Using AI in B2B Marketing: Use Cases, Examples & Where to Start

"If you're not embracing AI right now, you're not on the right path," says Ajith Krishnankutty, Head of Experiential Marketing at Capital Group.

This statement is backed up by what we found in our State of AI in B2B Video, which uncovered that 91% of marketers are currently using AI tools in their marketing strategy. Meanwhile, 84% believe that AI will be critical to achieving their marketing goals in the next 1-3 years.

Since the question about whether to use AI has seemingly been settled, it’s now time to determine which use cases worth the investment and how to harness AI to build more revenue and ROI.

B2B companies are currently using AI technology like advanced algorithms and machine learning to automate and enhance marketing processes.

While sometimes used interchangeably, AI and machine learning aren't quite the same thing. AI is a broader term that encompasses technologies that mimic human intelligence. Machine learning is included in this sweep, as well as natural language processing, robotics, and more.

Machine learning, on the other hand, refers specifically to algorithms that can automatically learn from data without being explicitly programmed to do so.

It's sort of a square/rectangle situation: Machine learning is always AI, but all AI isn't machine learning. However, many AI tools in B2B marketing are powered by machine learning.

Marketers report the highest value from AI in these areas:

• Content creation (77%)
• Reporting (59%)
• Video production (50%)
• Content repurposing (48%)

That tracks with what we're seeing across the industry: The highest-ROI use cases are usually the repetitive, time-consuming tasks that previously consumed the bulk of marketers’ time.

Following the rise of generative AI (GenAI), AI has earned its place in the content generation process.

Marketers are using GenAI tools to assist in creating content like:

• Videos
• Blog posts
• Social content
• Product descriptions
• Email campaigns

You'll notice we didn't say people are using it to fully create the content, only to assist. We've seen how important it is to keep a human touch present throughout the content creation process.

AI can save you time and, notably, help you scale your content efforts, but readers (and search engine algos) don't appreciate generic content ... at all. (About half of Americans think AI will worsen people’s ability to think creatively.) That's why incorporating unique perspectives and brand-specific expertise is absolutely crucial.

For content marketers, AI can be a powerful ally to the creative process. It doesn't, however, replace the indispensable human mind.

Most marketing teams are drowning in data from fragmented sources. One platform contains webinar engagement, but website analytics are in another, email performance is in a third, and CRM data is somewhere else entirely. Turning that into a cohesive picture of what's working has historically required hours of manual labor.

AI changes that in two ways:

1. AI can process and synthesize large datasets far faster than a human analyst, surfacing patterns and trends that would take days to find manually.
2. AI-powered analytics can move from reporting on what happened to predicting what will happen next, scoring leads based on behavioral patterns and recommending where to focus effort based on what's driven pipeline in the past.

This is also where buyer signals become powerful. Rachel Truair, CMO at Demandbase, makes the case that most B2B teams still treat signals as isolated events. When someone takes any action, whether it’s downloading a whitepaper or visiting a pricing page, each action gets its own workflow and score, but the real picture only shows up when you stack signals together and let AI handle the pattern recognition.

Look for platforms that pass granular engagement data directly into your CRM rather than requiring manual exports. The value of AI-powered analytics compounds significantly when the data flows automatically and you never have to think about that piece of the puzzle.

Traditional video production for B2B marketing involved a chain of manual steps that each introduced delay. Someone had to watch a full recording to identify usable moments, trim clips, add captions, resize for each platform, apply branding, and more.

For a team running four or five webinars a month (now the industry average, according to the 2026 B2B Webinar Benchmark Report), that backlog compounds faster than most teams can clear it. The result is that valuable content sits untouched in an archive instead of fueling campaigns.

AI collapses the chain. Engagement-aware AI identifies the strongest moments from a recording automatically and then gets the clip ready to review before posting. This year’s benchmark report also showed that almost all (97.8%) video clips generated in Content Lab were AI-generated, and Content Lab adoption grew 76% year over year. This proves that most B2B teams are running their content programs on AI now.

AI helps with personalization, too, by helping teams surface the right content, for the right audience, at the right time. Rather than manually searching an ever-growing video library for a clip that fits a specific campaign or account, AI can scan the full archive and pull the most relevant moments based on topic, speaker, engagement level, or audience profile.

Most teams are sitting on more usable content than they realize because of their events and webinar program.

Make the most of every event by repurposing it for a variety of channels, social media, newsletters, email, and more. In Content Lab, a single 45-minute webinar can turn into blog posts, social snippets, video clips, email campaigns, and more. (And it only takes a few minutes!)

Here are some of our favorite tips for repurposing content with AI:

• Turn every event into a content stream. Rather than treating a webinar as a one-time broadcast, plan your repurposing before the event even starts. Map out which clips you'll pull (Q&A moments, key quotes, product demos) and what assets the transcript will generate.
• Go both directions: spoken to written, written to spoken. If you said it on stage, it should live somewhere as text. If you wrote it, it should eventually find its way to a stage or camera. Thought leadership webinars become blog posts, while something like a well-performing LinkedIn article becomes the framework for your next panel discussion topic.
• Put the transcript to work. Beyond social posts and blog drafts, webinar transcripts are a useful source for FAQ content (psst: the questions people actually asked during the event are a great place to start), sales enablement copy, and email nurture sequences. AI can scan the transcript for each of these use cases in seconds.
• Keep quality standards high. Always review AI output before it goes live. The goal is to get to a strong 80% draft with AI, and then spend your time on the 20% that requires editorial judgment.

Note: All four examples came from the Future of AI Marketing event, co-sponsored by the AI Marketing Alliance.

Stacy Combest, Manager of Webinar Marketing at Hootsuite, was a team of one trying to juggle all things webinar- and events-related.

By using Content Lab to auto-generate blog posts from webinar transcripts and then embedding them on webinar landing pages and on-demand resources, her team saw a 150% increase in organic traffic to webinar pages, a 38% increase in on-demand views, and a 62% increase in webinar attendance rate.

Trinity Nguyen, CMO at UserGems, built her team's signal-scoring system using a framework she calls crawl, walk, run. Because AI can identify patterns more quickly than humans can, she’s been able to scale this framework over time.

Start with one or two signals your sales team already understands intuitively (this is the “crawl” phase). Trinity’s example was the signal of a brand champion changing jobs, opening up a chance to market to the new company. Once AI is scoring these, work on proving pipeline impact before moving on to more signals.

The key is to start slow as you work your way up to more. “If you start out the gate with 10 signals, you’ll get push back because it’s just too much,” Trinity explains.

Typically, it takes about three months to get to the “run” stage. In Trinity’s case, by that point, the full scoring system was in place, with AI handling the pattern recognition across stacked signals rather than a human manually triaging each one.

Dan Slagen, SVP of Marketing at Zapier, ran a company-wide Build-a-thon to encourage and increase AI adoption within the company.

Every person on the 75-person marketing team had to craft something using Cursor and Zapier's own MCP within 30 days. Most of the team had never opened a tool like Cursor before, so trepidation was high. The first few days were rough, but by the second half of the month, usage spiked.

On the final day, 50 builds were presented across nine categories, including a fully automated speaker management system for Zapier's annual conference that’s now in production.

Austin Lau, Growth Marketer at Anthropic, ran the company's entire growth marketing operation solo for nearly 10 months, without ever writing a line of code.

He advocates for a mindset shift: Use AI tools to blast through walls that have historically separated marketers from engineers or other experts. Take ownership of all the tasks in front of you, and use AI to teach you things you don’t know.

Here are some steps from Austin’s framework you can take right now:

1. Make two lists: one that includes manual, repetitive tasks, and one that includes things Claude might do better than you.
2. Pick one item from the list.
3. Open Claude and describe your current workflow for the item you selected.
4. Review the outputs and see if something piques your interest.
5. If yes, ask Claude to spec out a plan.
6. Put the plan into Claude Code and see what happens.

Going back to Zapier’s Build-a-thon example, remember that most of the 75 people who participated had never opened a tool like Cursor before. In order for the experiment to work, Zapier provided training sessions, office hours, a buddy system, and the space for people to “fail” before having to present to the larger group.

Philip Lakin, Zapier’s Director of AI Transformation, pointed out that most people are just shy about what they don’t know. The fix isn’t more technical training, however; the solution is to give people the time and space to create one small thing that works, so they can grow their confidence.

Once that happens, the momentum tends to accrue on its own.

Incorporating AI into your marketing strategies starts with using modern B2B marketing platforms with AI-powered features that can help you excel at what you already do well.

And for B2B marketing events, it doesn't get smarter than Goldcast. To see how Goldcast's AI-driven features can boost your impact, book a free demo today.

What is AI in B2B marketing? 

AI in B2B marketing refers to the use of technologies like machine learning and generative AI to automate and enhance marketing processes, including content generation, analytics, personalization, and predictive modeling.

The goal isn't to replace marketers but to free them up for higher-value strategic work. According to the State of AI in B2B Video report, marketers report the highest value from AI in content creation (77%), reporting (59%), video production (50%), and content repurposing (48%).

Do you need to know how to code to build AI-powered marketing tools? 

Nope, and Austin Lau of Anthropic is proof. He ran the company’s entire growth marketing operation solo for nearly 10 months without writing a single line of code, putting together a custom Figma brand-QA plugin and a Google Ads negative-keyword workflow using Claude.

Another example: Zapier's Build-a-thon put 75 non-technical marketers through a 30-day challenge to create real AI tools from scratch, and most had never opened a tool like Cursor before. The bigger barrier tends to be confidence, not technical skill.

How does AI help with buyer signals and lead scoring? 

AI stacks multiple buyer signals (content engagement, event attendance, job changes, intent data) and recognizes patterns across accounts. Teams that see the strongest pipeline impact typically start with a couple of signals their sales team already trusts and then layer in more once the early experiments prove out.

What's the most effective way to drive AI adoption on a marketing team? 

Zapier's Build-a-thon proved that structured, supported experimentation is the way to go. When Dan Slagen gave his 75-person marketing team a specific 30-day challenge and created space for early failures, the team went from overwhelmed to building production-ready tools within a month.

How do webinars and events feed AI-powered content repurposing? 

A single 45-minute webinar contains thousands of words of spoken content. That’s enough to generate a blog post, a dozen social snippets, an email campaign, and multiple short-form video clips automatically. AI identifies the highest-engagement moments from the recording, reformats them for each channel, and generates written assets from the transcript in minutes.

Hootsuite's Stacy Combest used Content Lab to auto-generate blog posts from webinar transcripts, embedding them on webinar pages and on-demand resources; the result was a 150% increase in organic traffic to webinar pages and a 38% increase in on-demand views. With AI, every recording becomes a content engine that keeps running for weeks after the event ends.