Here is a recap of the webinar, “Practically Speaking: A Look at How AI Can Be Implemented in Our Everyday Work.” The event was offered in collaboration with The DePaul AI Institute and DePaul’s Office of Corporate and Employer Outreach (CEO).

You can download handouts from the session here.

We covered real-world examples of how AI can be seamlessly integrated into everyday tasks. One highlight was demonstrating NotebookLM—a free tool that transforms large documents into digestible podcasts and analysis using Retrieval-Augmented Generation (RAG) and Google’s expansive context windows. The combination of RAG and large context windows helps reduce AI “hallucinations.”

In 2024, I made a presentation to The Broadway League’s 28th Annual Road Marketing Forum (“Yes, And… AI For Everyday”). To better familiarize myself with the organization, and to tailor my presentation for the audience, I used NotebookLM to analyze publicly available information – their website and their conference brochure.

NotebookLM is an AI-powered research and note-taking tool developed by Google Labs. It utilizes RAG (Retrieval Augmented Generation) to allow users to “chat with documents” as well as generating summaries, explanations, and answers based on the content uploaded. Since September of 2024, NotebookLM provides "Audio Overviews," which transform documents into conversational podcasts. Up to 50 sources can be uploaded to NotebookLM.

Example Podcasts

• 2024 Road Marketing Forum
• Source: Road Marketing Forum Confirmation Packet PDF
• The Broadway League | The Official Website of the Broadway Industry
• Source: Broadway League Website (site downloaded as a PDF – 3071 pages)

We emphasized the importance of using secure methods when dealing with confidential data. Whether it’s leveraging encrypted APIs or deploying AI models locally, protecting sensitive information is critical. Here at DePaul, our base LLM is Microsoft Copilot with Enterprise Data Protection. This service does not train models on faculty, staff, and student data, and securely encrypts our communication.

AI systems are only as good as the data they’re trained on. We addressed the inherent biases in many models and highlighted the need for transparency regarding training data and model provenance. This is crucial not only for ethical use but also for making informed choices about which AI solutions to adopt.

With advancements in reasoning models, voice-enabled functionalities, and new frontier models, we’re on the brink of a new stage in how we interact with generative AI and automated decision-making. These emerging trends reshape our approach to both personal and organizational tasks, providing both opportunity and heightened risk.

I’ve always believed that understanding AI isn’t just about learning how to use a tool—it’s about developing an informed perspective on its strengths, limitations, and ethical implications.

Just as a sommelier understands the nuances of wine, I encourage everyone to appreciate the provenance, pairing, and “price” of AI. It’s about selecting the right tool for the right task while being mindful of its capabilities and risks.

AI is evolving rapidly. That’s why I advocate for ongoing education—whether through short MOOCs, cross-functional team collaborations, or attending future events at that DePaul offers. The goal for all of us is to stay ahead of the curve and harness AI responsibly.

While AI offers incredible benefits, it’s essential to remain vigilant about potential downsides, such as data breaches, bias, and the erosion of trust. Thoughtful policies and proactive engagement with these challenges can help us navigate this complex landscape.