How are B2B marketers adopting AI?

Is AI the future of B2B marketing? Most probably.

But now, it’s a messy, promising present. Most are testing it out, some are implementing solutions, but how many B2B marketers know what they’re actually doing?

In this Data Story, we’ll be looking at some reports that illustrate how ready B2B marketing is for the big AI switch—and what are the biggest challenges this technology brings.

Of course, we’ll also provide some potential solutions, so buckle up…

Where do B2B marketers find AI the most effective?

Nowadays, it’s no more about if you’re using AI. 

It’s just about how you’re doing it:

It appears that content (52%) is the most widely-accepted form of AI utilization.

It’s where it all started from, after all. 

With AI tools getting better, it’s just a matter of time before most of the content online becomes at least a mix of AI and human. 

AI is also slowly overtaking design (30%) with the improvement of image generation tools like Midjourney and Dall-E, and most importantly…

AI video stocks rising? Video creation is far down this list—just 8%. But with how Google’s  Veo 3 is taking the internet by storm, it feels like it’s just a matter of time before generative video creation gets used massively.

There’s also quite a few useful applications like coding (39%), presentations (35%), and web development (25%), which can definitely make a marketer’s job easier and more effective.

What can you do: If you double down on content, don’t settle for generic. More than half of the industry is using AI content so you’ll have to spice it up with unique tone or data, and subject-matter insights.

There are also many other ways you might be ahead in B2B. Explore underutilized areas like video which are perfect for, say, explainers and case studies. And use AI to speed up behind the scenes. 

One final thing: Stay agile. What’s niche today might be “passe” in 6 months. 

How will B2B marketers add AI to their budget?

Budget is B2B marketing’s big, bad word. But we can’t skip talking about it.

With the emergence of AI, the talk of it eating into some of the traditional marketing budgets made sense. 

You should look at this from two perspectives:

• 48% are using AI to cut costs and replace other marketing expenses
• 52% are treating the AI budget as additive, meaning it’s a net-new investment.

What does this tell us: If over half of the industry is adding additional budgets, it shows that the perception of AI is changing. 

You’re not cutting costs. You’re adding up and forming a competitive advantage.

What can you do: Don’t look at where to cut. See where you can expand instead.

Think how AI can allow you new capabilities. Could it help you test more campaigns? Explore creating videos, something you never did before? Or make better reports for clients?

Also, if you’re in the 48%, it’s best to be clear—what are you removing in favor of AI? Will the quality suffer if you cut manual labor? 

Will AI output align with your brand voice if you’re trimming agency hours? 

All questions worth answering.

The Challenges of AI implementation

Let’s talk about something that’s rarely talked about—AI implementation.

While AI is becoming quite popular, it’s one thing to ask how to stop your YouTube procrastination binge. But asking it to accurately score leads or personalize outreach at scale is something completely different. 

And that’s just one of the potential issues with AI implementation.

So what are the biggest challenges? Check it out:

It seems that data quality is the pressing issue, with 53% claiming it to be the biggest challenge.

In other words, AI won’t really make you or your marketing team smarter. It’s the opposite—it will make your mess even more visible. At least until you become 100% certain of the accuracy of the output.

Systems integration and employee skill are equally important bottlenecks (43%). So when you add talent gap and legacy tech to bad data… It’s a perfect storm.

On the other end of the spectrum, you have ethical concerns (31%) and budget constraints (28%). 

These two things being low show that most are still in foundational mode when it comes to AI and it’s yet to mature to long-term governance and cost optimization concerns.

What does this mean for you: AI can be a multiplier, but only if your team knows what to do with it. 

So try to invest in upskilling yourself and your team, make sure the data adds up, and treat AI as a native part of your martech stack—so you don’t have integration issues.

And while we’re on that subject…

Are there plans in place for utilizing AI?

This is a crucial question for marketing teams.

If you consider AI as a part of your marketing strategy—do you have a system to make the most out of it? 

The answers are interesting:

Less than 20% have AI integrated into their systems. And 20% are not formally using AI at all, while some staff may choose to do it individually. 

The biggest (54%) group uses it “ad-hoc,” from case to case and with limited application. 

This means that there’s no strategy, no structure, and no real measurement.

What can you do: Move from experimentation to systematization. 

Pick a repeatable use case like say, social post drafting, content brief creation, or similar—and try to systematize it by creating a guideline.

But before you do, keep reading…

What issues do companies address in AI usage guidelines?

Sometimes, creating a guideline is easier said than done.

But companies that create guidelines focus on these: 

The top concerns are acceptable and unacceptable uses (78% and 66%), but only 27% address real-world application. That’s a gap between policy and purpose.

In a nutshell, most companies are focused on content boundaries and not strategy.

Just under half (48%) require disclosure when AI generates content. An underrated policy that might become even more important in the future.

Security measures (66%) are also high on the list, as are data handling guidelines (64%) which align with some of the biggest challenges we mentioned before.

The Crew’s opinion: It’s easy to say “don’t use AI for XYZ.” But what will you use it for? Build your AI policy around impact, not just rules. 

Start with creating a few high-impact use-cases (repurposing webinars into social posts for instance) and document how AI supports that goal. 

Then back it up with guidelines for bias checks, security, and transparency.

What impacts are B2B marketers already seeing with AI? 

If you used AI for something marketing-related, you must’ve felt some benefits.

Here’s what your peers say:

Most B2B marketers are happy that they’re doing fewer tedious tasks (51%). 

Writing reports? Extracting data from analytics? Creating content briefs? That’s all time-consuming stuff you can delegate now. 

Efficient workflows (45%) and improved content optimization (42%) come close behind, while interestingly—38% believe AI improves creativity. 

That’s a good sign. Instead of AI suppressing creativity, AI can be a creative engine. 

Of course, there are some negative impacts as well such as job anxiety (18%) or lower-quality content (13%). 

On the positive side, only 2% saw employee layoffs due to AI. Which shows that marketing teams are adopting AI in addition to expanding or changing existing roles within the business.

What can you do: Use AI to eliminate repetitive tasks (like report generation or data extraction), then upskill your team to focus on higher-value creative or strategic work.

For example, a content writer can evolve into a content strategiest—using AI for outlines and drafts while they focus on voice or brand positioning. 

If you’re proactive in reshaping roles alongside AI, marketers won’t perceive it as a threat, but an upgrade. This can reduce job anxiety and increase buy-in within your team. 

What measurable impacts are marketers expecting from AI?

There are only two kinds of B2B marketers today:

Those who are testing (or piloting) AI and those who have fully developed AI solutions. 

The difference between them? Expectations. 

Among those that are piloting AI solutions, 40% are expecting to see some impact across key areas. 

Naturally, those who have implemented solutions have greater expectations. 

However, both types think improving the quality of customer interactions will have the highest measurable lift, while increasing the quantity is the lowest.

What does this mean? Well, it appears that AI is being seen less as a volume driver and more as a “quality enhancer.”

In other words, B2B marketers don’t see AI as a spamming engine, as we first thought.

What can you do: If you’re currently piloting generative AI in your stack, focus on use cases that enhance conversation quality. 

Things such as AI-assisted personalization, smart summarization in sales follow-ups, or real-time chat enhancements can have a big, measurable impact. 

What’s the overall AI pulse among B2B marketers?

Let’s draw the line and see how we’re all feeling about AI. 

This report might make you feel like most B2B marketers are feeling the AI jitters…

But the situation appears to be the opposite:

More than half are somewhat optimistic about the outlook of AI in B2B, while 20% are very optimistic. Only 8% are cautiously skeptical, while 20% are neutral. 

This is a clear sign that the vibe isn’t panicky, but opportunistic. 

B2B marketers can see the potential of AI. Even if their current systems, skills, or data aren’t quite there yet. 

This gap between belief and execution is where the next wave of competitive advantage will emerge.

What can you do: You can use optimism as momentum, but it can also turn into a crutch. 

So don’t let “we’re excited about AI” become a stand-in for real progress. 

Pick one area where your team’s optimism can turn into traction, like building a pilot program, or launching an internal AI playbook. 

Optimism without action doesn’t produce anything substantial. 

Embracing the AI toolbox…

B2B marketers are clearly leaning into the tech, but there’s a big difference between dabbling with it and actually being proactive with it. 

It appears that the winners will be the ones who systematize and shape the strategy around it. And that can build and scale on top of it.

Hopefully, tips from this article can help you.