AI Adoption Statistics 2026: Market Growth, Usage & Economic Impact

Dive into AI adoption statistics for 2026. Learn about the 78% global adoption rate, projected $15.7T economic impact, workforce transformations, and the global trust gap.

AI Adoption Statistics: How Many Companies Use AI & Other Key Trends

By October 14, 2025, artificial intelligence will no longer be a futuristic concept but a daily reality in the business world. Yet, a stunning paradox defines this new era. Global organizational adoption has skyrocketed to 78% [55], marking a staggering 41.8% relative increase since 2023 alone, based on data from McKinsey and Stanford HAI [40].

Despite this rush, a mere 1% of companies describe their AI rollouts as ‘mature’ [31]. Why is there such a massive gap between using AI and mastering it? The data reveals a fractured landscape of deep disparities:

  • Investment vs. Performance: The United States leads with $109.1 billion in private AI investment [55], yet China is rapidly closing the performance gap on key benchmarks.
  • The Corporate Divide: Large enterprises are nearly four times more likely to formally adopt AI than small firms (41.17% vs. 11.21% [15]).
  • The Value Gap: Despite functional gains, over 80% of organizations report no tangible impact on their enterprise-level EBIT from their AI initiatives [40].

This is not a simple story of growth, but a complex tale of strategy, execution, and value. What follows is a statistical examination of what separates the few who have achieved mastery from the many who are still just experimenting.

The State of Global AI Adoption: A Quantitative Overview

The integration of Artificial Intelligence into the global business landscape is no longer a future forecast; it is a present-day reality. The numbers tell a story of breathtaking speed and scale, revealing how AI has become a dominant force in the modern economy.

The Acceleration Curve: From Niche to Mainstream

By 2025, an incredible 78% of organizations worldwide have integrated AI into their operations [55], [31]. This figure translates to approximately 280 million of the world’s 359 million companies now actively using artificial intelligence [31]. This near-total adoption is the result of a remarkable growth surge over just two years.

  • 2023: Adoption stood at 55% [40].
  • Early 2024: The rate jumped to 72% [40].
  • Late 2024-2025: The figure stabilized at the current 78%, marking a total relative increase of 41.8%.

However, the pace of this expansion is now shifting. The explosive 31% growth seen between 2023 and early 2024 has moderated to a more measured 8.3% increase. This signals a crucial transition, suggesting AI is moving beyond its initial hype and into the early majority phase of the technology adoption lifecycle.

The Generative AI Catalyst

What single force is responsible for much of this acceleration? Generative AI. According to a 2024 McKinsey survey, its use within organizations skyrocketed from 33% in 2023 to an astonishing 71% just one year later [40]. This 115% relative increase [40] represents one of the fastest technology uptakes in modern business history. 

In fact, GenAI’s adoption curve has outpaced the initial business integration of both cloud computing and mobile technology, establishing it as the primary engine driving the broader AI revolution.

The Expanding User Base

This corporate surge is matched by an explosion in individual use. Projections for 2025 estimate that the number of global AI tool users will reach 378 million [14], a massive leap from just 116 million five years ago. The largest single-year jump on record added 64 million new users [14].

A landmark 2025 KPMG report reveals a stunning reality: two-thirds of the global population now uses AI on a regular basis [37]. This trend is set to continue, with forecasts showing the United States alone will grow from 133 million users in 2025 to an estimated 241.5 million by 2030 [31].

A Fractured Landscape: Disparities in AI Adoption

While the impressive 78% global adoption rate suggests a world marching in lockstep into the AI era [55], the reality on the ground is far more complex. Beneath this single number lies a deeply fractured landscape.

The true story of AI adoption is one of stark contrasts, where geography, company size, and strategic maturity create a world of haves and have-nots.

Geographic Leadership and Lags

The global AI race is not a single event but a series of regional competitions, each defined by unique strengths in investment, implementation, and technical skill. The differences between North America, Europe, and Asia are dramatic.

North America: The Investment Powerhouse

The United States isn’t just participating in the AI revolution; it’s bankrolling it. A 2024 Stanford HAI report reveals that private AI investment in the U.S. soared to an incredible $109.1 billion [55].

To put that figure in perspective, it is nearly 12 times the investment in China and 24 times that of the United Kingdom [55]. This financial firepower fuels an 82% organizational adoption rate [31], with a staggering 99% of Fortune 500 companies now leveraging AI [31].

However, a more formal look from the U.S. Census Bureau tells a different story. Structured, firm-level implementation is much lower, though it is accelerating from 3.7% in late 2023 to 6.6% by late 2024 [31]. This highlights a crucial gap between casual tool use and deep enterprise integration.

The European Union: A Tale of Two Metrics

How widespread is AI in the European Union? The answer depends entirely on how you measure it. Broad surveys indicate an 80% organizational adoption rate [31], placing the EU on par with North America.

Yet, official Eurostat data reveals a starkly different reality. It reports that just 13.48% of EU enterprises have formally adopted AI [15]. This massive discrepancy exposes the divide between employees using AI tools and companies making strategic, top-down investments. Even within the formal data, the picture is fragmented.

  • The Leader: Denmark leads the bloc with 27.58% of enterprises using AI [15].
  • The Laggard: Romania sits at the bottom, with an adoption rate of just 3.07% [15].
  • The Rising Star: Sweden demonstrates the region’s dynamic potential, achieving a remarkable 14.72 percentage point increase in a single year, which translates to a 142% relative growth rate [15].

Asia-Pacific: The Rise of a Contender

While the U.S. dominates investment, Greater China is closing the gap with astonishing speed and momentum. In 2024, the region posted a 27% year-over-year increase in AI adoption, the largest gain anywhere in the world [31].

This rapid expansion is fueled by $9.3 billion in private investment and the development of 15 notable AI models in 2024 alone [55]. But the most critical development is happening at the technical level.

According to a Stanford HAI analysis, the performance gap between U.S. and Chinese models on key benchmarks like MMLU and HumanEval has collapsed. What was a double-digit difference in 2023 has shrunk to near parity in 2024 [55], signaling a major shift in the global balance of AI power.

The Corporate Digital Divide: Enterprise Size Matters

Beyond borders, the clearest dividing line in the AI landscape is the size of the company. A profound digital divide separates resource-rich large corporations from the small and medium-sized enterprises that form the backbone of the global economy.

Large Enterprise Dominance

The data is unequivocal: bigger companies are far ahead in the AI race. According to Eurostat, large enterprises with over 250 employees are 3.67 times more likely to formally adopt AI than their smaller counterparts [15]. This dominance is reflected across the board.

  • Strategic Maturity: According to McKinsey, large organizations are more than twice as likely to have established AI roadmaps, dedicated teams, and robust risk mitigation plans for cybersecurity and privacy [40].
  • Formal Adoption: 41.17% for large firms, versus 20.97% for medium firms and just 11.21% for small businesses [15].

Small Business Adoption: Tool-Based and Task-Oriented

While they lag in formal adoption, small businesses are not sitting on the sidelines. Instead, they are embracing AI in a pragmatic, tool-focused way.

An overwhelming 89% of small businesses worldwide now use AI tools for daily tasks such as content creation and data analysis [31]. 

The grassroots approach is delivering clear benefits, with over 60% of owners reporting improvements in both employee job satisfaction and productivity [31]. This trend is only set to grow, as U.S. firms with 100-249 employees expect a 62.5% increase in AI usage [31].

The Implementation-Maturity Gap

Perhaps the most telling disparity is not who is using AI, but how deeply they are using it. High adoption rates have created a dangerous illusion of progress.

A recent McKinsey analysis delivered a stunning reality check: a mere 1% of companies describe their AI rollouts as ‘mature’ [31]. This reveals a massive chasm between widespread experimentation and true, value-driving integration.

The maturity gap is the primary reason why over 80% of organizations report seeing no tangible impact on their enterprise-level EBIT, despite functional gains [40]. The data reveals exactly why this is happening.

  • Fewer than one-third of organizations follow key practices for AI adoption and scaling [40].
  • Fewer than one in five track well-defined KPIs for their AI solutions [40].

AI at Work: Deployment Across Industries and Business Functions

Where is AI actually making an impact? While adoption is widespread, its true power is concentrated in specific industries and business functions that reap the most immediate rewards.

A closer look at the data reveals a fascinating map of where AI is already indispensable versus where its revolution is just beginning.

Industry-Specific Adoption Rates

Which industries are leading the AI charge? While some sectors were natural early adopters, the data reveals a surprisingly wide reach across the entire economy, with clear frontrunners and a broad base of implementation.

Generative AI’s Broad Industrial Reach

Generative AI is no longer confined to Silicon Valley. Its adoption is widespread, with the Technology sector leading the pack.

IndustryGenerative AI Adoption Rate
Technology88% [31]
Professional Services80% [31]
Advanced Industries79% [31]
Energy and Materials59% [31]

What’s most revealing is the narrow 29-percentage-point gap between the highest and lowest adopters. This signals that GenAI is a broad-based industrial tool, not a phenomenon isolated to tech-centric fields.

Sectoral Deep Dives: Healthcare and Retail

In Healthcare, AI is already a cornerstone technology, with an incredible 90% of hospitals now using it for critical tasks like diagnosis and patient monitoring [65]. In 2023, the U.S. Food and Drug Administration approved 223 new AI-enabled medical devices, a monumental leap from just six approvals in 2015 [55].

The Retail sector is experiencing similar momentum, as 80% of executives expect to adopt AI automation by the end of 2025 [6]. The financial incentive is undeniable, with Netflix’s pioneering recommendation engine estimated to generate over $1 billion in revenue each year [11].

Functional Deployment Within Organizations

Inside the modern organization, AI is not a one-size-fits-all solution. Its deployment is highly strategic, creating hubs of activity in specific departments. The data reveals a fascinating split: general AI powers the company’s core infrastructure, while generative AI transforms its customer-facing operations.

Core Business Functions for General AI

According to a 2024 McKinsey report, the top functions for general AI use are IT, Marketing and Sales, and Service Operations. The IT department leads with 36% adoption, a figure that has surged by a third from 27% in early 2024, highlighting a focus on internal infrastructure and security [40].

On average, organizations deploy AI across three separate business functions [40]. In fact, 45% of companies now use AI in three or more areas, signaling its deep integration into core operations [31].

Generative AI’s Impact on Customer-Facing Roles

Generative AI has found its most powerful application in roles that directly touch the customer. Marketing and Sales departments report a 42% adoption rate for GenAI, more than double the overall average of 19% across all business functions [31]. This stands in stark contrast to operational departments, where GenAI has yet to make significant inroads. 

Adoption in Supply Chain management is just 9%, while in Manufacturing it is a mere 5%, clearly defining the current boundaries of its application [31].

A Closer Look at AI Technologies in Use

What does “using AI” actually mean in practice? The term covers a vast technological spectrum, from simple text analysis to sophisticated machine learning.

The specific tools companies deploy depend heavily on their size and strategic goals. Among companies using generative AI, the most common applications include:

  • Text-based outputs: 63% of users [40]
  • Image creation: Over 33% of users [40]
  • Computer code: Over 25% of users [40]

In the European Union, Eurostat data shows text mining is the most prevalent technology, used by 6.88% of all enterprises [15]. However, a significant capability gap emerges when looking at company size.

Large enterprises are far more likely to deploy advanced systems, with 20.58% using machine learning for data analysis and 20.40% using AI-based robotic process automation [15].

The Economic Engine: Market Size, Investment, and Business Value

What does it cost to build the future? When it comes to Artificial Intelligence, the price tag is measured in trillions, fueled by a torrent of capital and the promise of transformative economic returns. The numbers behind the AI economy are staggering, revealing a market undergoing explosive growth and beginning to reshape the financial performance of businesses worldwide.

Global Market Valuations and Projections

In 2025, the global AI market commands a stunning $391 billion valuation [25]. The tools and services segment alone makes up $244 billion of that total [59].

However, this is just the beginning of a steep upward climb. Projections from Grand View Research forecast the market will swell to an incredible $1.81 trillion by 2030, charting a compound annual growth rate (CAGR) of 35.9% [24]. 

While other forecasts offer a more conservative range between $826.7 billion and the $1.81 trillion high [29], the direction is unmistakable.

The Flow of Capital: Investment in AI

This market expansion is powered by colossal investments from both private corporations and national governments, each pouring billions into securing a competitive edge in the AI era.

Private and Corporate Investment

Private capital overwhelmingly flows from the United States, which poured an immense $109.1 billion into AI in 2024 alone [55]. This trend shows no signs of slowing, as 58% of businesses globally plan to increase their AI investments in the coming year [30].

Generative AI has become a primary magnet for this funding, attracting $33.9 billion in global investment in 2024, an 18.7% increase from the previous year [55]. This spending has direct operational consequences, with a 2025 study showing 63% of leading companies are increasing their cloud budgets specifically to support new GenAI workloads [31].

Government-Led Initiatives

Public sector investment is equally ambitious, with nations launching monumental projects to build sovereign AI capabilities. The scale of these commitments underscores the global strategic importance of AI development [55].

Country/RegionInvestment Commitment
Saudi Arabia$100 billion
France€109 billion
China$47.5 billion (semiconductor fund)
Canada$2.4 billion
India$1.25 billion

Quantifying the Business Impact

But what is the return on this colossal investment? The data reveals a story of immense potential, with early gains in revenue and efficiency tempered by the challenge of achieving broad, enterprise-level impact.

Revenue Growth and Economic Contribution

The projected economic prize is almost beyond comprehension. A landmark PwC study predicts AI could inject a colossal $15.7 trillion into the global economy by 2030 [47]. This economic uplift is expected across all sectors. 

Projections show Manufacturing gaining $3.78 trillion and the Wholesale and Retail sector adding $2.23 trillion [50]. At the functional level, the impact is already visible, with AI deployment in Marketing and Sales shown to increase qualified leads by as much as 50% [27].

Cost Reduction and Productivity Gains

Beyond growing revenue, AI is proving to be a powerful tool for slashing costs and boosting efficiency. A 2024 McKinsey report found that Service Operations (49%) and Supply Chain management (43%) are the functions reporting the highest levels of cost savings from generative AI [31].

Interestingly, while Marketing and Sales departments report the highest frequency of revenue increases (71%), most of these gains remain under 5% [31]. 

The greater promise may lie in long-term efficiency, with projections showing AI could improve employee productivity by 40% by 2035 [48]. Another study found that manufacturing businesses using AI already perform 12% better than their peers [42].

The Challenge of Enterprise-Level Value

Yet, a critical paradox emerges from the data. Despite these functional gains, a recent McKinsey analysis found that over 80% of organizations see no tangible impact on enterprise-level EBIT [40].

What separates the successful few from the rest? The same study reveals that the single attribute with the biggest effect on achieving bottom-line impact is the fundamental redesign of workflows [40]. Other highly correlated factors include tracking well-defined KPIs for AI solutions and direct CEO oversight of governance [40]. 

The data also contains a stark finding: headcount reductions are one of the organizational attributes most closely linked to realizing bottom-line value from AI [40].

The Human Equation: Workforce Transformation and Public Trust

Beyond the balance sheets and market projections, the integration of AI is fundamentally a human story. This technological wave is profoundly reshaping the global workforce, creating both immense opportunity and deep-seated anxiety.

At the same time, it is testing public confidence in the very systems that are becoming central to modern life.

The Evolving Workforce

Is AI a job killer or a job creator? The data reveal a complex and often contradictory answer, painting a picture of a labor market undergoing a dramatic and painful restructuring.

Job Displacement and Creation Dynamics

The story of AI’s impact on jobs is one of stark contrasts, with significant displacement occurring alongside the birth of an entirely new economic sector.

  • The Net Job Loss: A Forrester projection estimates that while AI will create 9% new U.S. jobs, it is set to replace 16%, leading to a net loss of 7% of the nation’s workforce [20].
  • Uneven Sector Impact: This threat is not distributed equally. An analysis of the UK reveals automation potential as high as 62.6% in Water Management but as low as 8.5% in Education [61].
  • Global and Gender Disparities: The International Monetary Fund assesses that 40% of all global jobs are exposed to AI [33]. The impact is particularly sharp for women, who are three times more likely to be displaced by this shift [13].
  • The Rise of a New Workforce: Yet, on the other side of the equation, projections show that by 2025, an estimated 97 million people will work directly in the global AI space [52], signaling a massive new employment category.

The Shift in Hiring and Skills Demand

This workforce churn is creating an intense and urgent demand for new kinds of expertise. Half of all organizations using AI report needing more data scientists, a need that is reshaping the job market [40].

In the last year alone, AI-related job postings in the Information sector skyrocketed by an explosive 79.56% [32]. In response, two-thirds of employers are now actively trying to hire talent with specific AI skills [31]. This fierce competition for talent is even upending wage expectations. 

In a surprising twist, 52% of employers expect to allocate a larger share of revenue to wages by 2030, a strategic move to attract and retain the human talent required to manage and innovate with AI [31].

Public Perception and the Trust Deficit

While businesses pour billions into AI, the court of public opinion remains deeply skeptical. A major gap has opened between AI’s technical capabilities and the public’s willingness to trust it.

Global Sentiment: A World Divided

How do people feel about an AI-powered future? It depends entirely on where you ask. A Stanford HAI report reveals a massive 47-percentage-point gap in optimism, ranging from a high of 83% in China to a low of just 36% in the Netherlands [55]. However, these views are not set in stone.

Since 2022, optimism has been rising in several Western countries, including a 10-percentage-point increase in Germany and a 4-point jump in the United States [55]. This suggests that perceptions are slowly, but measurably, warming.

The Trust Gap and Accuracy Concerns

Despite some warming sentiment, a fundamental trust deficit remains a critical hurdle. A recent KPMG report found that globally, less than half of people (46%) trust AI [36]. This skepticism is rooted in a disconnect between experts and the public, as well as firsthand experience with the technology’s flaws.

The Trust Deficit: Key Factors
Expert vs. Public Divide: There is a 33-percentage-point gap between AI experts, 76% of whom see benefits outweighing risks, and the general public, where only 43% agree [46].
Direct Experience with Errors: Skepticism is fueled by experience. Roughly three-quarters of people report having seen a significant mistake in an AI Overview [19].
Inaccuracy as the Top Complaint: The most common issue is inaccurate content, cited by 42.1% of users who experience errors [19].
Low Overall Confidence: This lived experience with flawed outputs explains why a mere 8.5% of people believe they can ‘always trust’ the answers AI provides [19].

Governance, Risk, and the Path Forward

With AI now a standard corporate tool, the conversation has pivoted from mere adoption to effective control. Capturing real value depends on mastering governance, navigating new regulations, and confronting the obstacles that still stand in the way.

Corporate Governance and Risk Mitigation

While effective AI governance must start at the top, a striking leadership gap is undermining its potential. A McKinsey analysis reveals that direct CEO oversight is highly correlated with bottom-line impact, yet only 28% of organizations place this responsibility with their chief executive [40].

What happens when high-level supervision is absent? The risks become alarmingly real. An astonishing 27% of firms admit to checking 20% or less of their AI-generated content before use, creating a massive exposure to errors and brand damage [40].

In response, a growing number of organizations are finally sharpening their focus on risk mitigation. They are now actively managing critical threats such as content inaccuracy and intellectual property infringement [40].

The Accelerating Regulatory Landscape

Governments worldwide are in a race to build legal guardrails around AI. In the United States alone, the number of AI-related regulations doubled to 59 in 2024, according to a Stanford HAI report [55]. This reflects a massive global trend, with legislative mentions of AI skyrocketing by a factor of nine since 2016 [55]. The result is a staggering expansion of regulatory oversight.

As of 2025, an estimated 60% of the world’s population lives in a jurisdiction covered by some form of AI legislation [54]. This represents a monumental leap from just 10-15% in 2020, signaling a new era of global governance [54].

Overcoming Barriers to Adoption

Even with soaring usage rates, fundamental obstacles continue to prevent companies from achieving mature, value-driving AI integration. An OECD study identifies several primary barriers [45]:

  • Uncertainty over return on investment
  • A lack of data maturity
  • A scarcity of specialized talent

Beyond these core challenges, qualitative findings reveal a critical disconnect. Managers often struggle to link AI’s powerful capabilities to tangible business problems and frequently underestimate the profound cultural shifts necessary for successful implementation [45].Financial concerns at the highest levels compound these issues. A survey found that 40% of executives believe AI is still prohibitively expensive, a perception that continues to stall deeper, more meaningful investment [26].

Frequently Asked Questions

How many companies use AI in 2025?

By 2025, a staggering 78% of organizations worldwide have integrated AI into their operations [55]. This figure represents approximately 280 million of the globe’s 359 million companies actively using artificial intelligence [31].

Which country leads the world in AI adoption and investment?

The United States is the undisputed leader in both AI investment and adoption. In 2024, its private AI investment hit a massive $109.1 billion, a figure nearly 12 times greater than China’s [55]. Furthermore, 82% of U.S. organizations now use AI [31].

What is the projected size of the global AI market by 2030?

The global AI market is on a trajectory for explosive growth, with projections for 2030 reaching as high as $1.81 trillion [24]. While forecasts vary, even conservative estimates place the market size at $826.7 billion [29], underscoring a monumental economic shift.

Which industries have the highest rates of AI adoption?

The Technology sector leads the charge with an 88% generative AI adoption rate [31]. Other top-performing sectors include Professional Services at 80% and Advanced Industries at 79%, showcasing widespread integration across knowledge-based fields [31].

How is AI expected to impact the job market?

AI’s impact on the job market is twofold, acting as both a force of displacement and creation. One Forrester projection indicates AI will replace 16% of U.S. jobs but also create 9%, leading to a net 7% reduction [20]. 

Simultaneously, AI is fueling a new economic sector, with a projected 97 million people expected to work in the AI space worldwide [52].

What percentage of the public trusts AI?

Public trust in AI remains a significant challenge, with a 2025 KPMG report revealing that only 46% of people globally trust the technology [36]. This skepticism is deepened by accuracy issues, as a separate study found that a mere 8.5% of users believe they can ‘always trust’ AI-generated answers [19].

What are the main barriers preventing companies from adopting AI?

Companies face several key obstacles to deeper AI adoption, according to an OECD study [45]. The primary barriers include uncertainty about the return on investment (ROI) and a lack of data maturity. A persistent scarcity of specialized talent also remains a major roadblock for organizations [45].

Conclusion

The era of artificial intelligence is no longer on the horizon; it has arrived. A staggering 78% of companies now use AI in some capacity [55]. Yet, this wave of adoption conceals a critical truth: a mere 1% of these implementations are considered mature [31]. This stark gap between widespread use and deep integration defines the current landscape. 

It creates a fractured reality, with deep divides separating North American investment from European formal adoption and distinguishing large enterprise dominance from small business agility. This technological shift is also profoundly reshaping the global workforce. 

While AI is projected to displace 16% of U.S. jobs [20], it is simultaneously expected to create a new economic sector for 97 million people worldwide [52]. Amid this transformation, a significant trust deficit persists. A revealing KPMG report shows that only 46% of the public currently trusts AI systems [36].Ultimately, the race for AI dominance is no longer about who adopts it first. The next chapter will be defined by who can master it, achieving the deep, value-driven implementation needed to unlock a projected $15.7 trillion global economic opportunity [47].

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49PwC (Responsible AI)https://www.pwc.com/us/en/services/consulting/library/artificial-intelligence-predictions/responsible-ai.html
50SAABRDShttps://saabrds.com/how-much-will-ai-contribute-to-the-global-economy-and-the-industrial-market/
51Salesforcehttps://www.salesforce.com/news/stories/generative-ai-statistics/#h-marketers-believe-generative-ai-will-transform-their-role-but-worry-about-accuracy
52Search Logisticshttps://www.searchlogistics.com/learn/statistics/artificial-intelligence-statistics/
53Semrushhttps://www.semrush.com/blog/ai-search-seo-traffic-study/
54Stanford HAI (2024 AI Index)https://hai.stanford.edu/ai-index/2024-ai-index-report/policy-and-governance
55Stanford HAI (2025 AI Index)https://hai.stanford.edu/ai-index/2025-ai-index-report
56Statista (AI Chart)https://www.statista.com/chart/17222/artificial-intelligence-marketing/
57Statista (Time to Million Users)https://www.statista.com/chart/29174/time-to-one-million-users/
58Statista (AI Forecasts)https://www.statista.com/forecasts/1451309/market-size-of-ai-us
59Statista (AI Outlook)https://www.statista.com/outlook/tmo/artificial-intelligence/worldwide
60TechJuryhttps://techjury.net/blog/google-search-statistics/#gref
61The Futures Centrehttps://www.thefuturescentre.org/signal/up-to-30-of-existing-uk-jobs-could-be-impacted-by-automation-by-early-2030s/
62The Vergehttps://www.theverge.com/climate-change/676528/ai-data-center-energy-forecast-bitcoin-mining
63Tractica (AI Market Forecasts)https://www.tractica.com/research/artificial-intelligence-market-forecasts/
64Tractica (NLP)https://www.tractica.com/research/natural-language-processing/
65Vention Teamshttps://ventionteams.com/solutions/ai/adoption-statistics