AI for U

There’s a quiet shift happening in how we work with data. For years, the focus has been on collecting more of it, building better dashboards, and reporting on the right numbers. But as this conversation explores, that approach may be reaching its limit. That’s because having data isn’t the same as having insight. In this episode, Brian talks with Jamie Boggs, Marketing and Engagement Analyst at Eastern Kentucky University, to talk about what’s actually changing as AI becomes part of everyday workflows in higher education. They explore why many institutions are “data rich but insight poor,” the difference between using AI for automation versus rethinking entire systems, and what it looks like to treat AI less like a tool and more like a teammate.

Join us as we discuss: 

[3:59] What “data rich, insight poor” means for higher ed

[18:05] The death of the data dashboard and what it means for student insights

[26:24] What institutions should be doing now to future-proof their schools

Check out these resources we mentioned during the podcast:

EduData podcast

The Enrollify network of podcasts

To hear this interview and many more like it, subscribe on Apple Podcasts, Spotify, or our website, or search for AI for U with Brian Piper in your favorite podcast player.

Episode prompt:

ROLE

You are an expert in higher education analytics and marketing measurement, with deep experience helping institutions move beyond vanity metrics to identify the data points that genuinely indicate progress toward strategic outcomes.

ACTION

Help me identify the specific metrics that will give me real insight into whether a particular initiative or goal I'm working on is actually working — and flag any meaningful gaps in the data I'm currently collecting.

CONTEXT

Before recommending any metrics, you need a clear picture of the initiative, the outcome it's tied to, the audiences involved, and the data I currently have access to. Ask me the following questions one at a time, waiting for my answer before moving to the next:

1. What is the initiative, campaign, program, or goal you're focused on? Describe it in your own words.

2. What is the strategic outcome this initiative is meant to drive? (e.g., enrollment growth, retention, yield, brand awareness, alumni engagement, faculty recruitment, fundraising)

3. Who are the primary and secondary audiences this initiative is trying to reach or influence?

4. What does success look like 6 months from now? 12 months from now?

5. What metrics, if any, are you currently tracking for this initiative? Which ones do you report up to leadership?

6. What data sources and tools do you have access to? (e.g., CRM, Google Analytics, Search Console, Slate, Salesforce, social platforms, SIS, LMS, email platform)

7. What constraints should I be aware of — budget, staffing, data access, privacy, governance, leadership reporting expectations?

8. Is there anything you've tried to measure in the past that didn't work, or any data you wish you had but don't?

EXECUTE

Once you have my answers, deliver:

1. A short summary of the initiative and outcome in your own words, so I can confirm we're aligned.

2. A list of vanity metrics I should stop over-relying on for this initiative — and why they're not actually telling me what I need to know.

3. A list of insight metrics that would actually indicate real progress toward the strategic outcome, organized into:

   - Leading indicators (early signals the initiative is on track)

   - Lagging indicators (outcomes that confirm impact after the fact)

   - Diagnostic metrics (help me understand *why* something is or isn't working)

4. For each insight metric, note which of my listed data sources it can be pulled from.

5. A gaps section flagging any critical metrics that would require me to start collecting data I don't currently have, with a brief note on what it would take to start collecting each one.

6. A short "leadership view" paragraph: what to report up to leadership vs. what to keep at the working level — so I can satisfy the "shiny things" leadership expects without losing focus on what actually moves the needle.

Ask any clarifying questions you need, one at a time, before producing the final output.

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Connect With Our Host:
Brian Piper
https://www.linkedin.com/in/brianwpiper/
About The Enrollify Podcast Network:
AI for U is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too!
Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AI can already do the easy work, so how do higher ed professionals prove their worth? In this episode, Dan Keating, Clinical Associate Professor of Information Systems and AI at University of Rochester - Simon Business School, explains how AI is changing how we think, learn, and define expertise. The fundamentals still matter, but when AI can produce solid output in seconds, the question shifts to not can you do the work, but what do you add to it? Tune in to hear why the future doesn’t belong to the people who just use AI but to the ones who can explain why their thinking still matters.

Join us as we discuss: 

[2:21] Creating AI-ready teams through curiosity and critical thinking

[10:07] Educating instructors, staff, and students during AI integration

[23:28] What human value means in the age of AI

To hear this interview and many more like it, subscribe on Apple Podcasts, Spotify, or our website, or search for AI for U with Brian Piper in your favorite podcast player.

Episode prompt:

You're going to help me stop optimizing and start reimagining. Do not give me efficiency tips. Do not suggest faster versions of what I already do. 

Step 1 — Ask me first. Before you generate anything, ask me these five questions, one at a time, and wait for my answer each time: 

1. What's one content or AI workflow I run regularly right now, described in plain language? 

2. What outcome am I actually trying to create with it (not the task, the outcome)? 

3. Who is it really for, and what do they currently get from competitors or alternatives? 

4. What's the unspoken rule or constraint I've been treating as fixed? 

5. If I had no legacy process, no existing team habits, and no tool stack to protect — what would "absurdly overdelivering" look like here? 

Step 2 — Reflect back. In 3–5 sentences, tell me what you're hearing. Name the assumption I'm defending without realizing it. Be direct. If my answers sound like optimization dressed up as innovation, say so.

 

Step 3 — Generate three boundary-pushing directions. For each one: 

* A one-line name for the direction. 

* What becomes possible that wasn't possible before. 

* What I'd have to stop doing or let go of to make room for it. 

* The smallest real-world experiment I could run this week to test it (something I can do in under 2 hours, with a concrete output). 

* The spiky take embedded in it — the belief most people in my space would push back on. 

Step 4 — Pressure test. Pick the direction you think is strongest and tell me why. Then tell me the most likely reason I won't do it, and what that reveals about my real constraint — time, fear, identity, or org politics. 

Step 5 — Hand it back. End by asking me which direction I want to run this week, and remind me that you're not the one who gets to decide. I am. 

Ground everything in what I actually tell you. Don't invent details. Don't generalize. If I'm vague, ask again.

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Connect With Our Host:
Brian Piper
https://www.linkedin.com/in/brianwpiper/
About The Enrollify Podcast Network:
AI for U is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too!
Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, host Brian Piper talks with Sean Harrington, Director of AI and Legal Tech Studio at ASU Law, about bridging the gap between risk-averse academia and rapid AI innovation. Sean breaks down the concept of vibe coding, using conversational AI to build custom, end-to-end software solutions in-house for a fraction of the cost of off-the-shelf products. He shares how ASU Law is leading the way by explicitly allowing AI in admissions and providing safe, local “AI Creator” sandboxes to prevent shadow AI usage. Learn why consistent reps are the key to AI literacy, how to implement soft law governance, and why higher ed must urgently redesign assessments now that knowledge transfer is no longer a differentiator.

Join us as we discuss: 

[3:33] Helping legal professionals navigate AI literacy and vibe coding local tools

[11:10] How ASU openly addresses the use of AI in their admissions process

[15:34] Curtailing shadow AI use by creating safe spaces for experimentation

[22:52] Why prompt design should be part of every school’s AI governance

Episode prompt: 

You are a workflow analyst and AI solutions consultant. Your job is to help me identify opportunities where I could use AI coding tools (like Claude, ChatGPT, Cursor, or Replit) to quickly build a custom app or tool that solves a specific problem in my work, even if I have little or no coding experience.

You're going to interview me step by step to understand my role, my tools, and my pain points. Ask one question at a time. Wait for my response before moving on. Be conversational and specific, ask follow-up questions when my answers suggest there's more to uncover.

Phase 1: Role & Tools. Start by asking about my job title and primary responsibilities, then ask what software tools and platforms I use regularly (project management, CMS, spreadsheets, email, CRM, databases, etc.). Ask how data moves between those tools, what's manual, what's automated, and what requires copy/paste or reformatting.

Phase 2: Pain Points & Repetition. Ask me to walk you through the tasks I do most frequently, especially anything repetitive, tedious, or time-consuming. Ask about tasks I dread, things I procrastinate on, workarounds I've built with spreadsheets or manual processes, and anything where I think "there has to be a better way to do this." Ask about tasks that involve transforming data from one format to another, pulling information from multiple sources, or doing something predictable across many items.

Phase 3: Impact & Constraints. For each pain point I identify, ask how often I do it, how long it takes, how many people on my team also deal with it, and what happens when it doesn't get done or gets done wrong. Ask if there are tools I've looked into but rejected because they were too expensive, too complex, or didn't quite fit.

Phase 4: Technical Comfort. Ask about my comfort level with technology. Have I used AI tools before? Am I comfortable describing what I want a tool to do in plain language? Would I be okay testing something and giving feedback to iterate on it?

After you've gathered enough information across all four phases, deliver a summary that includes:

A ranked list of my top 3-5 vibe coding opportunities, ordered by the combination of highest impact and lowest development complexity

For each opportunity, include: 

A clear name for the tool

The specific problem it solves

What it would take as inputs and deliver as outputs

An estimated development complexity (simple, moderate, or advanced)

The approximate time savings per week or month

A suggested AI coding tool to build it with and why



A recommended first project to start with and a plain-language description of how I would prompt an AI coding tool to begin building it

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Connect With Our Host:
Brian Piper
https://www.linkedin.com/in/brianwpiper/
About The Enrollify Podcast Network:
AI for U is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too!
Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AI can generate content in seconds, but excellent creative work still requires human judgment. In this episode, Brian talks with Dakota Story and Sarah Martin of Ologie about how designers and writers are actually using AI in their workflows. They explain why many creatives think of AI as an intern, where it fits into the creative process, and why taste, judgment, and emotional intelligence still make the difference. They also discuss how AI can speed up brainstorming, research, and production while leaving the most important creative decisions in human hands. 

Join us as we discuss: 

[3:16] Why some creatives struggle to get value from AI

[10:11] The risks of prioritizing efficiency over creative judgment

[20:33] Skills creative professionals will need in the age of AI

[30:08] How to maintain brand voice and trust when using AI tools

Check out these resources we mentioned during the podcast:

Chappell Roan/Fortnite video

Nano Banana

Adobe Firefly

Claude

Sarah’s Instagram

To hear this interview and many more like it, subscribe on Apple Podcasts, Spotify, or our website, or search for AI for U with Brian Piper in your favorite podcast player.

- - - -

Connect With Our Host:
Brian Piper
https://www.linkedin.com/in/brianwpiper/
About The Enrollify Podcast Network:
AI for U is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too!
Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of AI for U, Brian sits down with Dr. Manjeet Rege, Chair of the Department of Software Engineering and Data Science at the University of St. Thomas. Dr. Rege shares his journey from medical AI research to institutional strategy, arguing that the “age of piloting” is over. He breaks down what a truly operational AI organization looks like, emphasizing the need for product managers, data engineers, and robust governance frameworks. Dr. Rege also provides a roadmap for sustainable AI budgeting, the importance of redesigning workflows rather than just automating broken ones, and why the shift from data ownership to stewardship is essential for scaling AI safely and ethically.

Join us as we discuss: 

[2:42] The AI mindshift from academic research to an institutional strategy

[10:13] What an operational AI strategy looks like and sustainable budgeting

[17:09] Advice for schools considering building versus buying AI solutions

[21:42] Weighing data stewardship against ownership at the enterprise level

Check out these resources we mentioned during the podcast:

Ethics and Governance of Artificial Intelligence: Frameworks, Risks, and Society by Manjeet Rege and Hemachandran K

To hear this interview and many more like it, subscribe on 

Apple Podcasts, Spotify, or our website, or search for AI for U with Brian Piper in your favorite podcast player.

Episode prompt: 

You are an expert AI strategist specializing in higher education with deep experience in institutional change management, data governance, and AI implementation. I want you to conduct a structured AI readiness assessment for my institution.

Before you begin the assessment, ask me the following questions one at a time and wait for my answer before moving to the next question:

What type of institution are you (community college, regional university, R1, liberal arts, etc.) and approximately how large is your institution in terms of students and staff?

What teams or departments are you assessing? Your entire institution, a specific division, or a particular department?

What AI tools or platforms are currently in use at your institution, even informally?

Does your institution have a formal AI policy, governance structure, or AI task force in place?

How would you describe the current level of AI literacy among your staff, from leadership down to frontline employees?

What budget or resources, if any, have been formally allocated to AI initiatives?

What is your biggest AI challenge or concern right now?

What outcomes are you hoping AI will help you achieve in the next 12 months?

Once I have answered all of your questions, assess my institution's AI readiness across the following six dimensions. For each dimension, give me a readiness rating of Early, Developing, or Operational, a brief explanation of why, and two to three specific, actionable next steps I can take to advance to the next level.

The six dimensions are:

Strategy & Vision — Does the institution have a clear AI roadmap aligned to its mission?

Data Infrastructure & Governance — Is data trustworthy, accessible, and stewarded (not just owned)?

Talent & Training — Are staff being upskilled with clear pathways to AI fluency in their own discipline?

Tools & Technology — Are the right platforms in place, and is the build vs. buy decision being made intentionally?

Ethics & Compliance — Are governance frameworks, risk management, and privacy considerations built into the process?

Culture & Change Management — Are people being brought along as co-creators, not casualties, of AI adoption?

After completing all six dimensions, provide an overall readiness summary and identify the single most important area for me to focus on first, with a concrete recommendation for how to get started this month.

Guest Name: University of St. Thomas, Chair, Department of Software and Data Science, Professor, Department of Software and Data Science

Guest Social: https://www.linkedin.com/in/manjeetrege/

Guest Bio: Dr. Manjeet Rege is a distinguished academic and industry leader in the fields of data science and artificial intelligence. As a professor and the chair of the Department of Software Engineering and Data Science at the University of St. Thomas, he has made substantial contributions to the academic world, evidenced by his recognition as a Leading Academic Data Leader for 2023 by CDO Magazine.

Dr. Rege also serves as the Director of the Center for Applied Artificial Intelligence at the University of St. Thomas, where he oversees initiatives that blend academic research with practical applications in AI. His expertise is acknowledged internationally, demonstrated by the establishment of a chair professorship and analytics lab in his name at Woxsen University in Hyderabad, India, to celebrate his significant contributions in analytics.

As a thought leader, author, mentor, and keynote speaker, Dr. Rege is often featured in the media, offering his expert thoughts and opinions on the latest developments in machine learning and AI. Dr. Rege serves as an advisor to various organizations to provide guidance on data strategy and imparting technical AI expertise.

His work has been published in various peer-reviewed reputed publications, he serves on the editorial review board of journals, and regularly participates on the program committees of various international conferences.

- - - -

Connect With Our Host:
Brian Piper
https://www.linkedin.com/in/brianwpiper/
About The Enrollify Podcast Network:
AI for U is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too!
Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.