Executive Summary
Artificial intelligence (AI) has transcended the realm of experimentation to become one of the most
profound transformative technologies of the 21st century. Fueled by exponential gains in computing
power, data availability, and algorithmic sophistication, AI—particularly generative AI (genAI)—is re‐
shaping industries, redefining the nature of work, and altering the structure of the global economy.
This report provides a comprehensive analysis of this emerging economic model, examining trans‐
formations across key sectors, shifts in business strategy, and the evolving global power dynamics.
Our analysis reveals an economic landscape in rapid transition. While AI adoption is still in its early
stages for many, front-running organizations are already realizing significant gains in productivity, effi‐
ciency, and revenue. The technology is revolutionizing knowledge-driven fields, augmenting human
capabilities, and creating new avenues for value creation. However, these opportunities are not evenly
distributed. A significant gap is emerging between advanced economies (AEs) and low-income coun‐
tries (LICs), driven by disparities in AI preparedness, infrastructure access, and sectoral exposure. This
divergence threatens to exacerbate global income inequality, concentrating the benefits of the AI
revolution within a few nations and corporations.
Key findings indicate that AI could contribute trillions to the global economy, with projections suggest‐
ing the AI market could reach $4.8 trillion by 2033. This growth will be accompanied by massive labor
market shifts, requiring up to 12 million occupational transitions in Europe alone by 2030 and placing a
premium on technological and social-emotional skills. As data solidifies its role as a critical economic
asset, business models are evolving to prioritize innovation, personalization, and ecosystem collabora‐
tion. Navigating this new era requires strategic foresight, responsible governance, and collective ac‐
tion to ensure that the transformative potential of AI serves as a force for inclusive and sustainable
growth.
1. The New Economic Model in the AI Era
The advent of AI marks a fundamental shift in economic paradigms, moving beyond incremental im‐
provements to catalyze a complete reinvention of how value is created and captured. While past tech‐
nological revolutions centered on automating manual labor in manufacturing, AI is set to revolutionize
all knowledge-driven fields. This transformation is reshaping the platform, gig, and knowledge eco‐
nomies, altering how tasks are performed and redefining value across entire ecosystems.
Transformations in Economic Paradigms
The Knowledge Economy: AI’s most profound impact is on the knowledge economy. Generative
AI excels at processing unstructured data and automating cognitive tasks like writing, coding, and
analysis, which form the core of knowledge work. This is not merely about automation but aug‐
mentation. AI is enhancing human productivity and creativity through “fusion skills” where hu‐
mans and AI collaborate, allowing workers to focus on higher-value activities like strategic judg‐
ment, complex problem-solving, and innovation. Projections from the McKinsey Global Institute
(2024) suggest that by 2030, AI will drive a 25% increase in demand for technological skills and an
11% rise in social-emotional skills in Europe, while demand for basic cognitive skills could decline
by 14%.
The Platform and Gig Economies: AI is supercharging the platform economy by enabling hyperpersonalization, optimizing logistics, and enhancing user engagement at an unprecedented scale.
For example, Mojang Studios uses AI to process player sentiment data 66% faster, allowing it to
tailor experiences for millions of Minecraft players (World Economic Forum, 2025). In the gig eco‐
nomy, AI is transforming how freelance work is sourced, managed, and executed. While this can
increase efficiency, it also accelerates the pace of occupational transitions. Lower-wage workers in
roles susceptible to automation face a redeployment risk that is 3 to 14 times higher than that of
high-wage workers, risking greater labor market polarization if not met with proactive reskilling
initiatives (McKinsey Global Institute, 2024).
2. Sectoral Impacts and Transformations
AI’s influence is not uniform; it manifests differently across sectors based on their reliance on data, hu‐
man expertise, and digital infrastructure. Industries are moving from isolated pilot projects to enter‐
prise-wide integration, fundamentally altering their operational and business models.
Technology Sector
The technology sector is both a primary driver and a beneficiary of the AI revolution. Firms are making
massive investments in the foundational infrastructure required for AI, including specialized AI chips,
high-capacity servers, and data centers. According to the World Economic Forum (2025), technology
firms are spending heavily on R&D to develop the AI applications that will underpin transformations in
all other industries. This has created a feedback loop where advancements in hardware and software
fuel new AI capabilities, which in turn drive further demand for technological infrastructure.
Financial Services
The financial services industry is a leading adopter of AI, leveraging the technology for fraud detec‐
tion, risk management, and customer service. AI-driven chatbots and personalized services are be‐
coming standard. The London Stock Exchange Group, for instance, uses an AI-powered service that
has reduced query resolution times by 50% (World Economic Forum, 2025). The International Monet‐
ary Fund (IMF) notes that AI-driven productivity gains are particularly high in non-tradable service sec‐
tors like finance, which benefit from advanced digital infrastructure and a skilled workforce (IMF,
2025).
Manufacturing and Industry
In manufacturing, AI is enabling a shift towards “smart” factories and resilient supply chains. Key ap‐
plications include predictive maintenance, automated quality control, and production process optimiz‐
ation. Swiss Federal Railways (SBB) implemented an AI-powered visual inspection solution that re‐
duced inspection times by 60% and errors by 20-30% for critical train components (World Economic
Forum, 2025). This focus on operational efficiency is a primary driver of AI adoption in the industrial
sector.
Service Sector
Across the broader service sector, AI is revolutionizing customer experience. Over 70% of customers
feel AI improves their shopping experience by saving time and offering personalized interactions
(World Economic Forum, 2025). Companies are using AI to understand consumer habits, provide
tailored recommendations, and automate service operations. A California-based wellness start-up
launched an AI agent that reduced the number of queries requiring human assistance by 78%, show‐
casing AI’s potential to boost efficiency and allow businesses to focus on core operations (World
Economic Forum, 2025).
3. Corporate Investment and Real-World Case Studies
Global corporations are moving aggressively to integrate AI, recognizing it as a critical component of
future competitiveness. This is evident in both direct investment and the deployment of innovative, AIdriven solutions.
BMW Group introduced a platform with multiple genAI agents across its sales, supply chain, and
marketing functions. The system intelligently selects data sources to provide real-time insights,
improving productivity in corporate functions and on showroom floors by 30-40% (World Economic
Forum, 2025).
A leading technology firm developed a designer AI application that streamlines the creative pro‐
cess for new patterns by analyzing trends, sales data, and customer feedback. This tool empowers
design teams to create commercially successful collections, directly impacting sales and revenue
(World Economic Forum, 2025).
Beko, a home appliance manufacturer, developed a connected system of AI applications to over‐
haul its after-sales process. The system uses customer interaction data to predict issues, suggest
upsell opportunities, and provide real-time troubleshooting guidance to technicians, ensuring more
efficient service delivery (World Economic Forum, 2025).
These cases illustrate a clear trend: companies are moving beyond simple automation to embed AI
deeply into core business functions, from R&D and marketing to customer service and supply chain
management. However, adoption remains uneven. While 65% of organizations report using genAI in at
least one function, 74% report challenges in scaling AI across the enterprise (World Economic Forum,
2025).
4. Economic Forecasts and Projections
Credible international institutions project that AI will have a monumental impact on global economic
output, productivity, and the future of work.
Economic Growth and Market Size: UNCTAD (2025) projects the global AI market will grow 25-
fold from $189 billion in 2023 to $4.8 trillion by 2033. The IMF (2025) forecasts that AI could
expand global GDP by nearly 4% in a high-growth scenario over the next decade. Companies lead‐
ing in AI adoption are already outperforming peers by 15% in revenue generation, a figure
projected to more than double by 2026 (World Economic Forum, 2025).
Productivity Gains: The McKinsey Global Institute (2024) estimates that rapid AI adoption,
combined with proactive workforce retraining, could unlock 3.1% annual productivity growth
in Europe. This stands in stark contrast to a potential 0.3% growth rate without such measures.
Early adopters of genAI tools are already achieving 2.4 times greater productivity and cost savings
of 13% (World Economic Forum, 2025).
Future of Work: The transition will be disruptive. By 2030, an estimated 30% of current hours
worked in the U.S. and 27% in Europe could be automated, with genAI accounting for a sig‐
nificant portion of this potential (McKinsey Global Institute, 2024). This will necessitate massive
occupational shifts, with Europe potentially facing 12 million transitions by 2030, double the
pre-pandemic pace. UNCTAD (2025) notes that up to 40% of global jobs could be affected by AI,
with advanced economies being the most exposed but also the best positioned to benefit.
5. Evolving Business Models and Competitive Dynamics
AI is forcing a fundamental rethink of business strategy, shifting the focus from incremental efficiency
gains to wholesale business model reinvention. Over 82% of businesses now see genAI as a primary
lever for reinvention (World Economic Forum, 2025).
Value creation is no longer solely about producing a good or service but about leveraging data and AI
to deliver personalized, intelligent, and adaptive experiences. This has elevated data to the status of a
core economic asset. A strong digital core—comprising secure data, connected systems, and an
open architecture—is now a prerequisite for competitiveness.
Competitive dynamics are also shifting. Early adopters of AI are creating a significant performance
gap, outperforming their peers in revenue growth and cost efficiency. This advantage stems from their
ability to innovate faster, understand customers more deeply, and optimize operations more effect‐
ively. The rise of AI is also reshaping value chains, enabling the emergence of new intermediaries
while displacing traditional ones. Companies that fail to adapt risk being outpaced and marginalized in
an economy where intelligence and adaptability are the new currencies.
6. The Role of Data as an Economic Asset
In the AI-driven economy, data is the foundational asset. The effectiveness of AI models is directly pro‐
portional to the volume, quality, and relevance of the data they are trained on. This has transformed
data from a byproduct of business operations into a strategic resource that underpins innovation,
value creation, and competitive advantage.
The IMF (2025) highlights that access to essential data is a key determinant of a country’s ability to
harness AI. Similarly, the World Economic Forum (2025) emphasizes that a strong “digital core” with
secure and connected data is a critical enabler for AI adoption. Organizations that can effectively col‐
lect, manage, and analyze vast datasets are better positioned to develop proprietary AI solutions, per‐
sonalize customer experiences, and uncover novel insights that drive business growth. This reliance
on data also raises critical challenges related to governance, privacy, security, and ethics, making
robust frameworks for data management and trust essential for sustainable success.
7. Global Economic Shifts and Power Dynamics
The AI revolution is not unfolding uniformly across the globe; instead, it is creating new geopolitical
and economic fissures. The benefits of AI are currently concentrated in a few advanced economies,
threatening to widen the income gap between the global north and south.
The Widening Divide: The IMF (2025) warns that AI will likely exacerbate cross-country income
inequality. AEs are projected to realize up to twice the income gains of LICs, primarily due to
advantages in three areas: AI exposure (a higher share of jobs and sectors amenable to AI), AI
preparedness (stronger digital infrastructure, human capital, and regulatory frameworks), and AI
access (availability of advanced hardware and data). Even if LICs improve their preparedness, the
IMF concludes that these policy interventions are unlikely to fully eliminate the disparities.
Concentration of Power: AI development is highly concentrated. UNCTAD (2025) reports that
the U.S. and China account for 33% of AI publications and 60% of AI patents. Just 100 top compan‐
ies (excluding Chinese firms) funded 40% of AI R&D in 2022. This concentration extends to infra‐
structure, with the U.S. holding over half of the world’s global computing power. This dominance
gives a few nations and corporations immense influence over the trajectory of AI development and
governance. As of 2025, 118 countries, mostly from the developing world, have no involvement in
major AI governance initiatives.
Shifting Trade and Economic Dynamics: AI’s impact on productivity could alter traditional eco‐
nomic relationships. The IMF (2025) introduces the possibility of an “inverse Balassa-Samuel‐
son effect.” Traditionally, productivity gains in tradable sectors cause a country’s real exchange
rate to appreciate. However, because AI is expected to generate massive productivity gains in
non-tradable sectors (like healthcare and education) in AEs, it could lower the relative price of
these services, leading to a depreciation of their currencies. This could, in turn, improve the
current account balances of AEs, even as they invest heavily in AI.
Conclusion
The AI-driven economy represents a paradigm shift with the potential to unlock unprecedented levels
of productivity, innovation, and economic growth. Early adopters and knowledge-intensive sectors are
already demonstrating the transformative power of this technology. However, the path forward is
fraught with challenges. The rapid pace of change threatens to displace workers and widen the gap
between skilled and unskilled labor. More alarmingly, a structural divide is emerging on a global scale,
with a few technologically advanced nations poised to capture a disproportionate share of AI’s
benefits, potentially leaving developing economies further behind.
Addressing these challenges requires a multi-faceted and collaborative approach. For businesses, the
imperative is to move beyond experimentation to a strategic, enterprise-wide reinvention, focusing on
building a robust digital core, fostering a culture of human-AI collaboration, and investing in continu‐
ous upskilling. For policymakers, the priorities must include strengthening digital infrastructure, re‐
forming education systems to build future-ready skills, and creating social safety nets to support work‐
ers through the transition. On the international stage, inclusive governance frameworks are urgently
needed to ensure that the development and deployment of AI are guided by principles of fairness,
transparency, and shared prosperity. The coming years will be pivotal in determining whether AI
becomes a force for equitable progress or a driver of deeper global division.
References
AI in Action: Beyond Experimentation to Transform Industry – World Economic Forum (https://re‐
ports.weforum.org/docs/
WEF_AI_in_Action_Beyond_Experimentation_to_Transform_Industry_2025.pdf)
A new future of work: The race to deploy AI and raise skills in Europe and beyond – McKinsey
Global Institute (https://www.mckinsey.com/mgi/our-research/a-new-future-of-work-the-race-todeploy-ai-and-raise-skills-in-europe-and-beyond)
Technology and Innovation Report 2025: Inclusive artificial intelligence for development – UNCTAD
(https://unctad.org/publication/technology-and-innovation-report-2025)
The Global Impact of AI: Mind the Gap – International Monetary Fund (https://www.imf.org/-/media/
files/publications/wp/2025/english/wpiea2025076-print-pdf.pdf)
