By Kris Kaplan

I recently completed the eCornell Agentic AI Architecture certificate program. If I am being completely honest, my first thought when I logged into the first course was, “What have I gotten myself into?” That feeling lasted for a while, but I kept going anyway.

I signed up because I wanted to learn more about AI and because my peers signed up. Yes, peer pressure is real, even in IT, even when the outcome involves homework. About ten of us from IT at the SC Johnson College of Business enrolled together. Taking the course as a group made it easier to stay motivated, compare notes, and occasionally reassure each other that yes, the workload really was intense and no, we were not imagining it. By the end, when we finally finished and the congratulations were said, the most common question was, “So what are we going to do with all our free time now?”

Before the course, if you had asked me to define agentic AI, my best guess would have been something along the lines of a chatbot that had a confident personality. After the course, I would explain it much more plainly as systems that can reason, coordinate tasks, make decisions, and act across tools and data. That shift in understanding is valuable individually and powerful for the team as a whole. We now share a common vocabulary and baseline knowledge, which turns abstract AI conversations into productive design discussions.

I am not a developer, and the course did not magically turn me into one. I still cannot write code from scratch, and that is fine. What it did give me was the confidence to talk about AI in a knowledgeable way, ask better questions, and understand what is happening behind the scenes when AI solutions are proposed. That alone has been a huge professional win.

Surprisingly, the code was less scary than expected. eCornell did an excellent job presenting the exercises in a way that was approachable for non-developers. The coding activities were sophisticated cut and paste exercises where you changed a few key variables and suddenly understood a lot more than you expected. 

The course content felt immediately applicable to real IT work. As we moved through the material, it was easy to see how what we were learning connected to problems we were already trying to solve. This was not theory for the sake of theory. It was practical, grounded, and clearly designed for people who actually have to make systems work.

The true beauty of the coursework is that my team is already applying what we learned to a current cross-university data sharing pilot project. This effort brings together data from two units that use different systems to track similar information. The goal is to improve collaboration, eliminate duplicate data entry, and give both teams a near real time shared view of their data. We are using AI to identify fuzzy matches across datasets, group related records that would not align cleanly with traditional logic and summarize and surface insights in ways that standard code simply cannot do. Watching theory turn into practice has been one of the most rewarding outcomes of the course.

Taking this course together has given our IT organization new tools, new perspectives, and a stronger foundation for engaging with AI initiatives across the college. I now regularly hear phrases like, “We learned this in the course…”, or “I remember from the course….” One of the biggest changes I have noticed since completing the program is how our team approaches problem solving conversations. There is a clear willingness to suggest AI as part of the solution, rather than treating it as an experimental add-on or something we might explore someday. AI is now a legitimate and expected option in our discussions, not because it is trendy, but because we understand when and why it makes sense.

The course is intense. The amount of material covered is significant, and the homework is lengthy. But it is worth it. You do not need to be a developer to take this course. You just need curiosity, a willingness to learn, and a desire to make better and more informed decisions about how AI fits into your work. 

And yes, you may be wondering if I used AI to help write this blog post. The answer is absolutely. I would not want my new AI prompting skills to go to waste.

Kris Kaplan is the Associate Director of Enterprise Systems and Data Strategy at the SC Johnson College of Business, with a passion for turning complex data and systems into clear, actionable insights that support smarter, more effective work.

The eCornell Agentic AI Architecture Certificate, authored by Ayham Boucher, is offered fully online, spans roughly two months with 6–8 hours of study per week. Interested readers can enroll at any time via eCornell’s website (Cornell staff can register at a discounted rate here).