Generative AI in marketing: what it is and how to use it
Generative AI has rapidly moved from a novelty technology to a staple of the marketing tech stack: two out of three marketers surveyed by LinkedIn are currently using it. Yet only a quarter of marketers report having an "extremely good" understanding of how to apply AI in their day-to-day work. IBM research adds urgency to that gap, 67% of CMOs plan to adopt generative AI within the next year, which means the teams that figure out practical implementation now will have a meaningful head start.
Ready to move beyond the basics and into innovative, impactful ways you can use generative AI to make your work more efficient? We asked marketing leaders to share their favorite applications, well beyond drafting LinkedIn posts. Some of their answers may surprise you.
Why generative AI is reshaping the marketing function
McKinsey research estimates that AI adoption in marketing can drive a 3-15% revenue increase and a 10-20% improvement in sales ROI. Those numbers are compelling, but the more useful frame is what changes by marketing function, because the leverage points look different depending on where you sit.
For content and creative teams, the shift is about speed and volume without sacrificing brand voice. Generative AI can draft first versions, repurpose long-form content into social snippets and email copy, and suggest edits that make existing writing sharper. The constraint isn't creativity; it's the time it takes to move from brief to polished asset. AI compresses that cycle.
For demand gen and ABM teams, the opportunity is in audience research, account prioritization, and campaign personalization at scale. AI can analyze large datasets to surface which accounts are showing buying signals, generate tailored content for key accounts, and help teams move from insight to outreach faster than manual processes allow.
For marketing ops, the gains show up in data analysis, performance scoring, and workflow automation. Teams are using AI to combine engagement and pipeline metrics into composite asset scores, automate list hygiene, and reduce the manual work that slows down campaign execution. With 67% of CMOs planning to adopt generative AI within the next year, the pressure to build this operational muscle is real and immediate.
How marketers use generative AI: 12 real-world applications
These aren't hypothetical use cases. The following examples come from marketing practitioners who have embedded generative AI into their actual workflows, organized by the type of work they're accelerating.
1. Build and research an ideal customer profile
Understanding which buyers would be a good fit for your product or service, and knowing what makes them tick, is the cornerstone of good marketing. Coming up with the common qualities that make up your ideal customer profile (ICP) can take a lot of time and work, or it did, before generative AI.
Katie Robbert, co-founder and CEO of Trust Insights, is a big proponent of using artificial intelligence to fine-tune your ICP.
Katie Robbert headshot
"My favorite way to use generative AI day-to-day is with an ideal customer profile. We created an efficient set of system instructions to use with a Large Language Model that will ingest data about your company, your competitors, and your customers, and give you back an ICP analysis."
Customer experience strategist, researcher, and author Jay Baer notes that this type of research is becoming an increasingly common practice for savvy marketers.
Jay Baer headshot
"A well-known SaaS company built custom GPTs for each of their ICPs and trained them on customer attitudes and data. Then they ask these AI 'customers' to react to each potential piece of marketing and content. It's a real-time focus group."
2. Automate manual tasks
Eliminating busywork is one of the most powerful ways busy marketers can leverage AI. Mark Hinkle, founder and CEO of Peripety Labs, says he's excited about creating AI agents to automate parts of his team's workflow.
Mark Hinkle headshot
"It's part of my ongoing effort to reduce manual tasks and improve our overall efficiency. By continuously experimenting with and integrating new AI tools, I'm able to stay ahead in our fast-paced environment and ensure that our workflows evolve with the latest technological advancements."
Looking for an example of how you can implement AI into your workflows? Don't obsess over taking notes while you're brainstorming, simply record the call and put the transcript into ChatGPT or another generative AI tool to summarize and pick out action items.
3. Get a leg up on the competition
Chris Penn, co-founder and chief data scientist at Trust Insights, suggests using AI to analyze a competitor's strategy and inform your next steps.
"We extracted 1,900 competitor job openings and fed them all to generative AI, then asked the model to infer their 12-18 month strategy based on their SEC filings plus the job openings. We were able to produce an incredibly detailed competitive analysis and an action plan based on it."
You can also fine-tune your competitive messaging with the help of AI analysis. Check the reviews for competitor products and ask generative AI for a summary of the common negative review complaints and how your product can solve those.
4. Spot upcoming trends
Broaden the focus from discrete competitors to the broader market, and AI can help marketing teams of any size stay on top of, and even ahead of, industry trends.
"For my day-to-day work, the most helpful application of AI is research," says Kyle Lacy, CMO at Jellyfish. "I use it for researching industry trends and formulating massive amounts of data."
Kyle Lacy headshot
Follow his lead and use generative AI to analyze large datasets from social media, news articles, and other online sources to predict emerging trends. Then, you can create content or campaigns that align with these trends before they peak, positioning your brand as a leader in the space.
5. Research and strategize content
AI can analyze industry news, in-house and competitor content, and audience engagement data to suggest the types of content that will likely resonate with your target audience in the near future. This predictive approach can help B2B marketers stay ahead of the curve and focus on content that drives the most impact.
You can also incorporate data analysis into your content creation process by asking generative AI to analyze performance data and suggest content that aligns with proven, successful strategies.
"I'm not a math person by nature, so having AI in my corner has been a huge advantage," says Curt Woodward, a content director at ZoomInfo. "We use AI to combine metrics like leads and pipeline into one easy-to-understand score for each asset. Then, we have AI analyze the scores and produce a value-packed memo that guides us through our next projects."
And when it comes time to write the content pieces that are part of that strategy, AI is a huge research help.
"My favorite approach starts with using Perplexity for in-depth research. It's incredibly efficient at gathering relevant data and insights, which forms the foundation of my content creation process," Hinkle says.
6. Explore long-term strategies
Generative AI can also provide long-term, strategic research, delivering data-informed insights to power your marketing plans for the next quarter or next year. Prompt the AI tool to provide strategic advice for a specific period: for instance, you can tell it to "Analyze historical data and suggest marketing strategies for [insert goal] over the next [time period]."
"If you upload product or service details, ChatGPT does a fantastic job of forecasting sales under different possible economic conditions. It's speculative, but it makes me think about possibilities I hadn't considered," says Mark Schaefer, executive director of Schaefer Marketing Solutions.
With those possibilities in mind, you can apply the next-level creative thinking and specific insights that only an experienced marketer can bring to the table.
7. Repurpose content
It's a smart move to repurpose the insights from a webinar, podcast, or other event into additional content formats. It's even smarter to use AI to do it quickly.
"Generative AI is great for performing mundane tasks efficiently," says Avrohom Gottheil, founder and CEO of AskTheCEO Media.
"I have AI transcribe audio from my podcasts and generate summaries, highlights, and graphics which I then use to create posts and a newsletter."
"Previously, I would need a whole team to do what I can do on my own in about an hour. Essentially, generative AI frees me up to do what I do best, to get my clients heard over the noise on social media," he says.
8. Fine-tune writing
Using generative AI to create mountains of hasty content can result in generic pieces lacking human insight. The real power of AI in content creation comes from its ability to make your own writing stronger.
Instead of prompting AI to create a full first draft, consider feeding it what you've written and ask it for ways to improve what you already have.
"I'm a writer and love having a talented editor by my side," Schaefer says. "I don't use AI to create drafts, but it helps me 'sweeten' my writing by making clumsy sentences clearer, more compelling, and fun."
"I'm a writer and love having a talented editor by my side. I don't use AI to create drafts, but it helps me 'sweeten' my writing by making clumsy sentences clearer, more compelling, and fun."
9. Fill in the gaps
Many marketing teams are getting by with less: fewer tools, fewer team members, and smaller budgets. Whether you work on a team of dozens or act as a team of one, the power of AI can act as a force multiplier and help fill gaps.
Ann Handley, chief content officer of MarketingProfs, uses AI to create frameworks and visuals for her ideas.
"Like many B2B marketers, I'm a communicator and a writer first; it's how I think. So an intuitive tool that fills in the blanks and shores up skills for me, in this case, it helps me have a more visual brain, is incredibly useful."
The best use case of AI, according to Handley, is acting as a reinforcement. "That, to me, is the true innovation of AI: the small but important ways that it can help us be stronger, better communicators, helping us unexpectedly access those parts of ourselves we thought we didn't have," she says.
10. Streamline video creation
While videos are a high-performing content format, creating them can be much more time-consuming and expensive than other formats. But AI tools can streamline and optimize much of the surrounding work, creating videos more efficiently than ever.
"We find new AI-powered video editing platforms extremely helpful in streamlining production while enhancing creativity and efficiency," says Irene Lyakovetsy, founder and principal of SaugaTalks.
The benefit of AI for video production is that it enables Lyakovetsky's team to maintain strategic focus instead of getting lost in technical details.
"These tools enable us to quickly create polished, engaging content, allowing us to focus more on delivering impactful messages and less on technicalities."
"By automating repetitive tasks and providing smart editing suggestions, AI empowers our team to bring innovative ideas to life with greater ease and precision."
Hinkle has also found value in using AI to create short-form videos as a content multiplier.
"Another use case I've encountered combines AI tools to clone oneself for short-form video content, allowing marketers to maintain their brand consistently across multiple platforms while dramatically increasing their content output," he says. "It's a game-changer for creating personalized client outreach, marketing campaigns, and even internal communications at scale, all while preserving the authenticity of the presenter."
11. Customize and repeat
If you can't find a tool to do what you need, the beauty of generative AI is that often, you can create your own solutions.
"Building custom GPTs with ChatGPT is an often overlooked superpower in B2B marketing," says Paul Roetzer, founder and CEO of Marketing AI Institute.
A custom GPT is basically a personalized AI assistant that you train to perform a specific task. Then, when you need to do the task again, you don't have to start a new prompt from scratch. Here are some of the things you can build a custom GPT to help with:
Content written in a specific style or around a specific event
Acting as a particular persona for instant feedback
Analyze data to generate reports
Branding activities
Strategic planning
"With some knowledge and experience, plus natural language, anyone can build a custom GPT to drive efficiency, productivity, and innovation."
12. Boost your account-based marketing (ABM) campaigns
A successful ABM strategy is all about focusing on the right accounts and hitting them with a personalized campaign. Lacy likes to use AI to speed up the process of researching those key accounts.
"Gen AI can sift through massive amounts of data to deliver reporting on targeted accounts' earnings reports, organizational structure, and anything you can think of. This has freed up massive amounts of time for reps, marketing, and research teams to focus on other projects."
Generative AI can also craft highly personalized content for ABM campaigns, such as custom emails, landing pages, and even tailored whitepapers. By leveraging AI to create bespoke content for key accounts, B2B marketers can scale their efforts while maintaining a high degree of personalization.
ZoomInfo's GTM Workspace drives relevant personalization at scale. Go-to-market teams can rely on GTM Workspace to surface target accounts showing high purchase likelihood by reasoning across real-time intent signals, CRM history, and behavioral data, then generates personalized outreach grounded in that account context, using GTM Workspace's AI-generated outreach to craft messages that speak directly to the account's goals and priorities. Accounts scored by ZoomInfo were 43% more likely to convert into qualified pipeline and qualified 58% faster (Spekit).
Predictive AI vs. generative AI: how they work together in your marketing stack
A modern marketing stack relies on two distinct AI capabilities working in tandem. Predictive and analytical AI models analyze historical data to drive targeting, segmentation, scoring, and optimization. Generative AI models are the creative engine: they produce net-new content, copy, and campaign outputs. Neither capability is sufficient on its own. Predictive AI without a generative layer leaves marketers with signals they can't act on quickly. Generative AI without a predictive foundation produces content that's fast but untethered from real account intelligence.
ZoomInfo is an all-in-one AI GTM Platform built around this dual-capability architecture. The GTM Context Graph is the predictive and intelligence layer: it processes 1.5B+ data points daily, fusing ZoomInfo's B2B data with your CRM records, conversation history, and behavioral signals to reveal not just what's happening in your accounts, but why. That reasoning layer is what separates genuine buying signals from noise, and it's the foundation that makes generative AI outputs trustworthy rather than generic.
GTM Studio is the execution layer where marketers translate those signals into action. Audiences can be built in natural language, and plays can be launched across channels without engineering tickets. That combination, verified data feeding a reasoning layer, with a codeless execution environment on top, is what closes the gap between insight and campaign launch. Smartsheet saw an 84% MQL increase and a 26% improvement in opportunity rates after deploying ZoomInfo's marketing platform, a result that reflects what happens when predictive intelligence and generative execution work from the same data foundation.
Common challenges of generative AI in marketing and how to address them
Generative AI in marketing delivers real leverage, but it also introduces failure modes that teams need to anticipate before they scale.
The most widely cited risk is hallucination and accuracy. AI models working with poor or stale data don't just produce wrong outputs occasionally, they produce inaccurate results at speed and scale, amplifying errors in ways that are hard to catch downstream. The mitigation is straightforward but non-negotiable: ground your AI on verified, continuously updated B2B data before you deploy it in any customer-facing workflow.
Brand voice inconsistency is a subtler problem. Generic large language model outputs, without proprietary context, produce content that sounds like everyone else's content. The fix is to fine-tune or configure your AI tools with your brand guidelines, ICP definitions, and historical campaign data. The more proprietary context you inject, the more distinctive the output. Teams that treat AI as a plug-and-play content machine without this grounding step end up with volume but no differentiation.
Data privacy and compliance create real exposure in AI-driven marketing workflows. Using third-party data in AI pipelines without proper governance creates GDPR and CCPA risk, particularly for teams running multi-channel campaigns across regulated industries. Using privacy-compliant data sources matters here: ZoomInfo holds ISO 27001, ISO 27701, SOC 2 Type II, and TRUSTe GDPR/CCPA certifications, which means the data foundation your AI runs on is built for enterprise compliance requirements.
The attribution gap is the challenge that hits closest to home for demand gen teams. AI-generated campaigns are hard to attribute to revenue outcomes if you haven't built the measurement architecture first. Connecting campaign data to CRM pipeline data from day one is the only way to move from MQL volume to closed-loop revenue attribution. Teams that skip this step end up with engagement metrics and no revenue story, which is exactly the conversation no marketing leader wants to have with their CFO.
The role of data quality in generative AI marketing
Without quality data, companies risk creating inconsistent and inaccurate results at a speed and scale that quickly gets out of control.
Quality data is the essential ingredient for generative AI to fulfill its highest potential. Because generative AI is based on large language models that are primed to give human users satisfying answers, it's disturbingly common for models working with the wrong data to "hallucinate" inaccurate results at scale, amplifying run-of-the-mill errors in ways we can't always understand.
Integrating your own team's data with ZoomInfo, an all-in-one AI GTM Platform, can help ensure that generative AI efforts for business are working with the best possible inputs and giving you the most trustworthy results. With over 300 company attributes, including firmographics, org charts, technographics, and intent signals, ZoomInfo gives your AI the verified inputs it needs to produce trustworthy outputs. In a Fortune 500 competitive RFP analyzing 25 million contacts across vendors, an independent consultant concluded that no other competitor came even close (ZoomInfo Q4 2025 earnings call).
ZoomInfo's GTM Context Graph processes 1.5B+ data points daily, fusing your CRM records, conversation history, and behavioral signals with ZoomInfo's B2B data to reveal not just what's happening in your accounts, but why. Marketers and RevOps teams access that intelligence through GTM Studio, where audiences can be built in natural language and plays launched across channels without engineering tickets.
See how ZoomInfo's AI GTM Platform helps marketing teams get more from generative AI.
Building a generative AI marketing strategy: a practical framework
Deploying generative AI effectively requires more than selecting the right tools. Here is a five-step framework for building a generative AI marketing strategy that produces measurable pipeline outcomes.
Audit your data foundation. Generative AI outputs are only as good as the data you feed them. Before selecting tools, assess your CRM completeness, contact accuracy, and intent signal coverage. A fast AI running on stale data produces wrong answers at scale.
Define use cases by marketing function. Content teams, demand gen, and marketing ops have different AI leverage points. Prioritize two or three use cases per function rather than trying to automate everything at once. Specificity drives adoption and makes outcomes measurable.
Ground your AI on proprietary context. Fine-tune or configure AI tools with your brand guidelines, ICP definitions, and historical campaign data. Generic LLM outputs without this grounding produce off-brand content that sounds like every other company's content.
Pilot with a closed-loop measurement plan. Connect AI-generated campaign activity to CRM pipeline data from day one so you can attribute outcomes, not just engagement metrics. Define what pipeline contribution looks like before the campaign launches, not after.
Scale what works, retire what doesn't. Use performance scoring, like the approach Curt Woodward, ZoomInfo's content director, describes of combining leads and pipeline into a single asset score, to identify which AI-assisted programs drive pipeline and which produce MQL theater. Double down on what moves revenue.
Resource allocation matters as much as tool selection. BCG's research suggests that successful AI transformations allocate 10% of effort to algorithms, 20% to technology and data, and 70% to people and process change. That ratio holds for marketing AI rollouts: the biggest investment should go toward training, workflow redesign, and change management, not just software procurement. Teams that over-invest in tooling without the people and process layer consistently underperform.
Frequently asked questions about generative AI in marketing
What are the most effective generative AI marketing use cases?
The highest-impact generative AI marketing use cases for B2B teams are ICP research and audience building, competitive analysis, ABM campaign personalization, content repurposing across formats, and performance data analysis. The key is matching the use case to the marketing function: demand gen teams see the biggest lift from audience targeting and account prioritization, while content teams benefit most from repurposing and editing workflows.
What is the 10-20-70 rule for AI in marketing?
The BCG 10-20-70 framework suggests that successful AI transformations allocate 10% of effort to algorithms, 20% to technology and data, and 70% to people and process change. For marketing teams building a generative AI marketing strategy, this means the biggest investment should go toward training, change management, and workflow redesign, not just tool procurement. Teams that over-invest in technology without the process and people layer consistently underperform.
How does data quality affect generative AI marketing results?
Generative AI outputs are only as good as the data you feed them. Models working with stale, incomplete, or inaccurate contact and account data produce hallucinated or off-target outputs at scale, amplifying errors rather than fixing them. Grounding AI on verified, continuously updated B2B data, firmographics, intent signals, technographics, is the single most important step before deploying generative AI in any marketing workflow. Smartsheet's results, an 84% MQL increase and 26% improvement in opportunity rates, show what a verified data foundation makes possible.
What skills does a marketing team need to use generative AI effectively?
The five core skills for AI-enabled marketing teams are: strategic thinking (guiding AI with clear objectives), creative direction (evaluating and refining AI outputs), AI literacy (understanding what models can and cannot do), ethical judgment (data privacy, bias, and brand safety), and orchestration (coordinating AI tools across channels and workflows). Most teams already have the strategic and creative skills; the gaps are typically AI literacy and orchestration.
How do you measure the ROI of generative AI in marketing?
Measuring generative AI ROI requires connecting AI-assisted campaign activity to CRM pipeline data, not just engagement metrics. The most reliable approach: define a closed-loop measurement plan before launching any AI-assisted program, track which AI-generated assets and campaigns influence pipeline stages, and use a composite scoring model that combines leads and pipeline into a single asset score to identify what is actually driving revenue. Teams that skip the measurement architecture end up with MQL volume but no revenue attribution.