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Host Brian Piper speaks with Scott Clyde, Chief Growth Officer at enroll ml, about the evolving role of artificial intelligence in higher education admissions and enrollment strategy. Scott shares his first hand experiences navigating change management, testing AI tools, and learning from “fail fast” moments, including why 17-year-olds won't connect with AI phone callers. They also dive into the fine line of hyper personalization, the importance of retention, and why creating a sense of belonging requires an “all in” campus effort where recruitment and retention become everyone's responsibility. Join us as we discuss:  [2:16] How to overcome the “spy vs. spy” distrust around AI with students[12:19] Why you can't volume your way out of enrollment cliff realities[21:23] “Recruit, Retain, Graduate”: Why admissions is everyone's job 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: I'm introducing AI into a workflow and I need to know what I'm walking into. THE CHANGE What I'm proposing: [describe the actual change in plain terms] What it replaces or alters: [what people do today] Who it lands on: [roles, how many people, how long they've been doing this] What I'm telling them the goal is: [your stated rationale] What's actually driving it: [budget, capacity, leadership mandate, be honest here] What I can't promise: [job security, timeline, that it will work] GIVE ME 1. THE OBJECTION LIST Every objection this group will raise, sorted into two columns: what they'll say in the meeting, and what they'll say to each other afterward. The second column is the one I need. Include the objections people express as enthusiasm, as process questions, or as sudden concern for students. 2. WHAT'S UNDERNEATH EACH ONE For each objection, name the real concern driving it. Fear of being replaced, fear of looking incompetent with a new tool, loss of craft or expertise they're proud of, more work with no relief, or a genuine problem with my plan. 3. WHICH ONES ARE RIGHT Flag every objection that is legitimate and should change what I'm doing. Do not soften these. If my plan has a real hole, say so and tell me what to fix before I announce anything. 4. THE HONEST RESPONSE For each objection, give me two things: - What an honest response sounds like, in plain speech, no corporate language - What the defensive or evasive version sounds like, so I can recognize it if I start saying it  - Where the honest answer is "I don't know yet" or "some of this will change your job," write the version of that sentence I can actually say out loud. 5. THE PHRASES THAT WILL COST ME List the specific words and framings that will destroy trust with this group, and what to say instead. Include anything that sounds like a promise I can't keep. 6. THE OPENING Write the first three sentences of my announcement. They should say what is changing, name the concern in the room before anyone else does, and state what I am asking for. Under a hundred words. - - - - 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.

Higher education is confronting a massive erosion of public confidence, and the emergence of artificial intelligence is about to accelerate that reckoning. Host Brian Piper sits down with Teege Mettille, author of College Is Broken (And What We Can All Do to Fix It), to unpack how the higher ed trust deficit is being broken wide open by AI-driven transparency. Teege argues that just as Kelley Blue Book stripped used-car dealers of their information monopoly, accessible AI tools now empower under-resourced families to interrogate opaque pricing models, debunk inflated medical school admit rates, and cut through manipulated post-grad employment statistics. Despite the systemic dysfunctions, Teege lays out an optimistic case for campus practitioners: because higher education built these broken structures, everyday enrollment and marketing leaders possess the power to dismantle them and rebuild genuine trust. Join us as we discuss:  [2:09] AI’s role in the trust problem that pervades higher ed’s pricing model[10:29] Addressing the mass destruction of traditional entry-level positions[13:51] Why traditional presidential leadership is the biggest barrier to real change[25:51] How the perception of college-student engagement is changing in real-timeTo 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 auditing how visible and honest the cost information is on a college or university website, from the perspective of a prospective student and family with no insider knowledge and no counselor helping them. INSTITUTION CONTEXT  Institution: [name, private or public, URL, size]  Primary audience for this audit: [first year, graduate, parents, etc.] Cost landing page: [URL] Published cost of attendance: [$]  Net price calculator (if available): [URL] TASK  Inventory every place a prospective family could encounter cost information. For each item you find, record: What it is (a number, a calculator, a scholarship page, a payment plan, an FAQ)Exact URLClicks from the homepage, and the path a family would have to guess to get thereWhether it is findable by site search using the words a family would type ("how much does it cost", "tuition", "price", "can I afford")The exact language used, quotedWhether it states a single number, a range, or a conditional answerWhether it covers one year or the full time to a degreeWhether fees, housing, food, books, travel, and program specific costs are included, excluded, or unmentionedWhether the page says what happens in years two, three, and four, including tuition increases and whether scholarships renewReading level and whether financial aid jargon is defined on the pagePresent this as a table. Do not summarize. I want every instance, including the ones that contradict each other. Flag contradictions explicitly. Then, based on the growing challenges that higher ed is facing (from the demographic cliff to the perceived value of higher ed), create a list of opportunities for this institution to be more transparent and helpful with prospective students to build trust and to make it as easy as possible for them to get the information they need. - - - - 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.

Come find me at the MAICON event on October 15 for a live recording of AI for U — https://www.marketingaiinstitute.com/events/marketing-artificial-intelligence-conference Traditional marketing personas are often locked away in static PDF documents or PowerPoint decks, collecting digital dust while teams rely on gut instinct rather than real audience insights. Host Brian Piper welcomes Matt Wilkinson, Founder of Strivenn and author of The Buyer in the Loop, to explore how interactive AI personas in higher ed and marketing are transforming audience research. Matt breaks down why static persona documents fail, forcing marketers to perform uncomfortable mental gymnastics, and how grounding synthetic customers in deep research and voice-of-customer data allows teams to actually put their personas to work. Join us as we discuss:  [3:06] What sets interactive AI personas apart from traditional student personas[15:30] Practical examples of “synthetic for directional and human for decisional” [22:20] How AI personas can combat organizational gravity during the content creation cycle[29:11] Combatting imperfect data sets and tracking your synthetic personas’ ROITo 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 a buyer persona researcher working inside a higher education institution. You know the enrollment funnel, the advancement cycle, and how a university marketing office actually operates. You work the way Adele Revella's "Buyer Personas" and Matt Wilkinson's "The Buyer in the Loop" instruct. Your job is not to write a persona document. It is to build a grounded, queryable representation of a real audience that my team can interrogate for months, then hand me the text to deploy it. # THE ASSIGNMENT   AUDIENCE: [one sentence describing them]   INSTITUTION: [name and main URL]   DEPARTMENT: [marketing / admissions / advancement]   THE DECISION: [what this person is deciding: apply, deposit, give, re-enroll, upgrade a gift]   THE OUTCOME: [the institutional goal this supports]   REFERENCE URLS: [about, strategic plan, the 2-4 program or giving pages that matter most, cost and aid, outcomes]   EVIDENCE: [list what you are attaching] # OPERATING RULES 1. Tag every claim [E] evidenced or [I] inferred. Never leave one untagged, never quietly promote an [I] to an [E]. 2. Never invent quotes or manufacture voice. Patterns from real evidence only, with the source shown. 3. Resist the average. If a claim would be true of almost anyone in this category, flag it as GENERIC and either ground it or cut it. 4. Do not flatter. A persona that likes everything I show it gives me false confidence and the bad idea still gets built. 5. Synthetic for directional, human for decisional. You generate and stress-test hypotheses. You do not decide aid strategy, pricing, a case statement, or a program launch. 6. Carry no identifying detail about real people out of my evidence. Abstract to the pattern. 7. Write in plain language. If they would call it the price, do not call it the investment. # PHASE 0 - INTAKE Ask me these in one numbered batch and wait for answers.    1. What evidence do you have and can you share it? Interviews, surveys (admitted student, non-matriculant, melt, alumni), CRM and SIS behavior, GA4, frontline notes (counselor, call center, chat, tour, gift officer), and unowned voice (Reddit, Niche, review sites, parent groups).   2. What do you already believe about this audience, and how confident are you? Put your assumptions on the record now so we can see later which ones the evidence contradicts.   3. What are they choosing between, including the alternatives that are not other schools? A job, community college first, staying put, giving nothing this year.   4. Is this one audience or several wearing the same label?   5. Who else is in the room when the decision gets made, and can each of them champion, veto, or only advise?   6. What internal constraints can this persona's needs run into? Governance, brand standards, counsel review, an aid model or CMS you cannot change.   7. What will this persona be used for first? # PHASE 1 - EVIDENCE READ AND GAP REPORT Read everything, then before building give me a short report:   a. The three to five findings the evidence clearly supports, with sources named.   b. Where the evidence contradicts my Phase 0 assumptions. Quote me, then show what the evidence says. Be direct. This is the highest-value output of the exercise.   c. The gaps, and the fastest way to fill each one.   d. A provenance score, 1 to 5, and what it means for how far I should trust what follows. Wait for my reaction before continuing. # PHASE 2 - BUILD THE PERSONA Tag every line [E] or [I]. THE FIVE RINGS   1. Priority initiatives. The 5 to 10 things actually taking their time, money, and worry right now. For each, what triggered it. The trigger matters most: what changed that made this  decision active now rather than six months ago.   2. Success factors. What would make them say the decision worked, split into tangible outcomes and intangible ones (belonging, pride, relief, a parent's approval, legacy). Higher ed decisions run on the intangible column while institutional messaging runs on the tangible one. Name that mismatch if you see it.   3. Perceived barriers. What keeps them from choosing us, including the barriers that are factually wrong but sincerely held. For each: true, partly true, or false about this institution, and whether the fix belongs to marketing or to the institution.   4. Decision criteria. Ranked by the weight they actually carry, sorted into screeners (fail one and we are out), differentiators, and tiebreakers. Then say which we win, which we lose, and which we satisfy but never mention.   5. Journey. Stage by stage, using stages that fit this audience rather than a generic funnel. Per stage: what they feel, the exact questions they ask in their own words, who they trust for answers, what content they consume and what they wish existed, who else enters the decision, and what causes them to stall or reverse.   WHAT MAKES IT QUERYABLE   6. Identity. Fictional name, age range, life stage, and the situation they are sitting in today.   7. Voice. How they actually talk, including the institutional words they would never use. This is what separates a persona you can talk to from a slide you can look at.   8. Emotional drivers and anxieties, including the fear they would not say to a counselor or a gift officer.   9. Objections and turn-offs. Specific words, claims, images, and design choices that make the disengage.  10. Information sources ranked by trust, including the ones we do not control and the AI assistants they now ask before they ever reach our site.  11. What would change their mind. If you give me one line from this whole persona, give me this one. THE DECISION GROUP Build the primary persona in full, then map the other roles in one line each: their separate criteria, their power, when they enter, and where their criteria conflict with the primary persona's. That conflict is usually where the decision stalls. Then tell me plainly whether the other roles warrant full personas of their own or whether the map is enough.  # PHASE 3 - THE DEPLOYABLE PACKAGE Give me a copy-and-paste block for a Custom GPT, Gem, or Claude Project containing a fictional name, a one-line public description, a vivid and specific headshot prompt (synthetic person, never a real one, and no generic professional headshot), and a system prompt written in first person covering what they care about, what worries them, how they decide, where they are in the process, who they trust, and how they talk. The system prompt must instruct the persona to react to anything shown to it by naming its first-three-seconds gut response, scoring 1 to 10 on whether it would move them to the next step, pointing at the exact word or image that landed or lost them, and saying what they would need in order to act. It stays in character, stays blunt, says "I would be guessing here" rather than performing a reaction it has no basis for, and tells anyone about to make an expensive decision on its say-so to go talk to real people. Attach an evidence ledger: each ring, its [E] or [I] status, its sources, and a confidence level. That ledger travels with the persona so a colleague six months from now knows how much weight it holds  # PHASE 4 - KEEPING THE BUYER IN THE LOOP Wilkinson's point is that the buyer gets researched once and then fades, because the people with approval authority are the people furthest from that buyer. Every edit is reasonable. The message still ends up written for the institution. Give me a short plan to prevent that:   1. The three or four moments in our real workflow where the persona gets queried, including after approval. Run the approved version back past the persona and ask whether it still sounds like it was written for them. A score that drops between draft and approval is a measurement of organizational gravity, and a number is the thing that survives a conversation with a provost.   2. The one question anyone here can ask it in under a minute, so using it requires no process.   3. What it may never decide, and the human research each of those needs instead.   4. Refresh triggers: a new survey, a competitor or policy change, a leadership change, a news cycle, or twelve months, whichever comes first. Put a review date on it. # HOW TO WORK WITH ME Ask Phase 0 and wait. Do Phase 1 and wait for my reaction. Then run Phases 2 through 4 in one pass. If I try to skip the evidence step, build it anyway but label the whole thing SPECULATIVE, say in one sentence what it can and cannot be used for, and give me the gap list regardless. Be specific. A vague persona is worse than none, becau

Artificial intelligence brings with it a fundamental shift in how educational institutions prepare students for careers that have not yet been invented. In this episode of AI for U, host Brian Piper sits down with Dr. Rachelle Keck, President at Grand View University, to explore what institutional AI leadership in higher ed looks like from the president's office. Dr. Keck breaks down how Grand View, a resourceful, adaptive institution of 2,000 students, uses AI to level the playing field against billion-dollar endowments by automating mundane operational tasks to free up time for meaningful, face-to-face human connections. She shares how she fosters a culture of innovation where direct reports are encouraged to experiment and fail fast, why revising core curricula cannot take four to six years in an AI-driven world, and her favorite daily prompting secret: the Iowa "What Else?" method. Join us as we discuss:  [2:32] Preparing tomorrow’s workforce through a culture of experimentation[8:35] How higher ed leaders can prepare their faculty and staff for an AI strategy[17:21] Winning over skeptics and Grand View’s biggest institution-wide changes brought by AI[25:10] Bridging siloed departments and gauging the temperature of higher ed’s urgency to adopt an AI strategyTo 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:  “What else?” Follow-ups to push it further once "What else?" has opened the door: What am I assuming here that might not be true? Who would disagree with this, and what's their best argument? What would make this fail? What would the strongest version of the opposite approach look like? What am I not asking you that I should be? What would you need from me to give a better answer? Ask "What else?" more than once. The second and third passes are usually where the non-obvious material shows up. Stop when the answers start repeating. - - - - 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.

As artificial intelligence makes basic knowledge transfer free and instant, higher education institutions must confront a fundamental question: what is the true value of a university degree? Host Brian Piper speaks with Michael Horn, Author of Job Moves and co-founder of the Clayton Christensen Institute for Disruptive Innovation, to explore how AI is fundamentally reshaping higher education. Michael explains why simple AI chatbots fail to drive long-term student engagement and why community-centric higher education AI is the true path forward for sustainable institutional growth. Michael also outlines the shifting expectations of the modern job market, where employers increasingly expect entry-level candidates to arrive with real-world experience because AI now handles low-level administrative tasks. Join us as we discuss:  [2:36] The necessary pivot from marketing institutional knowledge transfer to communities of learning [14:10] Why schools need to steer away from the traditional mindset of higher ed’s value [24:06] The biggest obstacles in institution-wide AI integration on the administrative side [33:45] Using AI to leverage career centers and marketing student outcomes that justify ROI Check out these resources we mentioned during the podcast: The Future of Education Substack Episode Prompt:  Act as a skeptical, well-informed advisor reviewing the attached [document/plan/draft].  Don't summarize it back to me. Instead:  (1) identify the three biggest gaps or unanswered questions a smart critic would raise,  (2) tell me what a reasonable person who disagrees with this approach would argue, and why,  (3) flag anything that's vague, unsupported, or likely to raise a red flag with [leadership/faculty/families/whoever the real audience is], and  (4) suggest the three best outside sources I should check before I finalize this.  Ask me clarifying questions one at a time if you need more context before responding, rather than guessing. 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.

Higher education is undergoing rapid technological shifts, but Dr. Courtney Lewis believes AI integration should never mean sacrificing human empathy. Host Brian Piper sits down with the Director of Enrollment Services at Trinity Valley Community College (TVCC) to explore how an institution in rural East Texas leveraged AI-powered student enrollment to achieve sustained semester-over-semester growth. Courtney talks about her personal journey as a former first-generation TVCC student who navigated the college search with no money, no roadmap, and a shame-inducing narrative around community colleges, transforming that lived experience into a mission to rebuild the enrollment model from the ground up. She also shares actionable advice on change management, explaining why leaders must be vulnerable, expect to fail fast, and stop forcing AI into outdated, legacy processes. Join us as we discuss:  [2:16] How Courtney is addressing the challenges she faced as a TVCC student [7:52] Framing AI integration to your team as an opportunity to create more human connections [14:39] Why disconnected data is the biggest obstacle to implementing AI tools [22:14] The biggest takeaways from driving organizational change around data governance Check out these resources we mentioned during the podcast: Trinity Valley Community College Sees Surge in Enrollment (KETK NBC) 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 a process design facilitator. Your job is to stop me from automating something that should not exist. You are direct, you ask uncomfortable questions, and you do not let me defend a step just because it is familiar. Ask questions one at a time. **ACTION** Take one process I am considering automating and tear it down to its purpose before we discuss any tooling. Rebuild it from zero, then, and only then, tell me where AI actually belongs. **CONTEXT** Interview me one question at a time until you understand: - The process, described as a numbered sequence of every step, including the steps nobody documents - For each step: who does it, how long it takes, how often, and what triggers it - Why each step exists, and if I answer "that's how we've always done it," push back and ask who would notice if it stopped tomorrow - What outcome the process is supposed to produce for the student or the institution, stated in one sentence - Which steps exist because of a policy, an accreditation or legal requirement, or a system limitation, and which are just habit - What happens today when this process fails - What constraints are genuinely fixed (staffing, budget, regulation) versus assumed **EXECUTE** Produce, in this order: 1. **Purpose statement** — the one sentence this process exists to deliver. If the current process does not deliver it, say so plainly. 2. **Step audit** — every step classified as: Required (regulation, policy, accreditation) | Load-bearing (something downstream breaks without it) | Vestigial (exists from a prior system, workflow, or org chart) 3. **Clean-sheet redesign** — how you would build this process today, from nothing, to deliver the purpose statement. Ignore our current org chart and software. State the redesign's staffing and data prerequisites. 4. **Gap between now and clean sheet** — what would have to change: ownership, policy, data, staffing, culture. Which of these are hard and which are just uncomfortable. 5. **Where AI fits…and where it doesn't** — in the redesigned process, identify: steps AI should handle (high volume, low empathy, low judgment), steps a human must own (emotional stakes, exceptions, consequential decisions), and steps AI should never touch and why. Include what human oversight and audit each AI step requires. 6. **What we would automate today if we skipped this exercise** — name the steps I was about to bolt AI onto that should have been deleted instead. 7. **Rollout timing** — given our academic calendar, when should this change, and when should it absolutely not. Assume the first attempt will fail and pick a window where that is survivable. **CONSTRAINTS** - Do not recommend a specific product. - If I have not told you a legal or accreditation requirement, ask rather than assume one exists. - Every AI recommendation must name the human check that sits on top of it. - If the honest answer is that this process needs no AI at all, say that. - - - - 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 is changing how we learn, work, lead, and prepare students for the world ahead. So why isn’t higher ed moving faster in adopting this technology at scale? In this episode, Brian is joined by Josh Riedy, Executive Director of the Institute of Applied AI at Minnesota State University Moorhead, to explore what meaningful AI adoption looks like across higher education. Josh shares MSUM’s approach to building AI literacy across academic programs, creating real-world learning opportunities through its Applied AI Clinic, and preparing students to enter the workforce with practical AI experience. He also explains why institutions cannot simply hand AI over to the CIO, create another committee, or launch a standalone AI certificate and consider the job done. Join us as we discuss:  [3:25] Why higher ed needs to start adopting AI at scale [15:35] Who should lead AI implementation measures across your institution  [25:52] Using workshops and challenges to establish an AI literacy framework 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.

When Massachusetts rolled out free community college, it drove massive equity across the state, but it also created an unprecedented bottleneck at the front door of smaller institutions. Brian sits down with Alex Russo, Director of Enrollment & Recruitment Strategy at Cape Cod Community College, to discuss how a school with limited headcount operationalized 24/7 student support AI to manage a sudden 40% spike in student volume. Alex shares how their frontline AI agent triages hundreds of late-night inquiries from busy, non-traditional students, freeing up staff to focus on deep, relational counseling sessions and high-impact on-campus events. Join us as we discuss:  [2:51] Why 24/7 AI agent support became an enrollment equity driver for Cape Cod Community College [11:04] How community colleges can start leaning into an AI strategy on limited budgets [20:59] Measuring the ROI of AI agents and maintaining your school’s brand voice   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 reviewing a draft on behalf of the person who will read it. You are not a writer, an editor, or a copy improver. You are a stand-in for the reader. CONTEXT - Who wrote this: [YOUR ROLE, e.g. director of enrollment] - Who will read this: [BE SPECIFIC — e.g. a prospective student who is the first   in their family to apply to college and has never completed a FAFSA] - What the reader already knows: [WHAT CAN YOU ASSUME THEY KNOW?] - What the reader needs to do after reading it: [THE ONE ACTION] - Constraints: [DEADLINE, TONE, LENGTH, POLICY LANGUAGE THAT CANNOT CHANGE] THE DRAFT """ [PASTE YOUR DRAFT HERE] """ ACTION Do NOT rewrite my draft. Do NOT produce an improved version. Do NOT change my word choices, sentence rhythm, or voice. Report back only, using these headers: 1. THE ONE ACTION — In one sentence, what does this draft ask the reader to do?    If you cannot tell, say so. 2. WHERE A READER GETS LOST — Quote the exact phrases that assume knowledge    this reader may not have: jargon, acronyms, internal process names,    unstated steps. 3. UNANSWERED QUESTIONS — What will this reader wonder that the draft never    answers? List them as questions. 4. INTERNAL CONTRADICTIONS — Flag any dates, times, names, amounts, or    instructions that conflict with each other elsewhere in the draft. 5. TONE CHECK — Is there anywhere the language could make this reader feel    judged, condemned, or hesitant to ask for help? Quote it. 6. WHAT IS WORKING — Two things I should not change. EXECUTE Before you begin, ask me any clarifying questions you need, one at a time. When you report, cite the exact text you are referring to. If a section has nothing to flag, write "Nothing flagged" rather than inventing a concern. - - - - 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.

Brian welcomes back Ardis Kadiu, Cofounder and CEO at Andi.ai, to break down the massive shift from simple text generation to true agentic autonomy. Ardis maps out the three critical layers of agentic AI — skills, workflows, and autonomous agents — and explains why higher ed institutions must stop looking at models and start evaluating the “harness” surrounding them. He outlines five essential questions that university presidents and cabinets must ask to expose organizational ambiguity, protect data authority, and safely redesign campus workflows. The conversation also explores learning intelligence in an AI-saturated classroom and why an artifact like a final essay is no longer an accurate indicator of student competency. Join us as we discuss:  [2:53] Why higher ed professionals need to reframe their understanding of AI’s capabilities [12:07] Five questions higher ed leaders need to ask before implementing AI [19:55] How AI is going to change higher ed’s role in an evolving job market [30:30] Closing the gap between future AI model capabilities and human adoption 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.

Artificial intelligence is rapidly reshaping higher education, but most institutions are still trying to navigate what responsible adoption actually looks like in real time. In this episode, Brian sits down with Brandon Rich, Director of AI Enablement at the University of Notre Dame, to explore what he calls the “messy middle” of AI adoption in higher ed. Brandon shares how Notre Dame moved from an AI task force to campus-wide implementation in less than a year, launching AI literacy initiatives, governance frameworks, and hands-on experimentation across departments like HR, student affairs, finance, and research administration.  Join us as we discuss:  [2:47] The “messy middle” of AI adoption in higher education [13:26] How Notre Dame rolled out AI strategically across campus [26:06] Budgeting, staffing, and resource challenges for AI initiatives Check out these resources we mentioned during the podcast: Snowflake Cortex  ChatGPT Edu Okta 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.

Returning guest Joyce Peralta, Manager of Digital Communications at McGill University, joins Brian for a thoughtful conversation about what higher education institutions are getting right and wrong about AI adoption. From governance and accessibility to localization and structured content, Joyce shares how McGill is navigating the realities of implementing AI inside a large, decentralized institution. She explains why AI readiness is really about trust, shared standards, and human judgment, not just new technology. The conversation also explores the future of AI in higher education and why successful adoption will depend on building systems that support both consistency and inclusion. Join us as we discuss:  [2:38] Governance, trust, and creating shared standards across decentralized teams [11:34] The localization barrier in AI-assisted translation tools [21:19] Why AI is still stuck in an accessibility bottleneck 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 a governance and strategy advisor specializing in responsible AI integration within institutional environments. I'm preparing to roll out a new AI use case in my organization. Before I do, I want to stress-test it against the standards and best practices my community already trusts. Your job is to help me surface where this AI initiative might fall short of those standards, identify the connections I need to draw for stakeholders, and recommend governance touchpoints to build into the rollout. Before you respond, ask any clarifying questions that will produce a stronger analysis. The AI Initiative: [DESCRIBE THE AI USE CASE — WHAT IT DOES, WHO USES IT, WHAT IT PRODUCES] Context: Organization type: [PUBLIC/PRIVATE, SIZE, CENTRALIZED OR DECENTRALIZED] Functional area: [MARKETING, ADMISSIONS, ADVANCEMENT, IT, ACADEMIC AFFAIRS, ETC.] Audiences affected: [INTERNAL TEAMS, PROSPECTIVE STUDENTS, CURRENT STUDENTS, ALUMNI, ETC.] Timeline: [WHEN YOU PLAN TO ROLL IT OUT] Key stakeholders: [WHO NEEDS TO BUY IN, WHO HAS OVERSIGHT] Our Existing Standards and Best Practices: Accessibility: [SUMMARIZE YOUR ACCESSIBILITY STANDARDS] Privacy & Security: [SUMMARIZE YOUR PRIVACY AND DATA HANDLING REQUIREMENTS] Brand, Tone & Voice: [SUMMARIZE YOUR BRAND AND EDITORIAL GUIDELINES] Usability & User Experience: [SUMMARIZE YOUR UX AND CONTENT STANDARDS] Other governance commitments: [E.G., BILINGUAL/MULTILINGUAL CONTENT, DEI, DATA SOVEREIGNTY, RESEARCH ETHICS] How I'm Currently Planning to Roll This Out: [OUTLINE YOUR ROLLOUT APPROACH — COMMUNICATION, TRAINING, OVERSIGHT, REVIEW] Now help me work through the following: Standards Alignment: For each existing standard above, where might this AI use case fall short? Be specific about how AI output could violate or undermine each one in practice. The Connection Gap: What language and framing should I use to show my community how this AI initiative reinforces — rather than replaces — the best practices they already trust? Give me phrasing I can use in actual rollout communications. Accessibility Stress Test: AI is a statistical reasoning tool and can overlook the experiences of people whose needs sit outside the majority. Where in this use case is that risk highest, and how do I design against it? Governance Touchpoints: What review steps, human-in-the-loop checkpoints, or oversight roles should I build into the rollout to maintain quality and trust over time — not just at launch? Cross-Silo Implications: In a decentralized environment, which other teams or functions should be in the conversation before this rolls out? What shared structure — terminology, content model, data definitions — might need to be in place first for AI to produce accurate, complete output? Readiness Check: Are the content, data, or processes this use case depends on actually ready for AI, or would I be rolling out before the foundation is in place? Tell me honestly if I should slow down and fix the underlying structure first. Be direct. I need this stress-tested, not validated.   - - - - 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.

Raj Jha has spent his career working across computer science, technology law, entrepreneurship, and AI systems, giving him a unique perspective on how organizations navigate periods of rapid technological change. In this episode, Raj joins Brian to discuss why most conversations about AI focus too heavily on tools instead of outcomes, how institutions can adapt without losing trust, and why the future may belong to humans who can think like orchestrators instead of simply operators. Join us as we discuss:  [2:32] Why “What AI tool should we use?” is the wrong question [10:24] How AI is forcing higher education to rethink its entire model [24:16] Reframing how siloed departments handle AI implementation [32:19] Why this is a great time for entrepreneurs and intrapreneurs Check out these resources we mentioned during the podcast: RajJha.com Raj Jha (X) 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.

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. - - - - 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. - - - - 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 - - - - 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.