How to Teach AI Marketing Skills: 6 Ways to Go Beyond ChatGPT

AI technology evolves at the speed of light, which ChatGPT will tell you moves fast enough to circle the earth nearly eight times before you finish this sentence. That makes it incredibly challenging to keep up for anyone who wants to learn, let alone teach AI marketing skills to grad students and MBAs.

Generative engine optimization (GEO) is now listed right alongside search engine optimization (SEO) on job descriptions. Google Ads, Hubspot, Meta, and LinkedIn continuously roll out new AI-powered features for automation, audience targeting, personalization, content creation, market research and more. Understanding this landscape will be table stakes for the class of 2026.

To teach AI marketing skills is less about the where and more about the what. The platforms may evolve, but the driving underlying skill remains the same: Clear, strategic thinking.

Many professors fear that AI means the death of critical thinking. While that can happen, teaching AI marketing skills ensures that it won’t. Read on to learn more about six skills that will help your students future-proof their future careers.

AI marketing skills to teach: Prompt engineering

You know what they say: “Garbage in, garbage out.” If you teach AI marketing skills, that’s the core principle.

The most obviously AI-written essays that have come across your desk likely began with a vague, generic prompt. “Write me a [number] word essay on [topic].” AI will do exactly what you tell it. If there are gaps, AI will fill them in without context. The more clear and specific the instructions, the more relevant and accurate the output.

Imagine you’re tasking students with writing a marketing email. “Write me a marketing email about [product]” won’t do. They should also include as many details as possible, such as the product’s target audience and differentiators, the brand’s tone of voice, the email’s desired length, and the call-to-action.

AI marketing skills to teach: Customer research and audience targeting

Strong prompt engineering opens the door to limitless possibilities. Once your students have that down, they can conduct customer research and audience targeting with AI.

AI can analyze market data and find patterns, rapidly helping students identify market gaps, positioning opportunities, and competitive advantages. What AI can not do is provide context and bias detection. For example, the happiest and unhappiest customers are most likely to leave reviews, which means they aren’t necessarily representative.

To teach AI marketing skills, you must ensure students understand the importance of feeding AI diverse data and guiding it toward insights. Analysis and segmentation improve with a combination of behavioral (website analytics, purchase history), attitudinal (surveys, interviews), and observational (social listening, competitor analysis) data. From there, students can derive value with specific prompts such as, “Recommend specific strategies for highest value segments.”

AI marketing skills to teach: Budgeting

Budgeting has traditionally required extensive manual calculations, historical analysis, and some educated guesswork for good measure. With AI, students can think beyond static annual budgets toward agile, performance-driven budget management that replies to market conditions and effectiveness.

Examples include:

• Analyzing performance data to identify opportunities for budget optimization

• Recommending reallocations based on performance patterns

• Demonstrating how different touchpoints contribute to conversions throughout the journey

• Predicting performance ranges based on campaign variables such as audience, creative approach, and channel mix

When it comes to budgeting, the most important AI marketing skill to teach is validating AI predictions against students’ understanding of marketing fundamentals. Because AI sometimes hallucinates, it shouldn’t be taken at face value. Students must put on their critical thinking caps to make sure the AI-generated budget aligns with what they know of channel performance, audience behavior, and business objectives.

AI marketing skills to teach: Cross-channel strategy

Shiny object syndrome has always been rampant in marketing. The way AI evolves at breakneck speed has made that phenomenon explode. Strategically evaluating capabilities is a paramount, especially around cross-channel strategy.

There are more channels than ever. Coordinating them has traditionally been a manual process with significant challenges around tracking and attribution. One way to teach AI marketing skills is to have students approach AI through the lens of the customer journey, rather than individual campaigns. 

By analyzing customer behavior patterns, AI can create sophisticated customer journey maps. Your students can quickly understand which channels, messages, and cadences work best for each segment at each stage. That removes a lot of the guesswork from channel orchestration.

AI marketing skills to teach: Content creation

“If at first you don’t succeed, try, try again” was initially written about singing in the 1830s. But it could have been written about creating content with AI. Visual content is trickier than written because there are so many more variables. That makes clear, specific prompting even more important.

To teach AI marketing skills, make sure your students don’t skip the creative brief. In addition to campaign objectives and target audiences, helpful context includes brand voice documentations, visual identity standards, compliance checklists for regulated industries, and approval workflows, such as competitors’ colors to avoid.

AI marketing skills to teach: Performance analysis

AI’s measurement capabilities are a double-edged sword. It can aggregate data from multiple sources to create a complete picture, spotting patterns and predicting future campaign performance. However, the fact that AI can perform so many analyses so quickly makes it too easy to fall down unproductive rabbit holes.

Students first need to master the basic metrics for awareness, engagement, conversion, and retention. Only once they understand how these connect to business results should you teach AI marketing skills to go deeper.

The key is to think about what you want to measure before the analysis. That helps students avoid the same pitfall marketers have always worked to avoid: Being reactive and fixing problems rather than avoiding them in the first place.

To learn more about how to teach AI marketing skills to grad students and MBAs, click here for our free guide.