09-15-2026
Marketing has always been a field defined by its rapid adoption of technology, from the earliest predictive algorithms used for customer segmentation to the current wave of generative models reshaping content production. While students at the Mitch Daniels School of Business are on the front lines building AI-driven solutions, the faculty are the architects of the strategic mindset that ensures these tools create real value for the consumer. Siyi Yu, clinical assistant professor of management, shares the future of the AI-driven marketing landscape.
A: Honestly, all three, but mindset is what drives our philosophy. The tools themselves change too fast. If we only taught students which buttons to click on a specific platform, they would be obsolete by the time they graduate.
What we are really trying to build is a way of thinking:
Our focus is on building practical skills. In my Marketing Management course, students use generative AI for real tasks and submit a structured AI-use appendix. They must document their prompts, the decisions they made and how they verified the outputs. We even have an AI creativity bonus to incentivize sophisticated, high-value integration rather than just policing misuse.
So it's mindset first, skills second and the tools are the vehicle for both.
A: To understand where we're headed, it helps to see how we got here. Marketing as a field has actually been one of the earliest and most enthusiastic adopters of AI, long before the current generative AI wave. Predictive AI has been embedded in marketing practice for years. We use classification and clustering algorithms to build customer segments, recommendation systems to predict next-best actions, and propensity models to score leads. None of that is new. What's new is the generative layer — large language models for text, diffusion models for image creation — and more importantly, the pace at which these models are expanding both the breadth of tasks they can handle and the depth of reasoning they can bring.
I see two major trends shaping the next few years:
But here's the thing I always come back to — and this is something I emphasize with my students — all of these trends still need to pass what I'd call the value test: does it actually lower costs and create more value for the customer? Because if the answer is "it's cool but doesn't move the needle," the market will correct for that quickly. Marketing has always tracked technology closely for exactly this reason.
A: Yes. We are currently developing a new graduate course called "Artificial Intelligence: Strategy and Marketing" that we've proposed for the Spring 2027 term. The course is designed to fill a specific gap. We already have several offerings at the Daniels School that cover the technical and analytical side of AI — building predictive models, coding in Python and R, deploying AI solutions. This new course takes a strategic and managerial lens on AI: How does AI reshape competitive dynamics? How do firms make strategic choices about AI adoption and deployment? How do consumers and other stakeholders actually perceive and respond to AI-driven decisions? And how do you navigate the societal challenges — fairness, bias, explainability — at the leadership level? The proposed course tackles all of that through a case-study and discussion-based pedagogy.
A: This is a question our students are already raising proactively in class discussions. They have concerns about AI displacing creative professionals, whether algorithmic targeting can become discriminatory and whether we're automating away the human judgment that makes marketing strategic in the first place.
On our end, we're making sure the curriculum creates space for that kind of reflection rather than leaving it to chance. The new graduate course we're developing includes a dedicated session on the ethical and societal implications of AI in business, covering the three issues already mentioned — fairness, bias, explainability — and the question of how different stakeholders are impacted by AI-driven decisions. Students also have the option to build an AI governance framework as their capstone project to practice designing responsible solutions.
In my own courses, one of the mechanisms I use is the structured AI-use appendix. When students use AI for their assignments, they're required to document their prompts, the outputs they received, what they chose to keep or discard, and why. The act of writing down your reasoning forces you to confront moments where the AI hallucinated, where it produced something biased, or where accepting the output uncritically would have led to a worse outcome.
At Purdue, we bridge the gap between technical fluency and strategic judgment. We equip students to lead the next generation of marketing by focusing on a mindset that prioritizes value, ethics, and human ingenuity.
Ready to take your next giant leap? Find out more about the MS Marketing program.
If you would like to receive more information about pursuing a business master’s at the Mitch Daniels School of Business, please connect with us and a program specialist will be in touch!
