Artificial Intelligence (AI) is transforming almost every aspect of business, and B2B marketing is no exception. Any scan of trade press headlines declares that AI, and particularly Generative AI, is making marketers smarter, more efficient, and more successful. Or it’s going to put half of us out of work within a few years – depending on the writer’s perspective.

None of this hype is new to B2B demand generation professionals, who have been working with AI, Machine Learning and other Big Data technologies for a decade or more. We were using AI to build audiences, personalize messaging and analyze campaign results years before anyone had ever heard of ChatGPT or Midjourney.

So, we have a pretty good idea of where AI is working, where it has a way to go, and where good old human experience and insight still are your best strategic asset.

In this post, we’ll look at the state of AI in B2B marketing, the most common applications of the technology, and the pros and cons of relying on “artificial” intelligence when it comes to executing and optimizing your demand generation strategy.

AI Is the “Shiny Thing” Now and In the Future

AI is definitely here, and it’s only going to become more ingrained in B2B demand generation ops. A survey from earlier this year found that only 2% of B2B marketers said they are not using AI in any aspect of their operations. Just 2%. Between 2018 and 2020, AI adoption among marketers basically tripled, up to 84% overall, as reported by Salesforce. And the AI marketing sector is predicted to grow to about $100 billion in the U.S. alone by 2030.

A primary driver for this growth is using AI to reduce costs and automate some standard processes, according to industry bell cow IBM.

Leading platform vendors, including DemandBase and On24, are rolling out AI-powered features ranging from campaign analytics to chatbot training. (You can read more about these developments in our Demand Generation Digest.)

For the most part, marketers seem to be OK with the rise of AI. In fact, 88% of SMB marketers in a Mailchimp survey last year said their organizations need to be adopting AI more aggressively. A respondent to this year’s Content Marketing Institute survey called AI the “shiny thing” for 2024. We could not have said it better.

But as with any rapid change, it also appears that many marketers don’t fully understand exactly how AI works, or how that impacts the benefits they will realize by adopting the technology. A survey from Ascend2 cited paid advertising as a top application for AI, while only 12% of survey respondents said they were using AI for audience segmentation. We can say with some confidence that if you’re running programmatic, AI is helping select the audience for the run. That’s just how it works now.

So, there’s a lot of (mostly justified) enthusiasm for AI. But B2B marketing teams should also step back and ensure they have the basics covered before aggressively adopting the latest and greatest tech. And, not surprisingly, it all begins with data.

Bad Data = Bad AI

AI, including Generative AI tools such as next-gen chatbots, draws conclusions based on review of massive volumes of data. If the data is wrong, AI will come up with the wrong answer more often than not. When you hear about a chatbot making absurd mistakes – and there’s no shortage of examples out there – it’s typically because the AI simply consumed and indexed faulty “common knowledge” on the public internet.

This caveat is true for demand generation tools, as well. If your contact database is out of date or incomplete, AI will cut some really dodgy lists. If you haven’t trained your AI customer service chatbots with high-quality, carefully vetted product and service information, they will basically just make stuff up and alienate your best opportunities.

You’ve heard it before, but in an industry where data decay hit a record 70% last year, it’s worth repeating – if you implement AI on top of bad data, you’re just going to become more efficient at making costly mistakes. And nobody wants that.

Before implementing AI toolsets, be sure that you can trust both your first-party data and other sources, including your trusted demand gen partner. With that foundation in place, you can move ahead with a lot more confidence.

Where AI Is Really Working

Now that we’ve covered the AI basics, let’s look at where the tech is really making an impact in driving engagement at scale.

Data Aggregation + Speed

This is page one in the Big Data / AI story. B2B prospects use countless channels and sources to research a purchase, and you need to understand ALL that behavioral data to get a clear picture of their customer journey. AI can examine massive volumes of data, in multiple repositories, more rapidly than even the smartest humans could ever hope to. That’s really the basis of everything else it does.

Pattern Recognition

When it looks through all that Big Data, AI can find patterns that humans really can’t be expected to see. Or, more specifically, patterns that they may not even be looking for. A B2B demand generation specialist will run reports to check response rate by job role and geographic region. But they simply can’t be expected to ask about the same response rates from accounts where the CMO reports to a CRO, instead of the CEO. There are just too many possible permutations of the data to run through.

But not for AI. It can ask thousands of questions and ask thousands of follow-up questions based on what it learns. This capacity to parse every aspect of your contact and campaign response data is fantastic for audience segmentation, re-targeting and campaign personalization.

Predictive Analytics

You will hear this term a lot when reviewing features in demand gen platforms. It’s commonly used to simply mean “Based on our review of your data, we predict that your next campaign will perform at these levels.”

Technically speaking, that’s a bit of an oversimplification – pattern recognition (and just human review) can find trends in current campaign data that indicate doing more of the same is a good idea. If IT admins really like your whitepaper, you can predict it will perform well with more IT admins.

Predictive analytics (PA) takes this a step further and builds models on extended relationships between your immediate campaign data and what it knows about other markets, contact database and response behaviors. It’s a kind of seven-degrees-of-separation level analysis, and is possible only because AI can consume SO much data and find deep connections.

Genuine PA can be useful for projecting results when trying to open new markets or launch new products. But, we’ll add, you can also learn a lot from simply running a credible test campaign and basing your next steps on real results,

Campaign Optimization

However, they find patterns. AI-based campaign administration tools can quickly shift resources between creative channels and segments on the fly. Typically, these kinds of adjustments have been based on bare minimum performance thresholds, but with AI, your system can identify opportunities for the best return, not just an acceptable minimum.

In a climate where B2B marketing is expected to do more with already taxed budgets, this can be a huge win.


Next-gen chatbots may be the most obvious win for AI in this wave of tools. Recent surveys show that 57% of U.S. B2B marketers use chatbots in demand generation, and that bots raised lead volumes by 10% or 20%.

AI-powered chatbots use natural language models (NLMs), much like ChatGPT, to have more interactive conversations with site visitors. If you are investing in interactive landing pages and other high-value site content, a well-timed offer to engage with a smartbot can be invaluable in driving deeper engagement.

Content Creation

Quality content is essential to B2B demand generation. It’s also really expensive. So the idea of having a smart machine simply churn out a 1,200-word blog post is pretty attractive. And in use cases where volume is a primary concern – social posts, display ads, SEO copy – AI tools can be a real time-saver.

However, there are some drawbacks, at least with this wave of Gen AI. The Content Marketing Institute survey found that while most B2B marketers are using AI, uptake is slower among enterprises, at 58%. Among the concerns holding back AI adoption is accuracy (37%) and copyright (21%).

Typical use cases are for brainstorming topics (30%) and researching headlines and keywords – not generating final copy. In the eMarketer survey we cited earlier, 30% of respondents said they used AI tools to generate content and presentations.

We would be remiss if we didn’t note that 61% of B2B organizations in that survey lack an overall Gen AI policy. So, again, this is a case where rapid adoption of technology is sometimes outpacing solid business fundamentals.

Content Personalization

From writing custom email subject lines to micro-selecting what color car to show in a display ad, Gen AI tools can scale content personalization to a level that can’t be managed by individual copywriters. In fact, personalizing email content (32%) and subject lines (22%) are among the leading use cases in the Ascend2 survey, as well as other polls of B2B marketers.

We’ll add that there is actually some significant blowback among marketers against over-personalization – some buyers find the fact that you know so much about them a little off-putting. So, as always, there’s a balance to be struck between tech and a genuine human voice in your content plans.


Some Gen AI writing platforms are now integrated with SEO optimization tools that do keyword research and optimize content to target desired phrases. This is an obvious application of Gen AI, since a comprehensive SEO strategy requires a lot of content. It’s low-hanging fruit for saving time and money with Gen AI.

Where Humans Are Still Your Best Bet

There are a lot of areas where AI can support and optimize your demand generation strategy and operations. But, as you can see, there’s still a need for human experience and expertise to employ AI tools smartly and craft winning demand gen programs.

Innovative Campaign Design

Sometimes you have to step back from your plans for the next quarter and ask “Is this really what we want to be doing?”

Your team members and a trusted demand generation partner can ask the tough questions that may discover new targets, and more importantly, new goals. AI just isn’t going to give you that level of creativity – it finds patterns in existing data, not new possibilities. At least for now, trusting humans to conceptualize and schedule a controlled test campaign and gather actual response and conversion data is the best way to learn more about new opportunities.

Thought Leadership / Cornerstone Content

As we noted earlier, enterprise B2B marketers are using Gen AI tools, but mostly for topic brainstorming and outlining – not creating final copy, even for relatively basic blog posts.

This wisdom is particularly relevant to thought leadership content, which is growing in importance in many marketers’ overall strategy. (27% of respondents to the CMI survey say they plan on creating more thought leadership content in 2024.)

Gen AI tends to compile general wisdom and format it into language. Thought leadership is where your business stands out from the crowd, offering new insights or contrarian opinions. You can see where the two don’t mix.

Invest in the human process of creating thought leadership and cornerstone content that really differentiates your message. Gen AI can pick up from there, digesting and re-tooling this foundational content for delivery across multiple channels and platforms.

Campaign Review & Strategic Optimization

At the close of every campaign, your team and your demand generation partner should get together and close the loop, coupling what you’ve learned – including AI-powered analytics – and human insights to formulate next steps. This may mean re-shifting resources and responsibilities or staying the course for another quarter. But some level of human creativity and risk / reward assessment needs to be the ultimate determiner here.


People do business with people. At some point the human connection you’ve built within your team and with a trusted partner will be essential in moving your business forward. Your partner should embrace the idea that your success is their success. It’s a matter of culture, and it’s hard to quantify, but it’s essential. And machines don’t do that.

AI Is a Powerful Tool to Let People Do Their Best

AI is changing B2B demand generation, from the daily grind of writing email subject lines to developing long-term strategies. These new tools can be great time-savers and boost productivity, but they need to be incorporated into your B2B marketing practice based on quality data and a foundation of clear vision and business fundamentals set by your team.

More posts you may find useful.