252 Fresh Generative AI Facts And ROI Stats (2026)

By Jawad Khan.  Last Updated on October 24, 2026
By / Last Updated On: October 24, 2026
gen ai stats

TL;DR: The Generative AI Statistics That Matter Most
1. ChatGPT reached 900 million weekly active users in February 2026 and was nearing 1 billion by September.
2. The Gemini app passed 1 billion monthly users, and Google AI Mode passed 1 billion too.
3. 49% of US adults now use AI chatbots, up from 33% in 2024.
4. 89% of organizations use AI, but only 6% are AI high performers with real EBIT impact.
5. The top Google result gets 58% fewer clicks when an AI Overview appears.
6. Anthropic passed a $30 billion revenue run-rate, and GitHub Copilot has 50 million users.
7. 93% of marketers use AI for content, and 74.2% of new webpages contain AI-generated text.
8. 87% of L&D professionals use AI, mostly for voiceover, quizzes, and video.
9. 88% of students use AI, but only 64% of faculty have had AI training.
10. In a University of Maryland trial, only 15% of students used the AI tutor available to them.

Generative AI has moved from early hype into everyday use. People now use it to search, write, code, study, create content, run business workflows, and automate parts of their jobs.

To help you understand how big it has become, we’ve compiled probably the biggest list of generative AI statistics. These numbers will help you understand the scale of AI use, AI adoption in different fields, usage patterns for ChatGPT, Claude, Gemini and several other generative AI tools.

We spent hours digging up original research, surveys, and studies from some of the world’s most credible institutions to curate over 250 stats that will answer all your questions about generative AI and show you where we’re headed.

Excited? Let’s jump right in.

Read: The best online course platforms with generative AI features

ChatGPT Adoption and Usage Statistics

  1. 900 million+ weekly active users were on ChatGPT in February 2026, up from 800 million in October 2025, according to OpenAI. Growth has slowed from the 2025 pace, but the base keeps getting bigger.
  2. 50 million+ paying subscribers used ChatGPT by February 2026, according to OpenAI. Most people still use the free version, which matters if you expect learners to have paid AI tools.
  3. ChatGPT was nearing 1 billion weekly active users by September 2026, according to reporting by The Information.
  4. 18 billion messages per week went to ChatGPT by July 2025. Every seven days, this one AI system processed more messages than there are people on Earth. That is a useful reference point for how mainstream AI interaction has become.
  5. 2.5 billion messages per day went to ChatGPT by July 2025, up more than 5x from the 451 million daily messages recorded in June 2024. That growth rate in a single year confirms this is nowhere near a plateau.
  6. 29,000 messages per second went to ChatGPT globally by July 2025. In the time it takes to read one stat on this page, ChatGPT processed hundreds of thousands of requests.
  7. More than half of ChatGPT weekly active users had typically female first names by July 2025. In the first months after launch, roughly 80% had typically male names. The gender gap in AI adoption has closed in under three years.
  8. Nearly half of all adult ChatGPT messages came from users under age 26 as of mid-2025. Younger users are the primary audience. Any learning product or coaching program aimed at this generation needs to treat AI as a built-in part of how they work.
  9. About 70% of ChatGPT consumer queries were unrelated to work by July 2025, up from 53% in June 2024. The dominant use case for generative AI is now personal and non-professional. People use it for learning, planning, health guidance, and daily decisions far more than for corporate productivity.
  10. Nearly 78% of all ChatGPT messages fall into three categories. Practical Guidance, Writing, and Seeking Information account for almost everything. Coding, role-play, creative fiction, and relationship advice together make up less than a quarter of total usage.
  11. 4.2% of ChatGPT messages involve computer programming. That makes coding a much smaller share of actual AI usage than the public conversation suggests, and it means most people use ChatGPT to get guidance, improve their writing, and find information rather than to write code.
  12. 1.9% of ChatGPT messages involve relationships and personal reflection. Despite widespread concern about AI emotional attachment, the actual usage data puts this behavior firmly at the margins of how people interact with these tools.
  13. 40% of work-related ChatGPT messages involve writing tasks, making writing assistance the single largest workplace use case by a wide margin. If you use AI for one thing at work, writing is where it pays off most consistently.
  14. More than half of work-related messages from users in management and business occupations involve writing tasks, at around 52%. For anyone in a leadership, consulting, coaching, or content role, this is where AI delivers the fastest return.
  15. About two-thirds of ChatGPT writing messages ask the tool to modify or improve user-provided text rather than generate new text from scratch. People use AI as an editor and polisher far more often than as a ghostwriter.
  16. 49% of ChatGPT messages involve users asking for guidance, advice, or information. This is the largest single use mode, bigger than task completion. AI’s most common role is as a thinking partner and decision-support tool.
  17. 40% of ChatGPT messages involve users asking the tool to complete tasks and produce outputs they can use directly. At work, this share rises to 56%, meaning workplace AI skews more toward task execution than general consumer use does.

The ChatGPT usage data challenges several common assumptions. Coding is a small share of actual use. Most writing interactions involve editing existing text, and the largest single mode of AI interaction is people asking for guidance, advice, and information. AI’s primary role is as a thinking partner far more than an automation engine.

For course creators, that means the most useful prompts help you think, explain, and edit, not generate whole courses. Our list of ChatGPT prompts for course creators and our guide to generative AI use cases put this into practice.

Claude, Gemini and Other Leading Generative AI Tools

Here is a closer look at the usage statistics for the leading generative AI tools.

Google Gemini

  1. 1 billion+ people use the Gemini app every month, Google announced in August 2026, up from 400 million in May 2025. Sundar Pichai called Gemini Google’s fastest-growing product ever.
  2. Gemini app daily active users tripled in the year to Q2 2026, according to Alphabet’s Q2 2026 earnings call.
  3. 22 billion tokens per minute ran through Google’s model APIs in Q2 2026, up from 16 billion a quarter earlier (Alphabet).
  4. 9 million+ developers build with Google’s models every month (Alphabet, Q2 2026).
  5. Nearly 90% of the Fortune 100 use Gemini Enterprise (Alphabet, Q2 2026).
  6. Google’s AI tools will pass ChatGPT’s unduplicated US user count in 2027, eMarketer forecasts, because Google builds Gemini directly into Search.
  7. eMarketer projects that Google will drive most new US AI user growth through 2030, with unduplicated Gemini and AI Mode users stretching their lead over ChatGPT from 2027 onward.
  8. Google builds Gemini into Workspace, including Gmail, Docs, Sheets, Slides, and Meet, which puts it inside tools most knowledge workers already open every day.

Microsoft Copilot and GitHub Copilot

  1. 50 million developers use GitHub Copilot, Satya Nadella said on Microsoft’s FY26 Q4 earnings call in July 2026.
  2. 30 million+ paid Microsoft 365 Copilot seats were active by July 2026, and net seat additions more than doubled quarter over quarter (Microsoft).
  3. 77,000 organizations used GitHub Copilot in 2025, according to Microsoft’s annual report.
  4. 70% of Copilot users reported improved productivity in Microsoft’s published research.  
  5. 29% faster task completion was achieved by workers using Copilot compared to those without it, according to Microsoft’s own study.
  6. 64% of Copilot users reduced the time they spent processing emails. 
  7. 85% of Copilot users created first drafts faster. 
  8. 14 minutes per day average time savings was reported by Copilot users, equivalent to 1.2 hours per week or roughly 5 hours per month. 

Meta AI

  1. 3.64 billion people use Meta’s apps every day, according to Meta’s Q3 2026 results, which gives Meta AI a built-in audience across WhatsApp, Facebook, Instagram, and Messenger.
  2. About 1 billion monthly users were using Meta AI across Meta’s apps by May 2025, according to Mark Zuckerberg. Meta has not shared a newer user count.
  3. Meta AI is available in over 40 countries across Meta’s family of apps as of 2025, up from 22 countries at the April 2024 rollout. 
  4. Engagement per person in Meta AI more than doubled between April and October 2026, Zuckerberg said on Meta’s Q3 2026 earnings call.
  5. 10 million+ people in the US and Canada set up Muse personal agents within six weeks of launch, and about 25% of them use Muse on any given day (Meta, Q3 2026).

Anthropic Claude

  1. Anthropic passed a $30 billion annualized revenue run-rate in April 2026, up from about $9 billion at the end of 2025, according to Bloomberg. Anthropic still has not published monthly active user numbers for Claude.
  2. 1,000+ businesses spend more than $1 million a year with Anthropic, more than double the count in February 2026 (Bloomberg).
  3. 8 of the Fortune 10 use Claude, and Claude Code passed a $2.5 billion revenue run-rate in February 2026, according to Anthropic.
  4. 93% of Claude conversations produce an output such as an explanation, a document, or code, according to Anthropic’s June 2026 Economic Index. Explanations (17%) and documents or reports (15%) lead the list.
  5. Personal use makes up about 35% of Claude conversations on weekdays and a little under 50% on weekends (Anthropic Economic Index, June 2026).
  6. Claude is integrated into Amazon Web Services via Amazon Bedrock, giving it access to AWS’s base of hundreds of thousands of enterprise customers.  
  7. Claude handles augmentation-heavy tasks at a higher rate than ChatGPT based on usage patterns, with users more likely to treat it as a collaborative partner than to delegate tasks entirely, according to Anthropic’s Economic Index.  

Perplexity AI

  1. 100 million+ monthly active users were on Perplexity by 2026 across its search and agent products, according to the Financial Times.
  2. 780 million queries ran through Perplexity in May 2025, CEO Aravind Srinivas told TechCrunch. The company hasn’t shared a newer monthly count.
  3. Perplexity is positioned as an AI-first search engine rather than a general assistant, meaning its user base skews toward research and information-seeking queries rather than content generation or task completion.

AI Image Generators

  1. Midjourney has over 20 million registered users on its Discord servers, according to its last public disclosure in 2023. The current number is likely higher.
  2. DALL-E, Stable Diffusion, and Midjourney are the three most-cited AI image tools in research, but Canva’s AI image features have the widest consumer reach because they sit inside a design tool with 200 million+ users, according to Canva.
  3. 56% of influencers name Canva as their first-choice AI image tool, ahead of dedicated AI image platforms, according to Influencer Marketing Hub’s 2024 research.
  4. The Gemini app generates 150 million+ images a day, according to Google.

ChatGPT remains the largest standalone AI tool by active users. But the competitive picture has changed significantly in the last few years. 

Google’s embedded AI strategy means Gemini’s effective reach through Search far exceeds its standalone app usage. Meta AI’s access to billions of existing social media users makes its growth trajectory different from any other player. 

And Claude has carved out a specific position in research-heavy and enterprise workflows where output quality and reasoning depth matter more than conversational fluency.

The figures above also largely exclude the downstream usage from these platforms’ APIs, which power thousands of products and AI features built by third parties. The true reach of each model’s underlying technology is substantially larger than any single user count captures.

Most course creators will meet these models inside the tools they already use rather than in a chat window. See how platforms build them in with our reviews of Thinkific AI, Kajabi AI, the LearnWorlds AI Assistant, and Teachable AI.

The ROI Of Using Generative AI Technology

  1. 74% of enterprises using gen AI report ROI within the first year.
  2. 86% of enterprises that report increased revenue, note an increase of 6% or more.
  3. 84% of organizations can move a gen AI use case from idea to production in less than six months.
  4. 63% of enterprises report that gen AI has directly driven business growth.
  5. 86% of Gen AI Leaders plan to allocate at least half of their AI future budget to gen AI.
  6. 47% of organizations plan to use gen AI to develop new products, services, and business models

AI Search Statistics

  1. 73% of all web searches still happen on Google, according to SparkToro and Similarweb’s 2026 analysis. Search is changing, but it is nowhere near dying. The shift is that Google itself is becoming more AI-driven.
  2. More than one-third of US search results now show AI Overviews, according to SparkToro’s 2026 analysis. If you rank on page one in the US, there is a roughly 1-in-3 chance an AI-generated answer appears above your result.
  3. 2 billion+ monthly users saw Google AI Overviews by July 2025, Sundar Pichai said on Alphabet’s Q2 2025 earnings call. AI answers now reach more people than any standalone AI app.
  4. 1 billion+ monthly active users use Google AI Mode, according to Alphabet’s Q2 2026 earnings call.
  5. The average AI Mode search is three times longer than a traditional Google search, and follow-up questions rose more than 40% month over month in the US (Google, May 2026). People ask full questions now, not two-word queries.
  6. 58% lower click-through rate for the top-ranking page when an AI Overview appears, according to Ahrefs’ study of 300,000 keywords from December 2023 to December 2025. The same study measured a 34.5% drop in April 2025.
  7. 8% of Google visits with an AI summary led to a click on a regular search result, compared with 15% without one, according to Pew Research Center’s tracking of 900 US adults.
  8. Only 1% of visits with an AI summary included a click on a link inside the summary itself (Pew).
  9. Users ended their browsing session on 26% of pages with an AI summary, compared with 16% without one (Pew).
  10. 26.6% of internet users were exposed to Google AI Overviews in Q1 2025. More than one in four people who go online now regularly encounter AI-generated answers before they see traditional organic results.
  11. 18.3% of the world’s population used Google AI Overviews monthly in Q1 2025. This is a global-scale behavior change, not a niche feature rollout.
  12. 55.8 million AI Overviews were analyzed in Ahrefs’ 2025 study, covering 590 million search keywords. This is the most comprehensive dataset available on how AI Overviews behave across different query types and intents.
  13. 9.46% of desktop keywords showed AI Overviews at the time of Ahrefs’ study, rising to 12.8% by search volume and 16% for US desktop. The rollout is uneven across query types. Some queries almost always trigger AI answers while others rarely do.
  14. 97.7% of US AI Overview keywords had informational intent. AI Overviews dominate informational queries almost exclusively, which means if your traffic relies on how-to, what-is, or explainer content, your organic clicks face more pressure than almost any other content type does.
  15. 80.03% of AI Overview keywords were non-branded searches, meaning generic queries face the most AI Overview exposure. Branded queries, where someone searches your name specifically, trigger AI answers far less often.
  16. 71.67% of AI Overview searches had no CPC data, meaning most AI Overviews appear on non-commercial queries where advertisers are not bidding. The organic traffic most at risk from AI answers is informational content at the top of the funnel.
  17. Top 50 domains earned 28.9% of all AI Overview mentions in Ahrefs’ study. High-authority sites dominate AI citations just as they dominate organic rankings, which means building domain authority remains a prerequisite for AI visibility.
  18. 94% of AI tool visits came from desktop devices in SparkToro and Similarweb’s 2026 analysis. AI tools are used almost entirely on desktop, which aligns with their typical use cases of research, writing, analysis, and complex queries.
  19. Google received more traffic than the next 13 largest websites combined in SparkToro and Similarweb’s 2026 analysis. The internet’s attention remains extraordinarily concentrated, and distribution at scale still flows through a handful of gatekeepers.

AI search is growing quickly, but traditional search is not collapsing. The more accurate picture is that Google itself is becoming more AI-driven, and AI answers appear most often on informational queries. If you publish how-to content, educational resources, or explanatory articles, this is the part of the data that matters most to your traffic strategy.

The click data makes the cost concrete. When an AI Overview shows up, the top result loses more than half its clicks, and few people click the sources inside the summary. Pages that answer a question clearly and carry real expertise are the ones AI systems cite. We cover how to adapt in using SEO to sell online courses.

Generative AI Market Size Statistics

  1. $22.21 billion was Grand View Research’s estimate for the global generative AI market in 2025, covering the software, applications, and services built on generative AI models.
  2. $324.68 billion is Grand View Research’s forecast for the global generative AI market by 2033, reflecting a 40.8% compound annual growth rate. At that pace, the market roughly doubles every two years.
  3. 40.8% CAGR is Grand View Research’s projected growth rate for the global generative AI market from 2026 to 2033. Very few technology sectors sustain this kind of growth for more than a few years, which is why every major forecast carries significant uncertainty alongside it.
  4. $37.89 billion was Precedence Research’s estimate for the global generative AI market in 2025. The higher figure compared to Grand View Research reflects a broader definition of what counts as generative AI infrastructure.
  5. $1,206.24 billion is Precedence Research’s forecast for the global generative AI market by 2035, reflecting a 36.97% CAGR. These trillion-dollar projections should be read directionally. The uncertainty range is large, but they show where investment capital is flowing.
  6. 41% of generative AI revenue came from North America in 2025, according to Precedence Research. North America leads but does not dominate. Europe and Asia together account for the majority.
  7. 65.5% of generative AI revenue came from software in 2025, according to Precedence Research. The applications layer, not the model layer, is where most of the value gets captured. That distinction matters for anyone building a product on top of AI.
  8. $71.36 billion was MarketsandMarkets’ estimate for the generative AI market in 2025. It is the highest of the three major estimates because it uses the broadest definition of what counts.
  9. $890.59 billion is MarketsandMarkets’ forecast for the generative AI market by 2032, with a 43.4% CAGR. All three major research firms agree on direction while differing on magnitude.

These forecasts vary significantly because research firms define generative AI differently. Some count only software applications while others include infrastructure and services. All three major estimates point in the same direction, and all project extraordinary growth. The uncertainty in exact figures is real, but the direction of investment is not ambiguous.

Broader AI Market Forecast Statistics

  1. $390.91 billion was Grand View Research’s estimate for the total global AI market in 2025. This is roughly 18 times larger than generative AI alone, because it includes predictive AI, machine learning infrastructure, enterprise automation, and AI chips.
  2. $3,497.26 billion is Grand View Research’s forecast for the total global AI market by 2033, reflecting a 30.6% CAGR. The broader AI economy is one of the largest technology market expansions ever measured.
  3. $1.5 trillion is Gartner’s forecast for global AI expenditure in 2025, according to Reuters’ summary of major analyst forecasts, including hardware, software, services, and infrastructure investment combined.
  4. $2 trillion+ is Gartner’s forecast for global AI expenditure in 2026. The year-over-year jump from $1.5T reflects accelerating capital flowing into AI infrastructure, well beyond software adoption alone.
  5. $2.6 trillion to $4.4 trillion is McKinsey’s estimate of the annual value generative AI could deliver across industries through productivity, automation, and new product categories. This is a potential economic impact figure rather than a market-size estimate.
  6. $15.7 trillion is PwC’s estimate of AI’s potential contribution to the global economy by 2030. At this scale, AI would represent one of the largest single economic forces in history, roughly equivalent to adding another China-sized economy.
  7. $285.9 billion in US private AI investment in 2025, according to Stanford HAI’s 2026 AI Index, far ahead of Europe ($20.9 billion) and China ($12.4 billion).

These broader forecasts cover the full AI economy, including chips, infrastructure, enterprise software, predictive analytics, and generative AI combined. They are not interchangeable with generative AI market figures. They represent the total economic scale of AI as a technology sector.

Enterprise AI Statistics

  1. More than 1 million business customers use OpenAI’s tools, according to the State of Enterprise AI 2025 report. Enterprise adoption has moved well beyond early pilots into mainstream business deployment.
  2. 8x growth in ChatGPT message volume occurred across enterprise usage year over year. Workplace AI use is growing at an order of magnitude per year, not incrementally.
  3. 320x growth in API reasoning-token consumption occurred per organization year over year, showing that companies are deploying more advanced models for more complex work. The shift goes well beyond simply using AI tools more frequently.
  4. 6x more messages go from workers at frontier AI companies compared with median enterprise workers. Early adopters of powerful AI tools use them at dramatically higher rates than the typical corporate user does.
  5. 19x growth occurred in structured AI workflows including custom GPTs and Projects workspaces, according to OpenAI’s enterprise report. The shift is from ad hoc AI conversations to repeatable, systematic AI-assisted processes.
  6. 89% of organizations regularly use AI in at least one business function, according to McKinsey’s State of AI 2026 survey of 1,719 respondents in 97 countries.
  7. 44% of organizations say AI is now scaling across the enterprise, up from 38% a year earlier (McKinsey 2026). Among companies with $1 billion or more in revenue, 54% report scaling.
  8. In McKinsey’s 2025 survey, more than two-thirds of organizations used AI in multiple business functions rather than limiting it to one team.
  9. 56% of organizations use AI in three or more business functions, up from 51% (McKinsey 2026).
  10. 60% of organizations expect to increase AI investment in the next year (McKinsey 2026).
  11. 32% of organizations decided against buying at least one software product because they could build it themselves with agentic coding tools (McKinsey 2026).
  12. 9 million+ paying business users use ChatGPT, according to OpenAI’s February 2026 announcement.
  13. Nearly 500 Google Cloud customers each processed more than 1 trillion tokens in the past year (Alphabet, Q2 2026).

Enterprise AI has moved beyond pilots. The usage intensity data, particularly the 320x growth in reasoning-token consumption per organization, shows that companies are building AI into increasingly complex and consequential workflows, not simply giving employees chatbot access.

Workplace AI Statistics

  1. 40 to 60 minutes per day is the amount of time enterprise users report saving with AI at work, according to OpenAI’s enterprise study. That translates to roughly 20 to 30 hours per month, equivalent to more than three full working days recovered every month.
  2. 75% of workers in OpenAI’s enterprise survey said AI improved either the speed or quality of their work. Three in four employees using AI at work report a meaningful positive impact, a higher satisfaction rate than most enterprise software achieves.
  3. More than 10 hours per week is the time heavy enterprise AI users report saving. For context, that is more time than most organizations invest in formal training per employee per year.
  4. 58% of AI users say they now produce work that was impossible for them a year ago, rising to 80% among the most advanced users, according to Microsoft’s 2026 Work Trend Index of 20,000 workers.
  5. 65% of AI users fear falling behind if they don’t adapt quickly, but only 13% say their employer rewards reinventing work with AI when results fall short (Microsoft 2026).
  6. Only 26% of workers say their leadership is consistently aligned on AI strategy (Microsoft 2026).
  7. Culture, manager support, and talent practices explain 67% of AI’s reported impact at work, compared with 32% for individual mindset (Microsoft 2026). Training people on tools only covers a third of the problem.
  8. 21% of US workers say they do at least some of their work with AI, up from 16% in 2024, while 65% say they don’t use AI much or at all in their job (Pew Research Center).
  9. 80% of respondents say AI has improved their personal productivity, and 50% say it helps them make better decisions (McKinsey 2026).

AI at work is shifting from simple question-answering to systematic task support. The time savings data makes the business case straightforward. For knowledge workers, AI is delivering the equivalent of multiple additional days of productive capacity every month.

AI Workforce Impact Statistics

  1. 81,000 Claude users participated in Anthropic’s 2026 research on the economics of AI, making it one of the largest direct studies ever conducted on how people experience AI at work.
  2. 48% of AI productivity gains came from scope expansion in Anthropic’s survey, meaning users could do new things they previously could not do at all. Speed gains on existing tasks accounted for 40%. AI is expanding capability more often than it is accelerating the same work.
  3. Workers in AI-exposed roles reported greater concern about job displacement in Anthropic’s 2026 study, and that concern correlated directly with how much AI was already performing tasks in their occupation. People’s intuitions about AI risk track the actual usage data closely.
  4. Only 60% of early-career workers said they personally benefited from AI in Anthropic’s survey, compared to 80% of senior professionals. The gap matters for workforce development and learning program design, where the people most anxious about AI are also the ones with the most at stake.
  5. Workers reporting the largest AI speedups also expressed higher concern about job displacement, creating a pattern where the more productive AI makes someone, the more worried they become about what that implies for their role. This tension deserves direct engagement from learning professionals and organizational leaders.
  6. Highest-paid and lowest-paid occupations both reported some of the largest productivity gains from AI in Anthropic’s survey. AI is distributing benefits unevenly by occupation type, not uniformly across the income spectrum.
  7. 39% of respondents expect AI to reduce headcount at their organization in the coming year, up from 32% a year earlier, according to McKinsey’s 2026 survey.
  8. In McKinsey’s 2025 survey, 43% of business leaders expected AI to have little or no impact on overall workforce size the following year.
  9. In the same 2025 survey, 13% of business leaders expected AI to increase workforce size, seeing it as a source of new roles.
  10. Only 14% of organizations report an actual overall headcount decline from AI over the past year, far below the 39% that expect one (McKinsey 2026).
  11. About one-third of Claude users surveyed by Anthropic expect AI to handle most or nearly all of their tasks within 12 months, while 10% rate losing their own job as likely (Anthropic Economic Index, June 2026, about 9,700 respondents).
  12. 68% of Claude users say they learn more with AI, and 57% say AI has made their skills more valuable (Anthropic, June 2026).
  13. Speed (86%), scope (82%), and quality (69%) are the most reported productivity gains in Anthropic’s June 2026 survey, while only 27% report cost savings.

The workforce data points in two directions simultaneously. Workers see real productivity gains, particularly in scope expansion and the ability to do new things. Workers experiencing the largest productivity gains also express the highest concern about what that implies for their role. That tension deserves direct engagement from learning professionals and organizational leaders.

Workers expect AI to change their jobs, and most say they learn more with it. Companies that sell training have a clear opening here. Our guides to selling online courses to businesses and teaching adult learners cover how to build programs for that buyer.

AI Agent Statistics

  1. In McKinsey’s 2025 survey, 62% of organizations were experimenting with or deploying AI agents.
  2. 40% of large organizations are scaling AI agents in at least one function, up from 27% a year earlier, while smaller organizations held steady at 22% (McKinsey 2026).
  3. In the same 2025 survey, 39% of organizations were actively experimenting with AI agents.
  4. Fewer than 10% of organizations had scaled AI agents within any single business function in 2025 (McKinsey).
  5. About 2 in 10 organizations are scaling software coding agents, rising to 31% at larger enterprises (McKinsey 2026).
  6. 66.3% accuracy was the best AI score on OSWorld, a benchmark of real computer tasks, in 2025, still short of the 72.4% human baseline (Stanford AI Index 2026).
  7. 2.4 million+ weekly active users use Google’s Antigravity coding agent, and Google’s Agent Development Kit logged nearly 70 million downloads in Q2 2026 (Alphabet).

AI Productivity Statistics

  1. In McKinsey’s 2025 survey, 64% of organizations said AI was helping drive innovation.
  2. 37% of organizations report some positive EBIT impact from AI, essentially flat from a year earlier, according to McKinsey’s 2026 survey.
  3. Most organizations reporting EBIT gains in 2025 said AI contributed less than 5% of total EBIT (McKinsey). Financial impact stays modest for most businesses.

AI Impact on E-learning, Online Courses, and Training & Development

  1. 87% of L&D professionals already use AI, according to Synthesia’s AI in Learning and Development Report 2026. AI adoption in the training function is no longer an emerging trend. It is already the standard.
  2. 36% of L&D professionals use AI in defined, repeatable workflows, meaning they have moved past occasional experiments into systematic integration. The remaining majority are still in the exploration phase.
  3. 9% of L&D professionals are beginning to scale AI across their organizations. This is the leading edge. The practices this group builds today will define what the broader industry considers standard in two to three years.
  4. 84% of L&D professionals said faster production is AI’s biggest current benefit. Speed is the gateway benefit. It is what gets organizations to start, and it is real, but it is not the ceiling of what AI can deliver in learning.
  5. 66% of L&D professionals said AI improves the learner experience. That finding matters more than the production speed gains because it points toward AI changing the quality of what learners actually receive, which is harder to achieve than faster content creation.
  6. 63% of L&D teams use AI for voice generation or text-to-speech, making it the most common AI application in learning production. Video and audio learning content can now be created without studio recording.
  7. 60% of L&D teams use AI for quiz and assessment generation. This is among the most time-consuming parts of course development, and one of the strongest near-term applications for AI in learning.
  8. 52% of L&D teams use AI for video creation, a function that previously required either significant production resources or a dedicated specialist.
  9. 40% of L&D teams use AI search and knowledge assistants, meaning AI-powered tools for employees to find training content and institutional knowledge on demand.
  10. 38% of L&D teams use AI for translation or localization. For organizations operating across multiple languages, AI has removed one of the biggest historical barriers to global training distribution.
  11. 72% of L&D professionals believe personalized learning will deliver AI’s biggest future benefit in the sector. Adapting content to each learner’s pace, level, and gaps has been the promise of learning technology for decades. AI may finally make it practical at scale.
  12. 65% of L&D professionals expect AI to expand their organization’s internal learning reach, meaning AI will help them serve learners they currently cannot reach with existing resources.
  13. 55% of L&D professionals expect AI to deliver measurable business impact beyond course completion rates and downstream into actual performance change. The shift from tracking completions to tracking outcomes is the most important evolution in L&D measurement.
  14. 39% of L&D professionals describe themselves as cautious about agentic AI, which refers to autonomous AI systems that execute tasks without constant human oversight. The caution is appropriate. Agentic AI introduces real new challenges around accuracy and accountability in a learning context.
  15. 49% of L&D professionals are exploring AI tutors as an agentic AI use case. An AI tutor that responds to individual learner questions, adjusts explanations, and tracks progress in real time would change the economics of personalized coaching substantially.
  16. 43% of L&D professionals are exploring AI coaching or mentoring. AI that provides ongoing, responsive guidance rather than one-off answers represents the next meaningful frontier in workplace learning.
  17. 39% of L&D teams spend 5% or less of their learning and development budget on AI. Investment in AI tools is lagging behind adoption. Most teams get significant value from AI without significant budget allocation, which also means they have headroom to go much further.
  18. 58% of L&D professionals cite security as a major obstacle to AI adoption. Confidentiality of training content, employee data, and proprietary methodology is a legitimate concern, particularly for organizations in regulated industries.
  19. 52% of L&D professionals cite accuracy as a major AI adoption obstacle. Hallucination and factual errors are real problems in a learning context where credibility and trust are foundational to the program working.
  20. 74% of L&D professionals say their organizational culture encourages AI experimentation. Culture is often the hardest part of technology adoption, and three-quarters reporting a supportive culture is a meaningful structural advantage for the sector.

The L&D data shows a sector that has moved from experimentation to everyday use while still building the governance, budgeting, integration, and measurement infrastructure needed to get full value from AI. Production speed was the first benefit. Personalization and measurable business impact are what the field is working toward next.

Quiz generation, voiceover, and video are where L&D teams use AI most, and they are also where quality checks matter most. Before you publish AI-written questions, test them against real learner results. Our guides to creating quizzes, instructional design, and the best AI video generators go deeper.

AI Online Learning Statistics

  1. 67% of professionals responsible for building, managing, or scaling education programs currently use AI in their learning or training programs, according to Thinkific’s AI for Online Learning Report 2026. Adoption is the norm in this sector, not the exception.
  2. 17% of online learning professionals do not use AI yet but plan to adopt it. Combined with the 67% already using AI, that means 84% of the sector is either using AI or actively planning to, leaving only 16% with no current AI intent.
  3. 16% of online learning professionals do not use AI and have no current plans to adopt it. This group is competing in a market where the majority of their peers use AI to create content faster, improve learner experience, and scale their operations.

The online learning sector has reached a tipping point on AI adoption. With 84% of professionals either using AI or actively planning to, the operational and competitive gap between AI adopters and non-adopters will widen materially over the next two to three years.

For a wider view of the online learning market, including platform revenue and completion data, see our eLearning statistics roundup.

AI Education Statistics

  1. 80% of higher education teachers in a French survey of 30,000 participants had used generative AI tools in 2025, according to data cited in the OECD Digital Education Outlook 2026. Faculty AI adoption has moved from early adopters to a clear majority in under three years.
  2. 49% of higher education teachers in the French survey used generative AI to draft or prepare courses. Course preparation is the highest-value application for faculty because AI can substantially reduce the structural time investment of curriculum development.
  3. 68% of teachers in an international higher education study of 1,700 teachers across 52 institutions used AI in general, a figure that remained strikingly consistent across countries.
  4. 75% of AI-using teachers in that international study used generative AI specifically to create teaching materials. Among those who adopted AI, materials creation is the dominant use, which aligns with what L&D professionals report across organizational settings.
  5. 50% of upper secondary teachers in Estonia used generative AI tools in 2025, while 90% of upper secondary students did, according to OECD country data. The student adoption rate is nearly double the teacher adoption rate, which creates real design challenges for educators.
  6. 90% of upper secondary students in Estonia used generative AI tools in 2025. This is a preview of what adoption will likely look like across most developed countries within a few years. The question for educators has shifted from whether students use AI to how they design programs that account for it.
  7. 48% higher short-term task performance emerged when students used GPT-4 in a controlled study cited by the OECD. Students using AI outperformed those who did not in the short term. The design challenge is ensuring that AI use translates into durable learning rather than performance without understanding.
  8. 127% higher short-term performance emerged when students used a tutoring-focused GPT-4 system in the same research context. A tutoring-style AI that guides rather than answers directly produces dramatically stronger results than a basic AI assistant.
  9. 17% lower performance appeared after researchers removed AI access from students who had been using it. AI can improve measured task performance without strengthening the underlying capability. This is the central design risk for any AI-assisted learning program.
  10. 39.3% of student conversations with Claude involve creating or improving educational content, and 33.5% ask for technical explanations or solutions, according to Anthropic’s analysis of 574,740 student conversations.
  11. Coursework conversations with Claude most often produce documents or reports (21%), explanations (20%), and educational materials (11%) (Anthropic Economic Index, June 2026).

Students and AI

  1. 88% of students use AI, and 77% of faculty do too, up 16 points, according to the Digital Education Council’s 2026 survey of 45,398 respondents in 35 countries.
  2. Only 64% of faculty have had AI literacy training, and only 28% of respondents say most assessments reflect AI-era skills (Digital Education Council).
  3. 95% of UK undergraduates use AI in some form, and 94% use it for assessed work, according to HEPI’s 2026 survey of 1,054 students.
  4. 64% of US teens use AI chatbots, 54% have used them for schoolwork, and about three in ten use them daily, according to Pew Research Center’s survey of 1,458 teens.
  5. 59% of teens say students at their school use AI to cheat at least somewhat often (Pew).

What AI Tutor Studies Show

  1. Stanford’s SCALE Initiative found only 20 high-quality causal studies among more than 800 papers on AI in K-12 education. Most claims about AI and learning rest on thin evidence.
  2. In a University of Maryland trial with 2,379 students, only about 15% of students offered an AI tutor used it, and nearly 74% of their requests asked for answers rather than help.
  3. Students offered the Maryland AI tutor finished with final grades about 4 points lower than students without it.
  4. An NBER study across 18 schools found Khanmigo raised achievement by 0.06–0.08 standard deviations a year. 96% of students tried it, but only 17% of practice sessions with mistakes included a message to the tutor.
  5. In a Stanford SCALE study of 355 elementary students, nearly half never used the AI tutor they had, and users averaged 2–5 minutes a week against a recommended 30.
  6. Students who learned with AI scored 0.27 standard deviations higher on a later test taken without AI, and the gain held a week later. The gain faded when AI wrote the text for them (Contractor and Reyes).

AI in Colleges and Universities

  1. In an Inside Higher Ed survey of 376 provosts, about 70% use AI weekly, but only about 1 in 10 say their institution has a centralized AI strategy.
  2. 51% of provosts cite staff skills and capacity as a barrier to AI adoption, and 50% cite resistance (Inside Higher Ed).
  3. Only 11% of instructors have received comprehensive AI training, according to Instructure’s EdTech Top 40 data.
  4. “Determining where AI adds real value” ranks #1 on EDUCAUSE’s 2027 Top 10 IT issues list, the first time an AI item has topped it.
  5. AI-written abstracts in US STEM PhD dissertations rose from 1.7% in 2023 to 29.4% in 2026, according to an NBER working paper.
  6. 54% of adults considering education programs use AI tools in their decision, and 83% read AI overviews in search, according to UPCEA and Search Influence.

The education data shows strong adoption alongside a design challenge the field has not yet solved. AI improves measured task performance in the short term, but removing AI access causes performance to drop. AI can support students without necessarily building the underlying skills that durable learning requires. How educators design for AI use, rather than around it, is the central challenge.

The tutor trials point the same way as the OECD data. Access alone changes little. Students skip AI tutors they aren’t asked to use, and the ones who do use them often ask for answers. Gains show up when AI supports practice and explanation inside a planned course. If you add AI to a course, decide when learners should use it and when they should work alone, the same way you would plan a quiz or a discussion. Our guides to adult learning theory, cohort-based courses, and course completion rates cover the design side.

AI Marketing Statistics

  1. 93% of marketers surveyed by Ahrefs in August 2026 use AI to create or help create content, up from 87% in 2025.
  2. 89% of AI-using marketers manually check AI output, up from 80% in 2025, while 7% say more than three-quarters of what they publish is pure AI with no editing (Ahrefs 2026).
  3. A strong correlation of 0.737 exists between YouTube mentions of a brand and that brand’s visibility in AI systems, according to Ahrefs’ study of 75,000 brands across ChatGPT, AI Mode, and AI Overviews. This was the strongest individual predictor identified in the study, suggesting that brands discussed frequently on YouTube are significantly more likely to be mentioned by AI assistants.
  4. A very strong correlation of 0.779 exists between the brand outputs of the three AI assistants studied. ChatGPT, AI Overviews, and Google AI Mode largely agree on which brands to mention, meaning visibility in one major AI platform often translates into visibility across others.
  5. A very strong correlation of 0.821 exists between AI Overviews and AI Mode brand mentions, making it the strongest inter-platform relationship identified in the study. Google’s two AI systems are remarkably similar in the brands they surface, so efforts that improve visibility in one often improve visibility in the other.
  6. A relatively weak correlation of 0.326 exists between Domain Rating and AI Overview brand mentions. Traditional SEO authority signals still matter, but the study found that brand mentions, YouTube visibility, and broader web presence are much stronger predictors of whether AI systems mention a brand.

The AI brand visibility data points to a fundamental shift in what drives discoverability. YouTube presence and web mentions are stronger predictors of AI brand citations than domain authority or backlinks. If you want AI systems to mention your brand when relevant, the path is earned media and broad digital presence.

AI Advertising Statistics

  1. More than $1 billion is projected to be spent on AI-powered search advertising in the United States during 2025. This represents only a small fraction of total search advertising spend today, but it is currently the fastest-growing segment of the search advertising market.
  2. Nearly $26 billion is projected to be spent on AI-powered search advertising in the United States by 2029, up from just over $1 billion in 2025. That represents roughly a 25-fold increase in four years, making AI search advertising one of the fastest-growing advertising categories.
  3. 13.6% of total search advertising spend is projected to come from AI-powered search ads by 2029. By the end of the decade, more than one dollar out of every eight spent on search advertising could be directed toward AI-driven search experiences.
  4. More than 80% of AI advertising in 2026 is expected to appear alongside AI-generated content, such as traditional search ads shown next to Google AI Overviews, rather than inside chatbot conversations. Most spending classified as AI advertising today still resembles conventional search advertising with AI-generated content layered into the experience.
  5. $68.25 billion in global AI advertising spend is projected by 2030, according to eMarketer’s AI Advertising Forecast 2026. Growth is expected to be driven primarily by the expansion of AI-native search placements as products such as AI Overviews and AI Mode continue to scale.

AI search advertising is still a small fraction of total digital ad spend, but the growth trajectory is steep. For most course creators and knowledge businesses, the actionable implication is ensuring organic visibility in AI search through content quality, brand authority, and topic depth rather than through paid placement.

AI Content Creation Statistics

  1. 74.2% of newly created webpages contained AI-generated content in Ahrefs’ analysis of 900,000 newly created pages across 900,000 different domains. In practical terms, roughly three out of every four new pages published on the web now contain some level of AI-assisted writing.
  2. 2.5% of webpages in that analysis were classified as entirely AI-generated with no meaningful human contribution detected. Fully automated content remains a small minority of what is being published online.
  3. 71.7% of webpages contained a combination of AI-generated and human-written content. Human-AI collaboration has become the dominant content creation model, with writers using AI to accelerate production rather than replacing human involvement entirely.
  4. 25.86% of mixed-content pages showed moderate AI use, defined as between 11% and 40% of the page being categorized as AI-generated. This pattern is consistent with creators using AI for research, drafting, brainstorming, or expanding sections before applying substantial human editing.
  5. 20.50% of mixed-content pages showed substantial AI use, defined as between 41% and 70% of the page being categorized as AI-generated. These pages likely reflect an AI-first drafting process followed by human review, editing, and refinement.
  6. 15.51% of mixed-content pages showed dominant AI use, defined as between 71% and 99% of the page being categorized as AI-generated. At this level, human involvement is often focused on providing direction, making corrections, and performing final quality checks rather than writing most of the content themselves.

The content creation data shows that AI writing is already normalized across the web. The dominant pattern is hybrid content, combining human strategy and expertise with AI-assisted drafting and editing. Publishers who combine genuine expertise with AI execution capability will have the strongest position in this environment.

To get a sense of what’s possible, check out our list of the best Generative AI tools for content creators.

Consumer Behavior And Perception Regarding AI-Generated Content

  1. 49% of US adults use AI chatbots such as ChatGPT, Gemini, or Copilot, up from 33% in 2024, according to Pew Research Center’s February 2026 survey.
  2. Generative AI reached 53% population-level adoption within three years of its mass-market launch, faster than the PC or the internet, according to Stanford HAI’s 2026 AI Index.
  3. 50% of US adults say AI in daily life makes them more concerned than excited, while 10% say more excited (Pew).
  4. 31% of Americans say they interact with AI in any form at least several times a day, up from 22% in February 2024 (Pew).
  5. 50% of consumers correctly identified AI-generated copy in Bynder’s test of 2,000 US and UK consumers.
  6. US consumers spotted the AI copy 55% of the time, compared with 45% in the UK (Bynder).
  7. 56% of consumers preferred the AI-written version when they didn’t know which was which (Bynder).
  8. 52% of consumers say they would engage less with copy they suspect is AI-generated (Bynder).
  9. 20% of consumers would see a brand as lazy if its website copy seemed AI-written (Bynder).
  10. 26% of consumers would see a brand as impersonal if its website copy seemed AI-written (Bynder).
  11. 25% of consumers would see a brand as impersonal, and 20% as untrustworthy, if its social media copy seemed AI-written (Bynder).
  12. 19% of consumers would see a brand as uncreative if its social media copy seemed AI-written (Bynder).
  13. Consumers aged 25–34 were the best at spotting AI-written content in Bynder’s test.
  14. 22% of Americans say they use generative AI tools almost constantly or several times a day, according to eMarketer.
  15. 63% of postgraduates and 57% of college graduates interact with generative AI tools at least several times a week. 

Half of American adults now use chatbots, but using AI and trusting AI-made content are different things. In Bynder’s test, most people preferred the AI-written version when it wasn’t labelled, and half said they’d pull back from copy they suspect is AI. The same person can enjoy AI help and still discount a brand that sounds machine-made.

For creators and course businesses, the fix is to use AI for drafting and research and keep your own voice on the pages people judge you by, such as your homepage, sales pages, and social posts. Our guide to ChatGPT prompts for course creators shows where AI saves time without flattening your voice.

AI Creator Economy Statistics

  1. 86% of creators use generative AI in their creative workflows, according to Adobe’s 2025 creator research reported by TechRadar.
  2. 85% of creators view AI as a positive force in the creator economy. For a technology that many predicted would face strong resistance from creative professionals, the level of support is remarkably high.
  3. 81% of creators say AI helps them create content they would not otherwise be able to produce. For many creators, AI is expanding creative possibilities and output capacity rather than simply helping them work faster.
  4. 69% of creators worry about their content being used to train AI systems without their consent. This highlights the tension at the heart of the creator economy, where creators value AI tools while also expressing concerns about copyright, attribution, and ownership.
  5. 55% of creators use AI for media editing and enhancement, making it the most common creator-focused AI application. AI-powered editing tools have become a standard part of content production across video, image, and audio workflows.
  6. 38% of creators cite cost as a barrier to adopting AI tools, while 34% cite inconsistent output quality as a concern. The biggest obstacles to AI adoption among creators are practical workflow issues rather than philosophical objections to the technology itself.
  7. $37 billion is the projected size of US creator advertising spending in 2025, according to IAB data reported by Business Insider. Creator advertising spending is expected to grow 26% year over year, expanding at roughly four times the rate of the broader media industry.
  8. 278 AI-generated YouTube channels were identified among 15,000 popular YouTube channels in a Kapwing study reported by The Guardian. Those channels collectively generated 63 billion views, attracted 221 million subscribers, and were estimated to produce $117 million in annual revenue, demonstrating that fully AI-generated content has already become a meaningful part of the creator economy.
  9. More than 20% of videos recommended to new YouTube users in one study were classified as low-quality AI-generated content. Synthetic content is increasingly influencing what new audiences see on the world’s largest video platform, creating new challenges around content quality and discoverability.
  10. 87% of creators who use creative AI say it has sped up their business or follower growth, according to Adobe’s 2026 Creators’ Toolkit Report of 16,000+ creators in eight countries.
  11. 85% of creators say the final creative decision should always stay with the creator, and 57% say AI output usually needs moderate or extensive editing before publishing (Adobe 2026).
  12. 49% of creators always or often disclose their AI use, while 18% rarely or never do (Adobe 2026).
  13. 57.3% of creators use AI daily and 71.7% at least weekly, according to Kit’s survey of 550 creators. 89.2% always review or edit what AI produces.
  14. ChatGPT (73.3%) and Claude (69.8%) are the most used AI tools among creators, well ahead of Gemini (39.5%) (Kit).

The creator economy data captures a genuine tension. Creators overwhelmingly use and value AI while also worrying about how their content feeds the systems they depend on. Meanwhile, low-quality synthetic content is growing fast enough to change what gets surfaced to new audiences on major platforms. Being a high-quality, high-trust human creator is becoming a differentiator.

Creators who run communities have AI help built into their platforms now. Our reviews of Circle’s AI features and Mighty Networks’ AI Co-Host show what that looks like in practice.

AI Transformation Statistics

  1. Only 6% of organizations qualify as AI high performers that report both significant business value and meaningful EBIT impact from AI, unchanged in McKinsey’s 2026 survey.
  2. Half of AI high-performing organizations intend to use AI to fundamentally transform how their businesses operate rather than simply improve efficiency.
  3. About three-quarters of AI high performers have already scaled or are actively scaling AI across their organizations, far exceeding the adoption levels seen among typical companies.
  4. More than one-third of AI high performers dedicate over 20% of their digital budgets to AI initiatives, demonstrating a significantly higher level of investment than the average organization.

The Risks and Concerns About Using Generative AI

  1. 51% of organizations using AI report experiencing at least one negative consequence from AI deployment, showing that adoption often brings new operational and governance challenges.
  2. AI inaccuracies are the most commonly reported negative consequence among organizations deploying AI systems, highlighting the need for human oversight and verification.
  3. Organizations now mitigate an average of four AI-related risks, compared with two risks in 2022, suggesting that AI governance is becoming more sophisticated as adoption grows.
  4. 41% of learning and development professionals cite legal and compliance concerns as barriers to AI implementation.
  5. 36% of learning and development professionals cite integration challenges as a barrier to successful AI adoption.
  6. 29% of learning and development professionals say they need to learn more about agentic AI before embracing it.
  7. Fewer than half of K–12 computer science teachers feel adequately prepared to teach AI concepts, highlighting a growing skills gap in education.
  8. Stanford HAI’s 2026 AI Index counted a record 362 AI incidents in 2025.
  9. About 20% of organizations say AI operating costs, including token costs, have limited how much they use AI (McKinsey 2026).
  10. 72% of teachers worry AI lets students pass off AI-generated work as their own (OECD Digital Education Outlook 2026).
  11. 86% of adults considering education programs have concerns about the accuracy of AI answers, up from 68% in 2025 (UPCEA and Search Influence).
  12. About half of Americans who get news from AI chatbots say they see inaccurate news at least sometimes (Pew).
  13. Only 31% of Americans trust their government to regulate AI responsibly, the lowest of any country in Stanford’s 2026 AI Index survey.

AI adoption keeps climbing, but trust, accuracy, security, copyright, and workforce impact remain open problems. The organizations getting results use AI for repetitive work and keep people in charge of judgment, creativity, relationships, and quality control.

Educators, coaches, consultants, and course creators feel this most. AI helps you research faster, produce content faster, and serve more learners. Your expertise, track record, and relationships with learners are much harder to automate, and they are what people pay for.

AI Tools I’d Use Based on These Numbers

Statistics only help if they change a decision. Here is how I’d act on the strongest numbers in this report, and the tool I’d reach for in each case.

What the data showsWhat to doTool I’d use
60% of L&D teams use AI to generate quizzesDraft questions with AI, then check difficulty against real learner resultsThinkific or LearnWorlds AI Assistant
63% of L&D teams use AI voice and 52% use AI videoTurn scripts into narrated video lessons without a studioSynthesia for avatar video, Pictory for text to video, Descript for editing
87% of creators say AI sped up growth, but 57% say output needs heavy editingUse AI for first drafts of outlines, emails, and sales pages, then edit in your own voiceKajabi for courses, email, and funnels with built-in AI
Only 15% of students used the AI tutor in their courseBuild AI help into assignments with clear rules on when to use itYour course platform’s AI assistant, set up inside specific lessons
Top results lose 58% of clicks when an AI Overview appearsPublish clear, expert answers that AI systems citeYour own expertise, plus the steps in our SEO guide

My overall pick for course creators is Thinkific. I run six courses on it, and its AI tools help draft course outlines and lesson content inside the same platform that hosts and sells the course. Choose Kajabi if email marketing and sales funnels sit at the center of your business. Pick LearnWorlds if you need interactive video or sell training to companies.

For more options, see our roundups of the best generative AI tools, AI presentation makers, and AI image generators. If you’re building your first course, start with how to create an online course.

Try Thinkific free for 30 days

Data Sources

Every statistic in this article comes from one of the sources listed below.

Leave a Comment

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Related Posts

Leave a Comment

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Scroll to Top