Exploring Generative AI Use Cases in Marketing

Most of the conversation around generative AI in marketing feels stuck. It’s still focused on the magic trick of writing a blog post from a single prompt. I get the appeal, but it’s the least interesting thing you can do with this technology. It’s like using a new programming language to only write “Hello, World.” True generative marketing is about more than just automating content.

For me, the real leverage isn’t in replacing tasks. It's in building small, intelligent systems that drive business growth. It's about finding the friction in your marketing day: the manual data entry, the repetitive analysis, the blank page for ad content and inserting a smart, automated step. These aren't grand, company-wide initiatives. They’re small, personal workflows that give your marketing the leverage of a bigger team.

I’ve spent the last year testing these workflows. Some broke. Some were too clever to be useful. But a few have stuck, becoming core to how I operate. These are the practical, grounded use cases of generative AI in marketing that I keep coming back to.

Content Creation Beyond the Blank Page

AI content creation is the most obvious starting point, but we need to think beyond just generating final drafts. I’ve found the most value in using large language models (LLMs) as creative partners and specialised assistants, not just ghostwriters for our marketing content.

One of the first tools I built was a "Brand Guardian" GPT. I fed it our internal one-pagers on brand voice, messaging pillars, and target personas. Now, instead of staring at a blinking cursor, I can ask it questions like, “How would we talk about our new API integration to a technical project manager?” It gives me on-brand angles, metaphors and key phrases. It doesn't write the post for me, but it cuts out 30 minutes of throat-clearing from my content creation process. This approach enhances our entire marketing content pipeline.

This extends beyond simple copy to broader brand storytelling. By feeding an LLM our core brand narrative and customer success stories, we can ask it to generate different narrative arcs for new product launch campaigns. It helps us explore angles we might have missed, ensuring our content and story resonates across different channels, from a short video script to a long-form blog post or social media content.

This is especially useful for technical content. I recently had our lead engineer send me a wall of text about an update to our system's rate limits. I fed the whole thing to Claude and prompted it: “You are a technical writer who excels at analogies. Read the following engineering notes and generate five different ways to explain 'rate limiting' to a non-technical marketer.” The ‘water from a tap’ analogy it produced was as clear as any content I would have written on my own.

The goal isn't to abdicate the thinking. It's to use these generative marketing tools to explore more creative avenues, faster.

AI-Powered Personalisation That Actually Feels Personal

Personalisation at scale has always been the goal of modern marketing, but it often ends up feeling clunky and formulaic. "Hi [First Name], I saw you work at [Company Name]..." doesn't impress any customer anymore. Generative AI in marketing lets us add a layer of genuine relevance that was previously impossible without manual effort, improving audience targeting.

I’ve been experimenting with this in our lead nurturing flows. We use n8n to connect our CRM data from HubSpot to the OpenAI API. When a new lead downloads an ebook, a workflow triggers. It pulls their job title and industry data, then sends that data to the GPT-4 API with a carefully crafted prompt. Better personalisation starts with better data.

The prompt looks something like this: "Based on the following information—Job Title: [Title], Industry: [Industry]—write a single, concise sentence for an email that connects our product's value to a common challenge for this specific customer role."

The output is then added as a custom sentence in an automated follow-up email. It’s not fully AI-written email content, which I find can be risky. It’s just one hyper-relevant sentence that makes the other 90% of the template feel more personal. In a recent A/B test, emails with this AI-generated sentence saw a 15% higher click-through rate and a 5% increase in demo requests compared to our standard template. It's a small touch with a measurable impact on customer engagement.

This same principle can power interactive marketing experiences. Instead of a static lead magnet, imagine an AI-powered quiz on your website that asks a few questions and then generates a personalised recommendation or report for the customer. It’s a more engaging way to capture leads while providing immediate, tailored value and valuable data.

Sharpening Campaigns with Generative AI Applications

The highest-leverage part of any marketing campaign is the core creative concept. Generative AI applications have become an incredible tool for ideation and variation here, compressing the time from idea to execution for our campaigns.

For visual concepts, I’ve stopped briefing designers with vague ideas. Instead, I spend 20 minutes in Midjourney. For a recent campaign about the risk of "data chaos," I used prompts like a tangled, messy web of glowing fiber optic cables in a dark server room, photorealistic, cinematic lighting. In minutes, I had four distinct visual directions. We didn’t use the raw AI images in the final campaign, but this visual content gave our designer a concrete starting point that was miles ahead of a written brief. This process allowed us to launch a landing page A/B test 40% faster, and the winning variant resulted in an 8% lift in conversion rates.

The same principle applies to copy. For a new landing page, I’ll take the core value proposition and ask an LLM to generate 10 headline variations, each targeting a different psychological angle: one for urgency, one for social proof, one for curiosity, one that speaks to a pain point. This isn’t about finding the “perfect” headline content. It’s about quickly mapping the landscape of possible messages so we can make a more informed choice for our A/B tests. These AI-powered campaigns feel more focused from the start, improving our audience targeting.

AI-Driven Market Research and Predictive Analytics

This is one of the more underexplored use cases of generative AI in marketing, and for me, it’s been a game-changer. You can now perform sophisticated qualitative data analysis and even predictive forecasting that used to require expensive tools or days of manual work. This is where generative marketing provides deep strategic value.

For market research, I recently wanted to understand our customers' most common complaints and get insights into consumer behaviour. I exported a year's worth of support chat transcripts; a huge amount of customer data into a single text file. Claude’s large context window handled this data easily. I prompted it: “Analyse this customer support log. Identify the top 5 recurring themes of user frustration and analyse the underlying consumer behaviour. For each theme, provide a summary and three verbatim quotes that exemplify the issue.” In minutes, I had a report with actionable insights that would have taken a person a week to compile.

Beyond analysing the past, we're using generative marketing for predictive analytics. By feeding an LLM our last six months of email marketing data; subject lines, send times, and engagement rates, we can ask it to identify patterns correlating with high open rates. It can then generate predictive hypotheses for our next campaign, such as, "Subject lines framed as questions for a technical audience are likely to see a 10-15% higher open rate." This shifts AI from a reactive tool to a strategic forecasting partner, helping us understand future trends and improve our marketing strategies. The quality of our analytics has improved dramatically.

Automating the Annoying Parts of Marketing Processes with AI

The true power of any new technology reveals itself when it becomes boring. For me, that’s where automation comes in. The biggest wins have come from connecting generative AI in marketing to the other tools and platforms in our stack to eliminate the small, repetitive tasks that drain cognitive energy from the marketing team.

A simple one is meeting notes. We use Granola to record and transcribe calls. After each call, a webhook sends the transcript to a custom n8n workflow. The workflow sends the text to the GPT-4 API to summarise the call, list action items and identify requests from the customer. The output is then automatically posted to Slack and a new ticket is created in Asana.

Another powerful automation is content repurposing. When a blog post is published, an automation triggers. It takes the full text of the article and runs it through a series of LLM prompts to generate a 5-tweet thread for our social media platforms, a LinkedIn post draft, and three potential newsletter subject lines. This repurposed content is 80% of the way there, saving the marketing team an hour of work for every article, which frees up over 4 hours per month for more strategic work on our campaigns.

Getting Started: A Practical Implementation Guide

While these examples of generative marketing seem complex, starting is easier than you think. It's about finding one small point of friction and applying a simple, structured approach to your marketing operations.

Step 1: Identify the Friction
Don't try to boil the ocean. Find one small, repetitive task you do every week. Is it summarising meeting notes? Writing social media content from a blog? Manually researching new leads using external data? Start there.

Step 2: Choose Your Tools
You don't need a massive budget for these generative AI applications.

• LLMs: Start with accessible models like OpenAI's GPT-4 or Anthropic's Claude 3.
• Automation Platforms: Tools like Zapier or n8n are the glue. They connect your existing marketing apps (like Slack, HubSpot, Google Sheets) to the AI models without writing code.
• Image Generators: For visual content brainstorming, Midjourney or DALL-E 3 are excellent starting points.

Step 3: Build a Simple Workflow (A Recipe)
Let's take the lead enrichment example:

1. Set the Trigger: In n8n or Zapier, create a new workflow that triggers when a "New Form Submission" happens on your marketing platform.
2. Connect to AI: Add an "OpenAI" node. Connect your API key.
3. Craft the Prompt: In the prompt field, write your instructions. Use the data from the trigger step to insert dynamic information, like: "Based on the job title '[Job Title from Step 1]' and industry '[Industry from Step 1]', write a one-sentence email opener..."
4. Send the Output: Add a final step to "Update Contact Record" in your CRM, placing the AI-generated content into a custom field.

Step 4: Test, Iterate, and Scale
Start with non-critical, internal workflows first. Your first prompts will likely be imperfect. The key is to test the output, refine your prompts for better results, and once the system is reliable, scale it to more impactful marketing processes.

Navigating Ethical Considerations and Risks

Adopting generative AI applications isn't without its challenges. It’s crucial to be proactive about the ethical implications for your marketing.

• Data Privacy: When connecting AI to your CRM or customer data, ensure you are not sending personally identifiable information (PII) to models without proper consent and security protocols. Anonymise data where possible.
• Algorithmic Bias: AI models are trained on vast datasets from the internet, which can contain biases. If you use AI for audience targeting or to generate persona descriptions, always have a human review the output to check for and correct stereotypes.
• Transparency: Be transparent with your customer base. If a chatbot is powered by AI, disclose it. Authenticity builds trust, and trying to pass off AI as a human can backfire on your marketing efforts.
• Human Oversight: Never fully abdicate responsibility to the AI. The most effective use cases of generative AI in marketing involve AI as a co-pilot, not the pilot. A human should always be in the loop to review, edit, and approve AI-generated content before it goes live.

The Future: What's Next for Generative AI in Marketing?

What we're doing now is just the beginning. The next wave of generative marketing will be even more integrated and strategic, shaped by emerging trends.

• Hyper-Personalisation: We will move from personalising a single sentence to personalising the entire customer journey in real time. Websites will dynamically change their messaging and content based on consumer behaviour data.
• AI-Driven Strategy Formulation: Future models won't just execute tasks; they will act as strategic consultants. You’ll be able to ask, “Given our budget, customer data and market trends, propose three complete marketing campaign strategies for our Q3 launch, complete with channel recommendations and draft creative content.”
• Multimodal Creation: The lines between text, image, audio, and video generation will blur. Marketers will be able to generate complete, cohesive campaigns; a blog post, social media images and a short video script all from a single brief, ensuring perfect brand consistency.

The most durable and practical applications of generative AI in marketing are the small, compounding gains that lead to significant business growth. Start small, stay curious, and focus on building systems, not just generating content.

Frequently Asked Questions

How is generative AI used in marketing?

Generative AI is used for a wide range of marketing tasks beyond just writing copy. Key applications include personalisation of customer outreach at scale by dynamically generating relevant content, ideating visual concepts for campaigns, performing rapid qualitative analysis of customer data and insights, and automating repetitive processes like creating social media content.

What are some real-world applications of generative AI in marketing?

A few real-world examples include: connecting CRM data to an LLM to write a personalised sentence in an automated sales email based on a lead's industry; using image generation tools like Midjourney for visual content creation for a new ad campaign; and feeding hundreds of customer reviews into a model like Claude to instantly identify common themes and understand consumer behaviour.

How can AI automate marketing processes?

AI can automate marketing processes by acting as an intelligent step in a workflow created with platforms like Zapier or n8n. For example, a new form submission on your website (the trigger) can send the lead's data to an AI model (the action) to research the company and generate a summary, which is then added to the CRM and posted in a Slack channel for the sales team (the outcome). It’s about using AI to add an analysis or content generation step that previously required human intervention.