AI in B2B marketing: from experiments to impact

Last week we hosted a Lunch & Learn session at SPOTONVISION about AI in B2B marketing. Our guest was Sam Dewijngaert from Wolters Kluwer. The room was full of B2B marketers. Some were already experimenting with AI. Others were still trying to understand where to start.

The questions in the room were very recognisable. How do you scale AI in a global marketing team? Which tools should you choose? How do you keep content quality high? How do you get people to actually use AI? And how do you avoid creating a mess of disconnected pilots?

Because many B2B marketing teams are now past the first AI excitement. They have tested ChatGPT. They have written a few blogs faster. They have created summaries, emails and social posts. That is useful, but it is not transformation.

The real question is no longer: can we use AI in B2B marketing?

The real question is: what needs to change in our marketing team so AI can create measurable value?

AI is not a content shortcut

One of Sam’s clearest messages was that AI creates most value when it is scaled into existing marketing workflows. Not as a side project. Not as an experiment by one enthusiastic marketer. And not as a shortcut to produce more content without thinking.

At Wolters Kluwer, AI is being used to improve both efficiency and effectiveness. That distinction matters. Efficiency is about saving time, reducing manual work and speeding up production. Effectiveness is about better content, better campaigns, better localisation and stronger performance.

In practice, that means AI is used for content production, campaign ideation, research, translation, content repurposing, reporting and media creation. But the starting point is not the tool. The starting point is the use case.

Where is the work repetitive? Where does quality vary too much? Where do teams lose time? Where can faster production lead to better customer engagement? These are the right questions for B2B marketers.

Governance is not the boring bit

In many organisations, governance is treated as the thing that slows innovation down. During the session, it became clear that the opposite is true.

If you want AI to scale, governance is what makes it possible.

B2B marketing teams work with complex products, regulated markets, multiple regions, local languages and different buyer roles. That creates risk. AI can easily produce content that sounds confident but is inaccurate, off-brand or too generic.

This is why central governance matters. Teams need clear guidance on approved tools, brand voice, quality checks, legal requirements, copyright, data privacy and human validation. They also need clear roles. Who owns the platform? Who trains users? Who supports local teams? Who measures adoption? Who decides when a use case is ready to scale?

At Wolters Kluwer, the answer includes a central Centre of Excellence, local admins, team managers and end users. That may sound formal, but it is also practical. Without ownership, AI adoption becomes everyone’s hobby and no one’s responsibility.

Adoption of AI in B2B marketing is harder than tool selection

A familiar truth came back during the discussion. Choosing the tool is not the hardest part. Getting people to use it well is.

Many marketers are curious about AI, but still unsure. Some do not know how to brief the tool. Some do not trust the output. Some are afraid quality will suffer. Others simply do not have time to learn another platform.

This is where B2B marketing leaders need to be honest. AI adoption does not happen because a licence has been bought. It happens when people are trained, supported and shown what good looks like.

That means hands-on training. It means office hours. It means sharing examples. It means giving local teams room to test. It also means involving team managers early. They are the ones who can remove blockers, change workflows and help teams build new habits.

One of the most useful lessons from Sam was that process change should start earlier. If AI changes how content is created, translated, reviewed and published, then the process needs to change too. Otherwise, you simply put AI on top of an old workflow.

That creates frustration, not impact.

The human role becomes more important

There was also a healthy discussion about the role of people. AI can help with speed, structure, scale and variation. But humans still need to judge.

This is especially true in AI in B2B marketing. Our buyers are not looking for more words. They are looking for relevance, insight and confidence. They want to know whether a solution fits their reality. AI can help us create, repurpose and optimise content. But it cannot replace real customer understanding.

This is also where account-based marketing and AI come together. AI can help identify signals, build account insights, tailor content and support sales conversations. But without clear account choices and human judgement, we mainly automate irrelevance.

That is not progress.

The opportunity is to combine AI with sharper focus. Fewer accounts. Better insight. More relevant messages. Stronger sales and marketing alignment. More practical follow-up. That is where AI becomes useful for B2B growth.

The to-do list for B2B marketers

For me, the session led to a clear to-do list.

1. Start with business value, not with tools. Choose a few use cases where AI can save time, improve quality or increase commercial impact.
2. Build governance early. Define what is safe, what is on-brand and what always needs human review.
3. Train people in real workflows. Do not only teach prompting. Teach marketers how to use AI in campaign planning, content creation, localisation, account-based marketing and reporting.
4. Involve managers from the start. They are crucial for adoption and process change.
5. Protect local creativity. Central governance should create safety, not kill experimentation.
6. Measure what matters. Usage is interesting, but business impact is better. Look at time saved, speed to market, engagement, conversion, content reuse and pipeline influence.
7. Keep the human filter. AI can accelerate the work, but marketers must still bring judgement, ethics, customer understanding and commercial context.

Where to start

For many B2B marketing teams, the gap between experimenting with AI and using it at scale feels large. But the first step does not have to be complicated.

Start with one meaningful use case. For example, an account-based marketing play for a defined group of target accounts. Or a content workflow where one webinar becomes multiple assets for different buyer roles. Or a team training programme where marketers learn how to use AI in their daily work.

At SPOTONVISION, this is exactly where we help B2B organisations.

Our account-based marketing Hackathon helps teams turn AI, data and account insight into practical plays. Not theory, but a focused way to build momentum. Our B2B Vision Academy training on B2B and AI helps marketing teams understand the tools, the workflows and the changes needed to use AI responsibly and effectively.

AI will not fix unclear positioning, poor data or weak sales and marketing alignment. In fact, it may expose those issues faster.

But for B2B teams willing to make sharper choices, AI can be a serious accelerator. Not because it replaces marketers, but because it gives them more space to do the work that matters.

That was my biggest takeaway from the Lunch & Learn.

AI in B2B marketing is no longer about experimenting. It is about organising for impact.