My Interesting Conversations: What should we teach??

With so much going on in the AI space, business schools are struggling with how to keep up -- both in terms of how to use AI in their pedagogy, and how to teach students about it. I've been getting a first-hand look at this through recent discussions I have been having with the marketing department at Harvard Business School. While AI brings new possibilities, threats, and priorities into Marketing, students still need to learn the basics of how to craft a marketing strategy, how to understand marketing's role in contributing to the performance of a company, how marketing changes as a company grows and matures, etc. So much of a core introductory marketing curriculum still needs to stress the basics.

But we can't escape the changing landscape. So, I've been asked "what should MBA and executive education students learn about the opportunities, practicalities and risks of AI in marketing?" Of course, the school has already blended many cases that touch on AI into its curriculum, but stepping back, what should we, as a business school, teach??

Let's start with a short module that would just take a week -- a very intense one, though.? I am looking for your input, but here are some initial thoughts. As you look at these, think about what would be useful for your own teams to learn, and how you can bring it into your own environment:

• What is it really all about?? People need a simple, practical framework for understanding the different types of AI and GenAI. From the earlier forms of AI-based machine learning, predictive modeling, and optimization algorithms to today's LLM's, RAG architectures, and agentic tooling, lots of terminology has been bandied about. People need some navigation through this landscape, seeing the evolution of capabilities, what purpose each fits, and what is needed to make each work.?

Marketers have been using AI for quite a while, in many cases without realizing it, especially in how paid media operates.? Search, programmatic buying, optimization tools, etc. all are grounded in earlier AI capabilities. Now though, we are adding content creation, research exploration, workflow automation, data integration and management, and a wide range of other new possibilities that can transform how teams operate. There are also forces likely to change fundamentals such as SEO, SEM, attribution tracking, A/B vs. multivariate testing, etc.?

• How does it actually create value?? From what I have seen across dozens of companies, there's a simple ladder of AI use that teams gradually climb.? They start with "AI for Me," which is the widespread use of more personal capabilities, such as Chat GPT, Perplexity, and Co-pilot to help one's own productivity.? Then teams move up to "AI for US," where more end-to-end functions get transformed through new tools, such as marketing campaign creation and development, media budget optimization, and large-scale multivariate testing. At the highest level is "AI for Our Customers," where the customer experience and core value proposition are transformed through personalization, speed, or cost that empowers customers themselves with new capabilities. By knowing where you are on the ladder, seeing examples of how to move up, and managing each step explicitly as a way to generate value.

• Let me get my hands dirty! Nothing is as impactful as someone working hands-on with a range of AI tools. For this, let's have a series of 3-4 demos of tech tools, covering content creation, assessing one's Search positioning in AI engines, and doing customer research. After each demo, discuss how this can change both the strategy and operations of marketing, then give a situation to work through using the tool.?

• But isn't it risky?? Give a case example of a company, likely a regulated one, that illustrates the many kinds of risks involved, from security to privacy to bias to hallucinations. Talk through why the risks happen and then the range of ways one could try to address them, including "human in the loop," setting guardrails for the AI, using other AI tools, and other emerging responsible management approaches. Stress the importance of looking at AI-driven interactions from a customer's perspective, understanding the points of weakness in the flow of one's data, and the balance of completely autonomous vs. "bionic" (human/AI jointly executing).

• Where to start?? Set up a case situation where you are a marketing leader, facing pressure for more growth with less budget, and work through the angles where AI could drive value.? Discuss how to set up pilots, explore working in a more agile team mode, enhance the skills of your team, line up your data, and test new tools. Use examples from early adopters now seeing value.?

Along the way, I'll aim to build cases from brands adopting AI, from agencies figuring out where to focus their investment, and from the providers of AI-driven tools. Through a variety of perspectives, I believe it is easier to see patterns that someone can use for their own journey.?

So this is my starting point.? I know there are already many suppliers of AI training, and I follow a lot of them. But what I am trying to do here is blend it into a broader business or marketing education, and do so in a compressed timeframe.?

Am I on the right track?? All feedback welcomed! Thx!