This CIO is Making Her Entire Enterprise AI-Ready | 29M Students, Cambium Learning Group

Chirag Khanijo: Doctor Diana Kano, chief information Officer at Cambium Learning Group, now running technology for a company that serves over 29 million students and 3 million teachers, the 80%

Dr. Diana Cano: promises started falling to 15%, uh, results pretty quickly. Right?

Chirag Khanijo: The entire world is telling every CIO you need to be AI ready. What was the honest first reaction when you looked at the gap between vision, where the company was, and what needed to go and where it needed to go?

Dr. Diana Cano: I love Shadow It. Shadow it tells me what I'm, um, not getting, right, what I'm not offering to my customers.

Chirag Khanijo: When every partner or vendor walks into your room, everybody's claiming they can do AI. Ah, we are AI first. AI ready. AI native. AI powered. I am running out of objectives here.

Dr. Diana Cano: I can't compete with those salaries, but they can't compete with my mission. If you want to help students and teachers in the classroom every day, I always say, don't tell me you can do everything. Tell me what you're really good at, and then let's start there.

Chirag Khanijo: Welcome to CXO Spotlight. I'm your show host, Chirag Khanijo, and today we're going to talk about something that have been asked so many times that can we talk to a CIO who is having an impact at huge scale in terms of enterprises and people that get impacted with the technology that they bring, but who's also implementing AI and doing it beyond just pilots that is going into production and how are they doing it? What are the challenges they're running into? So Today's guest is Dr. Diana Cano, Chief Information Officer at Cambium Learning Group. A mechanical engineer turned into a CIO. Five industries, uh, GE, Honeywell, ETS Odyssey. Ph.D. in education from Rutgers and three U.S. parents wrote a book, Applying Physics to Management. And now running technology for a company that serves, uh, over 29 million students and 3 million teachers. So let's go. Let's get this show underway. Welcome, Diana. Welcome to the show. How are you doing today?

Dr. Diana Cano: I'm good, and thank you so much for having me. I'm excited to be here with you today and have this conversation.

Chirag Khanijo: All right, so, Dr. Diana, you started as a mechanical engineer, like I said, and went through ge, Honeywell, and almost like five industries that I could count. Um, and now you have 29 million students at Cambium Learning Group. What does, uh, jumping across or going across that many industries teach you about technology that staying in one place would have never taught you?

Dr. Diana Cano: Yeah, for sure. Um, I've had the privilege of working in different industries, and each one of them brings a new learning. Right. Um, it gives you a perspective, a broader perspective on how change happens, how industries have their own cultures, how companies have their own cultures, and how do we move things through these industries cultures for our products and services and get those to market. So from my perspective, uh, seeing how one industry adopts a new technology or tries a new process or takes on a new. A new business unit, a new acquisition, uh, how we do that might vary based on your industry or your company, but there are some things that we see that are the same. Right. So adoption can be hard. Uh, changing. Um, you know, it's easier to stay the same than to change. We think about forces of friction and, and forces that keep us in place. Right. We, we tend to stay in place. So that adoption cycle, it may vary by time in different organizations, but we still go through the same steps. You know, we get an understanding of the change. Um, we move through that change through, you know, deeper comfort with the change. Uh, so I think of myself as a chief change officer, right? Not a chief information officer, but. But a chief change officer, the one who is helping people understand the value of the technology that we're looking at. In my case, again, it could be process, it could be something else. And then taking that and saying, okay, what is it going to take to move us from where we are today to where we want to be? And that's the key. Where do we want to be?

Chirag Khanijo: Yeah. Uh, we are three minutes into this conversation, and Forces of friction came out very quickly. And I was going to ask about this because you wrote a book Applying physics to management. Right. And not, uh, a metaphor. You actually gave example of Newton's law applying to leadership and leading teams and everything. Um, can you talk a little bit about that? Because I'm very curious. It's a very scientific approach to leadership. I'm sure a lot of people would love to know.

Dr. Diana Cano: Yeah, sure. I mean, we, we draw on what we know when we do something new. So, uh, physics and calculus, I love those subjects. So mechanical engineering was a natural choice for me. When you're trained as a mechanical engineer, you think about the principles, um, in physics and taking that knowledge and applying it to other problems is really about how we learn. How do we transfer something. We learn here and apply it here. So the book we wrote is really about, um, helping engineers pull from that strength, that thing they've built over years. And it's not just the four years in college. It was the years in high school before that when you were taking those science Classes, it's the years in middle school when you were in the space club or whatever, um, your experience was there. Taking that and saying those strengths are so important now, how do you apply it to a new problem? And the new problem, uh, that we talk about is really, uh, a new manager. Sometimes engineers don't get the training that we need in management. Taking the principles you learned about how objects move can help us understand how people change and how they adopt change. And so we apply those principles. The book is not for everybody. If you don't love physics, it's probably not your book. Right. But if physics is sort of where you come from and what you've invested a lot in, we think about how to use that investment in your next challenge, in your next opportunity.

Chirag Khanijo: Yeah. What I found, I think it's for everyone, because in my opinion, um, a lot of people find physics and calculus very unapproachable. And, uh, it's hard, but when it's connected to something as simple as leadership principles and what I can do in my day, in the life, uh, as I'm working in a corporate or IT or a tech team, it just becomes approachable for me. So that's where I felt like, oh, so that's what Newton's law force actually meant, because I can see it applying in the forces at play at the workplace. Right. Um, all right, so let's talk about, uh, fun thing that we cannot have, Ah, a podcast about, which is AI. Right. Um, you are at Cambium. 29 million students, 3 million teachers relying on your products, and the entire world is telling every CIO you need to be AI ready. Uh, what was the honest first reaction when you looked at the gap between where the company was and what needed to go and where it needed to go? So how did you, um, your first contact with this problem, how did you see.

Dr. Diana Cano: Oh, sure. So when I think about AI ready, you know, we talk about AI literacy. Um, how do we build AI literacy? How do we build our capacity in that space? Um, you know, this didn't start with ChatGPT, right? So we've been in the space of AI for a long time in education. Um, but where it did really take hold in the last couple of years is how accessible AI solutions are to ourselves, to our customers, and how do we handle that? So making AI relatable to people, I think, is the most important thing. How can I use AI in my work and taking that curiosity, how could I do this? And turning that into confidence, it takes some time. So, um, one of the Things we did in our group is we didn't make the assumption that all technologists are comfortable with AI. We started on a curve and we started with a question. Um, it was a repeating sessions that we had for our team. We started with the question of what about AI in movies, video games, books. And we went into small breakout rooms and talked about that topic. How have we seen it in entertainment? Then we said, okay, well how can we use it in our personal lives? So we had a ton of. We did breakout rooms then and we talked about, and this is over time, uh, uh, there was time in between. Um, so we had like a recipe room, a travel room, right? So people were talking about how am I, how do I, um, plan my next vacation, how do I plan my next meal based on what's in my fridge? And so we talked about that. We found that people started to experiment with each of these sessions that we did. Experiment more with what the tools can do. Our last formal session was around how can we use AI at work? And we had a gentleman demonstrate how he was using it to sort through logs, come up with some, um, some insights from our log files. So then people could see that and it was demonstrated on the screen. And then we found more and more experimentation, more use cases that made sense, that provided value to people. The last example I have is we did a, uh, we have one of our team members demonstrate, um, Notebook LM and how they used it to create a, ah, work artifact. And so what we did is, um, uh, had an open session, uh, where folks could come in and um, watch uh, the use case. Within two days I had three presentations delivered to me that were created through Notebook. Right. So this is where we see it. We see this shift from how could I use it? And that curiosity leads to experimentation. What works? What doesn't work is not everything works. And then it goes to this confidence I'm going to use it to create a presentation out of these materials that I am. I, um, put together. And that is the path for us all to, to adopt. How does it work for me?

Chirag Khanijo: I think it's what I find very interesting in this is, is, is probably you're not seeing it because you're doing it. I'm finding it very valuable here. Is that on my side is that it's, it's as if, um, you have a task which is like an onion layer, like you need to get to the middle of the onion, which is that your enterprise should be AI ready. But until you peel the outer layer off people, what I loved about this example is that it was straight away the question was not that how will you use AI at your work. It was about can we first see how AI can be used in fun ways like movies, shows and things like that. So I became comfortable with AI. I started understanding in my day in the life job. And now I'm very curious, I'm excited. Right. And now the question is driving towards what would you do if you could? Right. And then you are, you're even further planting a seed with notebook elements saying see here is what could be done. And then I'm more excited because that, that, that onion is getting, I'm getting in there. I'm not why I'm saying this is because a lot of places I see caught the cart being placed before the horse where people are learning what is an LLM, what is a transformer model? What is all those things. But I think this, um, sort of, uh, why do I need it towards what is it towards how do I do it is such an interesting model to bring AI readiness to your organization, which is really good. Um, uh, I kind of want to push this forward that let's say your internal teams know this thing. Right. But then you have the leadership or the business side, the board, which also has some idea of what AI readiness means. Uh, and there's a lot of time there's a struggle in conveying the vision versus reality. How can a CIO answer this and handle that to convey that vision to board and to the business side?

Dr. Diana Cano: Well, we're doing a few different activities in that space. So if you think about it, what we were teaching in travel planning or recipe planning, we were teaching prompt engineering. Right. I don't like, I don't want to go to parks, I want to be inside in a museum or vice versa. So you're teaching people how to do prompt engineering by starting with something that is a non consequential low risk item. Right. So how do we measure those aspects? Right. As I teach prompt engineering, uh, to folks with a, with a, um, a uh, low risk, uh, task. How do I convert that then to more higher risk tasks? And if you think about it, doing the insights for the logs is one way that we do that. Right. Okay, so what are the results of those insights? Those insights turn into actions tickets that we get accomplished in it to reduce the noise in our alerts. Right. It turns into measurable outcomes that are a result of using the tools. So there are some things we're doing in terms of measuring that output and metrics there, how many Automations are we creating how many um, of our tickets are generated by AI Insights. And we can tag those and we can monitor those. We're also looking at the traditional ways too. How many folks are seeking learning opportunities, certifications? Um, we use a particular uh, learning platform and in that platform I'm working with our people experience team, um, to really figure out what is the curiosity at this point. Where are people looking for additional information? And as we turn that curiosity to confidence, we are looking at measuring people getting those certifications. Not everybody needs to get all certifications. Right. So we're working on three different um, pathways for folks to uh, progress. Uh, our team members that are in development, we are working with them on how do they use some of the tools to do coding assistance, uh, that sort of certification and training. But for other folks there are other certifications we can uh, offer them if they're interested in that more formal approach. Informally we ah, align that we have to figure out how to um, measure these operational improvements that are inspired, I say inspired by AI. Right, informed by AI. And those are the items that we're looking at. How do we measure that? How do we tag it Easily? I mean the last thing everybody needs is more documentation or administrative. But we certainly are trying to figure out how can we tag those value items, um, as being ones that we would not have known were it not for uh, using AI tools.

Chirag Khanijo: Yeah. Okay, so that's the vision. Now tell me what's actually in the way, like when you start making Camium AI ready? Um, there's a plan and you're executing on it, but what are the real hurdles that don't show up in presentations, but they are real hurdles that come in the way of doing this.

Dr. Diana Cano: Yeah, sure. The biggest hurdle I think that we don't talk about as much as CIOs in those kind of peer sessions. Um, we talk about the implementation of AI, the ring fencing of AI, the ensuring security of AI. We don't talk enough about how your business strategy needs to include AI, not the other way around. Right.

Chirag Khanijo: Yep.

Dr. Diana Cano: We're not implementing AI for AI sake.

Chirag Khanijo: Yeah.

Dr. Diana Cano: And at conferences we talk too much about that piece. Right. Um, we have to talk more about how do we inform our business strategy because it's a two way street. How do we inform our business strategy about what's happening in AI, both remember for your customers as well as your internal folks. And then how do you go about infusing AI initiatives within the business strategy? So my, um, you know, oftentimes one of the benefits I've had is, um, in, in moving from one industry to another or, uh, you know, coming to Cambium two years ago is I went on not a listening tour, I went on a learning tour. Right?

Chirag Khanijo: Yeah.

Dr. Diana Cano: I need to learn. I don't just listen to what the messages are. I need to turn those messages into activities, actions and work for my team. And then I need to play that back to those folks I meet with to make sure I learned it right. Um, you know, so when going on the learning tour, what I asked these leaders was, how can technology help your customers? How can technology help your teams find the right prospects? How can technology help your teams be more productive and do more of the things they love to do? And for me, the biggest gain is shifting our, uh, resources to doing the work that m matters in it. We get asked to do a lot of things right. We get asked to do there's a ticket for everything and everything's in a ticket. Right. So we could just do ticket management. But that's not really what matters. What matters is the work that's going to drive forward the business strategy. If that includes AI, great. Um, other automation tools, you know, we used to use robotic process automation. If that can help out. RPA is a perfectly fine, um, solution in some circumstances. So it doesn't matter what the implementation, uh, is. What matters is what problem are you trying to solve. And I think right now, as 2025 was so much about experimentation in different companies, about business use cases that worked and didn't work. CIOs need to be thinking more about how do I put my resources to the use cases that matter most to my business. How's that happen? And how do we not do some of the things that are in those tickets, Right. That don't provide that value to our end customers. So learning from those business leaders, understanding what they need, we've got a lot of tools in our tool belt. One of them is AI.

Chirag Khanijo: Yeah, yeah. And this is very profound because, um, the old thought process has been evolving. So what I used to see is that a lot of work deposited on the CIO's desk and they need to execute on that. And that has been changing for last few years where business and IT have come closer with cloud DevOps, agile product mindset. I have been seeing that. But I think, uh, last two, three years have been very fast like that, that those things have been folded up very quickly with AI. And in a way every company is becoming a tech company and that means that actually CIOs become the strategic business partner in telling what this thing can do and how quickly can it do. And I agree, AI is just one of the things. AI was always there. Now we are calling generative AI as AI. Almost like a, um, synonym. We're using it right. Um, which is really good. That um, which is why I say that's profound. Because I'm enjoying this very much that a business user is saying that, hey, I have two goals. Operational efficiency and revenue generation. Tell me what you can help me with. Most of the time I would think of a CRM pick. Operational efficiency. Let me optimize our tickets, do automation and RPA and whatnot. What you're saying is that I can help you with revenue generation, customer success, more prospects because we can apply these agile solutions to these problems. And a business person might not have ever known that the CIO can be a co pilot at doing that. Which is fantastic. Right? That it's a two way street now.

Dr. Diana Cano: Yeah. Can you say something interesting? You said something interesting about um, all companies are tech companies, right?

Chirag Khanijo: Are becoming one. I think so, yeah.

Dr. Diana Cano: I also think that all employees are technologists.

Chirag Khanijo: Yeah. How? I'm very curious how.

Dr. Diana Cano: Well we used to have these notions about shadow it.

Chirag Khanijo: Mhm. Mhm. Yes.

Dr. Diana Cano: Everyone is an IT person now.

Chirag Khanijo: Yeah. And it is said to be the time of shadow it with wipe coding and everything that everybody wants to participate and be a part of it. Yeah.

Dr. Diana Cano: And that's a great problem for us to have as CIOs. That's a great problem that moves us on that adoption curve more quickly because that curiosity moves to comfort, confidence. Right.

Chirag Khanijo: That's a refreshing take. I would have thought that as a CIO you would not like shadow it, but that's.

Dr. Diana Cano: I love shadow it. Shadow it tells me what I'm um, not getting. Right. What I'm not offering to my customers, where they are leaning in what they find value in. Shadow it tells you something about how you're doing. That's your feedback loop as a cio.

Chirag Khanijo: Yeah, true. I mean what was not being available with it that you had to build yourself and how I can now accelerate and do it for you. Right. That is so true. Um, one more aspect that I think we didn't cover yet on the reality of executing AI and other innovative solution is that which is money. Every CIO I talk to says that they've been asked to make company AI ready. Um, but nobody gave them a bigger budget to do it. And how do you find budget for AI? You know, when you're running uh, a company like, because budgeting is some of the thing that I think a lot of people struggle with. How do you plan for this thing?

Dr. Diana Cano: I believe that self funding and creating a track record of success for yourself then leads to more funding. So what do I mean by that? Right, so self funding means that you take a small project, um, take a look at what you're trying to accomplish in that work. Step one though is to say I need to eliminate some steps out of this process rather than just automating or using AI in the process. Right. Like let's do a lean six sigma kind of look at things. Let's pull out non value added tasks. Let's do that first M Because it'll cost you less to automate less and it'll keep your tech debt down later. Right. So if we take a big complex process, break it down into small chunks, take some of those small chunks and say, okay, do I have any non value added tasks in here? Let's take those out. Then let's automate it with AI or other tools. You do that first, you create an ROI for that, that's going to help you self fund the next one. And I mean when you're doing this at first you've got to keep that human in the loop. Right? We've got to keep the human in the loop in the beginning, make sure that we're doing all the right things with the automation and the AI, making sure we're not just following that, you know, red route, the most likely path forward. You gotta take some of those outliers, you gotta make sure that they're feeding in. Then you gotta do your human validation, do all that stuff. You gotta do your homework.

Chirag Khanijo: Mhm.

Dr. Diana Cano: Then when you release that, you measure your results, put that up on a slide, put that up on a dashboard, put that whatever is your communication path and then you'll start to self fund. You'll see that cycle come around again because people will see that you're not just looking at it as the next new shiny toy, that you're looking at it for its utility and how you're going to use it at your organization. Then the funding comes and I self fund all the time. I take money from here and I'll shift it over here in order to get something going and then start that self funding process.

Chirag Khanijo: It also, I think it's uh, a great feedback loop of trust in my opinion. Because if you started an experiment, then you kept that commitment all the way through because you showed the value. I mean, in a way, no matter what. C Suite role is everybody has a customer your side, you have a business as a customer. And when they see somebody making a commitment, doing experiment and self funding there's that trust feedback loop that oh they deliver on what they start right. Like it's not abandoned experiments along the way uh, which is interesting. Um, m moving on from budget. There's uh, something I keep hearing from UM CIOs that uh, which I don't hear on stage but I'm observing it very much that the best AI talent today, AI talent is you know on one way we have jobs, layoffs and things like that happening. But at the same time there is a very specific skill set that and mindset actually that this AI nimbleness required. I sometimes call it this AI native attitude towards things that ah, things that would could be made in three weeks actually are possible in two days. Nowadays we are seeing with the tools and everything, right? But a lot of us have come from this background of um, dev QA test, you know, prod and then we will do first we will do this, then we will do that and you know think about it, that your competition is making companies very quick and it's very quick to replicate things nowadays, right? That pressure has come to enterprise like yourself and you're like hey, we need to do some of this agile work in the native way that you can do things on cursor or things like that. Let's bring it here. But that also means that you need skills that understand this, that the things have changed and um, it can be done very quickly. A lot of these skills are joining OpenAI and getting half a million dollar stock packages and things like that. One of the very real problem that I see is that how do you attract, how does enterprise attract this talent that is running towards OpenAI or other people? Is that a problem that you see?

Dr. Diana Cano: So sure. But that's not a new problem. I mean today it's AI. It used to be cloud. Nobody had cloud resources. You know, I mean these have been issues we've dealt with as IT leaders um for many years. For my 30 some odd years, um, it may be different technology but the problem is still the same. So when we compete against other folks for talent, uh, if we're looking to hire, uh, I can't compete with those salaries but they can't compete with my mission. If you want to help students and teachers in the classroom every day you want to come work for us. If you want to have a significant impact on the lives of folks in the education space, we tackle those Meaningful issues. So if that's important to you, you'll come work for us. Um, you know, I build that talent within. So we also have to be very mindful of. We have amazing, talented people that may not know this latest thing, but they sure know how it should be applied to our environment, and that is super valuable as well. So we got to have that balance. We got to have that balance. You know, in it, we always talk about build or buy. Build or buy, right? So this is a bit about build or bring in, right? Do I build the resource, you know, with the capabilities, or do I bring in the resource? And there's, you know, it'll happen both ways. Right. But the key is also look at your own, look at your current assets and say, who's in? Right. Get them up the curve, give them the right experience, let them experiment. Um, and then work through that. That it's got to be a combination because I can't compete with those folks for, um, resources. I love the fact that those companies are building the amazing tech that we all get to use. So let's not, you know, like, that, to me, is amazing. Right. I need to find the people who want to apply that technology to a meaningful problem. And ours is education.

Chirag Khanijo: And, And I think you have an unfair advantage because every one of those kids or, or people were a student once. So, uh, so it's not a mission that they. It's a mission that there's a. Not a single person who has that skill will not relate to because they were somebody in a, in. In a college, in a school somewhere. Right?

Dr. Diana Cano: Yeah.

Chirag Khanijo: Uh, very cool. Uh, all right, how about. So we spoke about budget. We spoke about, uh, talent. Yes. How about partners? So when you have budget constraints, you've got, even without budget constraints, um, a healthy mix always has some partners, other talent, bringing support, people. And what I'm seeing is that when every partner or vendor walks into your room, everybody's claiming they can do AI. We are AI first. AI ready. AI native, AI powered. I, um, am running out of objectives here, but it is there, right. When you are sitting across a partner, I will call it a partner rather than a vendor. Ah. Telling you that our AI solution will transform, help, augment. Right. Um, how do you evaluate? How do you perceive? So

Dr. Diana Cano: we're all talking about what we can do in AI to our customers, and that includes us, right? So we walk into customers today and talk to them about the AI that's either embedded or available to them with our own products and services. We want to make sure that we are staying on the tech curve in a healthy way. So we all do it. What our customers ask us and what I ask partners is, what's it going to do for me? What's it going to do for me? So the question isn't different, right? So what I like is I go about, um, experimentation in a particular methodology. I start off with a proof of technology. Does the tech work? Does it do what's on the PowerPoint slide number two. Does it work for my use case? And that's when we get into a proof of concept, my use case. Does it help my salespeople find more prospects that they can then and make that an easier process for them? That's my use case. The next does it work in my environment? And I don't just mean the tech environment on my servers or in my cloud or whatever. I'm talking about my environment, right? My people process technology data. All of that data being probably the most important these days, because AI sits on top of that data. So I have to know if my data's, uh, up to the standards that I need. So I go through that process. Proof of technology, proof of concept pilot. I do all that before I get to production. So the right partner is along that journey with me. I make it their responsibility to be able to do the proof of technology. Right. Their stuff needs to work. But then does it work in my use case? And then does it work in my house? Those are the next two questions that I need to drive the answers to. So we should all be pitching AI, but not wild stories about what it can do. Let's talk about what it does do now, and let's do those things. Don't wait. Get in there, Try these, um, use cases that you think are going to be most valuable and see what it does. Uh, it does great work on simple tasks. We have proven that over the last year in my organization. Great work on simple tasks. You know, it's better than it used to be when we used to all get six fingers on all of our AI drawn items, right? So we're getting better on more complex tasks, but we still need to experiment with those. So partners need to be able to help us with what's going to work for me in my use case, in my environment before we can go to production. If a partner is not willing to take that journey with me, there's five other partners who are pitching the same AI capabilities that I can use. I have options. Um, and I think that's what makes this as a little different than our cloud Stuff we did, you know, 20 years ago. Whatever was, was back then shifting to the cloud was a, uh, organizational choice.

Chirag Khanijo: Yes.

Dr. Diana Cano: You know, this choice here is much more at the individual user level. And so in doing that there's a lot of different partners out there that can help solve a lot of different use cases. Um, anytime I coach a partner to come in and talk to us, I always say don't tell me you can do everything, tell me what you're really good at and then let's start there

Chirag Khanijo: because you have so many use cases, right. And you can match it to who, what can you know, who can do? Interesting. Um, I have a follow up on that because. And it's broader than just Cambium. I want to understand where this overall enterprise tech industry is going from your point of view. Right. Um, which is that, you know, for, for 30 years the buying has been sort of either software or services. You buy from um, third party off the shelf or platforms like aws, Azure, Google. I mean you can keep naming, right. Databricks, Snowflake, whoever, or you engage services companies to implement those or build custom things on that. Um, I'm seeing something very curious. I don't know if you relate to this or not. Um, lot of this money was services was because things were. Took long time to do, um, many people to do. And AI is conflating, not inflating but actually conflating these things into very quick turnaround. Um, so would uh, the pricing model, would the way a CIO expect to pay to the people change and become more outcome based? Because really those measures are becoming a little bit outdated that you know, price versus people versus things like that. Are you seeing that or not yet? What's your thought on that?

Dr. Diana Cano: I think that's the next wave and I don't think the major vendors have figured it out yet. They uh, you know, I spent 10 years in sales. The incentives for their salespeople are to hold or increase the seat licenses. Yeah. Uh, for services companies it's to hold or increase the revenue generated by the, you know, the people that are consulting.

Chirag Khanijo: Right.

Dr. Diana Cano: So if I'm in one of those businesses, talk about a disruption to your pricing model, uh, I'm going to make a little distinction because it is a solvable problem. It's just a different formula and you have to think about what's your business model versus what's your pricing model. And you can't change the way you approach your customers in your business model and not change the pricing model. M so I'm looking forward to seeing what 2026 brings in that space. Because if I'm going to buy an AI function.

Chirag Khanijo: Mhm, mhm.

Dr. Diana Cano: And that AI function means I might need less seat licenses, then my pricing model needs to reflect that sort of um, evolution.

Chirag Khanijo: True. Yeah. And that is exactly what I was asking. Well answered. I think that's, I, I was suspecting this will happen because I'm seeing the, you know, to, to. Another thing that is related to this is that when you apply AI to an existing workflow after implementing two failed things, you realize it's actually not a, ah, exact reflection of how people used to do these things with AI. Some things could be a parallel agent, something could be sequential. Just because human workflow used to work in a certain way doesn't mean AI workflow will do that. Right. Because AI has an ability to do parallel orchestration and things like that. The second, um, sort of result, uh, of that is what I was asking. That now if I'm having three people here who just automated the whole thing, it's like a forward deployed engine kind of thought process that may not need. So I think. Well answered. Uh, I'm very curious always that how this industry will evolve, uh, and evolve over a period of uh, time.

Dr. Diana Cano: Um, but again I'm going to say it's a little bit of uh, Groundhog Day. When we shifted from data centers and data center support when we did the outsourcing your mess for less kind of outsourcing, there was a pricing model associated with that. And then when we shifted to cloud, there was a pricing model associated with that. And so this is not the first time we've seen this kind of shift. But boy, talk about the time it took for cloud versus the time it's taking for AI.

Chirag Khanijo: There's a historical, uh, there's a very smart and interesting historical evidence for that. I don't know if you've read this book by Nicholas uh, Carr, uh, the story of it or the story of it and it says that it took uh, during the time of Industrial Revolution or even before that the, where you would have a textile company, uh, and you would have your own coal plant which will produce electricity for you. And it took 30 years to people to figure out the comet or Commonwealth Edison to figure out that there could be a socket instead of that whole coal plant that you plug and your electricity is coming through a wire. And it took 10 years or uh, 20 years for people to figure out that the whole data center could be as a service. And it took three years for everybody to figure out that AI could automate your entire process. So the paradigm is shifting very quickly, and there is a historical evidence for that. Super, uh, cool. All right, um, so we talk about hurdles. Uh, now let's talk about what's actually landed. I'm very curious about some AI use cases that you tried at Cambium, which weren't delivered real value, and which one did not live up to the hype, sort of.

Dr. Diana Cano: Okay. So I would say not living up to the hype. I like that a lot of people talk about AI failures. I don't like that. So not living up to the hype means it doesn't meet what I was hoping for. In my use case, it doesn't have the ROI I was looking for. It doesn't have, uh, the measurable outcomes I was looking for. Uh, and that's where a lot of us were in 2025. We were testing it for different, uh, um, operational automation. We were testing it for, you know, can it write my code? Well, if it takes me a decent amount of time to coach it, and then it takes me a decent amount of time to, you know, QC the output and. And how much time am I really getting out of that? Uh, you know, the. The 80% promises started falling to 15% results, uh, pretty quickly. Right. Um, so for Cambium, we've done, I would say, probably four major categories of use cases that we are finding really good value in. Um, one of them is around as a code assistant. Right. Not an independent coder, but a code assistant. Um, another one is around content creation. Uh, how can we create some content that lightens the load a little bit from the folks that are, um, uh, trying to create a personalized experience for students and teachers. Um, so to do that, the amount of content we need does go up. Um, and really doing that well and connecting and being relatable to the teachers and students is so important in our business. It improves our engagement, it improves their engagement with our products and services. We know that kids, when they're more engaged in reading, they spend more time reading. Right? Which, as an example. So we certainly make our way through, um, what it does well and what it doesn't do well and put the proper human loop in place, uh, to do that. Another item that we, uh, use case that we are looking at is, you know, basic productivity. And putting, uh, together a job description is a lot easier now. Um, putting together a presentation based on the information you have in. In a set of, um, repositories is a lot easier now. Uh, one of the things that works. We are putting into production right now is we're working with an amazing partner to use uh, our knowledge management tools in our help desk and creating an agent that will allow folks to have a self serve agent. Not rocket science, but certainly need some work to QC and pilot it. And why do we do that? So that when you're up at 2 in the morning and something's not working, you can get a hold of an AI agent to be able to help you out with that. So the productivity piece is another big piece of it. And that productivity is really about um, uh, 247 productivity type of work. Um, so as we go through our different use cases, we are finding these areas where it's working really well. Um, one of the major pilots we did last year that we are now rolling out to several of our business units in my, in my group is around um, cutting uh, down the administrative burden on our salespeople. And the thing I love about that as a next salesperson is uh, they want to go talk to customers, they don't want to write up their case notes, they don't want to uh, you know, pull together a bunch of. Back in the day we had to look up the codes, industry codes in a, in a book. Right? So you don't want to look up the industry codes and, and figure out who might be the next customer. I could call on being able to serve that information to them so then they can have a meaningful conversation with that customer. They can talk to that educator about how they might be able to use our products and services to help their students. Um, uh, in their, in their high stakes assessments. These are the conversations our people want to have. And if I can lift some of that burden from them, um, we proved, we increased talk time with customers. Now that may not be everybody's metric, right? Some people may say, diana, I don't want to increase my talk time with customers because you know, that's um, that means if I'm talking to customers, um, I'm, I have a problem, they have a problem. In our world, we're consultative. So that time with the customer really helps them understand how they can use our products and be successful with our products with their students. And so we just look forward to that. And in our world, each of these use cases improves our impact in schools, um, across the board. And so if we can do that, then we're doing our job in it.

Chirag Khanijo: Yeah, it's very cool. And you're in a unique position because uh, your external facing AI or external facing products that you build touches millions of students directly. Right. And um, so I'm very curious about AI in education. What would you say is a good use of AI in education so that it helps others as well?

Dr. Diana Cano: Boy, so much. This, this could be a whole nother, uh, a whole nother discussion. But let's, let's talk about maybe why education is a little different and maybe every industry is a little different. But I'll just say the ways that education is different from other, uh, things. Um, education industry is a risk averse industry and I love and respect that. That's because we are making consequential decisions. You know, life, life affecting decisions. I don't save lives every day, but I have a great impact on the lives of students and teachers every day. So what does that mean? That means that we have to be mindful and we have to follow a good process because what we do matters. It's not just a consumer product that it could fail or not succeed. We are in a business that it's important how we do our work. So what does that mean? Um, on the technology side, that means we have to just have a rigorous process for understanding how to prove the value, how to handle outliers. Um, we need to make sure that we are looking at the utility of the innovation. I always refer to myself as ah, um, a utility based adopter. Ah, because to me it's going to work for me. I'll get excited about any shiny toy, but it's got to work for me, right? So I got to make sure that I, uh, get that utility out of it. And what's nice about our organization is that we are, a lot of us, um, come from the education industry, having spent years in the classroom, myself included, teaching students, helping students understand, um, brand new concepts and apply new ways of thinking. So all CIOs need to know their culture in their industry and their company. And in our case, we are building products for educators, building them with educators. And these folks are so creative and so amazing. You go to uh, one conference in particular where teachers get up and talk about how they're using tech in the classroom. It blows you away how creative they are with the tools that they have access to. It's amazing. So we at Cambium, um, and I think you know, a lot of others in education technology, we are purpose driven. Our strategy is purpose driven, our culture is purpose driven. So our adoption is also purpose driven. It's not just for the sake of the tech, but it's gotta have a utility, it's gotta have A, um, value to us, um, because it's that important. The work is that important. So sometimes folks say, oh, you know, education is slow. And I say, no, education is purposeful. We make sure that the products and services we offer to students and teachers make a difference in their lives, that they're seen, valued, and supported in their learning journey. Like, that's the stuff that requires rigor. It requires us to be thoughtful about the technologies that we use. And then we have to be forthright about it. Right. We have to talk about how we make consequential decisions based on AI. AI has been used for 20 years plus in educational assessments. M. And, you know, grading essays, looking for, um, um, you know, where students, you know, could improve their writing. We've been using AI assistants for a long time, and it. And we know how to use it responsibly. This extension and this availability of AI for all of us is just using that strength we have, that DNA that we have in that responsible use of AI. It's just extending that even further. And we're not alone in education industry. Uh, there's a number of us out there that are in this space. Um, but I think we're a little different from, uh, other industries.

Chirag Khanijo: Yeah. Interesting. And I think I will just. That's my last question. But I always ask this at the end that, um, we live in the most exciting times. Um, and of the change that is happening around us, whether it's AI, automation, geopolitical, within education. Right. You, um, personally, what do you think in what excites you the most, uh, for the next three years, what are you most excited to see?

Dr. Diana Cano: I think we have an opportunity to improve the lives of our employees, build on their subject matter expertise with these new tools that are available to them, to bring them to new levels in their learning, their understanding, and their ability to contribute to the organization. And I think we have this amazing opportunity, if we do it responsibly, to personalize an experience for educators and students in a way that we have not been able to do until now. Everybody learns a little differently than each other. Everybody starts from a slightly different place, you know, to kind of come full circle, you know. Yeah, throw me an article about physics and I'll read it all day long. You know, throw me an article about something else. Maybe not. And to me, you know, being able to meet people where they are, whether it's in their career or it's in their education journey, we have a real opportunity to do that right now. And, um, I'm just excited about what that opens up for us.

Chirag Khanijo: This was really cool, I think. I love this discussion, and I'm going to give you one, um, interesting thought that I felt throughout this conversation was that, um, whether you realize it or not, what's happening is that especially in today's world, the context is sort of the king. Right? Everybody has their own context. For you, it's your physics that makes everything relatable for you. If, if it's combined with physics, I can see the excitement that you get and, and it starts making sense to you. Um, in a way, it's so, uh, interesting that the industry that you serve is education, in which the curriculums, the things have been carved and then everybody have to make up their own understanding. Isn't that amazing opportunity, uh, that it becomes personalized for everyone, contextualized to everyone, and everybody receives that. And whether you realize it or not, you were saying that, and you said it the same way for your employees as well, that, that for, uh, everybody is a tech person and now they can approach it so easily. Right. So that's my compliment that it's so amazing that the things become approachable because of the technology and not for the lack of, uh, which we sometimes fear from it. Right. Um, thank you so much, Diana. Um, and thank you to Cambie and best of luck to you and for the rest of the people. I think I'll put a lot of details in the show notes to where to find Diana and Camille. Thank you, uh, Nara, for the beautiful show.

Dr. Diana Cano: Thank you so much for the amazing conversation.