AI in B2B Marketing: 2025 Statistics Every CMO Needs to Know

In 2022, OpenAI’s ChatGPT became the fastest-growing user base in history, hitting 100 million users in just two months after launch.

In 2025, AI tools are mainstream and Generative AI (GAI) has sent shockwaves through the world.

For marketing leaders, this revolution poses pressing questions - ones you’re likely to have to answer at board level. How is generative AI transforming the way you work? What is the impact on content marketing and marketing automation? And crucially, how are you going to adapt your marketing strategy for the AI era?

So we’ve trawled the latest research reports and compiled the data you need to push for AI adoption and make marketing way more efficient and creative.

AI Adoption: Past The Tipping Point

The acceleration of AI adoption shows that the technology has crossed into mainstream acceptance. Its use is no longer just experimental—it's operational. 

McKinsey reports that organisations’ use of AI has accelerated markedly in the past year. 71% of respondents in their State of AI global survey said their organisation now regularly uses GAI in at least one function, up from 65% in early 2024.

Unsurprisingly, technology companies lead the charge, with 88% using Generative AI (GAI) in at least one function. Professional Services (80%) follow this, then Advanced Industries (79%), and Media & Telecoms (79%). Even traditional ‘laggards’ like Energy and Materials now show 59% adoption rates.

The transformation is being led from the top with Wharton finding that regular weekly use of GAI use among business leaders has nearly doubled, from 37% in 2023 to 73% in 2024.

Perhaps most significantly, 82% of businesses now see generative AI as a main lever for reinvention. This isn't about adding AI tools to existing processes—it's about reimagining how business gets done.

If your company isn't already implementing AI at scale, you're not just behind—you're becoming competitively vulnerable in real-time.

Generative AI Adoption in Marketing

In 2023, McKinsey identified four areas where 75% of the value that generative AI use cases could deliver would fall: Customer operations, software engineering, R&D, and marketing and sales.

They estimated GAI could increase the productivity of the marketing function with a value between 5-15% of total marketing spending, and increase sales productivity by approximately 3-5% of current global sales expenditures.

Two years later, their 2025 State of AI report shows that 42% of organisations are now using GAI in marketing and sales. While substantial, there is still significant room for growth toward McKinsey's predicted value potential.

The Content Marketing Institute found that 56% of B2B marketers’ organisations have AI at high to medium on their list of priorities for 2025. 21% say it is low priority, 11% don’t rate it as a priority at all.

The penetration of AI in marketing and sales functions varies significantly across industries - Technology (55%), Professional Services (49%), Advanced Industries (48%), Media and Telecoms (45%), Consumer Goods and Retail (46%), Financial Services (40%), Energy and Materials (33%). Healthcare Pharma and Medical is lower at 29%, likely reflecting regulatory and data sensitivity considerations.

The American Marketing Association (AMA) found that adoption by individuals is near universal. Nearly 90% of marketers reported using generative AI tools at work, with 71% using GAI weekly or more, and almost 20% using it daily.

They found that common uses for GAI in marketing include: writing (38%), content creation (33%), visual storytelling (28%), design (28%), editing (28%), brainstorming and ideation (27%), data analysis (22%), research (19%), SEO (8%), and personalisation or translation (5%).

When examining specific tools, ChatGPT and similar conversational AI tools dominate the marketing toolkit, used by 62% of professionals. Writing enhancers like Grammarly aren't far behind at 58%, followed by platforms with built-in AI features such as Microsoft Copilot and Canva at 52%. Visual content creation has also gained significant traction, with 45% of marketers turning to specialised generators like Midjourney and Lightricks for images and videos.

These findings suggest AI tools are becoming integral to workflows rather than occasional supplements and show a strong perceived value and practical utility. However, professionals are often using them independently through consumer applications, while their organisations lag in developing formal AI strategies and governance policies to manage this technology shift.

Generative AI’s Impact on Content Marketing

Let’s break down the key ways content marketing is being transformed:

Efficiency and Scale

One of the most immediate affects of AI is the increase in content production efficiency. Advanced AI writing and design tools can generate ideas, briefs, social updates, and detailed outlines instantly. 

According to Coschedule’s 2025 State of AI in Marketing report, 85% of marketers use AI tools for content creation. 75% believe it is giving them a competitive edge. 26% report that their content is more successful when AI is involved in its creation, with an additional 38% saying it was just as effective. 

83% of respondents said that AI had increased their productivity. Of the  84% saying that AI has improved the speed of delivering content, 29% reported a significant improvement in speed. 49% said AI saves them between 1-5 hours a week, with a further 25.4% gaining 6-10 hours a week.

The AMA echoes these findings. 49% of marketers reported gains in the quantity of content produced and 50% time savings.

In other words, marketers can produce more content for far less cost by integrating generative AI into their workflows. For resource-strapped marketing departments, these efficiency gains are game-changing.

Quality and Consistency

But does this mean we’re sacrificing quality for speed and quantity?

Trust in GAI outputs varies dramatically among marketers, revealing a complex relationship with the technology. 

Coschedule found that 55% of marketers have high levels of trust in AI content and insights, while the Content Marketing Institute's survey of B2B marketers found only 4% reported high trust levels. The majority, 67%, expressed medium trust, with 28% reporting low trust and just 1% having no trust at all. This variation likely reflects differences in specific applications, audience focus, topic complexity, and how trust is measured and defined.

For some teams, the level of trust may go a step too far given AI’s reputation for hallucination. Only 27% of respondents whose organisations use GAI say that employees review all content created by it. A staggering 30% reported that 20% or less of AI generated content was checked before use.

However, overall, many marketers discover AI can enhance content when properly implemented. Contrary to fears about a flood of low-quality content, 30% of respondents to LinkedIn’s 2024 benchmarking survey said AI improved their campaign ideation, and 52% of marketers surveyed by the AMA reported gains in content quality when using AI with human oversight. 

This suggests that successful AI implementation depends more on integration strategy than simply whether the technology is adopted. The industry appears to be settling on a collaborative approach rather than full automation. Nearly 50% of marketers believe the ideal balance involves human-driven content with AI assistance, showing that professionals view AI as an amplifier of creativity and productivity rather than a replacement for human judgment and strategic thinking.

Personalisation and Higher Engagement

Beyond time savings and volume, AI is expected to elevate the relevance and personalisation of content. According to McKinsey, 60% of B2B commercial leaders surveyed thought that AI will have a significant impact on lead identification, and 53% on personalised outreach.

However, there's a notable gap between these executive expectations and current implementation reality. According to LinkedIn's B2B Marketing Benchmarking report, only 33% of marketers report they are currently using GAI to personalise their marketing campaigns, suggesting significant untapped potential and growth opportunity ahead.

Interestingly, personalisation benefits currently rank lower among AI applications than might be expected. While 27% of marketers see more personalised content and campaigns as among the biggest benefits of AI, slightly more cite operational advantages such as cost efficiencies and increased productivity. However, 24% reported having more time to build relationships with customers, and 26% highlighted better data analysis and insights. 

This latter point is significant since robust data analysis is a prerequisite to effective personalisation, suggesting many organisations may be building the foundational capabilities that will enable more sophisticated personalisation later.

Generative AI systems can analyse customer data and tailor content to specific industries, firmographics, or even individual client preferences at scale. This means marketers can move from one-size-fits-all whitepapers or newsletters to highly segmented, personalised materials for different audience subsets. Dynamic content personalisation, once labour-intensive, is now workable in a more automated way. For instance, AI can help rewrite an e-book or blog post for different verticals (finance vs. healthcare), or adjust tone and messaging based on a prospect's role (e.g. CFO vs. CTO).

Crucially, personalisation drives better results. McKinsey reports it can reduce customer acquisition costs by up to 50%, lift revenues up to 15%, and increase marketing ROI by up to 30%. In one striking example, Vanguard used generative AI to individualise its ad copy, boosting LinkedIn ad conversion rates by 15%.

These figures suggest that personalisation represents one of AI's most promising but still emerging applications in B2B marketing. The combination of high executive expectations, proven ROI for early adopters, but relatively low current implementation rates indicates significant competitive advantages await businesses that can effectively bridge the implementation gap.

New Content Opportunities

Generative AI is expanding content creation beyond traditional text-based marketing, enabling new formats that were previously resource intensive or technically challenging for most organisations. According to McKinsey, while 63% use generative AI for text outputs, 36% are creating images, 27% code, and 13% each are producing video and voice/music content, demonstrating significant adoption of multimodal content creation. HubSpot found that 1 in 4 marketers plan to leverage using AI to turn text into multi-modal campaigns.

AI tools have the potential to democratise rich media production across formats. Image generators like DALL·E and Midjourney allow marketers without a large photography budget to create on-brand stock imagery. Adobe Firefly and Canva open up custom design to those without design expertise. And AI video platforms are becoming ever more capable, making previously time-consuming video production accessible to organisations without dedicated creative resources.

Integrating video generation capabilities into established platforms will speed up adoption. Tools like ChatGPT's Sora and Google's Veo 3 and Flow are being embedded in familiar interfaces, reducing the technical barriers that previously limited video content creation. Despite the heftier price tag attached, this suggests that multimodal content generation has the potential to drive the 13% video adoption rate significantly higher as capabilities mature and integrate into existing workflows.

Perhaps the most intriguing development is AI-generated spokespersons and virtual influencers. These digital personalities offer consistent messaging without the unpredictability or ongoing costs of human talent. While more established in B2C contexts, B2B applications are emerging, from AI avatars hosting webinars to virtual thought leaders maintaining social media presence.

Take UBS, for example. Using OpenAI and Synthesia, they plan to deploy AI-generated avatars of the bank’s analysts to serve an increased demand for research in video format. While they had published around 50,000 documents a year, video production capacity limited output to around 1,000 a year. Avatars will allow them to target about 5,000 avatar videos annually once the initiative is up and running.

These developments suggest AI is moving beyond process optimisation to enable entirely new content marketing tactics. The combination of established tool adoption and emerging capabilities embedded in familiar platforms shows that multimodal content creation will probably become standard practice, with competitive advantages flowing to companies that can thoughtfully integrate these capabilities while maintaining authenticity and strategic focus.

Implementation Challenges

While adoption rates show impressive growth, the reality of AI implementation reveals a significant maturity gap. Only 1% of company executives describe their generative AI rollouts as "mature," according to McKinsey, highlighting that most organisations remain in early deployment stages despite widespread experimentation.

This immaturity shows in fragmented approaches to AI integration. The Content Marketing Institute found that 54% of B2B marketing teams take an ad hoc approach to AI, experimenting without applying it widely. Only 19% reported that they have integrated AI into their daily processes and workflows.

Marketing teams are struggling with implementation challenges. HubSpot's research reveals that 54% of marketers feel overwhelmed by the prospect of implementing AI tools into their processes and workflows, suggesting that rapid technological advancement is outpacing organisational capacity to absorb change effectively.

McKinsey's findings illustrate this implementation gap, showing that only 21% of businesses have redesigned some workflows around AI. Most remain in earlier stages of integration. This suggests that realising AI's full potential requires not just tool adoption, but messier and more fundamental organisational transformation—a challenge that many companies are still learning to navigate.

Managing Risks and Governance

As AI adoption speeds up, concerns about quality, ethics, and governance are creating implementation friction. 

A significant worry among marketers is that AI content lacks humanity, with 40% citing robotic output as a key downside, according to LinkedIn. This is closely followed by concerns about plagiarism (34%), inaccurate information (32%), data privacy and security (31%), poor quality content (29%), and that AI will hinder creativity (24%).

Similar concerns translate into substantial barriers for non-adopters. Among marketers who haven't embraced GAI tools, accuracy concerns top the list at 35%, followed by corporate mandates restricting use (28%) and copyright concerns (26%). 

Governance structures are developing to address these risks. McKinsey reports that for 28% of organisations, the CEO handles AI governance, while 17% place oversight with their board of directors.

Formal guidelines remain patchier than they should be. The Content Marketing Institute found that 38% of organisations have established AI guidelines, while only 23% report that their marketing teams have specific guidelines. Among those with guidelines, 78% address acceptable uses in content marketing, 66% cover security measures, 66% cover unacceptable use of AI in content marketing, and 64% include data-handling protocols for AI platforms and outputs. 48% required transparency about AI-generated content, and 47% legal and copyright recommendations. Just 29% address bias mitigation—suggesting that governance frameworks are still maturing as businesse learn to balance innovation with responsibility.

Skills evolution

The skills gap represents perhaps the most critical barrier to AI implementation, with 43% of organisations reporting insufficient AI skills as their leading implementation challenge, according to LinkedIn. This shortage is so significant that two in five companies cite lack of AI skills as a primary reason they haven't implemented generative AI at all.

The skills challenge manifests across multiple levels. LinkedIn's data shows that AI skills training has become the fourth fastest-growing skill on the platform and the top digital skill in Q1 2024, showing a massive demand for capability building. 

However, training approaches remain inconsistent. The AMA found that while 54% of marketers have received company-provided training, 39% have trained themselves, and 37% have pursued formal training outside their organisation. Concerningly, 15% report receiving no training at all.

Companies are responding with varying degrees of investment in skill development. LinkedIn reports that 55% of organisations now provide training opportunities for GAI, but this still leaves nearly half without formal development programs.

The skills shortage creates a cascading effect on implementation success. When organisations lack AI expertise, they struggle with fundamental decisions about where to begin, how to integrate tools effectively, and how to maintain quality standards. 

This might explain why so many implementations and governance policies remain at the experimental stage rather than progressing to systematic integration. As the technology continues to develop rapidly, the skills gap threatens to become an even greater competitive differentiator, with organisations that invest in comprehensive AI literacy gaining substantial advantages over those that approach skills development ad hoc.

Strategic Imperatives for Marketing Leaders

Accept That AI is Non-Negotiable: The adoption curve has already decided this for you. The question isn't whether to use AI—it's whether you'll use it strategically or get left behind by competitors who do.

Solve the Skills Problem First: The biggest barrier isn't technology or budget—it's capability. Nearly half of organisations can't implement AI effectively because their teams lack the skills. This is your most urgent priority and biggest opportunity, since most competitors are struggling with the same issue.

Address Job Security Concerns Head-On: A quarter of marketers worry AI puts their job at risk. Ignoring these concerns will undermine your implementation efforts. Be clear about how AI enhances rather than replaces human work and reinforce this through training and role evolution. Change management will only succeed when reluctant team members see AI as making their work more valuable, not more vulnerable.

Don't Assume More AI Means Better Results: The businesses seeing success aren't the ones using the most AI tools—they're the ones using AI strategically with proper oversight. Quality control and human review remain critical, regardless of how sophisticated the technology becomes.

Focus on What Competitors Aren't Doing Yet: While everyone's experimenting with basic content creation, personalisation remains untapped. This is where you can gain a real competitive advantage while others are still figuring out the fundamentals.

Build Governance Before You Need It: Most organisations are flying blind—minimal content review, inconsistent guidelines, unclear oversight. The companies that establish proper governance now will scale faster and avoid costly mistakes later.

Expect Implementation to Be Harder Than Adoption: Getting people to use AI tools is easier than integrating AI systematically. Plan for this complexity rather than assuming adoption equals success.

The Messy Truth About AI Success

The data tells a story of transformation in progress. AI has moved from experimental tool to daily reality for marketers, delivering real efficiency gains and opening new possibilities for personalisation and content creation. But it also reveals an industry caught between individual enthusiasm and organisational readiness.

The question isn't whether to adopt AI—that decision has been made by the market. Your people are probably already using GAI, whether you want them to or not, and some of them are getting good results. But are they doing it despite your organisation, not because of it?

While you're debating governance frameworks in boardrooms, are they solving (and potentially creating) real problems with the tools already at their fingertips and learning best practices from random YouTube tutorials?

Only 19% of marketing teams have actually integrated AI into their daily workflows. Everyone else is still experimenting. That means there's still time to get this right—but not much. 

The question is whether you'll lead the transformation or be shaped by it.

Seeing the opportunity is one thing. Turning it into a scalable marketing strategy is another. That’s where we come in—combining strategic, sharp content, and smart automation to help B2B firms move from experimentation to real impact.