Ep 88 - Debugging Human Communication w/ Andrea Goulet

Send us Fan Mail The biggest risk in an AI-powered organization isn’t a lack of intelligence, it’s a lack of shared meaning. As tools get faster and output gets cheaper, teams can still stall, ship the wrong thing, or quietly lose trust because the human communication system can’t keep up with the speed of automation. That’s why I sat down with Andrea Gulet, founder of Debugging Human Communication and a longtime software industry leader, to treat communication like infrastructure you can act...

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Jason McGuinthe?

  Welcome back to Inspire AI, the podcast where we explore how leaders, builders, and communities can remain thoughtful, capable, and resilient in an AI-accelerated world. Today's conversation is about something most organizations underestimate until it breaks, and that's communication. As AI systems become more powerful, the challenge inside companies may not be access to intelligence. It may be the human systems responsible for interpreting, aligning, and acting on the intelligence together. My guest today is Andrea Goulet,

• 0:39Jason McGuinthe?

  founder of Debugging Human Communication, keynote speaker, and lifelong software industry leader. Andrea has spent decades helping organizations understand communication, not as a soft skill, but as infrastructure, something that can be analyzed, improved, and debugged like any complex system. This is a fascinating conversation about empathy, organizational clarity, technical leadership, and what becomes uniquely human as AI capabilities continue to scale. Welcome to Inspire AI, Andrea.

• 1:14Speaker 01

  Thank you so much. Thanks for having me.

• 1:16Jason McGuinthe?

  Awesome. Why don't you tell the audience about a little bit about yourself and what brings you here today?

• 1:21Speaker 01

  Yeah, so I have a background in organizational communications, marketing, really focusing on business services. And how do you like describe things that are you can't touch or see, essentially? That was my focus. And in 2009, a friend of mine from high school, we went to our high school reunion, and he said, I've been reading your stuff online, because I had been consulting at that point, and I really like what you have to say because you don't think about human language the way most people do. You're more of a programmer. And I was like, I don't code. What are you talking about? And he goes, no, you might not think you're a programmer, but you definitely think like a programmer, because everything's

• 2:04Speaker 01

  very structured. You're thinking about modularity. You're thinking about efficiency. And he said, my focus is on improving software systems. So you know, upgrading legacy code systems using agile software methodologies. I was like, I don't understand half the words you've just said. So I don't think I'm the right fit. But after, you know, a good period of due diligence, decided to come on board. I was the CEO for 15 years. And a few years into the business, Scott and I also got married. So we had a very big incentive to make sure that communication was really on point. And we ran into all of the normal engineering,

• 2:49Speaker 01

  marketing, sales, like challenges that you have. Because we had built the organization around communication and empathy, that was my number one, like, I can't join this unless I can, you know, take the skills that I know that work for organizational communication. I didn't see why they wouldn't work in software. But at the time, it was like, human communication and, you know, programming language, it was seen as like oil and water. Someone even tell me, it was like, you can't say empathy in software in the same sentence, because you'll be laughed out of the industry. I was like, I'm just gonna double down on this, because I think that people are wrong.

• 3:31Jason McGuinthe?

  Yeah, in your gut, that's just wrong, right? Yeah.

• 3:33Speaker 01

  Yeah, well, not just my gut, but also in my experience, because in all of my training about how you create efficient organizations, where you're deploying ideas, you're getting things out the door, you're delivering well, you're creating high-functioning teams, all of that is based on a very technical understanding of what empathy is. And so I just was like, software shouldn't be any different. Yeah, so started on the speaking circuit, that kind of, you know, you know, I've been a keynote speaker around this topic for a long time. And recently, we sold our business. It was called Corgi Bytes, so some listeners might know that. Our tagline was "Old Code, New Tricks." And have been doing some keynote

• 4:14Speaker 01

  speaking, but lately, I launched a program called Debugging Human Communication, where I'm working specifically with individuals mostly. So I've got groups and individual private coaching, because I believe through all of the research and all the experience I've done, that human communication is a system, not a soft skill. And when we think about it that way, it's very easy to notice where communication breakdowns are happening in a very similar way that we would debug our code. And when I dug into this topic, what really amazed me was that there's a lot of computational principles around this.

• 4:53Speaker 01

  And like 80% of them are immediately transferable to human communication. So knowing how computer science principles and knowing like how packets of information flow across a network, there is so much that is exactly the same in how humans get one idea from one brain to another.

• 5:16Jason McGuinthe?

  Just my intuition that that I roll with here, and that it's about humans created the software, right? And so they're embedding those those systems from their own brain into the technology that they created. So I imagine that's where it was where it transpired.

• 5:33Speaker 01

  Yeah, there's actually Jessica Kerr has a bunch of great work on semativity. And so that semativity. So, yeah, and that's exactly what you're talking about is where like humans are embedding our own ideas into the software. And then the software, as we interact with it, is also changing the way that humans communicate with ourselves as well.

• 5:55Jason McGuinthe?

  All right. So you're you kind of described communication problems as something you can debug, right? So what makes human communication behave more like a system?

• 6:06Speaker 01

  Yeah. So the best example, kind of the model at its broadest is from Claude Shannon. And if anybody's used Claude by Anne Fick, it was named after Claude Shannon. He was really the godfather of information theory. He founded the field. And quick backstory, in the 1930s, he got really interested in digital circuits. And, you know, things like electronic traffic lights and things like that were being built. But one of the challenges was that you had to actually build a prototype in order to test whether or not the system was working. And so things were becoming untenable. So for his master's thesis, he

• 6:47Speaker 01

  was like, hmm, there's got to be a way mathematically that we can test some of these things. So he found this obscure form of mathematics from George Boole from the 1700s, and said, oh, like, it was all these ands and nots and ors and ifs. And so he integrated Boolean computer, you know, math into how we test digital circuits. So that was kind of the beginning of information theory. And, you know, coined the term bit for binary digit. And, you know, really established kind of how we can mathematically and computationally look at things that are in the real world and test them with certainty to know whether

• 7:32Speaker 01

  or not they're going to work. So we see this in electronic circuits now all the time. And, you know, fast forward past World War II, he was hired by Bell Labs. And the problem that he was trying to solve was how do you get information, like the physical signal from one telephone to another with the highest fidelity? So that you're, you know, it's easy to hear. It's nice and clear. So based on that project, he established what he calls the general model of human communication. And there's a bunch of math behind it, right? And what this is, is that information starts at a source, it then has to go to a transmitter,

• 8:15Speaker 01

  it goes across a channel where it's impacted by noise, which is extra information that's added, that you have to then, you know, kind of figure out a way to filter out. And then it goes to a receiver, and then it arrives at its destination. Right? So that's kind of the general model. And what blew my mind when I was first learning how to code was that I saw this, and I was like, wait, I saw all of this in my human communication classes. Why is that? Is it like a derivative? So, you know, this is how we get bits of information across, you know, a computer network. But it's also how we get an idea from, you know, one person to another. There definitely are some, you know, things that we have to add

• 8:58Speaker 01

  to, you know, compensate for how humans have emotions, right? But we can look at other, you know, folks who have done some mathematical research here. So Noam Chomsky, for example, he did some work in terms of how do we look at syntax? And again, that like was directly about how we build compilers, for example. And it also leans to how do humans, you know, from a linguistic standpoint, how do humans process language? So right now, I'm using my larynx and my muscles and my throat and my mouth to physically get signals out that

• 9:41Speaker 01

  can be interpreted. It goes across a channel. So Shannon was only interested in the physical signals. But when we layer on some of those others, we've got language. So I was in the Netherlands and somebody came up to me and started speaking in Dutch. And I just had eyes glazed over because I didn't know what they were saying. But then they repeated what they said in English. And I was like, oh, I understand it because I speak English. And then there's the concepts. So for example, Scott and I, when we were first learning how to work together and I was learning about agile, he would say fail fast all the time. And as the CEO, I'm like, I don't want it. And it took us about six years to figure out that the problem was actually the concept level.

• 10:26Speaker 01

  Because I understood what failure meant. I understood what fast meant. But the difference was the context in which we were using them. So Scott, as a developer, he was using test-driven development and it was lots of failures, maybe 20 times a day, sometimes more, where it's getting something that you don't know out quickly so that you can iterate. And for me, as the CEO, I'm thinking in terms of 18 months from now. Right? I'm thinking like the trends and where we're going and things like that. So I was thinking long-term, huge risks. Scott was thinking very short-term, very low risk. And when we figured that out, that was a context mismatch, like a concept mismatch.

• 11:08Speaker 01

  And the way we resolved that was we used the term rapid result instead. And then it just resolved everything. So we both knew what we were talking about. And then we also have trust, which is John Gottman's research. So when we don't know somebody or when we're in an argument or there's been some conflict, that degrades our trust. But we meet people who are like, oh, my gosh, you get me. Oh, my gosh, we're working on the same problems. And so when we have that, there's kind of this immediate trust. So trust levels go up and down. And then it arrives at a destination, which is your brain or the listener's brain. So I'm talking about all this stuff. I may have used the term test-driven development. There are probably a bunch of listeners who are like, I don't even know what you're talking about.

• 11:51Jason McGuinthe?

  Right.

• 11:52Speaker 01

  So that's the idea is that we can figure out, oh, this term was the problem. And the reason was because of this kind of mismatch. And so what we're doing is we're taking the problem away from an individual's character. And instead, we're looking at it as a series of interactions that are both parties' challenges, that are impacted by environmental challenges. And that's something that in the software industry, we do inherently. Yeah, so there's a lot around experimentation and decomposing problems and abstractions

• 12:32Speaker 01

  that we use all the time in human language. We just don't know it and we just haven't named it. So for me, the reason you haven't heard of this is because most people haven't straddled both worlds, where they have a very deep understanding of the science of human communication, but also spent 15 years modernizing legacy systems and then have a deep understanding of the computer science side as well.

• 12:59Jason McGuinthe?

  Wow. I didn't want to stop you and ask about certain things. So that would have definitely disrupted your flow, but it all sounds great. I'll be honest. And I know my audience is going to take away a lot from this, so thank you. And as you see the AI becoming embedded more in organizations, do you think that communication will become more important or more fragile? And if so, why?

• 13:23Speaker 01

  I think it's both, honestly. I don't think that it's mutually exclusive. Because first of all, English is now how we prompt, right? So being really clear to a machine, it's forcing you to understand exactly what you're trying to say. It's forcing you to structure your communication in a way that tends to also work for humans, right? But at the same time, with the fragility, it's easy to only think about the machines and kind of compensate and think, oh, well, we can just have an entire army of agents.

• 14:06Speaker 01

  And so now humans don't need to talk to each other.

• 14:09Jason McGuinthe?

  Right.

• 14:09Speaker 01

  But we know from just how legacy systems work, there's another computational principle called Conway's Law. And so what this is, is that any system that an organization creates is inherently a reflection of the communication systems that the humans use to create that. So if we move people away from each other and just say, oh, you focus on this and you focus on this, we're inherently just creating a crystal palace of automation that will crash eventually because the system is so complex.

• 14:51Speaker 01

  So to me, I think it's about taking advantage of the optimization and the efficiency and things like that, but not at the cost of humans communicating with each other. Because that is so, so, so important and it needs to be balanced. So you see a lot of organizations, like Klarna, for example, is one where they had completely gotten rid of everybody in customer service. They went completely AI. And then it's like, oh, wait, everybody can tell. And they hired more human customer service. So I think the smart companies, Genworth is a great example here in Richmond. When I went to an AI-ready RBA event, I heard the CIO talk.

• 15:33Speaker 01

  And I really like their philosophy because it's about using the automation so that humans can have better conversations about the things that humans really connect with. And when you're talking about insurance, when you're talking about really things that impact your life, having a human there, there's just patterns that we're going to pick up that an AI, at least in its current state, can't. Or that humans are such good pattern matchers that we will notice it's AI and completely reject it, which then I think degrades the trust.

• 16:12Jason McGuinthe?

  Indeed, it does. Yeah, I was talking with a friend of mine at a squirrel's game the other day about, I probably shouldn't name any particular person here, but the systems that you were speaking of about Genworth sound familiar. And it's really smart, right? To layer in the human empathy and connectivity. And I hadn't noticed that people are rejecting the outputs in a more direct way than they have before, until I read an article the other day about this company had the CEO sent out a communication to his people and said, done with it. Like, you can't use it anymore. No one is using it properly.

• 16:57Jason McGuinthe?

  It's out and it's out for good. And that was in all areas because he was seeing shipped code is breaking down, communications internally were emails, Slack. It was all automated and people were just going crazy with it. And he was like, I'm sick of it. But it seemed extreme. And there's no putting the genie back in the bottle here, right? Like, if you kill, AI will rise again like a phoenix because it's so powerful. It might take a few generations of change because the current pattern recognitions are being rejected by humans and people are still struggling with using it appropriately. But it's so powerful that they can't stop it, right? And it's going to continue to evolve.

• 17:42Jason McGuinthe?

  So there's that.

• 17:44Speaker 01

  I tend to be very much a pragmatist when we talk about this. Because if we, I think the other thing is what AI are you using? And then what is the context in which you are using it? Yeah, and I mean, I've seen developers, because I work with a lot of software developers. Like, I coach them. And one of the big challenges is that now that they can be more productive, they're feeling a pressure to actually do way, way more than is healthy. So one of my clients, he's like, I have four context screens open at any one time. And he's like, I'm ending the day sweating because I feel this pressure to now code at the speed of my thoughts.

• 18:29Speaker 01

  And now that that's possible, because essentially what AI does, especially in the context of coding, is it makes the typing more efficient.

• 18:37Jason McGuinthe?

  Yeah.

• 18:38Speaker 01

  And so now that that kind of barrier has been relieved, that idea of thinking deeply is kind of being removed and thinking about what the problem is in exchange for going really fast. So I think that's something that culturally you can look at, right? But it doesn't mean eliminating AI completely. There's also a ton of use cases in science and in linguistics, where you're building your own models, but they're studying Black American English and the nuances of that and where in the United States, like different dialects are being used. And so they analyze 200 million tweets.

• 19:23Speaker 01

  And it was just like, they built their own model and things like that. And they were like, there's no way as humans, we could have analyzed this data. And now because they built their own model around this, there's a new paper coming out. And now they're getting all these insights that they never had before. And that's creating new insights for humans, new ideas for humans, new research directions. And I think that's kind of the thing is like, what is AI really good for? And what is the use case in which we could use it appropriately? And where is it just creating slop?

• 20:02Speaker 01

  That humans are going to reject. So for me, it feels a little bit more of it's a tool. And we should know where to use this tool and where it's like overkill or where it's going to destroy things. So in some cases, it's like you need a scalpel, but you're using a chainsaw.

• 20:22Speaker 01

  That doesn't mean that chainsaws are bad. It just means that in this specific use case, it was not the right tool for the job.

• 20:29Jason McGuinthe?

  For sure. Yeah. Let's double click on the the pattern recognition here. So you you've worked across many software engineering, operations, leadership, coaching, all. So, yeah. So what what patterns show up in every dysfunctional organization, regardless of the industry?

• 20:47Speaker 01

  Yeah, I think there's, there's many. One that all that was really surprising for me. When I was, you know, learning how to create a healthy culture as a CEO was how conflict shows up. So I kind of entered into the idea of like, conflict is bad. We don't want conflict. A high functioning team is is a team that doesn't argue with each other. And I was completely wrong. Conflict is essential for innovation and new ideas.

• 21:26Speaker 01

  Scott Page has a bunch of great work on this around when people from different perspectives are able to have empathy and communicate effectively with each with each other. You know, so kind of leveraging that model that I laid out where they're able to build trust, they're able to, you know, understand each other's concepts, you know, they've arranged themselves both in terms of how often they meet and which way that they meet. And that is kind of optimized. Then you have battles of ideas. So Scott and I ran into this for sure, where, you know, kind of the the biggest story is that one day we were co-located and one day he had his noise cancelling headphones on and he had for three days.

• 22:11Speaker 01

  And I had a client that had been having some conversations with, but it became really urgent that I needed to get his attention to solve like to answer a question that I couldn't provide an answer to, but it had gotten to the point where it was urgent. So I was like, I don't know what to say. I don't know how to get his attention. I was like, maybe I can pin him on Slack. So what I did was I ended up walking over and kind of sheepishly waving my hand and saying, do you got a sec? And what ended up happening, I took a deep breath because at that point I could rage quit and be like, what is wrong with you? How, why do you do this? I've worked with so many people over the years. No one has had this reaction. But he calmed down too. And then we had a really great conversation and I learned that

• 22:56Speaker 01

  inside his mind, as a software developer, his job was to build crystal palaces of mental models. And what had happened was that he was so close to solving this problem that just asking, like trying to figure out the analysis of how to answer the question, you got a sec? It's he was like, what is this about? How deep am I in? How long is it going to take? Because it's not a second. It's probably 20 minutes. And in the analysis of just trying to figure out whether or not that was a yes or no, he watched all of his work crumble. And he described it as, you know, inception, if you've seen that movie, where just those streets of Paris are folding over. And I was like, oh, my gosh, once I knew that, I was like, I don't ever want to do that again.

• 23:39Speaker 01

  So we sat down, we had conversations like, how can we solve this problem? Because we have to have a solution where I can interrupt you elegantly. Because if I can't do that, like, this is a huge business problem. And so we were like, why don't we just use the word inception? And if it's zero, then I know that I'm available. If it's a five or you don't respond, at least you know that I need you. And it just eliminated all of the organizational friction kind of overnight. So then we made that part of our organizational culture too. But that wouldn't have happened unless we had had the conflict. So, you know, when we have different ideas, when we can use empathy, when we can use effective communication,

• 24:24Speaker 01

  we literally get into a one plus one equals three situation. But the important thing is that the type of conflict needs to be about the work. It's called task conflict and not about the people. So not character conflict. That becomes just a vicious cycle that was the crucible of toxicity. But when we can shift that into task conflict and the way that we do that is through empathy, through understanding communication as a system, then we really get the benefits. One, I see kind of individuals who don't know how to speak up or, you know, a lot of times they're stuck in how, like, they're describing a lot of the implementation details,

• 25:08Speaker 01

  but they have to translate that into business impact and why it matters to different stakeholders. And so that translation skill is something that we work on. But I also work with teams. And so one of the teams that I worked with recently, one of the things we discovered was that no one wanted to talk about any kind of dissent. One of their core values was be kind. And so the way that people had interpreted that was kind of a shadow side where then no problems were. And they were going through a huge migration and there were technical challenges, but nobody wanted to say anything because they didn't want to go against the culture. So to me, again, it's that pragmatic point of view. It's like, let's look at this as a system. Let's, you know,

• 25:51Speaker 01

  figure out how to debug it. What is specifically going on here? And leaning on all of those heuristics, you know, that work with both computers and with human language and relationships. And I think that's where we can just get at the heart of what the challenge is. But it also can help us create context specific, you know, customized ways to solve our own problems in our own contexts.

• 26:18Jason McGuinthe?

  I want, I want to talk a little bit more about the empathy thing that you keep bringing up. So one of the most compelling ideas that I think I've heard you say is that empathy is a technical skill. What does the operational empathy actually look like inside of a high performing team?

• 26:34Speaker 01

  Yeah. So this is something that, you know, this is kind of a talk that, you know, put me on the speaker track. And it's funny because I had, this was the highest compliment I ever received. I had a software developer at one of the conferences. And a lot of people like lean back, really skeptical, hands, hands crossed. And he came up to me afterwards and he was like, I came to this talk specifically to tell you how wrong you are. And now I can't. So I think the biggest thing again here is to look at what empathy is and look at what empathy isn't. I think that one of the big problems that we have with empathy kind of culturally is again, that we use it as virtue signaling. And we use it as in the same vein as software or soft skills where it's like,

• 27:17Speaker 01

  I'm highly empathetic, you know, empathy is my superpower versus someone who is like, I don't know. I used to be someone who was like, oh, empathy is my superpower. But what I realized was I was actually operating out of hubris. Because I was operating out of the assumption that I could walk into a room and I could understand how someone else felt better than they could understand themselves. That is not empathy. That is hubris. That is hubris, right? So we think of empathy as this like intuition and being able to do that. And there is a bit of that. But the research shows us that what empathy is, is that it is a biological, functional evolutionary system that enables us to basically communicate faster so that we can collaborate and solve complex problems together.

• 28:03Speaker 01

  Because humans are the most hyper social species on the planet. And the way that we survive as a species is through technology and through modifying our environment. So when we have the probability that I meet someone that I don't know, and William Ives has some really great work on empathic accuracy, if if folks want to dive into this more. But if we were to meet someone and it was just pure chance that we were able to, you know, name what somebody else is feeling, right? It's about 5%. But humans, even with strangers, like that jumps to 20%.

• 28:44Speaker 01

  Where I can have a complete stranger and it's like, I kind of get a sense of how they're feeling. Like, that is a huge advantage. So the empathy superpower people are usually right about four out of 10 times. So I like to think of it as kind of like batting averages, like, you know, three out of 10 will get you in the Hall of Fame. And what I've noticed is that with the software developers that I've worked with, they're pretty accurate. So when we break empathy down into its component parts, it's kind of three. So the first is capacity. So empathy is something that fluctuates up and down based on context. If I'm sick, if I'm tired, if I'm overwhelmed, if I have too much to like process, too much information.

• 29:27Speaker 01

  Empathy is hard, even with people that we like, right? So what this is, is that, you know, there's two different systems in our bodies. There's the sympathetic, which is the fight or flight kind of nervous system. There's also the parasympathetic, which is the rest and digest is somehow how it's called. And so what these do is they, when either of these systems is are activated, they prompt us to think about survival differently. So when our sympathetic nervous system is activated, we are focused on self survival. You know, we are focused on preserving our personhood, right? So that we're not attacked by a tiger or whatever.

• 30:11Speaker 01

  When we are activating the parasympathetic nervous system, we are focused on social survival. We're focused on survival as the group. But this is why capacity is so important because it's about biologically aligning ourselves so that we can think about, you know, how do we optimize our communication? So there's kind of two steps there. So having the capacity, that is essential for being able to activate empathy. The next is connection. So this is both. From an emotional point of view, it's not necessarily about naming the emotions. This is the circumplex model of emotion where there's kind of two different axes. The first is activated or calm, the other is positive or negative.

• 30:56Speaker 01

  And so being able to notice like, oh, someone is calm and passive, like I can probably approach them that way. Or someone like there's a negative, highly activated. And then you kind of feel it in yourself a little bit, but you don't necessarily name it. Because for example, the way that somebody is processing grief, that could be what's driving that. Or it could be fear. And so you don't necessarily know that. And so that's where then the, you know, more logical side of our brain comes in where it's, okay, what's the context? What are you feeling? You know, what are your motivations? Those kinds of things. So the connection piece is both the cognitive empathy and then also affective empathy. And then the final is compassion.

• 31:38Speaker 01

  So this is about motivation and compassion in a technical sense is about recognizing suffering and then being motivated to do something about it. And one of the things that I say that is a big part of the empathy as a technical skill is that refactoring, which is going into a codebase and making it easier to work with. So that could be having more white space. It could be naming your variables in a way that's intention revealing. Like there's a lot of different techniques, but a lot of it is kind of for humans. Refactoring is inherently an act of compassion because you're looking at how the code could cause problems.

• 32:19Speaker 01

  And then you're being proactive about eliminating the pain of either yourself in the future or someone else in the future. And you're, you're doing something now to alleviate it. And so if we just look at it from a scientific point of view, that's what it is. And that is what you're doing. And so all of these people who have been told, oh, you're a bad communicator. You're bad with social skills, you know, you don't have soft skills, all these things. It's like, no, you're actually operating like really well in this. And again, looking at it systemically, so breaking empathy down into like, what is it really? And then how is it being executed really? And there is just so much interesting things around how culturally there are a lot of mismatches

• 33:04Speaker 01

  in how we assume what this is. And there are a lot of places where we have overlooked like refactoring, you know, just how compassionate that is of an act. Whereas otherwise people are like, ah, whatever, it's a waste of time. So.

• 33:19Jason McGuinthe?

  Wow. Yeah, no, a lot of that resonated with me. As you know, I'm going through performance management at, at the office over the last month or so. And a lot of these little like nuances you just spoke about are coming out. I was out and friend going through a tough, tough trials at, at, at home kind of a thing. And, and I'm, I'm picking up on, on a lot of what you're saying right here and challenging, trying to, to reflect in the moment, which is really hard when you have so, so much goodness to listen to. Right? I'm, I'm listening to you and I'm trying to see like, where do I place myself on this spectrum of zero to 20? And, and all of that, and it's all coming out.

• 34:02Jason McGuinthe?

  It's like flooding out, like, well, actually maybe it's flooding in. But I, I get the sense that there's so much here that I'm just, I'm gonna really value in the future. So thank you for all of that.

• 34:14Speaker 01

  Yeah.

• 34:14Jason McGuinthe?

  So what, what would you say happens when organizations automate communication? Just thinking about this, right? Before they build clarity or trust into the underlying system.

• 34:25Speaker 01

  Yeah, that's such a great question because I think the.

• 34:29Jason McGuinthe?

  Can I just say one more thing about that?

• 34:30Speaker 01

  Yeah.

• 34:31Jason McGuinthe?

  So as, as you said, and as anybody in and around AI driven organizations, especially ones that are building their own software are infusing their processes with, with agents and skills and any number of systems that are trying to make them more efficient. So I'm placing myself into that person's role. They're on a team, it's an agile team, maybe they have a job to do. They're, they're racing at the, the speed of light with their coding agents. What are they missing here? Right?

• 35:05Speaker 01

  Yeah, I think some of it is understanding what the technology does. And, you know, again, in, in like, where's the right tool to deploy? And I think in that, human communication definitely is something as well. So the way that I, you know, I kind of think about all of these different concepts and, you know, there's agents and there's all these, you know, different things. It's like, what is that really? And some people are like, oh, it's, it's a virtual employee. The way that I think about it is that, you know, is kind of like a car. Because as humans, again, our goal is to modify our environment for our species' survival. Right? And we do that through technology.

• 35:50Speaker 01

  Right? So this technology, like, how do we want to modify our environment? Right? So that's the goal. That's the destination, right? Is figuring. So, you know, if we think about it, then we've got the car, right? So I think of that as kind of the agent where it is a vehicle to get information like from one destination to another much faster than, you know, say, you know, we could before. Then a bicycle, we may have been operating at bicycle level before. It's like, we could only go so fast. Now agents, they allow us to move much faster and go to our destination faster. We also have the road. I think of this as MCPs, right?

• 36:34Speaker 01

  Where that's the connection point, right? That is what enables a car to move quickly. So if we have a fast car but no road, right? It's not going to be optimized. We also have directions. And so this is skills and you and I have had different conversations around skills. I think they are one of the most underutilized forms of technology when we're looking at LLMs. They're the turn by turn directions. Because otherwise you just have a car that is just going, but it doesn't know where it's going. So skills are such great instructions, you know, especially for automating customized business processes. But the other thing is that we have the driver, right?

• 37:18Speaker 01

  Or we have the passenger, right? If we're looking at self-driving cars, right? Or we have the person who like plots the destination, right? And says, here's how I want this thing to go. And if we forget that, we get car crashes, right? So we have to recognize that like I am a huge human in the loop, you know, fan where you have to have humans assess, is this system doing the thing that I want to? So that it can achieve its goals. And so the automation isn't the problem. Automation can be great because it helps us achieve things faster.

• 37:59Speaker 01

  It is when humans extricate themselves and say, oh, I don't have to be part of this process anymore. I can just put it completely on autopilot. Okay, if it's working fine, you know, great. But they're putting the system on autopilot. And because we're working with a complex system, things are dynamic, things change and things break. So you have to have people involved just to manage the entropy of the situation. Because, you know, nothing stays the same, especially when we're looking at software systems that interact with our environment. And what LLMs do is they make that entropy happen much faster.

• 38:39Speaker 01

  So, you know, to me, it's again about recognizing like, what are humans good at? Humans are really good at assessing the goals. Humans are really good at figuring out, assessing, is this on the right path? And there are a lot of different ways that you can automate that to achieve your goals faster. But, you know, just completely eliminating humans, I, I think that's a recipe for disaster.

• 39:03Jason McGuinthe?

  Mm hmm. For sure. So the, the clarity there, can, can we double click on how they can build clarity into what they're asking for from these agentic systems?

• 39:16Speaker 01

  Yeah, skills I think are great, right? Because what skills do is they force you to really think about kind of those turn by turn directions. And you can't be clear about an automation unless you're clear about the manual process. And so the clearer you are about what you actually want the system to do, that's where human collaboration comes in. Because it's, you know, like the automation breaks down. And, and this is regardless of whether or not we're using, you know, LLMs or something else. But the best way to automate is to have a really clear business process that's working manually.

• 40:00Speaker 01

  And then you optimize for it. So that's where humans really, you know, because again, it's like, what's the goal? What are we measuring? How do we know if it's effective, right? And figuring out and articulating in a very clear way, here's what we want for our organization. Is this, this, this, and this? And then the power comes in when you're able to use skills and MCPs and agents and all this stuff to do that faster, or at least at the rate that is appropriate. You know, sometimes you just need a cloudflare worker that like pings once a day. Sometimes you need a full-on pipeline that's just going to be, you know, automatic all the time. But again, this is like where you get token optimization too.

• 40:44Speaker 01

  Where it's like, what is the appropriate level of, you know, of tokens that you need in any given context? But yeah, I think that's where the humans, it's, it's really articulating what is the thing that we are optimizing for.

• 40:59Jason McGuinthe?

  All right. So you, you've definitely spent like over a decade, 15 years, you say, like the, working with legacy code and systems. How would you say organizations carry those legacy communication patterns? And how would they hold them back?

• 41:15Speaker 01

  Yeah, I, there's, there's a huge discussion kind of in the legacy code, refactoring, code craft community. Because I think this is one of the first areas that was jettisoned from the, oh, we don't need this anymore. We can have an LLM, we can use Claude or Cursor or, you know, Copilot or whatever to replace and do the automatic refactoring. But I think that. You know, my, my thing is that in the next couple of years, you're going to see that, oh, no, we actually do need experts who are in here. Because again, it's that, what do we automate? How do we think about what the best path is to do that in order to get our business?

• 41:56Speaker 01

  And I think this is where the communication skills for software developers then become so important. Because it's not about writing in a programming language anymore. We are typing out the dollar signs and the percentages and all of those different programming language syntax that feels like an alien language to 95% of the population. Now you're able to collaborate with domain experts in real time. And a great example of this, I spoke at the big Agile conference last year. And there was a hackathon, it was called Hack for Good. It was organized by April Jefferson. Like, I have seen so many of these hackathons where it's like, good intentions,

• 42:41Speaker 01

  but then things end up in prototype purgatory, right? There's nothing that actually of value that gets deployed and used. But what I saw was that in three days, they had brought in an organization that specialized in eradicating child abuse and neglect, right? Yeah, let's do that. Awesome. And so by using a methodology called Fast Agile by Quentin Cortel, which is more about making feedback loops real time.

• 43:13Jason McGuinthe?

  Mm hmm.

• 43:14Speaker 01

  What I saw was that the speed at which you could get features out in terms of prototyping, made it so that the domain expert could immediately give feedback. So the problem that we focused on was that there was a really, really useful intervention, but it wasn't being used because the data entry for the therapists who were like in the field was so burdensome that they just didn't use it. And so they were at risk of losing a grant. So we developed an iPhone app of, okay, what about this? What about this? And she was like, this is great, but I would want the numbers in this way because that's kind of how it works with this. And I would love to have it in colors. And I would love to see some like haptic feedback.

• 43:59Speaker 01

  And it was like, okay, boop, boop, boop, boop, boop. Now try it. So over the course of three days, there was something that was like actually working. And now it's being used. It's being rolled out to, you know, other organizations. More grant money is going in to, you know, things that are more effective. And, you know, we were all in tears because, you know, the, you know, the domain expert who was leading this was like, this will save lives. And to me, that's why I'm like, eh, it's not always bad. It's about recognizing that, you know, humans interacting with each other to figure out what is the goal, right? Different ways of using AI can help us have those better conversations.

• 44:41Speaker 01

  And I think one of the biggest shears is between developers and domain experts. But this is why human communication becomes so essential with software developers, because the, you know, the skill of having to communicate with humans and translate kind of those implementation details and to more higher level features and things like that. It, the feedback loop was so much slower, but now it's becoming so much faster. And that's why that human communication skill is becoming so much more important, not just for software developers, for everyone in the organization too.

• 45:14Jason McGuinthe?

  In an AI accelerated world where information is abundant? That is absolutely true. What other human capabilities do you believe are, are becoming truly scarce and, but valuable?

• 45:27Speaker 01

  Yeah, I think critical thinking, for sure. Deep listening, right? So being able to take time, kind of think through. I think, you know, having a culture and kind of having the operations to activate our parasympathetic nervous system. Where then that way it's like, you know, I have worked at different organizations over the years and some of it's like, oh, we have this beautiful campus. And like, you can go outside, but like culturally, it's like, no, you can't really do that. Like, if you do, then your scene is not working.

• 46:01Jason McGuinthe?

  Yeah.

• 46:01Speaker 01

  And I think that's some of it too, is looking at culturally and figuring out what is the importance? Because a lot of times we think, if you're not typing as fast as you possibly can, and if you're not deploying something as fast as you possibly can, then you're not contributing value. When sometimes, not all the time, right? Slowing down so that you can think critically, so that you can activate that social, you know, goal, you know, idea where it's not just that individual survival. It's the social survival, right? It looks like you're not necessarily working, right? Because of, because that's how we've defined productivity right now. But, you know, so I think assessing kind of what are those norms?

• 46:45Speaker 01

  And, you know, figuring out what are kind of the operational tools that are going to help us kind of get there effectively.

• 46:53Jason McGuinthe?

  When you said, I had to chuckle, sorry. And when you said, it's beautiful outside, but I can't go because I'll be looked at like I'm not working. I have experiences where people make those statements. And I'm like, what is wrong with this, with the culture here, right? Like, why do you believe that for yourself?

• 47:11Speaker 01

  And so then it's like, they feel like they can't do it, so they just don't do it, right?

• 47:15Jason McGuinthe?

  Yeah.

• 47:16Speaker 01

  So it might not be that anything is just wrong, but we're just conforming. So Edward Shine, or Edgar Shine, I really like his work on what culture is. And so he defines it as three things. It is shared behaviors, shared expectations, and shared learning. And so, you know, if we don't explicitly say, please go outside. It is important to our culture, right? This is how we get creativity, right? Then, you know, there are explicit, you know, expectations, but then there is also implicit expectations. And so if we don't see the behavior, then we might imply it's not useful or it's not allowed. But it's not actually not allowed.

• 48:01Speaker 01

  We're just, we could go outside and then someone's like, oh, that's, okay, maybe I'll do that too.

• 48:06Jason McGuinthe?

  Yeah.

• 48:07Speaker 01

  But some of it is just defaulting to, this is the way that everybody operates around me. And I don't want to risk being seen. Amy Edmondson has some great work in her book, Teaming, around image risks, where people don't want to be seen as ignorant, disruptive, negative, or there's one more.

• 48:24Jason McGuinthe?

  So what's the difference between a team that communicates efficiently? And a team that communicates intelligently?

• 48:32Speaker 01

  Yeah. Again, I think that this is kind of not mutually exclusive. Because I think that a team that communicates intelligently does communicate efficiently. So if we look at, again, that Shannon Weaver model, right? The idea of what effective communication is, is I am getting an idea from my brain into your brain as quickly as possible with that, at the highest fidelity possible. So without losing information, right? And that means that conceptually, I need to figure out like, what is the thing I want you, I want to say? And then what is the thing I want you to understand? But there also is that like, how can I get that message there quicker?

• 49:16Speaker 01

  And so there are things like feedback loops. There are times where, you know, especially when things are very complex, you want people to be on site because you need all of that rich information. You need people to be bumping into each other and having those different conversations. But for this, something like this, it would be really hard for us to schedule a time where we were in the same studio, right? So we're able to get the same fidelity without having to be in the same physical location. So again, it's like, how do you structure your, you know, communication infrastructure? And so how do you look at the channels and, you know, so Slack? And how all of that is organized and built? So that you can get messages across the network efficiently.

• 50:01Jason McGuinthe?

  I really need your brain power on the synthesizer that we're building. I think about that a lot. Whenever I'm building out that system, I'm like, what would make this better in, in these particular areas? And developing this new workspace for the, the 60 day roadmap, the mapping exercise that we're doing here in AI Ready? It has me rethinking some of that structure and how we can leverage different work streams and things like that. And how those work streams can coordinate amongst themselves and more operationally across the board. So it's fascinating. We're actually recording a podcast.

• 50:40Speaker 01

  Yeah, so these are conversations that we're already having about the communication infrastructure. Right? So for example, you know, there's a lot of information happening over on Discord, right? That's the channel that the organization has chosen. The idea isn't necessarily that we have to move where those conversations are happening, but we do have to be able to, you know, capture those conversations so that they can be analyzed. So that's an example of building some automation, where what's a technologically appropriate way to do that? And then you start having conversations around privacy and, you know, speed and how much and things like that. And again, that's where the human conversation becomes really important. Because if you just go, oh, let me just scrape all of it.

• 51:23Speaker 01

  It's, it's like, that's where some of the problems come in. Using these tools to enable us to have better human conversations so that we can be on the same page around, okay, what is the actual goal? What are the priorities? And then what are some of the automation, you know, techniques that we can use to achieve our goals faster?

• 51:43Jason McGuinthe?

  Yeah, yeah. Okay, cool. I want to circle back to how communication develops in organizations. And, you know, leaders know that it's, it's super important for their, their teams, themselves, their, their thought processes. Now, tell me how? Sorry, I keep getting distracted.

• 52:02Speaker 01

  You're fine.

• 52:03Jason McGuinthe?

  Yeah, I was thinking of your camping trip and the board meeting and. My mind is going in many different directions. All right. I'm like, I'm seriously overwhelmed with this conversation and I'm gonna-

• 52:16Speaker 01

  I get that a lot.

• 52:18Jason McGuinthe?

  I, I love it. I do. And I'm also becoming cognizant of the time that, that is, it's gonna exceed an hour, which I haven't done before. And I don't know that there's like a real rule around that, but I imagine it does lose its effectiveness-

• 52:34Speaker 01

  Yeah.

• 52:35Jason McGuinthe?

  -in people's attention.

• 52:36Speaker 01

  Well, if you wanna kind of just wrap it up, we can totally do that now. This is probably a good place.

• 52:40Jason McGuinthe?

  Yeah.

• 52:40Speaker 01

  Unless there's like a burning question that you've got.

• 52:43Jason McGuinthe?

  I really, yeah, I really don't, but you've been so thorough is the thing, right? It's like, gosh, like, most people do not answer so effectively and thoroughly. It's just, they just don't have as much to say and they don't have all this industry knowledge and yada, yada, like, different experiences. Yeah, I almost feel like I'm, I'm, I'm in your audience when you're speaking, you know? So it's like-

• 53:09Speaker 01

  Yeah. Yeah, and I think this is kind of what my clients like because then it's not a, oh, you should make eye contact all the time. I think this is kind of where a lot of communication coaches, just if you don't have the other understanding of how systems theory and like how all these things work, like, you know, you can end up giving advice where it's like, okay, in that context, that might not be the right thing. So for example, one of my clients, the challenge that he was working on was that he had kind of a all hands meeting for his work. They were gonna bring in like the 200 people on the IT team to a physical location. And he was like, I am going to hate this.

• 53:51Speaker 01

  I'm introverted, I'm shy, I like, don't know what to do when we talk to people. So everything that I do, my coaching is all around experimentation. So what we discovered in kind of the, the process of decomposing this problem was that the reason that he was struggling wasn't necessarily that he was shy, it was that he was analyzing the conversation before it even happened.

• 54:17Jason McGuinthe?

  Hmm.

• 54:17Speaker 01

  And so, a lot of times, decomposition is another important thing that we do in software development, where we're taking, you know, a problem and we're breaking it down to its teeniest, teeniest, teeniest, tiniest thing that can be tested. So you've heard Atomic Habits and things like that. So what we ended up doing was like, okay, well, how can you make sure that you don't, that you're not spending all of that cognitive overwhelm? And also, you're like, you know, you've heard eye contact, but you don't feel like you're going around and staring at everybody so that you feel really uncomfortable. And so, the experiment that we came up with was just pay attention to the expressions of the people around you. And if you see someone else make eye contact with you, or do a head nod, or do a small

• 55:02Speaker 01

  smile, all you have to do is mirror what they do. It's the only thing you have to do. You don't have to come up to them, you don't have to talk to them, you just mirror. And so, a few weeks later, he came back and he was like, I don't know what happened, but that was the best time I ever had. I connected with so many people. I didn't realize that there were so many people who were passionate about refactoring, and so now I'm creating this group. And so, it's like, all of that from, you know, we think that communication is this like, big, huge thing, but the way that we navigate complex systems is a lot of times by just breaking them down into their smallest component parts and then executing the system.

• 55:44Speaker 01

  And then you try and it's like, whoa, that actually had a lot of impact. There's, again, in systems theory, this is called leverage points. Don Almetto has a great book, Thinking in Systems, that goes over this. So, but yeah, I think that's, that's part of it, is that, you know, that feeling of overwhelm is what a lot of people feel, right? And when I'm naming out loud, it's like, here are all of the different possibilities and here are all the different things, but also here are all of the heuristics that we can lean on and say, oh, in order to achieve my goal, I know that leverage points are a thing. So then we can bring it down to what's the smallest problem, but what's the smallest

• 56:27Speaker 01

  thing that we can test to observe in each interaction? So, you know, I work with my clients where it's every single interaction is a tiny test. Okay, try this thing. Like, don't even think about any kind of conversation or anything, just mirror back. If somebody nods their head, you nod your head back. That's all you do. It's all you do.

• 56:47Jason McGuinthe?

  Okay.

• 56:48Speaker 01

  And so then every interaction becomes, you know, a mini experiment. And that's how we learn. That's the scientific method. That's how we explore patterns. And it takes this burden of, I'm not a good communicator, to, you know, I am learning how to communicate effectively with the people that are around me in the context that I'm in, given the goals that I have.

• 57:14Jason McGuinthe?

  That is great advice, by the way. So, I wanna wrap it up here, Andrea, with one final question, because it takes not just, you know, listening to great advice, but also having your own mental mindset shifts, right? And I know that intuitively and academically because of the, you know, books and experiences I've had in life, but if you could give one final piece of advice for a single mindset shift for navigating the next few years of AI-driven change, what would it be?

• 57:53Speaker 01

  I truly believe that if we start to look at human communication as a system and understand its properties and how it behaves, and we focus on that, given all of the things that, like, systems theory and Conway's Law and all of these things, that that is how we create healthy software systems that will help us achieve the goals that we're looking for. And sometimes that is pausing and allowing ourselves to have more empathy. Sometimes that is going super fast because we have a challenge that we need to resolve as quickly as possible. But paying attention and really thoroughly understanding the science of how humans communicate,

• 58:38Speaker 01

  that's going to directly translate into how we build our AI systems, and then also how we automate, how we leverage the tools, and which problems we focus on solving.

• 58:51Jason McGuinthe?

  Wow. Okay. That was fun. I thoroughly enjoyed that conversation. Thank you so much for joining me on the podcast today, Andrea. We've got a lot of work to do to help this community, and I'm super grateful for your contributions. So, without belaboring it any, any further, thank you again, and I look forward to working with you on the synthesizer. And enjoy your camping trip.

• 59:19Speaker 01

  Thank you.