6 Examples of AI in Digital Marketing | AI Skills Explained

Here are six powerful examples of how AI is used by digital marketing companies. These are real examples that the average marketer can implement without special software or tech knowledge.

I wanted to show how you can improve speed and quality in digital marketing using common artificial intelligence platforms like ChatGPT and Claude. You can do these kinds of things tomorrow in the workplace without complicated setups or agentic workflows.

The examples are drawn both from my own experience as a digital publisher and from interesting case studies. They demonstrate that AI skills for marketing students and professionals often come down to understanding the capabilities of the tools at your disposal.

1. Create More Ad Variations

In this example, I gave ChatGPT the copy from a successful Meta ad and asked it to develop six new versions built around different customer motivations.

Example

Task: Produce new ads without abandoning a proven concept.
Inputs: The original ad, landing page, target customer and campaign goal.
Traditional method: Brainstorm and write each variation separately.
AI method: Ask the tool to retain the offer while changing the hook, angle and call to action.
Tools: ChatGPT or Claude, plus Canva.

ChatGPT identified that the original ad appealed mainly to customers wanting to save time. It produced alternatives based on saving money, avoiding mistakes, getting faster results and reducing effort.

I selected three genuinely different angles, edited the copy and built the ads in Canva. The variations could then be tested against the original rather than replacing it.

Key takeaway: Use artificial intelligence to explore more creative angles from a proven idea. It increases the number of worthwhile ads you can test without multiplying the time spent brainstorming.

2. Test More Email Subject Lines

Jennifer O’Brien of Calms Jewelry used ChatGPT and Claude to produce subject-line variations for a major sale.

Example

Task: Create subject lines for a sales email.
Inputs: The offer, campaign goal, audience, brand guidelines and original ideas.
Traditional method: Write each variation manually and test only a few options.
AI method: Generate and refine multiple subject lines in a conversation, then select candidates for testing.
Tools: ChatGPT or Claude.

O’Brien explained the sale and its goal, supplied some of her own ideas and asked the chatbot to improve them. One useful suggestion was to swap two words so the more important one appeared before the subject line was cut off in the inbox.

The process reduced hours of rewriting to minutes and gave her more variations to test. She still rejected suggestions that did not follow the Calms Jewelry brand guidelines.

Key takeaway: AI makes it easier to create and test wording variations efficiently. Human judgement is still needed to choose suitable options.

3. Build Repetitive Content Faster

In this example, Buffer used a reusable prompt to produce the basic outline for every entry in its Social Media Glossary.

Example

Task: Draft dozens of glossary entries in a consistent format.
Inputs: The term, required sections, audience and editorial rules.
Traditional method: Plan and draft every entry separately.
AI method: Use one prompt to generate the same basic outline for each term.
Tools: ChatGPT, Claude or Gemini.

Buffer reported that the prompt halved the time spent deciding what to include and how to present each term. Its content team then edited the drafts and added sources and useful detail.

The method also works for product descriptions, FAQs, course profiles, location pages and other content produced in a repeated format.

Key takeaway: AI is especially useful when many pages require similar elements. Define the format once, then apply it repeatedly without starting from a blank page.

4. Diagnose Campaign Performance

I exported 60 days of Google Ads data to a CSV file and used ChatGPT to investigate why lead generation had weakened in the second month.

Example

Task: Find what caused a decline in leads.
Data: Campaign, clicks, cost, conversions, conversion rate and cost per lead for two months.
Traditional method: Calculate the changes and compare each campaign manually.
AI method: Upload the CSV file and ask the tool to rank the changes most responsible for the decline.
Tools: ChatGPT, Claude or Gemini.

I asked ChatGPT to compare the two months, show its calculations and identify which campaigns accounted for most of the fall in conversions.

It found that one campaign accounted for 14 of the 18 lost leads. Clicks had increased, but its conversion rate had fallen from 6.2% to 3.7%, pushing up the cost per lead. I checked the figures in Google Ads before investigating the campaign further.

Key takeaway: Artificial intelligence is good at handling and interrogating data dumps. When you have relevant performance data on hand, use it to answer business questions.

5. Find What Customers Care About

In this example, Clorox used artificial intelligence to analyse retailer reviews and identify what customers valued about Burt’s Bees lip balm.

Example

Task: Identify the product qualities customers care about most.
Data: Reviews from Amazon, Walmart and other retailers.
Traditional method: Read a sample of reviews and tally recurring comments manually.
AI method: Group reviews by product attribute and compare the sentiment for each one.
Tools: ChatGPT or Claude for a small-scale version.

The analysis found that customers cared more about the lip balm’s scent than its moisturising ability. Clorox could use the finding to guide product messaging and creative decisions.

A digital marketer can try the same method by uploading customer reviews to ChatGPT or Claude. Ask the tool to rank recurring benefits, complaints and purchase motivations, then check the findings against the original comments.

Key takeaway: Customer reviews contain useful marketing evidence, but the important themes can be difficult to see. AI can turn a large collection of comments into ranked insights for positioning, advertising and website copy.

6. Repurpose Content for Different Channels

Unilever used generative AI to turn more than 100 pieces of Dove influencer content into versions for different audiences and platforms.

Example

Task: Extend the reach of successful influencer content.
Inputs: Still images and short video clips from Dove creators.
Traditional method: Resize, shorten and rewrite each asset separately.
AI method: Remix the originals into different sizes, formats and lengths.
Tools: Canva, Adobe Express or CapCut.

The adapted assets supported a Dove campaign that generated more than 3.5 billion social impressions. Unilever reported that 52% of purchases came from new Dove customers.

A smaller marketer could apply the same method to a webinar, customer video or product demonstration. One source asset can become short clips, social posts, email content and website material.

Key takeaway: AI can adapt proven content without recreating every asset from scratch. Start with material that already works, then change the format for each audience.

Benefits of AI in Marketing

The six examples show how a typical marketer, not a technology specialist, can successfully use AI.

Artificial intelligence speeds up data analysis, produces more campaign variations, reduces repetitive work and turns large volumes of customer comments into usable insights. It can also help adapt content and campaigns for different audiences and channels.

In each case, AI is used as a tool rather than a click-and-publish system. The marketer supplies the data, sets the objective, reviews the output and decides what to test or implement. None of the examples depends on an autonomous agent running the entire process.

A key disadvantage is that AI lacks human judgement and cannot be fully trusted to interpret results or implement marketing decisions. Even so, the examples show clear benefits across analysis, creative testing, content production, customer research and campaign customisation.