AI in Marketing Statistics 2026: 35 Stats on Adoption, ROI and Trust

The global AI market is on track to grow from $94.81 billion in 2020 to a forecast $1.675 trillion by 2031, and the slice aimed squarely at marketing is climbing past $107 billion by 2028. Adoption has already crossed the halfway line: 56% of marketers now run AI in production, and 70% name generative AI the most important consumer trend to watch in 2026. Yet the same data exposes a widening gap. Consumer comfort with brand AI fell from 57% to 46% in a single year, and nearly 60% of marketers worry the technology could cost them their jobs. This report pulls 35 data points into interactive charts and data tables, then translates them into the use cases and ROI evidence that actually move a 2026 marketing plan.

Key findings:

• The global AI market is forecast to reach $1.675 trillion by 2031, up from $94.81B in 2020, a 17.7x expansion (Statista Market Insights)
• The AI-in-marketing market is projected to grow about 9x, from $12.05B in 2020 to $107.54B by 2028 (The Insight Partners)
• 56% of marketers have integrated AI into data-driven work (17% extensively, 39% in select areas) (Ascend2)
• Generative AI tops the 2026 trend list at 70%, ahead of CTV/streaming (63%) (Mediaocean)
• 53% of new unicorns in 2025 were AI startups, up from 6% in 2015 (CB Insights)
• Consumer spend on AI mobile apps hit $1.42 billion in 2024, a 274% year-over-year jump (Appfigures)
• Consumer comfort with brands using AI fell from 57% (2023) to 46% (2024) (Qualtrics)
• Marketers worried AI may jeopardize their role rose from 35.6% to 59.8% in one year (Influencer Marketing Hub)

Every chart and table below names its original primary source: Ascend2, HubSpot, CB Insights, Appfigures, Attest, Capterra, Econsultancy, the World Economic Forum, and others, with modeled market forecasts attributed to Statista Market Insights. Two sections go further than the survey data: we add live deployment counts from our own detection crawl of tens of millions of active domains, and live AI-crawler traffic from Cloudflare Radar. You can browse every visual in our interactive charts library, and for the wider picture see our AI adoption trends report.

The AI market backdrop: how big the opportunity is

Marketing AI does not exist in a vacuum. It rides on top of one of the fastest-growing technology markets ever measured. The global AI market grew from $94.81 billion in 2020 to $186.93 billion in 2024, and Statista Market Insights models it reaching $1.675 trillion by 2031. The only contraction in the eleven-year series is the 2022 dip, a brief funding pullback before the generative-AI wave reset the trajectory. We break this curve down further in our dedicated AI market size statistics report.

That growth is spread across every technical segment. Across AI robotics, autonomous and sensor technology, computer vision, machine learning, and natural language processing, the market is forecast to reach its maximum value at the end of the period, with machine learning the largest single segment at roughly $552 billion by 2032. The table below ranks the five segments by projected 2032 size.

AI segment (2032 projection)	Relative scale
Machine learning	Largest, about $552 billion
Natural language processing	Second
AI robotics	Third
Computer vision	Fourth
Autonomous & sensor technology	Smallest

Source: Statista Market Insights. Machine learning's ~$552B figure is the only labeled value in the source chart; the remaining segments are ranked by projected size.

The portion of that market built specifically for marketing is smaller but compounding fast. Estimated at $15.84 billion in 2021, AI in marketing is forecast to exceed $107.5 billion by 2028, roughly 9x growth, with figures from 2021 onward calculated by Statista based on The Insight Partners data.

Employers expect the underlying skills to become standard. In the World Economic Forum's survey of 1,000 employers, information and technology services and telecommunications tied at the top: 66% of employers in each expect AI and big data to be core workforce skills between 2025 and 2030. Even the lowest-ranked industries kept the skill above one in three workers.

Industry	Expect AI & big data as a core skill (2025-2030)
Information and technology services	66%
Telecommunication	66%
Financial services and capital markets	61%
Insurance and pensions management	58%
Education and training	56%
Automotive and aerospace	54%
Medical and healthcare services	51%
Government and public sector	50%
Electronics	44%
Supply chain and transportation	44%
Real estate	43%
Production of consumer goods	42%
Retail and wholesale of consumer goods	41%
Infrastructure	39%
Professional services	37%

Source: World Economic Forum.

Consumer spending on AI creative tools is heavily concentrated. Between January 2023 and December 2024, the United States accounted for 66.3% of global consumer spend on AI visual generator and editor apps, more than ten times China's second-place 6.3%.

Investors have noticed. The share of new unicorns that are AI startups climbed from 6% in 2015 to 53% in 2025, meaning more than half of all new billion-dollar companies are now AI businesses. CB Insights notes that one in five of those 2025 unicorns are AI agents.

Funding concentrates at the top. Among machine learning operations and platform startups, OpenAI raised over $11.3 billion, roughly 19 times the second-place Scale AI ($602.6 million). All ten leading MLOps startups had raised more than $100 million.

AI / MLOps startup	Total funding (Jan 2024)
OpenAI	$11,300M
Scale AI	$602.6M
Adept	$415M
Cohere.ai	$414.9M
Anyscale	$259M
Inflection AI	$225M
Weights & Biases	$200M
Hugging Face	$160.2M
OctoML	$131.9M
AI21 Labs	$118.5M

Source: NFX, January 2024.

What this means for marketers: AI budgets now compete inside a market growing toward $1.7 trillion. The platforms and skills you adopt in 2026 will be table stakes by 2030, not differentiators, so the cost of waiting compounds faster than the cost of experimenting.

AI use cases in marketing: where the work actually happens

Headline market numbers say AI is big. The survey data says how marketers actually use it. As of January 2025, 56% of marketing professionals had AI in production: 17% extensively across multiple channels and 39% in select areas. Another 26% were exploring it, leaving only 13% with no plans at all.

When marketers name the tactics they apply AI to, the top of the list is content and communication work. Content creation and enhancement leads at 37%, with email optimization (36%), social media management and ad targeting (35%), and content personalization (33%) clustered right behind.

Influencer marketing has adopted AI almost wholesale. About 60% of marketers already use AI for influencer work: 37.8% on a limited basis and 22.4% extensively, while just 9.5% have no plans to adopt it. Our full influencer marketing statistics report tracks where that spend is going.

The purpose behind that adoption is dominated by language. Natural language processing accounts for 49% of AI tool use in influencer marketing, nearly double machine learning (28.7%). Notably, deepfake technology already ranks third at 24.3%.

For broader digital experience delivery, chatbots (38%) and personalization (36%) lead the use cases marketers find most impactful, with brand intelligence, content intelligence, and product recommendations close behind. Generative tasks such as content authoring, video, and image generation all cluster around 28-29%.

AI digital-experience use case	Marketers citing it (2024)
Chatbot	38%
Personalization	36%
Brand intelligence	33%
Content intelligence	31%
Product recommendations	31%
Content authoring	29%
Video generation	29%
Image generation	28%
Customer journey optimization	27%
Content performance predictions	26%
Content findability	25%
Digital asset discovery	25%
Asset tagging	18%
Ideation	17%
Briefing	16%

Source: Sitecore, 2024.

Ecommerce shows the broadest, flattest adoption of any segment. Customer service and support leads at 37%, but every listed application, from data analysis and image generation to copy generation and admin tasks, sits within a narrow 30-37% band.

One 2024 finding has aged quickly but is worth keeping for context: as of early 2024, 52% of marketing and media leaders used no AI content tool at all. Among those who did, ChatGPT dominated at 29%, roughly ten times its nearest rival. (For how far ChatGPT has scaled since, see our ChatGPT statistics breakdown.)

AI content tool	Share of marketers using it (early 2024)
None	52%
ChatGPT	29%
Other / don't know	15%
Google Bard/Gemini	3%
Midjourney	3%
Canva	2%
Adobe AI	2%
JasperAI	2%
Grammarly	2%
Google AI	1%
Microsoft (Azure/Copilot)	1%
Bing	1%

Source: WordPress VIP, early 2024.

What this means for marketers: Start where adoption is already proven, content, email, personalization, and customer service, rather than chasing novelty use cases. The fastest, lowest-risk ROI sits in tasks your peers have already validated at 30%-plus adoption.

Which AI marketing tools are actually deployed on live sites

Surveys measure intent. Our crawler measures deployment, and the two tell different stories. TechnologyChecker scans the public tech stack of tens of millions of active domains every month. In our most recent complete crawl (July 2025), OpenAI-powered integrations were detectable on 41,764 domains, the single most-deployed AI-native signature we track. Every other standalone AI tool trails far behind it.

The visible tip is the standalone AI tool. The hidden mass is the marketing stack already running it.

AI-native tool	Category	Live domains (Jul 2025)
OpenAI	AI chatbots & assistants	41,764
LucidWorks	AI search & discovery	8,275
DALL-E	AI image generation	3,038
Clare.AI	AI chatbots & assistants	2,368
Gamma Labs	AI content generation	620
Synthesia	AI image generation	259

Source: TechnologyChecker detection crawl, July 2025. Counts are domains where each tool's signature is live in the page source.

Now set those numbers against the marketing platforms sites already run. Traditional automation and live-chat tools dwarf every AI-native product, and most of them have since added their own AI features.

Marketing platform	Category	Live domains (Jul 2025)
MailChimp	Marketing automation	313,840
Klaviyo	Marketing automation	191,547
Tawk.to	Live chat	145,895
HubSpot	Marketing automation	107,974
Tidio	Live chat	39,806
Intercom	Live chat	22,512

Source: TechnologyChecker detection crawl, July 2025.

MailChimp alone sits on more than seven times as many domains as every AI-native tool in the table above combined. The takeaway is not that marketers ignore AI. It is that AI mostly reaches their sites as features inside the platforms they already pay for, Klaviyo's predictive sends, HubSpot's content assistant, Tidio's Lyro bot, rather than as a wave of new AI-only products bolted on to the front end. The 56% adoption figure from surveys and the few thousand standalone AI deployments we detect are both accurate; they describe different layers of the same stack.

What this means for marketers: Audit the AI you already own before buying more. The fastest path to measurable AI in marketing usually runs through a feature toggle in your existing email, CRM, or chat platform, not a net-new tool that adds another login and another integration to maintain.

Marketer insights: budgets, trust, and the things slowing AI down

Among B2B marketers, AI proves most effective at the front of the funnel and the back office. Targeting audiences (43%) and analytics and reporting (41%) top the list, ahead of personalization, email, and content creation, which cluster at 35-36%. This mirrors the broader marketing automation market trends we track across 29.9 million active domains.

Looking ahead, marketers rank generative AI as the trend that matters most. In a November 2025 survey, 70% named generative AI among the most important consumer trends for 2026, ahead of CTV and streaming (63%). The metaverse, by contrast, has collapsed to last place at 12%.

Enthusiasm comes with caution. Only 13% of marketers fully trust AI insights without human checks. The vast majority sit in a "trust but verify" middle: 33% validate AI insights with human review and 35% rely mostly on human judgment.

That caution shows up in spending. Nearly half of marketers (47.6%) allocate under 10% of their marketing budget to AI-driven campaigns, and only 19% commit more than 40%. Most organizations are still testing AI with small budget slices rather than betting the bulk of spend on it.

When organizations do invest, the leading strategy is internal upskilling. 27% say training courses and workshops are their main AI adoption play, ahead of partnering with AI vendors (16.7%) or hiring external experts (15.9%).

Generative AI specifically is mostly live or near-live inside brands. About 57% are already running GenAI: 30% implementing initial solutions and pilots, 27% with solutions in place and being evaluated. Just 2% report a company policy that prohibits its use.

So what holds the rest back? When marketers list barriers to adopting new AI tools, data privacy concerns rank first at 41%, with training and time investment close behind at 39%. Tool sprawl and legacy-system integration tie at 34%, an interoperability problem as much as a trust one.

Barrier to adopting new AI tools	Marketers citing it (2025)
Data privacy concerns	41%
Training and time investment	39%
Too many tools that do similar things but don't connect	34%
Integration with existing or legacy systems	34%
Prefer a different AI tool than the company invests in	27%
Resistance to change within the organization	27%
Role security concerns	26%
Ethical or legal compliance concerns	22%
No barriers experienced in the past year	11%

Source: HubSpot, 2025.

The biggest organizational challenge with generative AI is output you cannot fully trust. 35% cite concerns about reliability, including hallucinations, ahead of skills gaps (30%) and security risks (29%). Nineteen percent also worry they are not moving fast enough, a reminder that fear of falling behind sits alongside fear of the technology itself.

Organizational GenAI challenge	Organizations citing it (2024)
Reliability concerns (e.g. hallucinations)	35%
Lack of skills or training	30%
Security risks	29%
Lack of a clear strategy	25%
Brand-safety concerns	24%
Difficulty integrating AI into daily workflows	24%
Not moving fast enough	19%
Limited or poor-quality data	18%
Limited budgets	18%

Source: Econsultancy, 2024.

In social media specifically, the leading concerns are human rather than technical. Maintaining authenticity (43%) and the value of human creativity (40%) top the list of GenAI challenges.

The benefits side of that same survey explains why marketers push through those concerns. Increased efficiency (38%) leads, but easier idea generation, more content, and enhanced creativity (33-34%) all rank above pure cost savings (32%). Speed and ideation, not just budget, drive the appeal.

GenAI benefit in social media	Marketers citing it (2024)
Increased efficiency	38%
Easier idea generation	34%
Increased content production	33%
Enhanced creativity	33%
Reduced costs	32%
Reduced turnaround time	26%
Competitive advantage	20%
Audience personalization	18%

Source: Capterra, 2024.

At the individual level, the daily frustrations are about quality. 43% of marketers say generative AI sometimes produces inaccurate information, followed by bias (34%), irrelevant output (31%), and prompting difficulty (30%).

Challenge using generative AI	Marketers citing it (2025)
Sometimes produces inaccurate information	43%
Sometimes produces biased information	34%
Content not always relevant to the goal	31%
Difficulty prompting to the desired goal	30%
Content too surface-level or vague	29%
Lacks up-to-date info on trends and events	23%
Cannot create truly original content	19%
Sometimes produces plagiarized information	19%
Concerned about what AI tools do with data	15%
Helps summarize large amounts of data	14%

Source: HubSpot, 2025.

All of this lands on the people doing the work. The share of marketers worried AI may jeopardize their role jumped from 35.6% in 2023 to 59.8% in 2024, a 24-point rise in a single year, tracking the mainstreaming of generative AI in everyday workflows.

What this means for marketers: Treat reliability and integration as the real blockers, not enthusiasm. Budget explicitly for training and human review, and consolidate overlapping tools before adding new ones, since tool sprawl is now as big a barrier as data privacy.

Consumer insights: how shoppers actually feel about brand AI

Marketers have moved fast. The final question is whether consumers are coming with them. On the demand side, the answer is broadly yes: roughly half of consumers in four English-speaking markets are open to AI-assisted shopping research, led by Canada at 51%, the US at 49%, the UK at 47%, and Australia at 43%.

Country	Consumers open to AI-assisted purchase research (2025)
Canada	51%
United States	49%
United Kingdom	47%
Australia	43%

Source: Attest, 2025.

Who actually uses AI apps skews sharply male. AI utility apps are 89.1% male, and every category, from transcription to music to writing tools, leans male. Education apps are the most balanced, at 31.4% female users.

It also skews young. The 18-24 cohort is the largest demographic in every AI app category, peaking at 65% for companion apps and 56% for education. Writing tools and transcription draw the oldest audiences, with 23% of users aged 50+.

Consumers are increasingly willing to pay. Spending on AI mobile apps reached $1.42 billion in 2024, up 274% from $380 million in 2023, a sign that paid AI apps moved from novelty to mainstream spending in a single year.

When consumers describe the upside of brands using AI, they reward operational speed. Faster customer support leads at 47%, ahead of helping staff do their jobs (38%) and more creative advertising (35%).

Consumer-seen advantage of brand AI	Consumers citing it (2025)
Faster customer support	47%
Helping staff do their jobs	38%
More creative advertising	35%
Cost savings passed on to consumers	32%
Greater product innovation	32%

Source: Attest, 2025.

The downsides they name are overwhelmingly about losing the human element. Loss of the human touch (59%) tops the list, with job losses and the inability to speak to a real person tied at 57%.

Those concerns add up to a trust problem. Only 26% of consumers say they trust brands generally to use AI responsibly, meaning nearly three in four withhold that trust.

And the gap is widening, not closing. Consumer comfort with brands using AI fell from 57% in Q3 2023 to 46% in Q3 2024, an 11-point drop even as brand adoption accelerated.

The discomfort is sharpest where AI replaces something visibly human. 51% of consumers are uncomfortable with AI virtual brand ambassadors standing in for celebrity spokespeople. Comfort rises for behind-the-scenes work: AI writing descriptions and taglines (43% comfortable) or deciding ad placement (42%) nearly break even.

What this means for marketers: Use AI loudly where it speeds things up, such as support and recommendations, and quietly where consumers expect a human. Disclose AI use and keep a clear human escalation path, because the trust gap, not the technology, is now the limiting factor.

AI marketing ROI and real-world use cases

The survey data shows where marketers apply AI. Independent research shows what it returns. McKinsey estimates that generative AI applied across marketing and sales could add the equivalent of roughly $463 billion in value annually, with productivity gains worth 5 to 15 percent of total marketing spend. HubSpot's State of Marketing data points the same way: about 80% of marketers now use AI for content creation and 75% for media production, and 61% say marketing is going through its biggest disruption in 20 years.

The strongest returns come from AI compressing an existing workflow, not inventing a new one.

Those returns concentrate in a handful of repeatable use cases, most of which map directly onto the adoption rankings above:

• Personalization at scale. Product recommendations and tailored content (36% of digital-experience use cases) let small teams run the kind of one-to-one targeting that once needed a full CRM team. Predictive personalization is where brands like Nike have reported lifts in repeat purchase behavior.
• Content and creative production. The single most common tactic (37%) covers drafting, repurposing, and localizing copy, plus AI image and video generation for ad variants and social posts.
• Customer service automation. Chatbots top both the digital-experience list (38%) and consumer-perceived benefits (47% cite faster support), making service the clearest win-win for brands and buyers.
• Predictive analytics and targeting. B2B marketers rate audience targeting (43%) and analytics and reporting (41%) their most effective AI applications, using models to score leads and forecast behavior.
• Visual commerce and virtual try-on. AI visual tools power try-before-you-buy experiences; beauty and apparel brands such as L'Oréal have built AI diagnostics and virtual try-on into the path to purchase.

The pattern across these examples is consistent: the brands seeing measurable returns are not the ones generating the most content fastest. They are the ones embedding AI inside governed, repeatable workflows that protect brand voice and keep a human in the loop, exactly the discipline the trust and reliability data argues for.

What this means for marketers: Tie every AI pilot to one of these proven use cases and one metric (response time, conversion, content cycle time). The ROI case is strongest where AI compresses an existing workflow, not where it invents a new one.

What the data adds up to

Three patterns run through all 35 data points. First, adoption is no longer the story, integration is. A majority of marketers already use AI, generative AI is the top trend they are watching, and the open question has shifted from "should we use it" to "how deeply and where."

Second, the constraints are human and organizational, not just technical. Reliability and hallucinations lead the challenge lists, but data privacy, skills gaps, tool sprawl, authenticity, and job security all rank highly. The biggest barriers to getting more value from AI sit in budgets, training, and integration, not in the models themselves.

Third, the consumer trust gap is widening as deployment speeds up. Comfort fell 11 points in a year, only a quarter of consumers trust brands to use AI responsibly, and discomfort spikes wherever AI replaces a visibly human role. The marketers who win the next phase will be the ones who treat that gap as a design constraint, using AI where it earns trust, such as faster support and better targeting, and keeping humans visible where consumers expect them.

How AI search is changing who finds your marketing

AI assistants now crawl the web at a scale that rivals classic search, and that traffic is becoming the new top of the funnel. Over the 28 days ending June 6, 2026, Googlebot led all measured bot requests at 26.8%, followed by Meta-ExternalAgent (12.8%), ClaudeBot (11.0%), and OpenAI's GPTBot (10.8%), according to Cloudflare Radar. AI crawlers from Anthropic, OpenAI, and ByteDance now individually rival the traffic of Bing's crawler.

AI crawlers now decide which brands show up when buyers ask an assistant for recommendations.

Crawler	Share of bot requests (28 days to Jun 6, 2026)
Googlebot	26.8%
Meta-ExternalAgent	12.8%
ClaudeBot	11.0%
GPTBot	10.8%
Bytespider	10.4%
Bingbot	8.2%
Applebot	6.9%
Amazonbot	5.3%

Source: Cloudflare Radar, ai/bots/summary/user_agent, 28 days to June 6, 2026.

Most of that activity feeds model training. Cloudflare classifies 51.9% of AI crawl requests as training, 35.0% as mixed purpose, and only 9.8% as search, so the majority of AI crawling is building the models that will later answer buyer questions, not indexing pages for a results list. The web has started writing rules for these bots: GPTBot is now the user agent that the most domains name in robots.txt, ahead of ClaudeBot, Google-Extended, and CCBot.

For marketing teams, the practical consequence is direct. If your robots.txt or CDN blocks GPTBot, ClaudeBot, or PerplexityBot, you remove your brand from the data these assistants draw on, and increasingly from the answers they generate. Getting cited in an AI answer now follows the same playbook that wins our own marketing technology statistics coverage: allow the citation crawlers, structure pages as clear question-and-answer passages, and keep your data fresh.

What this means for marketers: Treat AI crawler access as a marketing setting, not just an IT one. Audit robots.txt for accidental blocks of the search and citation bots, then structure your highest-intent pages so a model can lift a clean, sourced answer, because that is how brands surface in ChatGPT, Perplexity, and Google's AI results.

Put together, the data points to a clear sequence for the year ahead: start with the AI you already own, prove it on a known use case, keep a human in the loop, open access to the AI crawlers, then measure what gets cited.

Frequently asked questions

Why are AI use in marketing statistics important in 2026? They show where adoption, budgets, and consumer sentiment actually sit, rather than where the hype suggests. With 56% of marketers already running AI and the AI-in-marketing market heading past $107 billion by 2028, benchmarking against real numbers is how teams decide what to fund and what to skip.

What is the projected AI in marketing market size? The market for AI in marketing is forecast to grow from about $12.05 billion in 2020 to $107.54 billion by 2028 (figures from 2021 onward are forecasts based on The Insight Partners data). It sits inside a broader AI market projected to reach $1.675 trillion by 2031 (Statista Market Insights).

What are the latest AI in marketing adoption statistics? As of January 2025, 56% of marketers had integrated AI into data-driven work (17% extensively, 39% in select areas), per Ascend2. Among brands specifically, around 57% are already running generative AI in pilots or production (Econsultancy).

What are the most effective AI use cases for marketing? The leading tactics are content creation and enhancement (37%), email optimization (36%), and social media management and ad targeting (35%), per Ascend2. For B2B marketers, audience targeting (43%) and analytics and reporting (41%) are rated most effective (Act-On). Chatbots and personalization lead digital-experience use cases.

Which companies are successfully using AI for marketing? Brands across retail and ecommerce use AI for personalization, customer service automation, and visual commerce. Reported examples include L'Oréal's AI beauty diagnostics and virtual try-on and Nike's predictive personalization. The common thread is embedding AI in repeatable workflows rather than one-off content generation.

How is consumer comfort with AI marketing changing? It is falling. Consumer comfort with brands using AI dropped from 57% in Q3 2023 to 46% in Q3 2024, and only 26% of consumers trust brands generally to use AI responsibly (Qualtrics). Discomfort is highest where AI replaces a visibly human role, such as a virtual brand ambassador (51% uncomfortable).

What are the biggest risks of using AI in marketing? Marketers cite output reliability and hallucinations (35%), data privacy (41% as an adoption barrier), and integration with legacy systems (34%) as top organizational risks. On the people side, nearly 60% of marketers worry AI could impact their roles, up from 35.6% a year earlier.

How many websites actually run AI marketing tools? Fewer than the survey numbers imply. In our July 2025 detection crawl, OpenAI integrations were live on 41,764 domains and other standalone AI tools ranged from a few hundred to a few thousand each. By comparison, MailChimp appeared on 313,840 domains and HubSpot on 107,974. The pattern suggests AI is reaching marketing mostly as features inside incumbent platforms rather than as new AI-only products.

Does AI search affect how brands get discovered? Yes. AI crawlers now make up a large share of web traffic: GPTBot and ClaudeBot each accounted for roughly 11% of measured bot requests in the 28 days to June 6, 2026 (Cloudflare Radar), rivaling Bingbot. Blocking those crawlers in robots.txt can remove a brand from the answers ChatGPT, Claude, and Perplexity generate, so AI crawler access is now a marketing concern.

About the author

Emre Elbeyoglu is a growth marketer and entrepreneur with 15 years in SEO and growth. He reverse-engineers how search engines and AI ranking systems decide what to surface, then applies those patterns to acquire customers and scale products, including his own bootstrapped SaaS startups. He writes about AI, GEO, and growth experiments at GrowthMarketing.ai and across TechnologyChecker.io.

Methodology and sources

All figures in this report are drawn from a Statista dossier, "Artificial intelligence (AI) use in marketing." Statista aggregates the underlying studies; each chart and table here credits the original primary source, including Ascend2, HubSpot, CB Insights, Appfigures, Attest, Capterra, Econsultancy, Influencer Marketing Hub, Klaviyo, Sitecore, Act-On, Mediaocean, NFX, WordPress VIP, the World Economic Forum, YouGov, and Qualtrics. Market-size and AI-in-marketing forecasts are modeled by Statista Market Insights and The Insight Partners. The ROI and use-cases section draws on additional external research from McKinsey, HubSpot, and published brand case studies, each linked inline.

Survey-based figures reflect different samples, dates, and geographies, ranging from a 312-respondent global marketer survey (Ascend2, January 2025) to a 23,730-respondent multi-market consumer study (Qualtrics, Q3 2024) and an 8,500-to-34,680-respondent 17-market advertising study (YouGov, January 2024). Percentages may not sum to 100 because of rounding or multi-select questions. The "AI technologies market by segment" table reports machine learning's labeled 2032 value; the remaining segments are ranked by projected size, since the source chart does not label them individually.

Two sections use first-party and live data rather than the dossier. Tool deployment counts come from the TechnologyChecker detection crawl (most recent complete crawl: July 2025), which records the technology signatures present in the public page source of active domains; counts are signature-level and re-verified on a recurring crawl. The AI crawler figures come from Cloudflare Radar (ai/bots and robots_txt datasets, 28 days to June 6, 2026) and represent shares of aggregate request traffic, not absolute volumes.

Every visualization in this post is available in the TechnologyChecker interactive charts library. For related analysis, see our reports on AI market size statistics, AI adoption trends, marketing technology statistics, and social media advertising insights.