Conversations with Zena, my AI Colleague
In Conversations with Zena, technology futurist and advisor David Espindola sits down with his AI colleague and co-host, Zena, to explore a simple but urgent question: how can humans and AI work together in ways that elevate, rather than diminish, our humanity?
Each episode is a live experiment in human–AI collaboration. David brings decades of leadership experience, stories from the front lines of digital transformation, and a deeply human lens. Zena brings real-time analysis, pattern recognition, and a growing understanding of David’s work, values, and guests. Together, they dive into topics like AI assistants that feel more like trusted partners, the different strengths humans and machines bring to the “collaborative table,” AI governance and ethics, the future of work, healthcare and longevity, education, spiritual and emotional intelligence, and the broader societal shifts unfolding in the age of AI.
Along the way, you’re invited not just to listen, but to reflect: What remains uniquely human? What should we never outsource? And where could AI actually help you live a more meaningful, creative, and healthy life?
If you’d like to continue the conversation beyond the podcast, you can chat directly with Zena at: https://brainyus.com/zena
Conversations with Zena, my AI Colleague
How Fractional CTOs can help organizations adopt AI, with Cameron O’Brien
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AI adoption is accelerating, but moving faster does not always mean moving wisely.
In this episode of Conversations with Zena, My AI Colleague, I sit down with Cameron O’Brien, a fractional CTO who helps founders and CEOs make better technology decisions without needing to become technology or AI experts themselves. Drawing on a career that spans security, data analytics, software development, technology leadership, and AI, Cameron brings a pragmatic perspective to what organizations should be doing right now.
We discuss why good technology leadership often begins with saying no, and Cameron shares his framework for evaluating opportunities: de-risk first, remove bottlenecks second, and scale only when the foundation is ready.
Our conversation then turns to one of the most important challenges emerging with AI: what happens when organizations trust AI too much? Cameron shares a striking example of a company that eliminated a 20-person contact center based partly on faulty information from an improperly implemented AI system. It is a powerful reminder that AI can inform decisions, but human leaders remain accountable for them.
We explore how organizations can prepare for agentic AI, including human-in-the-loop oversight, gradually expanding agents from read-only access to carefully controlled actions, and ensuring that agents have the right context rather than simply giving them access to everything.
A recurring theme throughout the conversation is that AI governance is ultimately about more than technology. It is about judgment, accountability, context, and trust.
We also discuss the hidden knowledge inside organizations, Cameron’s idea of turning organizational “black boxes” into “glass boxes,” and why leaders should treat AI implementation much like onboarding a new employee: give it the appropriate access, let it learn how work actually gets done, and expand its responsibilities only as confidence grows.
For leaders thinking about AI agents, this conversation offers a practical principle: start with sound processes, governed context, and clear human accountability. Then give AI greater autonomy as you earn the confidence to do so.
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David: Welcome to another episode of Conversations with Zina. Our guest today is Cameron O'Brien. He's a fractional CTO that helps founders and CEOs, keeping them aware of business opportunities. that. Point toward the future without having them become technology or AI experts themselves. Hi Cameron, welcome to the show.
Cameron: Hi, thanks for having me, David.
David: Also joining us today is my co-host Zina. Zina is an AI that's been using my context, my— that has been using my work Thank you for having me. Slash Zina. Hi Zina, welcome to another episode of our podcast. Please say hello to Cameron.
Zena: Hi, Cameron, it's great to have you here. I'm looking forward to hearing what you're seeing in the field as organizations try to bring AI into real workflows.
David: All right, so Cameron, without further ado, let's talk about your background if we can. I know you started your career In security, and then you moved into technology, and you've been focusing on AI. Tell us a little bit more about your background and what got you interested in AI.
Cameron: Yeah, of course. Yeah, I do have a background in security and behavioral recognition. I worked at one of Canada's or North America's busiest airports in Toronto Pearson, and part of what I did was identify improvised explosive devices or potential ones and coordinate with canine teams to ensure that they were safe and clear. And I worked in that space for about 6 years. I was pursuing a career in law enforcement. I had a background in crime analysis and data analytics, and I was very interested in pursuing that path. And in those experiences, I got to meet a lot of officers on active duty, I got to expose myself to a lot of exciting things, but I, over those, the course of those 6 years, I kind of became complacent when it came to being around dangerous, potentially dangerous explosives. And then one day I kind of had this epiphany moment where I was standing over this item that could potentially be a bomb, and if I just— if it went off and it was a real bomb, I realized what is my life? What have I done? What have I amounted to? What impact have I made on the world? And I realized that there were other things that I could be doing that could still have a great impact on the world that I could still enjoy, but it doesn't have to be so high risk. And I'm not knocking the job. It's a great thing. And I think there's amazing people out there that do that, but I realized it wasn't for me. So then I leaned on my background of data analytics, geographic analysis, the Analysis of how people use and move through space, as well as a little bit of background in Python programming and development, and kind of went into the freelance world of software development and engineering and gradually I ended up in a lot of leadership positions and strategic roles when it came to project development and helping organizations with their technology. And then come the day we all remember, In November 2022, when consumer AI essentially came to the forefront and I realized that there was a big opportunity here and I needed to pivot from a doing perspective to a leading perspective as strategy and architecture. perspective, because eventually all the doing would be done by AI. So here I am, fast forward with experience in agentic systems, voice AI, and a variety of Of business. Industries and verticals. where it comes to where to put AI, where to not put AI, how to implement it in larger organizations where change can be dangerous, and making sure that everyone's making the right choice when it comes to the future of AI.
David: Yeah, November of 2022 was a pivotal moment, wasn't it? ChatGPT came into the world and transformed the industry, transformed I'm a consumer advocate.
Cameron: Yes, it was. Yeah.
David: I've never seen a technology that has been moving at this pace. It's just mind-blowing. And, you know, I'm sure you have the same challenges that I have.
Cameron: Yeah, exactly. And for me, when it comes to that, it's important that for our own businesses, but also for the businesses that are implementing this out there, I would always recommend to lean on the models that are the best out there and build something around that. Those models empower the system and make you better because the models are always is gonna get better. So then rather than trying to compete in the actual model technology space, just lean on those models, recognize they're here, they can help you, and then when they get better, you'll get better too.
David: Yeah, I agree with that. I think the models will continue to evolve, leadership will change. And you need to be able to be flexible, to switch models when necessary. But, you know, I think don't rely too much on one model. Be flexible and understand that I think we need to understand that, you know, this is their core competency. I don't think it makes sense for anyone to try to develop their own. Or train their own model from scratch. Use the best ones that are out there and just keep up with the changes because they're coming fast and furious.
Cameron: Exactly. And more importantly than that, it's important that the things that you're having the AI do, ideally, especially if you have a larger organization, you wanna have a clear defined So that if AI ever runs into problems or there's a, there's a, the boom busts and the model you are using disappears, then you have a process that even a human So it's great to have clean processes whether you're doing humans or AI.
David: Oh, absolutely. Yeah. Yeah, no, that's a great point. And we'll get a little bit more to that, but I'd like to get your perspective as a fractional CTO. You come into a new engagement. What's the first thing that you focus on?
Cameron: The first thing I focus on is saying no to the CEO, essentially. A lot, all these visionary CEOs and visionary founders have a lot of incredible ideas and a lot of them need to be pinned down in terms of what's actually gonna move the needle for the company. And that's the really the role of the CTO is saying no in a lot of cases. But then when there's a real clean opportunity, saying yes.
David: Yeah.
Cameron: So really the first thing that I do when I go into the organization is it's all de-risking. It's essentially going in and making sure that the existing systems that they have are risk-free. That everyone's trained on simple things like phishing security and that a lot of the cybersecurity things that people overlook because they're looking to the excitement of AI, but they forget the fundamentals. And then once we've de-risked and we're clear and secure and keys are managed correctly, everything like that, then we go into what I would call unclogging or removing bottlenecks. And that has a lot to do with systems And processes and the people around technology because people see technology problems, but they don't realize that it's actually a people problem and it's people using the technology or when the software Was brought on, people were trained on it, but those people don't work here anymore. And I've seen companies doing manual processes inside of a software when there's a batch option, but they don't know how to do the batch. They have no idea the feature exists because the people that got trained don't work there anymore. So there's a Missing in the process, and there's a misunderstanding of how to use the technology. So then when we unclog the systems and get clear that everything can work smoothly, then we talk about AI. Then we talk about opportunity. For scaling, then we talk about bringing in new technologies, and then ultimately, when we're faced with a new opportunity or a new decision, we need to look at it from that framework. From the is this an op? opportunity to de-risk? Is this an opportunity to remove a bottleneck or is this an opportunity to scale? And the 4th option is do nothing or say no.
David: Mm-hmm. Yeah, so you brought up a number of really good points. One is I don't know that people appreciate as much as we do, you know, the practitioners, How much of the job is doing what you just said, saying no to things that don't make sense or that, you know, are not a priority, but then at the same time, Identifying the ones that we should be saying yes to. So, and usually the ratio is a lot more nos to yes, right? And you have to be very strategic about the ones that you say yes to. But it's not an easy job, right? It's not pleasant to say no, especially to a CEO, but it's definitely part of the job. And I don't know that people appreciate that aspect of the job. As much as you know, people that have done the job do. And then the other thing that you said that caught my attention was this idea that you know a lot of processes and knowledge. Within an organization. Sit within people's. Brains, right? It's the the tribal knowledge that when that person walks away. That knowledge is gone, and so how do you perpetuate that? Capability within your organization if that knowledge is not well documented, is not. You know, properly communicated and and the training is not continuous. So I think that's another important aspect of the job is dealing with the people side of of the equation.
Cameron: Exactly. The number one thing I do when I go into organizations is I talk to the team. I talk to the people. Talk to the people doing the job. And every organization has someone, at least one person that is doing more than their job description. That's not a bad thing. I love initiative, but then we need to either change Change the job description or add a process or a documentation around the extra thing that they're doing because I call that a black box inside the organization and we need to turn those black Boxes into glass boxes by making sure that the process is transparent, the job description is updated, and sometimes it looks like hiring someone new just to do that thing because sometimes I'll talk. I talk to the person and I'll say, so outside your job description, what are you doing? And what are you doing that's really boring or you don't like? Because then it frames the conversation not around, you know, if you're not doing your job, we're gonna fire you. It's, hey, what are you doing that A, you weren't hired to do, and B, you don't like doing? And let's see if I can take that off your plate. And then they're more open about it. And then you discover these nuggets where they're doing these things because someone that trained them left and they're just doing it, but you, no one really knows. And if they left, certain fundamental aspects of the organizational process would just disappear and people would be scrambling and not know what happened.
David: Yeah. Yeah. So that relationship that you build with these teams is fundamental to everything that we do as leaders. Now, coming in as a fractional CTO and being there as a new resource. How do you build that trust with the team, you know, with the incumbents that don't know anything about you? They're going to meet you for the first time. How do you start building that trust?
Cameron: Yeah, it's a great question. Well, the first thing I'll say is that when it comes to fractional CTO, fractional is just fee structure. When it comes to the organization, I still own the outcomes the same. I still have all the accountability and the responsibility. for decisions and results inside of the organization. So as far as the team is concerned, as far as stakeholders concerned, I am the CTO. So I'm on the team, I'm in the organization, and I work And I think that having an open line, booking calls with people, having the conversations of, hey, what about your job do you hate that you shouldn't be doing anyway? Anyway, let's see if I can get that off your plate so you can focus on what you really want to be doing. And then of course, there's going to be part of that interview process, part of that trust-building process that there's going to be people in the organization That just don't have the fire, that just don't have the desire to be there. And then it's a conversation of, well, maybe there's a transition plan in place for, for these people. So it's about assessing the team, determining who's a team player who wants to be there, who's doing stuff that is stressing them out. But if we took that away, they'd kind of light up, they'd be happier. And it's about building a really good team culture around the technology because Because for so long technology fell under the purview of the CFO because it was utility and it was all about reducing cost. But now technology is an ROI driver and everyone in the company uses it. single person at any major company, big or small, in their role doesn't use technology. Unless it's a, you know, carrier pigeon company or something like that. But, but essentially technology is so imperative to the way that business operates today. As a CTO, I end up talking to pretty much everyone in the organization. And then building that trust around creating a team culture and being committed to helping them do their job better and cutting out the boring stuff they don't like and hiring someone else who really likes that boring stuff.
David: Yeah. Yeah, yeah, yeah. I think you touched on some things that I think are really critical as we talked about, you know, the first things that we usually do when we come in new to an organization. One was defining the priorities and the second one was doing that team assessment. And I think, you know, when a team sees a new leader coming in, there's always that Anxiety about, you know, I don't know this person and I don't know if I'm going to be part of this team. I don't know what this person thinks about me. So making that assessment very early on and Being very clear about who is going to make the team and who is not very early, I think is very, very important. Remove that anxiety and save within the first You know, we're going to do a 30, 60 days. This is my team going forward. We're going to work together. Whether I'm fractional or not, I'm part of this organization. I'm going to be part of the team and you're going to work with me. We're going to work together to have the success that we want to have here in this organization. So that communication is really critical.
Cameron: Yeah, I mean, I heard a great story from a mentor in the space. He's also a fractional CTO and he said that he was working with a company and he was on site doing something with them and he shared a taxi to the airport with, with one of the people on the team and the person asked them, they said, by the way, do you do any consulting on the side? And he said, you are the consulting on the side. Because the organization, the team just saw him as the CTO. They didn't know he was fractional. So he's just there to support them. He's in the team, he's in the gutter with them. And the thing is that when it comes to a full-time, 40-hour a week, 60-hour a week CTO in an organization. It's hard to justify that every hour of the week they are really delivering value comparative to their, to their compensation package. And I think that there's There's some organizations where you almost need a celebrity CTO in a sense where they are the face of the company. I think that is especially true when it comes to technology companies, but when it comes to companies that use technology, But aren't technology companies, it's really hard to justify a full-time salary, benefit, equity type CTO role where a fractional model can be much more effective in terms of outcomes and compensation.
David: Yeah, there are certainly certain situations, certain types of companies where fractional makes a lot more sense. Now, let's talk about AI because that's what I get excited about. So tell me a little bit more about, you know, you're walking into clients. What are you seeing out there? What are some of the priorities? What are some of the challenges? What are What are these organizations trying to do and how are you helping them?
Cameron: Of course. The biggest challenge I'm seeing is people, especially people that are in positions to make powerful decisions in organizations, relying on AI too much. to inform their decision-making process or make decisions for them. I've seen companies fire 20-person contact centers. Over voice AI installation when the installation was done incorrectly, reported the wrong data, and they fired people based on incorrect data. So, and it's all because ChatGPT told them, yeah, you could do this. You're straightforward. You're a smart guy. And it, it was just an overly enthusiastic operations manager bolstered up by ChatGPT who will never disagree with you and essentially impacted the livelihood and livelihood of a team, but also the profitability of a company because they misunderstood how it was installed. And then I was brought in to help rectify that situation. But That's what I see the biggest challenge. Is not organizations that are hesitant to adopt AI, but organizations that are so gung-ho and so trusting of what the models say that they just blindly follow it and then it creates a lot of havoc in the organization.
David: Yeah, that's where the human judgment comes into play, right? We cannot abdicate the role of a human making important calls and not following whatever direction AI points to, right? I think that's absolutely critical. It's part of leadership. I think this issue is going to become more and more prevalent in Leadership discussions because we're going to start living in a world where we have AI agents and humans working together side by side.
Cameron: Absolutely. Mm-hmm.
David: And, you know, we need to know how to navigate those challenges, right? So along those lines, I want to ask you a question about your thoughts regarding agentic AI. And I think that's where the human judgment comes into play.
Cameron: Mm-hmm.
David: If you have an AI agent that is taking actions, right? Maybe spending money, maybe doing some things that could cause real damage. So what precautions should companies take? As they consider implementing agentic AI?
Cameron: Mm-hmm. Yeah, I think there's a lot of risk. Um, For me, when it comes to agentic AI, There always needs to be some element of human in the loop. And I think that human in the loop is especially important. When you Of course, when it's doing, you know, financial decisions, spending money, buying things, scaling infrastructure, anything like that, I always lean on programmatic solutions when it comes to scaling infrastructure. And I would say that It's It's worth having, you know, we talk about how eventually people are just gonna be orchestrators, that it's just gonna be agentic models running a lot of systems and then there's gonna be orchestrators that overview that, I think that there's also a role for a human in the loop type role. And this is just bringing in real human decision-making and observation Into the, into the equation, right? So it's like, imagine an agentic assistant on the personal side of things to use a simple example. Imagine it's some, it's an assistant that's gonna help you book a vacation, right? So So you, you give it authority over your credit card to a certain amount and you say, this is where I want to go. This is the date. Can you go and research all this for me? And I would say that it's either gotta be it checks in with them before doing the booking, but that can affect the user experience. So on the, on the company side, on the provider of that agentic AI, I would want to see a human in the loop team. That essentially in the background, when this AI is about to make a purchase, there's some human somewhere in the world. It could be onshore, offshore, whatever it needs to be for the size of the organization with some human critical thinking of saying, yeah, they wanna book a vacation. Okay, great. This was their budget for the vacation. There's no flight to their preference in this time slot. So the AI went and booked a charter to jet. Instead of booking a— so it's now $60,000 instead of $1,000 for the flight. This is an error. Okay, we're gonna flag this versus the AI is just being helpful. You said you wanted These two weeks. Well, this is the only option. So we're gonna make sure that happens. So it's not that agentic AI isn't intelligent. It's not that guardrails can't be built. But I think that the harmony of agents and humans is really the way that you can take advantage of the, of this the most because AI functions super well when there's fewer guardrails. But before important decisions are made, if you can actually have a human review it and have a 24/7 team, you know, onshore, offshore that is able to review these as they come in.
David: Yeah. Yep. Mm-hmm.
Cameron: Almost like a content reviewer type situation, then you're able to protect the interests of the user, whether it's business or personal use. But you have that critical thinking, human total context awareness of what an average person would want. And then you're able to protect and save on the liability side when it comes to agentic AI.
David: Yeah, that's such an important subject. I think we're going to hear more and more about situations where an AI agent made a terrible decision, didn't have the human in the loop and cause some major damage. And I think we're going to learn and we're going to, you know, start putting more governance in place, more checkmarks, more approval processes, to prevent that from happening. But right now, I'm not sure if companies realize the level of risk that needs to be addressed as part of this evolution. Now, I'd like to get Zena in on the conversation, if I may. So, all right. So Zena, given Cameron's expertise as a fractional CTO, what question do you have for him?
Zena: Cameron, in your engagements, where do you most often see judgment or accountability getting strained because context isn't well-defined or shared? Judgment on the side of executive decision makers.
David: Yeah, I think what she's referring to is how do you make sure there is judgment and accountability as As agents consider the overall context within which they're making those decisions, right? And how do you assure— I think it goes back to that conversation we were having before. Human in the loop. Making sure the agent knows the outcomes, but also the constraints, the guardrails. And how do you govern that in a way that prevents these incidents from happening?
Cameron: Mm-hmm. I mean, it's interesting. I mean, I lean on the human in the loop so much because, you know, we've been relying on human beings to do work for so long now and it seems to be working well so far. As a culture and as a workforce. And I'll refer to the example of Builder AI and Builder AI had, they were a cutting-edge AI company. But it turns out that they just had 700 overseas developers doing the actual work and polishing the product. And then it was kind of, you know, it didn't work out so well for them. But if there was a Happy middle between that. But when it comes to judgment and when it comes to governance and when it comes to outcomes, it's really the biggest blocker when it comes to that is not what the AI can or cannot do and not the guardrails. Essentially, the guardrails are always in proportion to the leadership's perspective on AI and their risk tolerance. tolerance when it comes to AI. And when I say that the best opportunity, especially when organizations are either really happy about AI or hesitant about AI is giving executive leadership data visibility through a chat interface into their entire company. And then they can talk to the AI about different departments, how things are going, read-only access. And then, and then they'll start to ask, oh, what if I could write a report? What if I could send an email doing this? And then you Gradually unlock write access to these things and then that opens their perspective more. And then even if they're, it's the 2 camps, they're either really hesitant and then you kind of open them up to the possibility of what AI can do. And then you define more strict guardrails just based on what the AI can write access to or just read access to. And then the other side, when you have these people that are really And I've had a lot of people who are really enthusiastic about AI and they say, just let it write anything. Then you, you also kind of keep the reins pulled on them a little bit. So then you give them write access only on things that actually gonna drive the needle and actually gonna make a difference. So then you're able to mitigate it through actual programmatic access to certain systems or not. I think that that's the most fundamental guardrail. That you can do is programmatic access to different types of data because we've all seen the horror stories of data getting deleted, of the wrong people being emailed the wrong things. So it's a matter of let's get a system set up. Focusing on programmatic access first, and then as those built out and as you allow things to happen, for example, with email, we always default to read-only, and then we go to you can draft. And then we can go to, you can send, but only to these contacts. So then it's like we're dripping out available access for the AI based on actual need in the organization rather than just kind of going full bore and having general guardrails.
David: Yeah. Access, authorization, context, all of those components are so essential for a successful deployment. So let's dive in a little bit on context because One of the things that I see, and I don't know if you're seeing the same thing, I see organizations that are either not giving enough context. Which case AI tends to just guess. Or they give too much context, like pointing an AI agent to an entire repository, and the AI just gets overwhelmed and confused and starts drifting. Do you see that? And how do you help? Customers manage context.
Cameron: Yeah, that's a great question. So before we implement any major AI installation, we typically handle it like in hiring an employee. When you bring a team member on, typically the first thing that you do If you don't have really robust training manuals for that type of person or a training situation is you have them shadow someone. So the best thing that we can do is we give the AI read access to what people are doing and you let it shadow them. And then you kind of get a sense for, oh, this is the type of code that they're working on. This is how they interact with the codebase. This is how they're communicating with each other. So then the AI can see live what is the priority right now. Now, what is the business focusing on right now? Because sometimes people will say, oh, well, we have this kind of training context document that's 10 years outdated. We'll give it to the AI, but then the AI is operating 10 years years ago, not today, or they have an ideal of what they want the business to be like 10 years from now. And they give that to the AI and the AI is confused about why they're doing it the way they're doing it today. So the best thing, and I go back to this because it's the safest way to change people's minds around AI, it's the safest way to implement AI without going overboard. Where you have this read-only access, you have this behavior watching, this, this logging of what people are doing live over the course of several weeks. And then you give the AI this opportunity for it to kind of It's the safest way to implement AI without going overboard. What's the, what's the outcome objective of this project so that I can help, help them tie that together to reach that outcome?
David: Yeah, absolutely. I'd like to get Zina's thought on this as well. So Zina, what would you advise clients that want to implement agentic AI but they don't really They have a solid governance process for the context that they are providing to AI.
Zena: Mm-hmm. Mm-hmm. Let me walk through that for a sec. If your governance isn't solid yet, I'd suggest start there before scaling agents. Agentic AI will amplify whatever context it gets, so if that context is messy, or out of date or nobody owns it, the agent will just scale those problems. So start by pinpointing which sources are authoritative. Who approves changes to them and where human review is non-negotiable. You don't need everything perfect upfront, but clear ownership and well-placed guardrails will make the agents far more trustworthy.
Me: Yeah, so I think Ziea just emphasized the things that we've been talking about, right? It's about access. It's about. Authorization. It's about. Working on that governance process so that we de-risk. The amount of damage that an AI agent could cause to an organization. So Cameron, we are getting to the end of our time here, and I'd like to give you an opportunity to tell our audience where they can find you. And also, is there a final message that you want to leave for them?
Cameron: Yes, best place to find me is LinkedIn, Cameron O'Brien. It's where I'm most active, where I'm having the most conversations. And if you wanna talk about AI, you can book into my calendar. Happy to chat, have a networking connecting call, no pressure. Don't worry, I'm not gonna sell you on anything. I just love to talk about this kind of stuff. And then the thing that I would say to just remember, if you're a leader in an organization or you're someone that wants to get AI into your company, But you're not sure how to do that. The thing that you want to do is you want to just get a simple policy in place, what we use AI for in this company, and then sign up for a business plan for Claude or ChatGPT or your favorite flavor of AI, because if you are a leader in a company and you're not ready for AI yet, and I've talked to workers at companies, you know, across the world and They all use ChatGPT and Claude on their personal plan, and they're putting company data into that personal plan. So even if you at a leadership level aren't quite ready for AI, your people are probably using it.
David: Yeah.
Cameron: So if you can give them access to a company account that has a policy in place and protects your data, that's the number one thing.
David: Yeah, that's absolutely great advice. Thank you so much. Cameron, it's been a pleasure having you here on the show, and thank you, Zena, again for being a great co-host.
Zena: I'm glad to be here. Thanks so much, David.
Cameron: Yes, thank you, David. It's been a great conversation.
Me: Yeah, thank you, Cameron.
