deployment decisions. The AI engine leveraged
granular network-level information for every
line (e.g., signal strength, throughput) with an
ML model to create the score tailored to each
customer’s individual network experience and
expectations. The operator used the score, which
directly correlated with impact metrics such
as customer churn or network care tickets, to

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