The Economic Impact of AI Adoption: Measuring Productivity and Competitive Advantage in International Enterprises

The American Journal of Management and Economics Innovations

22

https://www.theamericanjournals.com/index.php/tajmei

TYPE

Original Research

PAGE NO.

22-29

DOI

10.37547/tajmei/Volume07Issue06-04

OPEN ACCESS

SUBMITED

24 Arpil 2025

ACCEPTED

22May 2025

PUBLISHED

10 June 2025

VOLUME

Vol.07 Issue 06 2025

CITATION

Farrukh Avezov. (2025). The Economic Impact of AI Adoption:
Measuring Productivity and Competitive Advantage in International
Enterprises. The American Journal of Management and Economics
Innovations,

7(06),

22

–

29.

https://doi.org/10.37547/tajmei/Volume07Issue06-04.

COPYRIGHT

© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.

The Economic Impact of AI
Adoption: Measuring
Productivity and
Competitive Advantage in
International Enterprises

Farrukh Avezov

Global Business Development Director at Twin, Co-founder of Oliy Aql
Uzbekistan Moscow, Russian Federation

Abstract:

This article covers the economic impact

analysis

by

quantitative

assessment

through

implementing artificial intelligence technologies. It
analyzes how AI solutions are Productively applied and
the formation of sustainable competitive advantage in
multinational corporations. The relevance of this study
is justified by the rapid growth that exists in corporate
and venture investments in AI, a forecast of global AI
spending to reach USD 632 billion by 2028, and a need
for companies to adapt business processes as fast as
possible to keep them competitive in the international
market. The novelty of the work lies in a
comprehensive synthesis of industry statistics (CB

Insights, IDC, McKinsey, PwC, OECD), resource‐based

theory of the firm, and the concept of dynamic
capabilities to explain sustainable advantages. The
methodology includes descriptive and comparative
analysis of financial metrics, macroeconomic forecasts

of AI’s added value, and detailed case studies of

implementations at Google, Amazon, Nike, and
Starbucks. The significant results show that, in general,

artificial intelligence raises firms’ labor productivity by

0

–

11%, improves Overall Equipment Effectiveness in

manufacturing,

quickens

Time‐to‐Market,

and

increases Customer Lifetime Value in retail and
services. Generative AI can add between 1.3% and
9.3% of revenue in different fields of business; also, the
worldwide economic impact is approximated at USD
2.6

–

4.4 trillion per year. Sustainable competitive

advantage emerges at the intersection of VRIN
resources (unique data, algorithms, infrastructure) and

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firms’ ability to sense, seize, and transform new

opportunities rapidly. At the same time, the key
constraints are regulatory costs, the capital intensity of
infrastructure, and the hype cycle effect. This article
will be helpful for managers, strategists, and analysts
of multinational corporations, as well as consultants
and researchers assessing the economic efficiency and
competitive benefits of AI implementation.

Keywords

: artificial intelligence; economic impact; labor

productivity; competitive advantage; VRIN; dynamic
capabilities;

generative

AI;

macroeconomic

contribution.

INTRODUCTION

In recent years, the global artificial intelligence market
has transitioned from an experimental phase to one of

large‐scale investments by corporations, venture capital

funds, and sovereign wealth funds. According to data
[1], total corporate investments in AI rose from USD 92
billion in 2022 to USD 142.3 billion in 2023

—

an increase

of almost 55% in a single calendar year, making this

segment one of the fastest‐growing technology markets.

Venture capital is following the same trajectory: in 2024,
startups working with AI raised USD 100.4 billion [2],
accounting for 37% of global venture financing and 17%
of deals

—

the highest figures on record [3]. The average

round size increased to USD 23.5 million, reflecting

project maturity and investors’ confidence in their

commercial potential [4].

Corporate budgets for AI implementation and support
are expanding just as rapidly. IDC forecasts global
spending on AI technologies will reach USD 632 billion

by 2028 [6]. Such long‐term commitments indicate that

companies no longer view AI as an auxiliary IT tool but
are embedding it at the core of their operational and
product models.

Under these conditions, international enterprises face
economic and strategic pressure to rapidly transform
their business processes through AI to maintain
competitiveness in global markets. This combination of
financial benefit and competitive necessity underscores

the relevance of further analysis of AI’s impact on

productivity and sustainable corporate advantages.

MATERIALS AND METHODOLOGY

The study's materials and methodology rely on analyzing
29 sources, including industry reports, government
publications, academic articles, and empirical case
studies. Comprehensive data on corporate and venture
investments were drawn from CB Insights reports [1

–

4],

while forecasts of global AI spending were sourced from
IDC studies [5, 6]. Economic mechanisms and models of

competitive advantage are based on Barney’s resource‐

based theory of the firm [11], as extended in the works
of Ristyawan [12] and Simón [13] on organizational
dynamic capabilities. Empirical cases and practical
recommendations

were

taken

from

European

Parliament reports [7], Samsung SDS cost‐optimization

studies [8], and IBM and FIU research on personalization
and predictive analytics [9, 10].

Descriptive statistics and comparative analysis methods
were employed to assess the economic impact
quantitatively. Values for revenue, operating expenses,
and capital expenditures were aggregated by year and
sector based on publicly available data from CB Insights,
IDC, McKinsey, and PwC [1

–

6], [15

–

16]. The evolution of

AI’s contribution to macroeconomic indicators was

evaluated using forecasts from McKinsey [15], PwC [16],
and the OECD [19]. Analysis of benefit distribution
across industries was conducted using McKinsey

diagrams illustrating generative AI’s influence on

business functions in various sectors [15].

Qualitative analysis of competitive advantages drew on

the resource‐based theory of the firm [11, 12] and the

dynamic capabilities concept

to interpret firms’ ability to

sense and transform new opportunities [13, 14]. Case
study methods included detailed reviews of AI solution
deployments at Google [22], Amazon [23], Nike [24], and
Starbucks [25]. To assess manufacturing and logistics
effects, metrics for Overall Equipment Effectiveness [20]
and labor productivity were analyzed using data from
Clear Object and Gartner [21].

Regulatory context and risk management were
considered by comparing the EU AI Act (dataset audits,
Art. 10) [28], NIST AI RMF recommendations [11],

Canada’s Algorithmic Impact Assessment [12], and
Singapore’s Model AI Governance Framework [14].

Identification and management of hype cycle risks were
based on analysis of the Gartner Hype Cycle 2024 [29]
and surveys by MIT and McKinsey on the maturity and
scalability of AI initiatives [17, 18].

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RESULTS AND DISCUSSION

The economic impact of artificial intelligence in the
corporate environment is conventionally defined as the
aggregate

change

in

fundamental

financial

parameters

—

revenue growth, reduction of operating

costs, and more efficient use of resources. The European
Parliament report emphasizes that algorithms can
significantly

enhance

decision-making,

reduce

expenditures, and optimize production factors across
nearly all economic sectors, thereby increasing returns
on capital and labor resources [7]. Corporate reviews by
Samsung SDS illustrate this at the applied level:

expenditures on deploying open models in the cloud are
allocated among GPU time, RAM, and data storage,
allowing precise calculation of the breakeven point and
reducing total cost of ownership compared to
purchasing commercial API licenses [8]. Figure 1
illustrates the stages of AI model development, showing
cost growth for services and skills along the vertical
axis

—

ranging from the least expensive stages at the

bottom, through data operations, model

fine‐tuning and

RLHF, to the most resource‐intensive stage of creating

and training proprietary models

—

while the horizontal

axis contrasts a focus on structured data at left with
documents and unstructured sources at right.

Fig. 1. AI Model Development Stages and Costs [8]

If the economic effect reflects the first derivative of
technology adoption, then competitive advantage
characterizes the durability of these outcomes over
time. A study [9] shows that AI-based predictive
analytics enables companies to forecast demand more
precisely, thereby outpacing competitors in pricing and
product decisions. IBM highlights another dimension of
advantage

—

scalable

personalization:

algorithms

generate real-time recommendations, increasing
conversion rates and reducing customer acquisition
costs [10].

From the standpoint of the resource-based theory of the
firm, unique data, algorithms, and computing
infrastructure satisfy the VRIN criteria

—

value, rarity,

inimitability, and non-substitutability

—

and therefore

can serve as sources of long-term advantage. In his
seminal work, Barney [11] establishes a direct
relationship between control over such resources and
above-normal profits, a link confirmed by later empirical
reviews demonstrating how AI assets become strategic
barriers to industry entry [12].

However, valuable technology alone does not guarantee

success; the decisive factor is the firm’s dynamic

capabilities to sense, seize, and transform opportunities
under conditions of uncertainty. Study [13] defines
dynamic capabilities as the ability to integrate, build,
and reconfigure resources amid rapid environmental
change, emphasizing that AI accelerates the feedback
loop between market signals and organizational
response.

By connecting these two theoretical frameworks, one
can conclude that competitive advantage emerges at
the intersection of the VRIN characteristics of AI

resources and the firm’s ability to reconfigure them for

new market opportunities continually. The absence of
either component diminishes the economic effect, as
evidenced by a survey [14]

—

74% of organizations report

scaling difficulties even after pilot success. Companies
that

establish

organizational

mechanisms

for

developing,

updating,

and

disseminating

AI

competencies record lower unit costs and higher
revenue growth rates from new products, as confirmed

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by recent industry meta-studies on AI-initiative
profitability.

Macroeconomic estimates by leading consultancies

converge on the view that AI’s econo

mic contribution

already rivals the GDP of major economies: McKinsey
calculates that generative models can add USD 2.6

–

4.4

trillion annually to the global economy

—

boosting the

aggregate impact of all AI types by 15

–

40% and equating

to the combined GDP of the United Kingdom [15]. As
shown in Figure 2, generative AI can contribute 1.3

–

9.3% of revenue depending on the industry: the most

tremendous impact occurs in high-tech sectors (4.8

–

9.3% or USD 240

–

460 billion), followed by banking (2.8

–

4.7% or USD 200

–

340 billion) and healthcare (1.8

–

3.2%

or USD 150

–

260 billion). The most significant effects

appear in marketing and sales (dark-blue cells across all
rows), substantial gains in customer operations
(especially in banking and insurance), product R&D
(pharmaceuticals,

electronics),

and

supply-chain

management (manufacturing and telecommunications).
Software development and risk management see
moderate benefits, whereas strategy, finance, corporate
IT, and HR show the most noticeable efficiency gains.

Fig. 2. Generative AI use cases will have different impacts on business functions across industries [15]

PwC, extrapolating productivity growth and consumer
demand, forecasts a 14% increase in global GDP by 2030
(USD 15.7 trillion) [16].

Academic research adds further detail on benefit
mechanisms. Report [17] finds that 80% of data and
analytics leaders expect a business environment
transformation driven by generative AI, identifying the
ability to rapidly integrate models into existing decision-
making processes as the key success factor. These

findings align with McKinsey’s State of AI survey, which

reports that 78% of respondents use AI in at least one

business function and 71% regularly apply generative
tools, six percentage points higher than six months
earlier [18]. Figure 3 shows that after a slight dip in 2022
(from 56% to 50% of companies using AI in one or more
functions), a sharp rise began in 2023

–

2024: by the end

of 2024, 78% of organizations employed AI in one or
more functions; 63% in two or more; 45% in three or
more; 28% in four or more; and 16% in five or more. The
most striking growth is in multifunctional deployment

—

firms using AI in three areas nearly tripled, and in four
regions, almost quadrupled

—

indicating a shift from

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isolated experiments to broad AI integration across all key business processes.

Fig. 3. Business functions at respondents’ organizations that are using AI, % of respondents [18]

These qualitative data support the conclusion of
consulting and academic works that measurable
productivity gains and competitive superiority arise only

where AI is embedded in an organization’s strategic

contours rather than existing as isolated experiments.

Labor productivity

—

measured as output per employee

or hour worked

—

remains the primary indicator through

which firms capture economic returns from AI: an OECD
meta-analysis across nine advanced economies for
2016

–

2021 showed that mere presence of AI solutions

in one primary business function correlates with a 0

–

11% productivity increase at the enterprise level [19].
For manufacturing sites, the analogous throughput
metric is Overall Equipment Effectiveness: AI-driven
computer-vision systems can automatically capture
telemetry on Availability, Performance, and Quality,
thereby raising OEE to the global benchmark of 85%
without manual data entry [20]. In service industries,
critical metrics are Time-to-Market and Customer
Lifetime Value; Gartner emphasizes that generative AI
accelerates product and campaign launches by
eliminating

content

bottlenecks

and

making

personalization a continuous feature, directly reducing
TTM and increasing CLV [21].

Empirical cases illustrate how these metrics change in
practice. Google deployed a DeepMind AI system for
autonomous control of data centre cooling: the
algorithm optimizes cold-air delivery in minute-by-
minute intervals. It reduces cooling energy consumption
by 40%, equating to double-digit operational cost
savings and increased labor productivity since the same
compute volume now requires less energy and fewer

maintenance staff [22].

Amazon cemented this effect at the logistics level: by
early 2025, over 750,000 robots were operating in its
warehouses, and analysts estimate potential annual
fulfillment cost savings of USD 10 billion if 30

–

40% of

U.S. orders pass through next-generation robot-centric
centres; this directly increases OEE and lowers per-
package handling costs [23].

In retail, Nike uses computer vision in its Nike Fit service:
foot scanning via a mobile app creates a 3-D model and
provides exact shoe sizing; the company reports a
substantial reduction in returns, and industry reviews
note that incorrect sizing underlies most fashion-item
returns, so reducing this rate directly extends CLV and
frees working capital [24].

At Starbucks, the Deep Brew predictive platform
personalizes offers for 34.3 million active Rewards
members. In 2024, the base grew by 13% year over year,
and the CEO attributes increased visit frequency and
average ticket size to AI-generated targeted offers; this
is reflected in metrics as simultaneous increases in CLV
and reductions in marketing CAC due to higher
conversion of existing customers [25].

Thus, across industries, AI demonstrably improves
fundamental performance indicators: in manufacturing,
OEE and energy costs; in logistics, order-handling costs;
and in retail and food service, return rates, visit
frequency, and customer lifetime value. The consistent
factor in all successful cases is measured pre- and post-
implementation metrics, preventing the conflation of
technological enthusiasm with real productivity gains.

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Multinationals reap the most significant AI benefits for
long-term competitiveness where the technology
accelerates innovation cycles, makes personalization
the default, and enhances supply-chain flexibility.
Breakthrough models such as Gemini already act as in-
factory co-scientists: Google demonstrates how the new
version generates hypotheses for drug development,
reducing lab iteration timelines from months to days
[26]. In the consumer sector, Unilever uses digital twins
of packaging and formulations, enabling marketing
teams to produce photorealistic content without
physical photoshoots and to launch new products faster
than competitors can secure studio time [27].

New constraints accompany this accruing advantage.
First, regulatory costs: the EU AI Act mandates testing,
audit, and registration of high-risk systems [28]. Second,
the capital intensity of infrastructure: IDC forecasts that
global spending on AI-supporting technologies will reach
USD 337 billion by 2025, a significant portion of which
will go to specialized GPU clusters and cooling [5]. The
hype cycle effect remains a real threat: Gartner places
most generative tools at the peak of inflated
expectations and notes rising project withdrawals due to
the gap between demo potential and operational
readiness; analysts warn that without clear business
metrics and systematic risk management, firms risk
entering the trough of disillusionment and losing
investment momentum [29]. Collectively, this indicates
that sustainable competitive advantage is shaped not

merely by AI adoption per se, but by a firm’s ability to

extract value simultaneously from rapid innovation,
personalization, and flexible supply chains

—

and,

critically, to systematically mitigate the regulatory,
financial, and organizational costs that inevitably
accompany a technological leap.

In summary, this analysis demonstrates that
implementing AI solutions in manufacturing, logistics,
retail, and services leads to significant improvements in
key metrics

—

from labor productivity and OEE to

reduced TTM and increased CLV

—

in the cases of Google,

Amazon, Nike, and Starbucks, and that by accelerating
innovation cycles and personalization, firms secure
additional competitive dividends. At the same time,
long-term advantage is achieved only through strict
adherence to measurable KPIs and proactive
management of the regulatory, financial, and
organizational costs that invariably accompany the
digital leap. The concluding section will formulate

practical recommendations and strategic insights for

multinational enterprises seeking to convert AI’s

potential into sustainable economic superiority.

CONCLUSION

In the present study, it has been demonstrated that
large-scale implementation of artificial intelligence
technologies in multinational corporations yields short-
term economic effects, such as revenue growth,
reduced operating costs, and increased returns on
capital and labor, but also lays the groundwork for
sustainable competitive advantage. Analysis of industry
reports and empirical cases (Google, Amazon, Nike,
Starbucks) confirmed that precise measurement of key
indicators before and after implementation (OEE, labor
productivity, TTM, CLV, etc.) enables objective

evaluation of AI initiatives’ effectiveness and prevents

digital enthusiasm without real returns. Macroeconomic
forecasts by McKinsey and PwC point to trillions of
dollars in added value and waves of GDP growth.
However, they also emphasize that, without
coordinated

efforts

to

develop

infrastructure,

competencies, and flexible organizational mechanisms,
these figures risk remaining potential rather than
realized impacts.

From the perspective of the resource-based theory of
the firm, unique data, algorithms, and computing power
meet the VRIN criteria and become strategic barriers to
market entry. At the same time, research on

organizations’ dynamic capabilities underscores that

technology alone

—

absent the organizational reflex and

the ability to sense, seize, and transform new
opportunities

—

will not lead to sustainable growth. The

fact that 74 % of companies failed to scale pilot projects
illustrates the risk of a gap between proven utility and
mass adoption [14].

These findings provide practical recommendations for
multinational enterprises. First, AI solutions must be
integrated into strategic frameworks

—

from supply

chains to product development and customer-facing
operations

—

and each project should be aligned with

specific KPIs. Second, firms should establish internal
processes for training and disseminating competencies
to ensure a rapid response to market changes and
technological shifts. Third, regulatory requirements,
financial risks, and the hype cycle should be anticipated
by implementing robust audit, testing, and expectation
management systems.

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In conclusion, we emphasize that a complete
transformation under AI influence is possible only
through a systemic approach: combining technological
investments with the development of dynamic
capabilities and rigorous control of economic,
organizational, and regulatory dimensions. Such a
synergistic

approach will

enable

multinational

corporations to achieve isolated technological successes
and secure long-term competitive advantages in the
face of intensifying global competition.

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