Working in an entrepreneurship center on a college campus, I am surrounded by curious, entrepreneurial students. Like most students today, ours are digital natives. They have never known a world without the internet, laptops, smartphones or apps. For them, technology adoption comes naturally and artificial intelligence (AI) is often their trusted go-to expert.
I am not a digital native. Technology adoption for me is a slower, evolving process. AI is a helpful tool, not my ultimate authority. How then do I teach, coach and collaborate with the students I support?
Companies face a similar dynamic — they are a mix of digital and non-digital natives. How do you onboard, coach and collaborate with the early-career talent you hire? One key strategy is to accelerate building real-world context alongside AI adoption.
I created a pilot course to explore these differing perceptions of AI and build practical context for our students. It’s called “AI vs. Humans: Who Builds a Better Business?” The course challenges student teams to use AI to construct a startup in the coffee market and then critique each other’s enterprises — much like companies do through a formal project review process.
We asked students to exclusively rely on AI to identify a problem, assess market potential, design a solution and build a business model. Along the way, we asked them to reflect on the answers AI provided and develop a list of core assumptions to test with humans. What did AI come up with? We saw concepts ranging from a smart lid that measures caffeine consumption to custom bottled coffee delivered to your door, a matchmaking app for remote workers and local cafes, and an upcycling program turning grounds into artisanal soaps. They were creative, fun ideas with seemingly huge market potential — or were they just entertaining ideas detached from reality?
That is what our students still need to discover through human interaction, not algorithms. They will begin building the visceral, 3D context needed to determine if their coffee venture is viable:
- Research: They will read industry journals, annual reports and trade articles to map patterns, generate testable assumptions and internalize market insights.
- Observation: They will spend time in local coffee shops, restaurants and retail stores tracking the authentic customer journey firsthand.
- Interviews: They will meet with owners, baristas, distributors and manufacturers to test their AI-generated assumptions, learn from real-world feedback, and pivot toward success.
We designed this class to help students understand that AI alone, without human context, does not equal expertise. True expertise is human context combined with AI insights and context can only be built through human experience.
You can apply this exact framework within your own company. Encourage project teams to intentionally critique AI output, document underlying assumptions and execute a plan to test those assumptions with real stakeholders. This approach creates space for experienced leaders to leverage AI for greater efficiency while helping emerging professionals build the critical context needed for future decision-making.
Moving forward, we plan to make one simple change: we are renaming the course. It is not AI versus Humans. It is AI and Humans. ●
Mindy Baierl is an Adjunct Faculty Member; Senior Director of the Veale Institute for Entrepreneurship at Case Western Reserve University’s Weatherhead School of Management
