What are the key takeaways from this Practical AI episode?

Key insights include: **Pre-build Ethics Charter:** Establish explicit AI boundaries before writing a single line of code. International Mission Board spent two days with eight people drafting a charter defining what AI will and will not do. This framework allowed Don Barger to immediately decline a request to build an AI counseling chatbot without deliberation, because the decision was already made.; **Delegation Risk Matrix:** Evaluate every AI task across two dimensions — reward (better outcome or greater efficiency) and risk of error with its consequences. Tasks with high reward and low error risk are clear candidates for automation. High-risk tasks require tight deterministic guardrails. This framework mirrors how managers delegate to human employees and controls the AI's operational "leash."; **Data Ownership Before AI Deployment:** Noblis catalogued over 1,000 datasets before deploying agentic AI, ultimately identifying 115–120 critical datasets with 30 designated data stewards. Without a named human owner who understands data quality, timeliness, and access permissions, agentic AI systems cannot be safely or reliably deployed in compliance-sensitive federal environments.

What did Don Barger discuss on Practical AI?

Three practitioners from the Midwest AI Summit share real-world AI deployment experiences: Don Barger from International Mission Board on ethical AI boundaries, Mark Lay from Noblis on CIO-level AI governance decisions, and Darren Taillie from AeroCore Technologies on deploying text-to-SQL and vision verification agents in an analog industrial operation. Key topics include: **Pre-build Ethics Charter:** Establish explicit AI boundaries before writing a single line of code. International Mission Board spent two days with eight people drafting a charter defining what AI will and will not do. This framework allowed Don Barger to immediately decline a request to build an AI counseling chatbot without deliberation, because the decision was already made.; **Delegation Risk Matrix:** Evaluate every AI task across two dimensions — reward (better outcome or greater efficiency) and risk of error with its consequences. Tasks with high reward and low error risk are clear candidates for automation. High-risk tasks require tight deterministic guardrails. This framework mirrors how managers delegate to human employees and controls the AI's operational "leash.".

How long is this episode of Practical AI?

This episode is 47 minutes long. SignalCast provides an AI-generated summary so you can get the key insights in about 3 minutes.

Practical AI

Abundant Intelligence at the Midwest AI Summit Part 2

November 5, 2026

47 min episode · 2 min read

Don Barger

Episode

47 min

Read time

2 min

Topics

Productivity, Leadership, Design & UX

AI-Generated Summary

Published Nov 5, 2026

Key Takeaways

✓Pre-build Ethics Charter: Establish explicit AI boundaries before writing a single line of code. International Mission Board spent two days with eight people drafting a charter defining what AI will and will not do. This framework allowed Don Barger to immediately decline a request to build an AI counseling chatbot without deliberation, because the decision was already made.
✓Delegation Risk Matrix: Evaluate every AI task across two dimensions — reward (better outcome or greater efficiency) and risk of error with its consequences. Tasks with high reward and low error risk are clear candidates for automation. High-risk tasks require tight deterministic guardrails. This framework mirrors how managers delegate to human employees and controls the AI's operational "leash."
✓Data Ownership Before AI Deployment: Noblis catalogued over 1,000 datasets before deploying agentic AI, ultimately identifying 115–120 critical datasets with 30 designated data stewards. Without a named human owner who understands data quality, timeliness, and access permissions, agentic AI systems cannot be safely or reliably deployed in compliance-sensitive federal environments.
✓Text-to-SQL Agent for Self-Service Analytics: AeroCore deployed a natural language database agent inside Microsoft Teams, enabling leadership to query operational data directly without analyst involvement. Tasks previously requiring 30 minutes to one week now return results in seconds. This eliminated the analyst bottleneck where a two-person data team spent 40 hours weekly answering routine data requests instead of strategic work.
✓Vision Model for Work Order Verification: AeroCore runs a daily automated agent at 8:15 AM that uses a vision model to cross-check technician-submitted aircraft photos against work order entries. The agent flags tail number mismatches and abnormal procedure notes. The result: a previously recurring multi-department end-of-month reconciliation meeting was cancelled entirely due to zero incorrect work orders.

What It Covers

Three practitioners from the Midwest AI Summit share real-world AI deployment experiences: Don Barger from International Mission Board on ethical AI boundaries, Mark Lay from Noblis on CIO-level AI governance decisions, and Darren Taillie from AeroCore Technologies on deploying text-to-SQL and vision verification agents in an analog industrial operation.

Key Questions Answered

•Pre-build Ethics Charter: Establish explicit AI boundaries before writing a single line of code. International Mission Board spent two days with eight people drafting a charter defining what AI will and will not do. This framework allowed Don Barger to immediately decline a request to build an AI counseling chatbot without deliberation, because the decision was already made.
•Delegation Risk Matrix: Evaluate every AI task across two dimensions — reward (better outcome or greater efficiency) and risk of error with its consequences. Tasks with high reward and low error risk are clear candidates for automation. High-risk tasks require tight deterministic guardrails. This framework mirrors how managers delegate to human employees and controls the AI's operational "leash."
•Data Ownership Before AI Deployment: Noblis catalogued over 1,000 datasets before deploying agentic AI, ultimately identifying 115–120 critical datasets with 30 designated data stewards. Without a named human owner who understands data quality, timeliness, and access permissions, agentic AI systems cannot be safely or reliably deployed in compliance-sensitive federal environments.
•Text-to-SQL Agent for Self-Service Analytics: AeroCore deployed a natural language database agent inside Microsoft Teams, enabling leadership to query operational data directly without analyst involvement. Tasks previously requiring 30 minutes to one week now return results in seconds. This eliminated the analyst bottleneck where a two-person data team spent 40 hours weekly answering routine data requests instead of strategic work.
•Vision Model for Work Order Verification: AeroCore runs a daily automated agent at 8:15 AM that uses a vision model to cross-check technician-submitted aircraft photos against work order entries. The agent flags tail number mismatches and abnormal procedure notes. The result: a previously recurring multi-department end-of-month reconciliation meeting was cancelled entirely due to zero incorrect work orders.

Notable Moment

When asked to build an AI counseling chatbot for people with no access to human counselors, Barger declined by citing a pre-existing ethics charter. His counterargument was direct: when the wrong tool is deployed, something can produce worse outcomes than providing nothing at all.

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Episode Transcript

Narrator: Welcome to the Practical AI Podcast, where we break down the real world applications of artificial intelligence and how it's shaping the way we live, work, and create. Our goal is to help make AI technology practical, productive, and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place. Be sure to connect with us on LinkedIn, X, or Blue Sky to stay up to date with episode drops, behind the scenes content, and AI insights. You can learn more at practicalai.fm. Now onto the show. Chris: Welcome to another episode of the Practical AI Podcast. I'm Chris Benson, your co host. And this week, we're going to bring you three more of our speakers from the Midwest AI Summit. They are three speakers who are dealing with AI issues on an everyday basis that many of us are coping with. Those three speakers are Don Barger from the International Mission Board, Mark Lay from Nobelis, and Darren Taillie from Evercore Technologies. Next week, I will be back in the usual format with a guest speaker, but in the meantime, I hope you enjoy these three. Thank you very much. Dan: I want to introduce our first speaker, Don Barger. I've I've known, Donnie for a a a long time, and he's taken kind of the whole journey of Prediction Guard with us. And I've... I'm so impressed with how he has stewarded the International Mission Board forward in terms of their AI journey and adoption. He's the director of innovation and artificial intelligence at the International Mission Board. He spent a lot of years in Latin America and different parts of the world, with really deploying strategies rooted in orality and storytelling. He's gonna be talking about building AI trust, what we said yes to and what we didn't. So welcome Don Barger from the I and B. Don: Appreciate it. Yeah, so I've known Dan for a while. The first time I met Dan was at a much smaller meeting than this that he was actually leading, Again, another meeting he was leading about AI. And at that meeting, I was the dumbest person at that meeting. It was hands down. I walked in, I'm like, I don't think I knew what I was coming to. And I could say that if it hadn't been for Dan at that meeting and then some other meetings that we met at, and we sketched out some stuff in a hotel in London in the lobby, and next thing you know, we're really doing a lot of crazy stuff. So every organization that's building AI eventually runs into questions about, Should we be doing this? So that's what I want to share with you today, the journey that I've been on, who I am, the oddness that I am the one that's speaking to here. I'm not your typical AI presenter. Normally, you would have somebody that …

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