The Boring AI Show

In this episode...
Tim and Tally sit down with Brent Paugh, Business Intelligence Manager at Mind Over Machines, to dig into the data layer behind AI. The conversation centers on a practical truth that shows up again and again across healthcare, business intelligence, automation, and everyday AI use: AI is only as useful as the context, data, governance, and human judgment surrounding it.
The episode starts with a pair of AI news stories that set up a broader discussion about the relationship between AI, data, and human expertise. From there, Brent joins Tim and Tally to explore what it takes to make AI useful inside organizations, including data quality, business context, governance, and trust.
The conversation covers why AI struggles without shared definitions and structured information, how organizations can think about security and access when deploying AI tools, and where human judgment still plays a critical role. Along the way, the group shares practical examples of AI in action, from analytics and synthetic data to knowledge management and content creation.
Throughout the episode, one theme keeps resurfacing: successful AI initiatives depend on more than the technology itself. The strongest results come when AI is paired with well-managed data, clear business context, and thoughtful human oversight.
Topics Covered
What “clean data” means in practice
Why AI needs business context, not just access to data
Structured versus unstructured data
Why humans still need to define terms like customer, revenue, region, and inventory
The importance of semantic models for BI and AI
Using AI with dashboards and trusted business metrics
Why AI outputs need expert validation
Cognitive laziness and the risk of over-trusting AI
Data classification, restricted data, sensitive data, PII, PHI, and HIPAA considerations
AI connectors and the danger of unfettered system access
Using synthetic data to protect private information
Predictive analytics, machine learning, and early cancer detection
The baseball statistics example: clean domain data plus human expertise
AI-readable communication, including headings and structured documents
AI slide generation across Copilot, NotebookLM, and Miro
Claude agent swarms and Dynamic Workflows for large knowledge-graph updates
Using AI as a thought partner and role-based reviewer
Resources
Articles and News Mentioned
AI for Good: AI and the Future of Inclusive Mental Health Care
AI for Good Global Summit 2025
Mayo Clinic: AI Detects Pancreatic Cancer Up to 3 Years Before Diagnosis in Landmark Validation Study
Forbes: Samsung Bans ChatGPT and Other Chatbots for Employees After Sensitive Code Leak
CIO Dive: Samsung Employees Leaked Corporate Data in ChatGPT
Organizations Mentioned
AI for Good
International Health Charity Association
Mayo Clinic
Tools Mentioned
GameChanger
Claude Opus 4.8
Claude
ChatGPT
Microsoft Copilot
Copilot in PowerPoint
Google Gemini
NotebookLM
Miro Sidekicks
Perplexity
Redgate SQL Data Generator
Thanks for listening! Visit us at @mindovermachines.com to learn more about us!
Want to stay up to date on our latest AI insights? Subscribe to our newsletter here!

In this episode...
Tim and Tally sit down with Jessy Jordan, professor of philosophy at Mount St. Mary's University in Emmitsburg, Maryland, to dig into what AI is doing to critical thinking. The conversation starts with discussion around the Pope's first encyclical, Magnifica Humanitas. Jessy pulls out the three things that matter most: a moral vision centered on human dignity, a pointed warning about AI monopolies and the danger of leaving transformative technology in the hands of a very few.
Then Jessy takes the conversation into the trenches of university education. He describes the university as an intellectual gym and explains why students who outsource their cognitive work to AI aren't just cutting corners on a paper, they're skipping the reps that build judgment, structure, and confidence in their own competence. Tim connects that to what he's seeing in the workforce too, where experienced professionals are accepting AI outputs without validation and people who have been doing their jobs for years are hitting the easy button without ever checking what came out the other side.
Topics Covered
The Pope's encyclical Magnifica Humanitas and what it says about AI
AI monopolies and who controls transformative technology
The IKEA chatbot example: automation paired with reskilling
What a university education is actually for
The university as an intellectual gym
Cognitive offloading and what students lose when AI does the thinking
Why generalists are going to be in high demand
Using AI as a thinking partner without losing slow reflection
Practical AI use cases: summarization, audio tools, and slide creation
Resources
Articles and News Mentioned
Pope Leo XIV's Encyclical: Magnifica Humanitas
Time: Pope Leo Uses First Major Papal Text to Warn About Dangers of AI
PBS NewsHour: Pope Calls for Robust Regulation of AI
Fortune: Barnes & Noble CEO Clarifies Stance on AI-Generated Books
PYMNTS: IKEA Turned 8,500 Call Agents Into Design Consultants
Mississippi Statewide AI Framework
People and Organizations Mentioned
Jessy Jordan, Ph.D. — Mount St. Mary's University
Algorithmic Justice League
Mind Over Machines Resources
Tools Mentioned
ElevenReader by ElevenLabs
Miro
Thanks for listening! Visit us at @mindovermachines.com to learn more about us!
Want to stay up to date on our latest AI insights? Subscribe to our newsletter here!

In this episode...
Tim and Tally talk about the AI for Humanity event, hosted at The Towson University College of Liberal Arts. The idea for the event was simple: give liberal arts students - those who normally don't get the first say in tech - the opportunity to shape the future. Tim and Tally recap the various group teams that presented their solutions to a modern AI problem in a hypothetical scenario. 
The conversation explores how AI governance spans from a technical problem to a social problem. Tim and Tally discuss the different approaches student teams took, including ethics-first frameworks, impacted-population governance, stage-gate review processes, and even a critical anti-AI perspective.
The episode also features interview clips from students, faculty, and professionals answering the question: “What advice would you give to business professionals just starting to use AI?”
Topics Covered
 AI governance as a liberal arts challenge 
 Towson University’s AI for Humanity event 
 Responsible AI policies and human-in-the-loop review 
 Ethics-first AI governance 
 Impacted-population governance 
 Stage-gate review processes for AI use 
 Whether every problem actually needs AI 
 Building AI tools with affected communities, not just for them 
 AI as a collaborator, not a competitor 
 Practical advice for business professionals starting to use AI 
 Using AI carefully without treating it as an answer machine 
 Practical AI use cases for learning, organization, dashboards, and workflow support 
Resources
Event Sponsors
Baker Donelson
APWU Health Plan
The Coca-Cola Company
Maryland Technology Council
Event Links
Towson University: Students Develop Solutions for Responsible AI Use
CLA AI for Humanity Competition
Towson StarTUp at the Armory
WMAR: Towson University Hosts AI for Humanity Challenge
Mind Over Machines Resources
Articles and News Mentioned
Gates Foundation: Making AI Work for More People
Anthropic: Gates Foundation Partnership
Reuters: Anthropic and Gates Foundation Launch $200 Million Partnership
Reuters: Elon Musk Loses Lawsuit Against OpenAI
Arnold’s Pump Club: This Is Your Brain on AI
Thanks for listening! Visit us at @mindovermachines.com to learn more about us!
Want to stay up to date on our latest AI insights? Subscribe to our newsletter here!

AI is getting weirder, more useful, and more consequential all at the same time and that's exactly why we wanted a clear, grounded conversation about what's coming next. Tim and Tally start with what's been grabbing our attention lately, from a looming governance event with Towson University to a reminder that AI systems can pick up odd patterns and biases (yes, "goblins" and "gremlins" included) through the data and feedback we give them. 
From there, we get practical about the future of work and enterprise AI adoption. We talk about why many organizations will feel "AI disillusionment" after big spending and scattered Copilot rollouts that never connect to a real use case. The fix is not more hype, it's better measurement and better change management: define metrics that matter, look beyond time savings, and build a culture where people can use AI without fear of being judged for "shortcutting" their work. Along the way we tie AI back to purpose and core competency, because roles and meaning don't disappear just because tasks get automated. 
Then we dig into MIT Tech Review's "AI things that matter right now," including agent orchestration (think an AI assembly line of specialized agents), artificial scientists, open source models and vendor transparency, world models that move beyond text into simulated environments, and humanoid data that raises real privacy, consent, and labor questions. We also name the cultural backlash and resistance we expect to keep growing, and why that pushback can be healthy if it leads to more responsible AI governance and ethics. If you're trying to understand AI trends in 2026 without the noise, hit subscribe, share this with a teammate, and leave a review. What's the one AI trend you're most excited or worried about right now?
Links:
2026 10 Things That Matter in AI Right Now | MIT Technology Review
Where the goblins came from | OpenAI
Mouse Heaven or Mouse Hell? (Calhoun's Universe 25) | Science History Institute
World Labs (Fei-Fei Li's spatial intelligence company)
Ghost Work by Mary L. Gray and Siddharth Suri
A Roomba recorded a woman on the toilet | MIT Technology Review
Measure What Matters by John Doerr (the OKR book)
Thanks for listening! Visit us at @mindovermachines.com to learn more about us!
Want to stay up to date on our latest AI insights? Subscribe to our newsletter here!

AI can generate a recap that sounds like a podcast. The real question is whether it captures what you actually meant.

We’re back for Season 2 of The Boring AI Show with a hands-on experiment: we feed Season 1 transcripts into NotebookLM and listen to the AI-generated audio summary. It’s impressive, fast, and genuinely useful, and it also shows a common failure mode of generative AI summarization: it can compress so hard that it drops context, blurs specific stories, and quietly changes the meaning. That becomes our jumping-off point for a practical lesson you can use immediately at work.

From there we break down the difference between prompt engineering and context engineering. Prompts are what you ask the model to do. Context is what the system knows and what it is allowed to use, including your files, constraints, and approved definitions. We share an easy framework (who, do, what, how), talk about why “What is AI?” is a terrible prompt, and explain how better constraints reduce generic outputs and hallucinations. We also touch on choosing tools based on the job, from ChatGPT 5.5 and newer agent features to Claude, Miro slide decks, and creative workflows that support better storytelling instead of cookie-cutter slides.

We close with what we’re watching for Season 2: more real use cases, real organizational change, the coming AI backlash, and how education and community efforts shape responsible adoption. Plus, our AI wins of the week include students emphasizing human-in-the-loop design and a “second brain” knowledge graph using Obsidian with Claude to speed up case studies and slide decks.

Subscribe, share the show with a friend, and leave a review if this helps you cut through the hype. What topic or use case do you want us to test next?
Thanks for listening! Visit us at @mindovermachines.com to learn more about us!
Want to stay up to date on our latest AI insights? Subscribe to our newsletter here!