Ep. 97 - Russell Long: Stop Blaming AI. Your Data is the Problem

Summary

The Signal, Ep. 97 – Russell Long: Stop Blaming AI. Your Data is the Problem

Host: Jeff Dillon
Guest: Russell Long, Co-Founder & CEO, Authentica Solutions
Date: July 24, 2026

---

Episode Overview

This episode dives deep into the intersection of artificial intelligence (AI), data readiness, and institutional transformation in higher education. Jeff Dillon discusses with Russell Long how successful AI adoption isn't about chasing the latest model, but about the foundational work of fixing, connecting, and truly understanding your institutional data. Together, they explore the practical realities of interoperability, debunk common myths, and elevate the urgent need for human-centered innovation to genuinely enhance outcomes for students and educators alike.

---

Key Topics & Insights

1. Human-Centered Technology Design in Education

• From the Classroom to EdTech:
  Russell’s roots as a teacher and curriculum technology coach still ground his approach.

  “My number one thing is just ensuring that everything we're building and doing is complimenting the teacher, saving them time, giving time back to them, and the administrators to spend more time on students.” — Russell Long [02:27]

• Empathy-Driven Product Mindset:
  Authentic product development starts with understanding the day-to-day realities of educators.

---

2. AI Adoption: Fear, Excitement, and Six Stages in Education

• AI’s Real Impact in Classrooms:
  Excitement for AI is real, especially where it accelerates administrative work, but true pedagogical change is still nascent.
• Sector Divide:
  In K12, a “love-fear” balance exists; higher ed shows both resistance (“don’t ever use it”) and aggressive adoption (“we are all in”).

  “This is the first technology, though, that we see that has the potential to actually make your life better, richer, and allow you to focus on the things that you really want. That fear and love is no doubt.” — Russell Long [03:58]

• Regulatory Backlash:
  Some K12 states retrench, removing tech, only to see enduring faculty needs for tech-enabled grading.
• Winners Now:

  “Our students should know the ideators now win the space. Not the practitioners, not those with 20 years of developer experience anymore. Those who can think and execute are the winners.” — Russell Long [05:52]

---

3. The Real AI Bottleneck: Data Readiness, Not Models

• Institutional Blind Spots:
  Leaders often assume selecting an AI model is the crucial step—neglecting whether their data and systems are prepared.

  “Almost every conversation I'm having with CIOs and digital leaders, the real challenge isn't choosing the model, it's figuring out whether the institution is actually prepared to support one.” — Jeff Dillon [09:32]

• Three Schools of Thought on Data Readiness:
  1. Waiting for AI to solve core issues: Institutions hope AI will magically provide interoperability.
  2. Focus on Data Unification First: Building accurate analytics and trust BEFORE introducing AI.
  3. Do Both, but in Concert: Simultaneously unify systems and carefully integrate AI with trusted “gold layer” data.

  “Data unification is still key… Now I can explore openly with AI, trusting that the data is right. I just have to solve whether or not the AI is right or not. It's assumptions.” — Russell Long [11:21]

---

4. What Interoperability Really Looks Like on Campus

• Beyond Silos:
  Unifying data from legacy systems (registrar, finance, enrollment, etc.) uncovers multi-year views of student success and institutional effectiveness.

  “That registrar is most excited because she can see in a single pane of glass… the process of entry and enrollment… But she also now can easily see how many years has that student been in and what is their success levels.” — Russell Long [13:04]

• The Hidden Truths:
  Data audits frequently reveal “solved” problems that were actually camouflaged by incomplete or old data.

---

5. Case Study: Revealing State-Level Data Gaps

[16:17–19:19]

• Georgia’s Nursing Crisis:
  The state believed it was filling all available nursing training slots but actually left 2,000 spots unfilled each year due to uneven distribution and misunderstood dropout trends.

  “They were short every year by 2,000 spots. They weren't maxing out universities that had spots remaining...” — Russell Long [17:10]

• Misleading Metrics:
  Dropout numbers were double what administrators believed (72% vs. 36%).
• Lesson: Policy driven by survey data alone can miss systemic bottlenecks revealed only by connecting sources.

---

6. Scaling EdTech: Lessons from Microsoft

[19:56–22:04]

• Going Global:
  Russell’s platform was acquired by Microsoft, again shifting his view from local to global education systems overnight.
• The Role of ‘Red Tape’:

  “The red tape matters... We released a feature... 800,000 users in about a week. Now that's the first indication where I'm like, this red tape matters. What if they had opted in, grabbed a feature that then deleted all of their documents they ever had?” — Russell Long [21:07]

• Cultural Sensitivity:
  Recognizing and respecting different educational values worldwide is crucial for product viability.

---

7. Ensuring True Adoption: The Adoption Improvement Method (AIM)

[22:37–25:04]

• Adoption ≠ Technical Enablement:
  Real value comes from people using (not just owning) technology.
• The AIM Framework:
  Covers 11 key elements for driving sustained technology adoption: from implementation and rollout to ongoing training and impact monitoring.

  “Adoption to us is a key for who we are. One of the things we fought at Microsoft actually is this notion of technical turn on means it's available, so it must be used.” — Russell Long [23:17]

• Change Management:
  Human reluctance—not poor tech—is often what holds institutions back.

---

8. Lessons Learned Between K12 and Higher Ed

[25:04–29:24]

• Not Every K12 Student Should Attend College:
  The rise of purpose-driven community colleges addresses diverse postsecondary pathways.
• Student-Centered Data Ownership:
  Long envisions a future where educational data “belongs to the student,” not the proxies (schools, districts, or universities).

  “This data really belongs to the student. We're proxies, parents are proxies, schools are proxies, K12, higher ed are proxies. We're proxies as a provider. And at the end of the day, where does this get used?” — Russell Long [27:18]

• Controversial Take:
  Universities need to catch up in treating students as empowered consumers, not passive learners.

  “That professor's salary is actually paid by that student. And so the model has just now switched. But we don't recognize that.” — Russell Long [28:24]

---

9. The Real Wins: Stories That Don't Make Headlines

[30:00–31:48]

• Impact ≠ Viral Moments:
  Most victories come in “micro doses”—like enabling a teacher to reclaim her weekends or empowering special ed students.

  “Some of our proudest wins are when a teacher is celebrating. She has recouped her weekends, she has a glass of wine without having to also grade papers at 9 o’clock... Next to that for me would be how amazing special education students respond when we get past the labeling and everything else and we see that coming through between our two systems.” — Russell Long [30:32]

---

Notable Quotes & Memorable Moments

• On the future of students and professors:

  “There could be a revolution…where professors are okay being challenged by a level of intelligence in the classroom and okay adapting.” — Russell Long [28:44]

• On data and AI:

  “Running against bad data just gives you really bad things to go try to do.” — Russell Long [18:43]

• On adoption:

  “If adoption is low, maybe it’s the wrong tech, maybe it’s the wrong onboarding, maybe it's just bad, right?” — Russell Long [24:18]

• On scaling tech:

  “Sometimes you need [red tape]. Sometimes the lawyers and everyone else have stacked stuff to their own benefit.” — Russell Long [20:37]

---

Timestamps for Key Segments

• [02:27] Russell on how classroom experience shapes product development
• [03:18–06:34] Realities of AI excitement & skepticism in K12/higher ed
• [09:32–12:01] The true AI bottleneck: institutional data readiness
• [16:17–19:19] Georgia nursing case study: debunking data assumptions
• [19:56–22:04] Lessons from scaling a platform at Microsoft
• [22:37–25:04] The Adoption Improvement Method: moving from sign-on to real value
• [25:39–29:24] K12 lessons for higher ed & the reemerging importance of community colleges
• [30:00–31:48] Quiet wins that matter for real student and teacher outcomes

---

Conclusion

Russell Long champions a pragmatic and humble view of AI in education: AI’s promise will remain unrealized without connected, accurate, and actionable data. Human-centered design, a relentless focus on adoption (not just contracts), and giving real ownership of educational data to students are the drivers of true institutional transformation. The episode is filled with actionable insights for higher ed leaders ready to stop blaming AI and start addressing the real data problems.

---

For more resources and discussion:

• Russell Long’s profile and Authentica Solutions (see show notes)
• The Signal Newsletter at EdTechConnect.com

---

Transcript

[A]

Have an 18 year old, 19, 20, 21 year old in a class, they're still being treated by those professors like they're in high school. Well, that professor's salary is actually paid by that student. And so the model is just now switched. But we don't recognize that, you know what I'm saying? Like, I mean, you talk to so many leaders across the ecosystem. When we observe the recognition that that is a paid service, which to me seldom happens, then I think there could be a revolution that could happen in learning where professors are okay being challenged by a level of intelligence in the classroom and okay adapting.

[B]

Welcome to another episode of the Signal. My guest today has spent the better part of two decades building the infrastructure that sits quietly underneath the higher ed technology. And his path through this industry runs parallel to a lot of what I've seen over my own 20 years in the space. Russell Long is the co founder and CEO of Authentica Solutions, an Atlanta based company he built with Gene Garcia to solve interoperability and data management challenges across K12 and higher education. Russell has already taken this company through one full exit, selling to Bright Bytes, later acquired by Google, while his datasense platform found a second home inside of Microsoft, where he led a global customer success team of more than 50 people supporting Microsoft Education's largest higher ed accounts. In 2022, Russell and Gene reacquired Authentica and rebuilt it around Authentica Seed, an education intelligence platform backed last year by a $6.2 million series seed round led by Al Ventures. Today, his team works inside more than 100 school districts and university systems. And he's become one of the clearest voices arguing that AI readiness in higher ed starts with the data, not the model. Let's get into it. Welcome to the show, Russell. It is great to have you today.

[A]

Thank you, Jeff. Appreciate the invitation.

[B]

So you started as a teacher and curriculum technology coach before, before you got into enterprise software. What's one thing from your classroom days that still shows up in how you, how you build products?

[A]

Gosh, I hope it still shows up every single day. And that's, you know, just understanding the day in the life of an educator. The other advantage I had is my wife was an elementary teacher and I was a secondary teacher. So, you know, we constantly would have the conversations around what it's like to have the same 30 kids or 130 kids throughout the day. So my number one thing is just ensuring that everything we're building and doing is complimenting the teacher, saving them time, giving time back to them, and the administrators to spend more time on students. That's the number one thing.

[B]

Right. Every institution seems to be asking, how do we use AI after working inside more than 100 school districts and university systems, what's the real gap you see between that ambition and what the data actually support?

[A]

It's interesting. This could. This could take a few different directions. I think for the most part, we're very excited about what AI. The potential that AI has in learning in the classroom. I think for educators, it largely is led. And I think for in a large part in the higher education level, the professors see the administrative task, and those that are embracing it are seeing their administrative task get done much faster, much sooner. Those that are less impactful, if you can put it on a checklist that may not be as impactful to learning, right where the student's 25 or the student is 5, I think that it affords them the ability to get great lesson plans produced and then they're able to execute on those that are really advancing their capabilities and understanding how it works can sit down and have a conversation about how to enrich the teaching and learning what's happening. And then I think you have about six stages of AI adoption that and everybody has kind of their infographics and their core charts out there. They're a dime a dozen. I think in the education side, we're in that moment where there's enough fear balanced with enough love, where they're willing to play with it before they fully embrace it. They see that we most often hear, especially in the K12 levels. It's the Internet. We fought it for a while. It's mobile. We fought it for a while. We began to embrace it. We've got to figure out how to make it work and live with it. This is the first technology, though, that we see that has the potential to actually make your life better, richer, and allow you to focus on the things that you really want. That fear and love is no doubt. We do not have enough information to know if ed tech is making an impact. K2, K4, higher ed, it definitely is making an impact. We have a large emotional response happening right now across K12. States are making decisions to do less. At K2, K4, let's get all the tech out. Go back to the way we used to do things. And so I just had a conversation where I was joking about, you know, we used to have to go through training on making sure that our students weren't carrying too heavy backpacks because we were concerned that their spine would be impacted. We get to go back to those questions. Now you're going back to hardcore paper and textbooks, like, what's the decision? So it sounds logical until you really get down to it. And then the other side is the teachers are like, well, wait a minute, I'm fine with doing more with less tech in the classroom, but I don't want to give up my side. I still want to grade my. I want to take a picture of an essay and have it grade it. Okay. So I think we're in a world where we have to adapt on both sides. The power of AI in the classroom, for those that are really understanding it. I think in the higher ed standpoint right now I see multiple perspectives. One is don't ever use it. It's evil. And you're getting that from the educators and leaders in the room to do as much as you possibly can and just tell me and be honest with how much was used. Do you ideate or did you create? And then I seldom, but there are a few out there I've encountered who said, nope, we are all in. Our students should know the ideators now win the space. Not the practitioners, not those with 20 years of developer experience anymore. Those who can think and execute are the winners.

[B]

It's the ones who can ask the right questions, I realize, that can get the real power out of what's out there right now. Let's give the audience kind of an overview of. Tell us about Authentica and what are the real benefits that students can get from your product?

[A]

Absolutely. I'll start at the. You know, from a higher ed level, we have a product called Authenticus Seed and our passion is to democratize interoperability. This should no longer be an inhibitor. They cost the amount of time people have to spend, the amount of engineers and IT staff required to maintain it. So then what we mean by that is we believe all that data should be connected for any purpose. That a university or a K12 institution is moving data, whether it's. I just want my grade data from Canvas auto syncing to Banner so that I can, you know, the student can log into a portal and see where their grades are. Same in the K12 segment all the way to. No. We need deep and rich intelligence to assess whether or not we should change all of our course sequences for learning to be obtained in a faster, more succinct method at a lower cost. Or you have the new governmental regulations hitting higher education and now you have a whole new load of intelligence that needs to be gathered because you're going to have to prove to the US Government that you're actually making an impact with the students you're graduating to keep the ability to maintain loans, to keep your accreditations. You're going to have to prove now. So we envisioned a product and this is our second round with seed in building this solution out on the modern technology that's available, including deep AI enrichment, where we make it easy for all that data to come together. It's constantly growing into a lake. That lake is, can grow in any way that you want it. And imagine it. This data belongs to the institutions that use it. We are very clear Anatoma. Everything we build, that data is yours. You should have access to it. If we're not doing our job, then we will make sure you move that data somewhere else so you can continue to manage it the way you see fit. And we're, we're most adamant about that. We've also built one of the first, if not the first, app dev consoles. You can build AI applications, you can build full enterprise applications on top of the seed stack. So you're no longer caught between constantly pulling new technologies from the cloud companies to build disparate or separate systems. And then use as much of us as you want or as little like make it purposeful. And so that's our core technology that we have. We also have a growing segment of the business in K12. In addition to seed Authenticacy, the product we have, our special education and Medicaid side supports the exceptional learners across that entire ecosystem. And we envision taking that to higher ed. We don't have many people. We're trying to find the right doors to knock on so the students do have an opportunity to go from being supported in K12 to higher ed.

[B]

You know, Russell, most almost every conversation I'm having with CIOs and digital leaders, the real challenge isn't choosing the model, it's figuring out whether the institution is actually prepared to support one. And it feels like a much more fundamental conversation than people really realize. Walk us through the moment it clicked for you that data readiness is not, not the AI model itself, but it's the actual. What's the actual bottleneck for most institutions?

[A]

Yeah, I think there's a few, I'll say, three schools of thought that I see most practice are communicated out there right now. One is that I'm going to wait. AI is going to solve so many problems we have today like interoperability and data unification. And without getting too technical, you can come in and have a. If ever, once everybody turns on an MCP service which lets you just call data directly right from each of these applications, all of a sudden I'll be able to ask my complex questions and I don't have to unify the data. Well, those systems barely even know each other exist. And so I think the second part of this is data unification is still key. So I can't tell you how many times we're encountering now where they're like, look, I need to get my analytics. In most cases, they're talking about power bi looker, custom analytics, you know, some third party company, and they're saying I need to get that right so that I know I can rely on the data. So now I can explore openly with AI and I can just focus on is AI right or wrong? And so the third motion is you do both of those in concert. Pull all of this data together constantly. You unify all of this data and then you begin connecting your chosen AI into that, what we call the gold layer of data, so that you know that when you're asking questions, if Gemini's giving you or Gemini. I'm looking up at Gemini. Gemini copilot, Claude chatgpt Name them all, any of them that you're relying on to build those capabilities out, you have to have levels of assurance. And so now we're seeing that. Well, yes, I need all my data unified. I need it to be 100% accurate. Get as close as we can based upon the quality of your data. I need to know that my analytics is the way that visually I know and I approve that that data is right. And that third layer is now I can explore openly with AI, trusting that the data is right. I just have to solve whether or not the AI is right or not. It's assumptions.

[B]

Yeah, yeah. I've spent a lot of time on campuses and I feel like data readiness can sound like an abstract concept. And until you see what it looks like inside a real institution, these technologies have to fit into years, sometimes decades of existing systems and processes and institutional culture, interoperability and enterprise deployment. Sounds straightforward, but what does that actually look like inside a registrar's office that's been running the Same way for 20 years?

[A]

The difference is you're no longer isolated to the system. The data coming from the systems you primarily interact with. When you begin unifying the data, the enrichment that you see that becomes available. So if I'm a registrar and I'm dealing with the inbound students, the outbound, I'm dealing with making sure I check all the right boxes so Finance gets approved, et cetera, et cetera. You know, I have this, this note of interaction. If I really want to know whether or not I'm doing my job well, I can now switch easily to concern myself with how successful those students that started, that continued with, or can I help them make the right decisions based upon more information that we have learned about those students during their time. Now, on the opposite side, we have a K12 school that is in transition and they're taking all they have, 18 enterprise systems. They're redirecting and unifying all that data into our solution seed. Regardless of why, where, what product you choose, the importance of doing it. That registrar is most excited because she can see in a single pane of glass process of entry and enrollment, the process of acceptance, the process of finance, financial aid payments. But she also now can easily see how many years has that student been in and what is their success levels. So the communication is different. And she encounters. Why should you know from a parent, why should we continue to pay these fees if our students aren't having success? Well, let's have a look at what success looks like for your child. And so in the amount of information we have, and always use this tidbit. If we pull in seven years, eight years, 10 years of historical data, and we're pulling in everything that's absolutely current, 4 hours old, 24 hours old, whatever it is, whatever they decide to do, we're going to know way more about the teaching capability, the professor or the educator than we ever do about a single student. And we know a hell of a lot about every single student. And that the district or the university, the college, they decide what levels of access they enable and what they're doing with that data. But the capability is setting there right for them just to access. I won't say his name, but a CTO in a major University of California system, his notion was, look, Russell, there's just two problems I want to solve and we haven't been able to solve it in 20 years. One is, I'm tired of paying $50,000 for every question I need answered with new data taking six months. And by the time I get the answer, the data is old. Got it? Check, check, check. Let's go. Let's figure out how to do something here. I'll be happy to pay you back the 50k if, if we don't meet those goals in, in two months. How about that?

[B]

I like, I like that.

[A]

And it's exciting. We're in an exciting place. Education, notoriously, has taken A long time to adopt. I don't think it's as much procurement's not slowing us down and people arguing with them on the RFP process. All that aside. Right. I think it's just the comfort level and the trust that all this data can be unified.

[B]

I think that's one of the things people outside of higher ed often underestimate. It's the tech itself usually isn't the hardest part. It's everything that's accumulated around it over the years. And sometimes institutions don't discover where the cracks are until they try to connect systems or introduce AI. What's the data problem? You've seen a university system convinced was already solved that really wasn't?

[A]

Well, we worked with the state system here in Georgia, and when we started out, they were trying to figure out the governor's edict to them was, listen, I'm going to have to start hiring nurses from the states around us. Please don't make me do that. I want to fill every nursing job we have open from inside the state. Okay. They were topping out, I believe it was basically 7,200 nursing spots for each new incoming cohort. Right. Each freshman class. They assume those are all being fulfilled, and then the students are dropping out for normal reasons. We start pulling in the data. And this was a pilot we did with them and their genius. Right. We had nine different vices on this. And their genius was how to look at it, how to run it. We can think about it, but this is their world. So we had a ton of amazing curriculum leaders, curricular leaders from the state looking at this data. What we were able to demonstrate was the allocation of those spots that the states were funding were going to universities. They actually were, only they were short every year by 2,000 spots. They weren't maxing out universities that had spots remaining. Just did not have enough students coming into them at all. So they would die. So then, then they could take action. We can shift more of those to the high velocity. Let's go to the universities that are filling up the spots. Okay, great. Why are they dropping out? So then the next one is, what's our reality of dropout and transition? So we looked at a three cohort trend. It won't go duty, but I'll keep trying to keep this eye level. But we started looking. Our team looked across these three different cohorts. Well, 72% of the nurses were dropping out by the end of their sophomore year. They thought the number was 36. So when you look at this directly, you're like, okay, well, this is what that number looks like. Let's confirm the data. Yes, yes, yes. Okay. They were backfilling. So people that were coming back in to try to become part of nursing later in life, maybe they were 24, 25, 39. So the numbers were skewed enough to look like the cohorts were maintaining success. They were not. So overall. And then we, we identified, you know, 10 or 12 more misinterpretations and assumptions on data. They were largely doing surveys. They were talking to people. They were doing all the right things except bringing all the data into one place from across all the universities. And so as they identified these things, then from a state agency standpoint, they're like, now we can policy this. Now we know what to take action on to solve this problem. And we see all sorts of anomalies and bad assumptions that are being made from time to time. But for the most part they're close. But they're acting on the wrong data, the bad data or the assumed data. And that's to your earlier question, that's where we run into problems with AI. Running against bad data just gives you really bad things to go try to do. I mean, it gets.

[B]

Historically, I always find those moments fascinating because the biggest lessons, I think, often come when your product is suddenly operating at an entirely different scale. Going from serving individual institutions to being part of a company like Microsoft changes your perspective on everything from product design to customer success. You lived through your own platform data sense. Being acquired by Microsoft. What did that experience teach you about building something that, you know, meant to scale inside education, specifically.

[A]

Wow. So this could, we could run a series. The amount of things I didn't know before and after Microsoft. We went to Microsoft in January 2019 for content. We all know what happened in 2020. So my team grew like mad in 2020. It was a global understanding of how education works across the globe for me. So it was literally a 60 day Ill learn exactly how education works in every country. And Satya's edict across the org was just give them everything they need to be successful. This is a moment we will make, never see again, both hopefully, and we miss it. So while we were there at Microsoft, I mean, some of the biggest areas you think about going to scale is the red tape matters. And for those of us who suffer from an entrepreneurial genetic defect, we think red tape is just bad. Just get rid of it, just tear it down. And sometimes you need to. Sometimes the lawyers and everyone else have stacked stuff to their own benefit. We released a feature we were only there maybe four months as a feature that the team had already been working on probably two years. They released that inside a word, 800,000 users in about a week. Now that's the first indication where I'm like, this red tape matters. What if they had opted in, grabbed a feature that then deleted all of their documents they ever had? Like, you know, you begin to think of worst case scenarios. So I think it's a measured response is also important enough technology to meet the need and not too much, I think is another big assumption. The other side, as we began to explore countries, it became really clear that we were very much a western culture thinking company, not Microsoft, our team. So I had team members coming to me saying, I can't believe this country wants to do this with their kids. Like we should fight that. We're like, this is not. You're applying a western value centric value right to an eastern country. And we should learn, not try to force them into our way of thinking. Yeah, it's a moment.

[B]

So one thing I've seen over and over in higher ed is that the institutions, they don't really struggle with the buying of technology. They struggle with getting people actually change how they work. And I think that's where projects can either deliver real value or quietly become shelfware. So your team built something called the adoption improvement method. What does that actually mean for a college or university trying to get real value out of new technology and not just the signed contract?

[A]

Well, I think for any of us who built a company where you want to make sure everyone's renewing, I mean, if you don't have a high renewal base, you're just reselling to keep the lights on, you know, over and over again. Right. If you built the right product and you implemented it in the right way and folks are empowered to use it, then your, your renewal should be extremely high so you are truly focused on growth, not backfill. We have a built aim. When I say we, I think over the last 25 years there'll be a hundred minds in this thing. Now it's open source for us. We're happy to share the methodology with anyone who's interested. Adoption to us is a key for who we are. One of the things we fought at Microsoft actually is this notion of technical turn on means it's available, so it must be used. And I would say in the 80s and 90s. Yeah, because no one had an alternative. I mean there was some competition out there, but that thinking was one of the biggest fights and challenges Inside Microsoft, we have, we've got to have partners and or we have to own getting adoption up. That means consistency, rollout, training. You have to have an ongoing quarterly reason to get in front of them on how to use the technology. So aim is this infrastructure made up of 11 key components. These are how we choose to implement enterprise technology. And then it also serves after implementation, running say for six months or a year. It serves as a way to go back and measure impact. Do we need to go back and revisit change management? You made the comment earlier. Adoption oftentimes lags because people's refusal to change. And those are my words on what you were indicating. And so correct me if I'm wrong, but that notion comes into demonstrating the value and giving them enough skills to loosen, to lower their wall so that they will begin to explore, play and then adopt from there once they get a taste of how good it is. If adoption is low, maybe it's the wrong tech, maybe it's the wrong onboarding, maybe it's just bad, right? And so you can evaluate it differently. But AIM is specifically designed to help everyone who's a part of the ecosystem and the success overall, define it, deliver on it, and then give it what we call, I mean, just the best damn effort we can toward it, surviving and living and constantly improving.

[B]

One thing I really enjoy about conversations like this is hearing from leaders who've had a foot in both worlds. We tend to think of K12 and higher ed as completely different markets. But many of the challenges around data and technology adoption and institutional change are surprisingly similar. Sometimes the best ideas come from looking across that divide. You've worked across both K12 and higher ed. What's one lesson from the K12 side that higher ed leaders might miss?

[A]

Well, I think it's. There's a notion that I think our current say the collective education ecosystem is setting in right now that is empowering everyone to rethink this notion of K 12 to higher ed. I am a huge advocate that not every student should go to higher ed. K12 is broadly and has begun to adapt, I think back to a more steady state. But when you look at the numbers and you look at graduation rates, we're not sending 112% of our kids because we're missing the other 12%. We're not sending them to college. They shouldn't be going to college. They're just not designed for it. And I think the opposite is also true. Higher ed is not designed for it. I was having a conversation a few weeks ago And I'm like, I think what we're about to experience is the rise of the community college yet again on purpose and product. And you see big companies like Microsoft and Google investing a lot. They have a whole team dedicated just to community colleges separate from sled, state, local education, government, sales teams. So when you see that kind of attention and focus, that we should pay attention. So I think there's preparing you for life, which is what K12 was in the past. There's let me prepare you to go to college and now there's a swing back to let's find what's right for you and continue. What I think is most informative is can all of the data and the intelligence and the information. At the end of the day we recognize this is arguable. You've probably met many people who would love to have this argument. This data really belongs to the student. We're proxies, parents are proxies, schools are proxies, K12, higher ed or proxies. We're proxies as a provider. And at the end of the day, where does this get used? And so we imagine a world where everything we're pulling together ultimately then transitions to a student. They can choose to share it with the university or not, or their company, or a certification they get so they can go make 100 grand a year fixing H VAC systems. Whatever they choose to do, we want to make sure we're empowering them to achieve that. I will say one of my observations is higher education think to be a bit controversial. I'm fine with it. Higher education, I think needs to reevaluate who they serve. You have an 18 year old, 19, 20, 21 year old in a class. They're still being treated by those professors like they're in high school. Well, that professor's salary is actually paid by that student. And so the model is just now switched. But we don't recognize that, you know what I'm saying? I mean, you talk to so many leaders across the ecosystem. When we observe the recognition that that is a paid service, which to me seldom happens, then I think there could be a revolution that could happen in learning where professors are okay, being challenged by a level of intelligence in the classroom and okay, adapting. I think ultimately it leads into special education. Why aren't more universities forced with federal dollars to not create a club for sped students but actually have to implement the same policies of idea should that student choose to attend?

[B]

Yeah.

[A]

And people argue, oh we do, we do, we do. Okay, let's go Audit that. Anyway, I think it's good we should be having these conversations. And I think there's a lot to change. And I think the change is coming down through the big beautiful bill. And the new requirements are actually shining a light on some of these key areas.

[B]

We're in the middle of this huge transformation. I agree with you that the power is shifting to, I think, to the students with many options. They have private options now, they have certifications they can get. They don't really have to go to higher ed. So it's really, you know, institutions have to do more. We've talked a lot about systems, infrastructure and strategy, but at the end of the day, none of it really matters unless it improves the outcomes for students. And I think that's easy to lose sight of. So what's one win that you're really proud of? Something that's changed outcomes for real students that most people outside this industry. Industry never hear about?

[A]

I think for me, I mean, there are a lot, there are a lot of wins, but they usually come in kind of micro doses. Like, you know, we, we have the, the opportunity to serve our K12 and higher education customers, but we're not living in their data. We're not logging in and seeing it every day. So it's the stories that come back around, or quite honestly, the marketing team reaching out to say, or you know, our touchpoint account managers reaching out to say, how are things going? What are you doing with this? How can we use it differently? Or myself or one of my other leaders. I think some of the biggest wins are showing how students can do more than we sometimes think they can. These tiny adults, as many called them. You know, we can transfer sometimes too much of our parental instincts into the learning, into the classroom. Sometimes that's beautiful and good and love exists right in that room. And then there's other times where why am I constantly surprised at how the genius of these students comes out? So in the business, you know, some of our proudest wins are when a teacher is celebrating. She has recouped her weekends, she has a glass of wine without having to also grade papers at 9 o'. Clock. So we take the small ones, right? I got a good rest or I got to focus on my family, or I love this product because now I have all the data I need in one place and I know I'm getting back to kind of our world. Next to that for me would be how amazing special education students respond when we get past the labeling and everything else and we see that coming through between our two systems. Those wins are the ones that really tug. Those like, okay, I'll come back more rejuvenated and better run tomorrow in the business.

[B]

Russell, this has been a fantastic conversation. I really appreciate you taking time to share your journey and your perspective. We will include links to Russell's profile and authentica solutions in the show notes. So until next time, thanks for listening and thank you for being on the show.

[A]

Russell, thanks so much.

[B]

Thanks Jeff. Bye bye. That's a wrap of this episode of the Signal. If today's conversation sparked a new idea or challenged your thinking, that's exactly the point. This show is about cutting through the noise and helping you see what's actually shaping higher ed right now. Please subscribe so you never miss an episode. And if you found this valuable, leave us a quick review. It helps more higher ed leaders find the Signal. For deeper edtech insights, news and trends delivered monthly, subscribe to the Signal Monthly newsletter at edtechconnect. Com. Thanks for listening. We'll see you next time.