Beyond chatbots: Agents that tackle your SOPs

As AI reshapes the workplace, employees and leaders face questions about meaningful work, automation, and human impact. In this episode, Jason Beutler, CEO of RoboSource, shares how companies can rethink workflows, integrate AI in accessible ways, and empower employees without fear. The discussion covers leveraging AI to handle routine tasks (SOPs or "plays") and reimagining work for smarter, more human-centered outcomes.

Featuring:

• Jason Beutler – LinkedIn
• Chris Benson – Website, LinkedIn, Bluesky, GitHub, X
• Daniel Whitenack – Website, GitHub, X

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• Framer – Design and publish without limits with Framer, the free all-in-one design platform. Unlimited projects, no tool switching, and professional sites—no Figma imports or HTML hassles required. Start creating for free at framer.com/design with code `PRACTICALAI` for a free month of Framer Pro.

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Creators and Guests

Host

Chris Benson

Cohost @ Practical AI Podcast • AI / Autonomy Research Engineer @ Lockheed Martin

Host

Daniel Whitenack

Guest

Jason Beutler

What is Practical AI?

Making artificial intelligence practical, productive & accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, LLMs & more).

The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!

Jerod: 00:04

Welcome to the Practical AI Podcast, where we break down the real world applications of artificial intelligence and how it's shaping the way we live, work, and create. Our goal is to help make AI technology practical, productive, and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place. Be sure to connect with us on LinkedIn, X, or Blue Sky to stay up to date with episode drops, behind the scenes content, and AI insights. You can learn more at practicalai.fm.

Jerod: 00:36

Now, onto the show.

Daniel: 00:48

Welcome to another episode of the Practical AI Podcast. This is Daniel Wightnack. I am CEO at Prediction Guard. I'm joined as always by my cohost, Chris Benson, who is a principal AI research engineer at Lockheed Martin. How are you doing, Chris?

Chris: 01:03

Hey. I'm doing very well today. How's it going?

Daniel: 01:05

It's going great. Wrapping up the year, reflecting over the past year. It's always a kinda time of the year to reflect, and it's been a great year for for the podcast and for for business and for other things. So, yeah, just feeling I think feeling particularly blessed as we kinda head into the head into the end of the year. And I think, also blessed to have made a bunch of good connections this year to people that are that I'm learning from.

Daniel: 01:35

One of those is our guest today, Jason Butler, who's CEO at RoboSource. Welcome, Jason.

Jason: 01:42

Hey, thanks so much.

Daniel: 01:43

Yeah. Yeah. It's great to have met this year, I think from a variety of directions, a bunch of different connections, but you did also joined us at the Midwest AI Summit, which was, fun. Chris was there. I know that you have a particular passion for kind of thinking about the way that people work and helping them do good work and meaningful work, I guess.

Daniel: 02:13

Yeah. Do you wanna expand on that idea a little bit? Because I do think it's interesting to think about that topic when people are grappling with, is AI taking over my job? What is my purpose at my job? What jobs are gonna stick around?

Daniel: 02:31

What jobs aren't gonna stick around? From your perspective, what does that mean? Like helping people do good work, or from your perspective, what does that mean kind of in in light of the current ecosystem in which we live?

Jason: 02:43

Yeah. Well, I just think people wake up in the morning and they want their jobs to matter. Wanna feel like they're making a difference. And so they they want they wanna build relationships. They wanna build community.

Jason: 02:54

They wanna they wanna know that what they're doing is having a strategic impact on the community they're with, the people they're with, the the people they're spending their day to day with. And I think when that happens, you work different. I I I think my grandfather read this story, but he he would tell it to me often when talking about, you know, he knew someone that worked at a factory and spent all their time working basically just doing the same thing over and over again, was happy as he's ever been. And the new guy had come in and was doing the same thing and just felt miserable the whole time and felt like he was it was meaningless and that what he was doing didn't really matter at all. And couldn't understand why this older guy was so thrilled.

Jason: 03:33

And he's like, all we're doing is putting the same screw into the same hole and all these different different vehicles or whatnot. And the older guy goes, no. I'm protecting my kids. I'm protecting my children. I'm protecting the the millions of families that are gonna buy this car because I put this thing on right.

Jason: 03:48

So his version of meaningful work made him joyful about the day to day stuff that he was doing. So I got really passionate around that. I was like, how can we how can we help people know that what they're doing matters and help give them some context around that? And so part of that is finding the things that feel meaningless and removing that. And that's really where our our organization started, almost fifteen years ago now, was how can we find things that that people feel in their day to day isn't important, isn't adding value, isn't adding meaning, and how can we move that aside so that they can focus on the stuff that is unique to them, that allows them to bring that value?

Jason: 04:27

And that's different for everybody. It's not like again, the grandpa putting on the the screw was happy as could be because he was making a difference and the 25 year old was feeling meaningless. Like, they were doing the same job. So it's not the job you're doing, it's the impact that you can have when you are able to get the things out of your way that mentally keep you from being effective.

Daniel: 04:47

Do you think that in our world of AI influencing every job at almost every level of any organization, in your interactions day to day with the folks that you serve and work with, do you see that shifting in terms of what people think is meaningful or is there maybe just more fear that what they feel is meaningful might be going away or or or something like that?

Jason: 05:17

You know, most of the people that I interact with are afraid that that their leadership is gonna see their work as not meaningful. And that's where I think the fear is coming in is this uncertainty around how leadership's gonna view them. And so what they're trying to do in my conversations with them is they're trying to figure out how can I make sure that the leadership knows that I'm adding value and that I'm doing meaningful things? And this world of AI is throwing some, you know, ambiguity into that because where how they used to add value is starting to shift. And as that starts to as that becomes more and more, exacerbated and we're now AI is doing more and more of the kind of monotonous or repetitive tasks that you used to bring value around or even some of the analytic tasks where it's starting to say, hey, I'm seeing insights that you might not even be able to see.

Jason: 06:10

We start to question our own value and how we interact with that with with the organization.

Daniel: 06:15

But I guess that's where I

Jason: 06:16

keep coming back to this as a psychological problem because business is done with people. Like, business is a people enterprise. Like, it it it always has been, and I think it always will be. Yes. We can do tasks, and and AI can help take some of that off.

Jason: 06:32

But at the end of the day, we still there's there's still people involved on how business gets done. There's still relationships that drive that. You know, my my daughter just graduated from college and it's like, how do you get a job? Well, you go meet people. Like, it's people that help you get jobs.

Jason: 06:48

Like so it's not the automated systems. It's it's when you meet someone face to face, that's where you find find work. So so I think in the day to day, like, yes, some of our tasks are shifting and we're afraid of the value that we bring, but there's still relational components. There's still a human to human piece that exists, and we've got to embrace that. And I think the more we embrace it, the more we're gonna see our impact and the and the meaning that we can bring to that organization is just going to accelerate.

Chris: 07:17

I love I love kind of that. It's a it's an optimistic take in a, you know, that is doable and, and, you know, that we've all kind of lived by for years. And it's very easy in the current environment to kind of forget that. And I think, I think, you know, the young people entering the workforce today, you know, maybe have not had the benefit of those experiences that give someone a little bit older, that perspective. I am curious, acknowledging all these industries that AI is impacting are changing rapidly now, and that management at lots of different companies across industries are trying to navigate that dichotomy between human interaction and the fact that we have all this amazing automation available and they're trying to find applications so they they want the benefit of the automation to make their businesses more efficient.

Chris: 08:16

But at the same time, as you pointed out, it's a you know, the workforce is human. And it's not only that, but it's not homogenous. Every situation is a bit different. Every employee is a bit different, the jobs are different. There's a lot of diversity in terms of both people and process there.

Chris: 08:32

So as you're kind of coaching people into this brave new world that we're all navigating now, how do you approach different management teams about navigating those challenges and, you know, not only finding the efficiencies that they're looking for to be profitable, but also reassuring their workforce, and all that uncertainty that there is a place for you the employee in the future. Because I know a lot of a lot of people out there are looking for that right now. I'd love any insight you have in terms of how how you tackle that.

Jason: 09:06

Yeah. That's a fun question. That's a lot there's a lot to unpack there. Here here's what I believe. At least where AI stands today, it does not have access to all of the context in order to make the decisions that a business actually wants made.

Jason: 09:20

And therefore, the real value that happens is in the conversation with people to solve problems that, frankly, the technology can't even comprehend because it doesn't even know exist. So from a management standpoint, my my first approach is get the people in the room on a Zoom call, get them in a place, having conversations about the real challenges you're dealing with. Now, can we be more efficient around that? Yeah. Let's record the let's record the conversation.

Jason: 09:51

Let's pull a transcript. Let's have the AI extract some actionable things from that. Even go so far if you're in software, let the AI code the thing you all just talked about. But at the end of the day, the real value is the conversation that happens because we're all aware of the context of what we're trying to solve. So you bring in, you know, Chris, your perspective and Daniel, your perspective and my perspective, put us all into a room.

Jason: 10:13

We're coming up with a very different solution than if I'm just sitting in the room by myself with AI. And so I I just I embrace the yes. Let's be AI focused. Let's make sure that we're leveraging these tools. That's just good business.

Jason: 10:27

Like, it's a tool. We should be using it. You know, I I I don't buy a a a drill or a power screwdriver and then sit it in my garage and not use it. I buy it to use it. So let's use it.

Jason: 10:37

It's good business. But let's use it in the right ways. And to use it in the right way, you have to start with people. And so I just think from a management standpoint, it's the same formula. It's not really changed, though it feels like it has.

Daniel: 10:53

And do you think part of that kind of responsibility that's growing for management in those cases is, part of what I've seen is maybe there's a from the top statement that goes out, you know, we will transform our business with AI, right? And actually the management isn't also using AI or leading kind of by example in that. And certainly there's, management I'm sure that are, but how do you think about, I'm wondering your perspective on kind of like those of us that are supervisors or those of us that are managers, how we can actually lead by example in showing how kind of, I guess, human and AI can team together to, like you say, create efficiencies, but also bring in that valuable human element like you're talking about?

Jason: 11:52

Yeah. I I think an interesting thing I've observed with my team, because I can talk specifically about how they're working. We set out and we're like, we're gonna be AI driven. And so we looked at our current workflows. We looked at all the steps of our current workflows, and we're like, which of these workflow pieces of the workflow can we, you know, either replace with AI or augment with AI in some way, shape, or form?

Jason: 12:13

And so we went through all of our so we're soft we do a lot of software development. Right? So we have an SDLC. So we're going through all the steps of our SDLC, and we're like, oh, we can automate this step. And so we break it all down.

Jason: 12:24

Here's what ended up happening. It was like we were more inefficient. We we started having all these weird, like, oh, we gotta move stuff into different places for AI to help. And it was like, this isn't this isn't right. We're we're inefficient in our approach.

Jason: 12:37

When we took a step back and we're like, what if we assumed that AI could do things that right now we've assumed it can't do? Like, so what if we'd, like, completely rethink our entire software development life cycle from a concept of maybe AI could do all the things that we were trying to do? So, like, for instance, right now, we create a, like, a product requirements document. It's like so when we were looking at doing AI, we said, hey. Let's have AI create the products requirements document.

Jason: 13:05

Okay. It did it did okay, and then we had to work with it. What if we don't need a product requirements document? Like, what if we can all sit like, what if we rethink the entire workflow? And what if we record the conversation we're having around what the feature should look like and then take that straight to a AI project plan on how to execute that in the code.

Jason: 13:23

Skip the seven steps in between around how that's working. Maybe that's viable. So I say that to say, I think we have to start from, like, from scratch. I think we have to take a step back and kind of throw out the way we do work currently and rethink from a whole new perspective because the tools are they just work different. And and they're they're tools that we we don't even know how to think about yet.

Jason: 13:49

And so you've got to challenge your kind of base assumptions for you to actually start to to put real tools and real power into place. So that's where I like to start is like, let's, you know, let's start from scratch. Like, literally, let's throw out what we had. And that's hard. Change management is hard because that was somebody's five years worth of effort to get that SDLC right.

Jason: 14:11

And we're saying, hey. What if we don't do it? Like so that there there is a a relational thing, a relational management piece that needs to happen there to make that accessible. But when you do, all of a sudden, what used to take us weeks is starting to take us days because the tools are that powerful.

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Chris: 15:45

So Jason, I wanted to actually even go a little bit farther into that what you were describing, because that I love where you're taking that in terms of kind of reimagining that. I think I think a lot of people stumble at that point in terms of what it takes to reimagine, and trying to figure out like, as they're looking at their own process, and they're they're looking at trying to find places to fit AI capabilities in to solve their problems more efficiently than they had before. You know, it comes down to you know, once upon a time, it was kind of just like the mundane tasks. But then we you know, generative AI came along and like the the imagination, if you will, of AI is is remarkable. And so I think that's led to a lot of ambiguity or confusion in terms of where the all the the plethora of AI tools can can bring those benefits on on that.

Chris: 16:42

Can you talk a little bit about like, how do you get someone to start down that path and level set the initial assumptions that they're gonna make in their own process, recognizing it's a little bit different across organizations and teams? How do you get there? Because I mean, AI is so capable now that trying to find that that point of application that just getting out of the the the starting block, I think, is a challenge.

Jason: 17:06

So take a slightly different approach to that and the way that I answer that. My son's a a musician. He's an artist. He writes he writes a lot of music. When he sits down to write a song, we often end up in writer's block.

Jason: 17:24

We end up just stuck. We're like, I don't know what to do. So he starts playing with it, he's like, I know the chords. I know how to put them together. I can play licks all day long.

Jason: 17:35

Nothing's connecting. What I find though is when I say, hey. Let's pick up a cover song. Let's grab the Beatles. Let's start rocking on the Beatles a little bit.

Jason: 17:44

And we just start goofing around. And we're just fiddling around playing the Beatles, singing it together, and and, you know, all of a sudden, he'll look at me and he'll go, oh, I have an idea. And then he starts riffing on a new song. So sometimes it's just the practice of playing and letting your mind go that lets you create new ideas. And and I think the same thing applies in AI.

Jason: 18:08

If we're not just being creative and playing with it, I'm not sure we're gonna find creative new ways on how to plug it into our process. So we've gotta just embrace that it's gonna be messy, but we gotta play. Like, my son and I, we don't play the Beatles perfectly. We've been working on the Eagles recently. Right?

Jason: 18:29

Like, you know, little sister golden hair. And it's like, we can play on this sounds awesome. But though by just playing those chords and seeing and seeing how other people are doing it, it starts to get your mind thinking differently. Same thing with AI. You might not have a great use case for it right off the top of your head.

Jason: 18:45

Part of that is because we don't even know how to think about it yet until we start playing with it enough for our minds to start to make those connections that we never would have made without it. So I encourage people just to get in and start throwing stuff at it. Not because they're necessarily gonna get a lot of great value out of what they're throwing at it, because most of the people I talk to are we're still treating it like Google. I mean, like like, we haven't really pushed it to the new limits. But by getting in and asking it those questions and seeing what it's doing and learning just little bits and pieces here and there, all of a sudden, they start to go, oh, what if?

Jason: 19:22

And because they have the experience and the connection to their processes and the way they do work, it naturally goes to, oh, man. If I didn't have to do these five things, can you imagine what I would do with my time instead? But they don't know to think about it because they've not played with it enough to even know. So I like just to play. Like, it's a it's unconventional, but I think it does it is what spurs the creativity.

Daniel: 19:45

And maybe part of that play is what leads you to have some of these insights around like, oh, maybe the automation or maybe the AI driven process should actually look different than its human equivalent, right? Otherwise you sort of don't know where it should look different or where it shouldn't. I always, I love, read some of Richard Hamming's work and some of what he talks about is like seeing so many different generations of technology that have mechanized certain processes or automated certain processes. Basically his observation is the ones that end up transforming things are those that do a process but in a way different than the equivalent human process. And I think there's kind of the common way of saying this is like, if you automate some well established process, you just sort of get a very efficient bad process, not a new transformative process.

Daniel: 20:50

I find this though very, very difficult in actual conversations with people to have them grasp this idea because the tendency is for us to say, well, I have this process that X person does in my company. I would like to automate that now. And I know you think about automation all the time. How do you get people to get Maybe part of it is building that intuition with getting hands on with the tools, but how do you get to that point of from here is our process that we execute manually to the here is how we might do it with an AI augmented approach, which is necessarily, I I mean, if it's going to be transformative, like we're kind of making this assumption should look different than a human process.

Jason: 21:44

Yeah. And you're so right. Almost every client I could start with is, here's the thing I want you to automate it. Yes. Here's a task I want you to automate it.

Jason: 21:52

And I'm like, you could have done that ten years ago. Right. A lot of what you're asking to do is not new. So, there is this this kind of a mental gap or a hurdle between, you know, pure automation and what AI is capable of doing. To answer your question on how we actually get to the the transformative, I I do think depending upon who's having the conversation, there's different ways you can go about it from a conversational standpoint.

Jason: 22:16

I tend to be pretty high energy, and I tend to be very inquisitive. So that plays to my strengths. So when I'm in there having a conversation, I'm like, oh, we're excited. Look at all the cool stuff we can do. Hey.

Jason: 22:27

Check this out. Like, I'm high energy with them all. And then they say, well, here's what we're doing. And I'm like, oh, why are doing that? Really, I'm using the five why strategy of, like, I'm digging into what is the root cause of why they're actually making the decisions in the process they're making so that I can then start to offer alternatives around, if this is the outcome you really want and you did these six steps to get to that outcome, maybe there's a different way we can get that same outcome.

Jason: 22:54

And maybe the the six steps aren't necessary anymore. But for me, I do that through just high energy and creative, inquisitiveness. So

Chris: 23:04

As you dive into that, I'm curious. And you kinda talked about the, you know, that initial engagement with the CEO and stuff like that. But to to to kinda circle back and bring the employees in, as you're going into that process, how do you wrap those employees into it in a in a positive and productive way, so that they also see that same value that you you know, you automatically do because you're doing this, but that the CEO is as well as starting to see so that they're engaging, you know, with that same energy and creativity instead of instead of worry or concern, that kind of thing. How do you how do you navigate that human element as you're going through the beginning of this process, flow?

Jason: 23:46

Yeah. So being candid, I'm still figuring a lot of that out.

Chris: 23:50

As are we all, I think. Yeah.

Jason: 23:51

So so that that is hard. Yeah. That being said, we do know that change management is a science. It has been studied. I am reading on it constantly right now.

Jason: 24:04

So I I think the same principles will apply. I do think there is an idea a concept of leadership has to buy in. If leadership's not bought in, the cost of change is gonna be too great. So we've gotta get leadership bought in. Once we have leadership bought in, then I do think there is some idea of I hate the word committee, but bringing in people who are on the front lines and are actually dealing with the reality of work to be a part of the conversation.

Jason: 24:33

I also think it's important for the the leadership to be in the room and reaffirming intent. I know there are scenarios, and I'm not ignorant enough to say that there aren't scenarios where AI is gonna replace jobs. I know there are. Most actually, all of the businesses that we've worked with understand that we're not in a we're not lacking we're not we're not wanting people to leave. We're wanting people to work on other things.

Jason: 24:59

So they're not coming in going, we're trying to eliminate place positions. They're coming in saying, we have so much opportunity that we can't take advantage of. We wanna get you moving in the in these other places. Leadership's gotta be real vocal about that and, has to be very, very clear and transparent on that front. So I I think we gotta get the front line in place.

Jason: 25:18

I think we gotta get leadership communicating expectation, and then we've gotta create and this is what I'm trying to figure out right now, is how to create environment where we can play in a safe place to start to spur some of that creativity. Yeah. I've not figured out how to do the the the play part yet where it's fun. It still feels like they're learning engineering stuff.

Daniel: 25:38

Yeah. And some of this too, I guess some of the points that you made around like different people being motivated by different things. Certainly in the If we just take AI out of the picture, if a business is operating and someone really just loves to write handwritten letters, right? And they're like, I'm not gonna send any emails. I'm just going to write handwritten letters to everyone and we're gonna communicate that way.

Daniel: 26:07

Obviously there's a inconsistency with how actual business operates now. Maybe part of the disconcerting thing right now is that we sort of don't quite know how business will operate as things kind of as this adoption happens, right? So we don't know if doing the thing that is the equivalent of writing handwritten letters instead of sending emails, right? And I guess that's still just, I mean, it seems like sometimes, so I'm also curious on your perspective on this, Jason, because it seems like sometimes that if we look out at the world, every business is using AI pervasively. And sometimes like I got on a call the other day and I asked like, Hey, are you using AI any way in your business?

Daniel: 27:03

And the response was, Oh, everybody is. What kind of question is that? In my experience, actually, that is very much not the case. I'm wondering about your experience. Maybe just to give people comfort that are listening to this, most of the calls I get on folks are are not what I would consider having adopted AI and are using it pervasively across their business.

Daniel: 27:28

Would you what what is your experience? I'm hoping it's the same.

Jason: 27:31

It is it is exactly the same. Everyone says they're embedding it into every part of their processes, and nobody is actually doing it in any way, shape, or form effectively. And so, you know, one of the conversations I have with clients most often is they're like, well, I'd love to use it more, but every time I wanna go ask it a question, I have to spend fifteen minutes telling it all the background information so it'll answer it appropriately. I was like, yeah, that seems reasonable. Like like that that was what I would expect.

Jason: 28:02

So those are most people are not using it. And if they are, CEOs like to say they're using it because they're using it to generate social media content and marketing and some other, like, you know, they roll right emails for them, things along those lines. But actually using it in a productive, way that's creating efficiency, I I run into very few companies that are doing that.

Chris: 28:23

You know, I've been, I've been kind of pondering one of your previous answers as we've continued talking in the back of my mind. And, you know, you talked about like the navigating the human side is, you know, there's the psychology around it. And, and it occurs to me just to just to throw it out that it's, it's, we have a habit of framing, you know, bringing AI in as a new problem. But in a sense, it's really not. Because if you go back before, you know, this moment, where where we have generative AI and other AI models that we're bringing in, And it's creating that sense of uncertainty.

Chris: 28:58

If you go back in time, and I'm gonna I'm gonna make a reference that maybe will make you smile to like the movie office space. And at that point, they had kind of a two comic characters, which maybe some folks in the audience remember called the two bobs, which were talking about process. And the employees in the company were very concerned about their jobs, and they were going through that. And as it occurs to me, it's much the same concern, it may not be whether that process, know, which had nothing to do with AI or really even technology in the movie, you know, but but that that notion of process automation being a frightening thing for employees to be thinking about. Am I safe?

Chris: 29:36

Am I gonna be okay? And it's still really what we're talking about now. We're just talking about kind of AI as an actor in that in that sequence. So and I know one of the points of the movie is you're still trying to figure it out. Humans are complicated or emotional.

Chris: 29:51

There's not a quick answer that people just get happy about. And as you were saying very honestly, which I really appreciated, that you were like, you know, that you were still trying to learn your way into that, which I think is a fantastic answer because it's so it's so honest that, you know, it's not just reframing. I'm just wondering is is I throw that that kind of office space analogy. Does that resonate with you in terms of do you do you think there is a a truth there that is sore somewhat timeless? And is there anything, you know, what is new potentially in the AI being thrown into the equation on top of that office space timeless aspect?

Jason: 30:28

Yeah, that resonates deeply. Actually, I'm gonna have to go pull out that clip and use it in my next talk. It's the same problem y'all. Yeah. We're literally like like, we don't like change.

Jason: 30:40

Like, no one does. You know, we're we're very happy to know what's like, how things are being done and not have you know, don't move my cheese type of idea. Right? So, you know, these these problems have have been around for a while. So this is a new flavor of it.

Jason: 30:55

And it's one that feels like the magnitude of the of the wave is larger. It feels like it's gonna maybe rock more boats than other changes in the past have, but but it's the same problem. And so I I feel like we we should be able to, leaders, be able to use the the same tools that have been used in times past to help people navigate this currently. And I think that should bring comfort to us, because it's we are solving somewhat of a similar issue. Now, again, I I don't wanna downplay the impact of a of artificial intelligence.

Jason: 31:31

Its impact is significant. Like like, there there is a lot that this is going to impact. But I I do think we're, people wise, kinda dealing with the same issues.

Daniel: 31:42

And this kind of leads into something I I would love to talk about because I I get the sense just having seen what what you all are doing with Process Coach, which is one of the things that that you're offering as a product, which we were talking about this a little bit before we started recording. I'm all for anything that's not just another chat bot. I love it when people are thinking about different ways of interacting with AI and integrating it into their kind of business processes or the way that they work, which, you know, as folks have already, have heard from you, you are thinking all the time about, you know, the way people are working and what they find meaningful. But I'm wondering if you could just give us a little bit of backstory on Process Coach, because I do think, you know, the way that you're approaching this integration of AI and the way that you're thinking about automation and what you're doing maybe is different than of the ways that other people are approaching it.

Jason: 32:45

Yeah. Well, we're pretty excited about this this tool, mostly because, again, we're we thought if you're a business, particularly a small and mid sized business that's trying to figure out how to navigate this, and you're trying to get people, your team in line and and and, like, hey, this is actually gonna be valuable to you. There's a lot of fear. There's a there's a lot of a lot of things that kinda stay in your way. And and a lot of that comes down to how do we get the day to day operations so that it actually is leveraging AI in some way, shape, or form.

Jason: 33:19

So when we took a step back and we're talking to our clients and looking at at everything, we're like, you know, MBAs all say, to scale your business, you have to have business process. And I think we all know that to a certain degree, that, you know, if you don't have something standardized, it's impossible to really to improve it. So we understand that, but very few people, I think, have actualized it. And we thought, man, if we could create an environment where we could simplify figuring it out, get it get it defined, and then actually make it useful. So a lot of the automation around process ends up getting too fragile.

Jason: 33:52

I don't know if if you've experienced that or not, but, like, the high level, sure. Those are those deterministic steps, like, gather all the information, put it all in the CRM. Like, okay, those you can pretty much easily go down the line. But when you get into, like, what does gathering all the information mean? And there's, like, a million little issues in the decision tree.

Jason: 34:12

Well, and I can help solve a lot of that. So if you combine the two together and we create a standard operating procedure, we actually call them plays because we think more in that that, like, playbook mindset. But you you create a play that's like, here's how we're gonna solve this problem. And we let AI manage the context of all the information that's happening from step to step and from person to person. All of a sudden now my job becomes pretty easy.

Jason: 34:37

And what we end up seeing is, you can interact with the with the AI agent and say things like, I don't know what to do here. What would you do? And it will go, well, I have the context of what's happening. I have access to your various tools. Let me go do some research and answer it for you.

Jason: 34:53

And the answers are pretty good. And so we're freeing people up from the monotony of hitting all their websites, doing all their things, all these tools, they're able just to run a standard operating procedure and it and the AI is kind of managing how it all gets done. This opens up some options for business. And that that's what we're we're pretty excited about. I'm still learning how to talk about it.

Jason: 35:16

Alright? The product is relatively new. But, as we're we're getting it in people's hands, this rethinking of how your processes look, Now put on top of, I have access to your tools. I have access to your knowledge base, and I'm managing who gets assigned what so that we're all working on a process together. All of a sudden now, we just have a much, much wider, much richer, deeper context for the conversation that's actually happening in between us as people and between the computer so that it can do the work for you.

Jason: 35:48

And that's pretty cool. And we're getting some really neat outcomes from that.

Chris: 35:51

Yeah. It strikes me that that you're kind of you're kind of fulfilling that thing that you're talking about before, in that, you know, as we have talked over the course of the show about the the psychology and the expectations, the humans involved, that you're bringing your tool, and you're putting it not only in front of the management, but as you talked about kind of a committee of the people that are doing the work, where all that knowledge is currently residing and helping them without replacing them. You're helping them do better things with that knowledge. And so you're you're you're sort of giving those humans a bit of superpower for free without it being a threatening thing. And I've just I'm I'm wondering if maybe by using the tool itself in that context, it's actually kind of starting to assuage some of the concerns and fears that those employees might otherwise have.

Chris: 36:46

It seems like a fantastic strategy to give them a good experience upfront that that starts off by saying, I'm kind of implicitly acknowledging your fears, but here's an experience to to guide you going forward.

Jason: 37:00

Yeah. You're dead on. And that's what that's what I get excited about it. And I'll even talk with the leadership. It's like, hey, let's not go in and just throw a whole agent in there and say, hey, we now have agents is off and running.

Jason: 37:11

Nobody trusts them. Like, I I I'm cool with agents, but let's let's ease into it a little bit. Let's let the let's let the team become comfortable with it. So we create this hybrid where we literally start off and we're like, let's create the process, you know, a through z, and each step is getting handed to a person. And we interact with, like, teams, Slack, email, text message, so that they're not having to log in to another system.

Jason: 37:36

Right? So it kinda feels like you're talking to just an external employ external team member. And so you're just having a conversation. It's asking you questions. Hey.

Jason: 37:44

I just had a great meeting with Daniel. Help me onboard him. Hey. In order to do that, I need to know the company. I need to know and it's asking you questions.

Jason: 37:50

You're just kinda going back and forth with it. Then as you get tools and the AI tools and MCP servers, everything open up, then it starts going, you just want me to add that for you? Yes, please. It now starts to feel like it's a helpful assistant until eventually you get to the point and AI gets smart enough because every new model that comes out is getting better. You now get to a point where it's like, just do it for me.

Jason: 38:16

Like like, I've gotten to a point where I trust you now, and so just do it for me. That's our strategy for adoption is, you don't have to train anyone how to use email. They know how. So change management barrier goes down. It's just asking them questions.

Jason: 38:31

Get it so that they feel more comfortable over time and that it's doing work for them. And then eventually flip it into agent mode and it just goes off and does it. But you know what it's doing because you were in the loop while it was getting, structured in the way that it should be executing in the first place. It's just a very accessible way for small and mid sized businesses to get their team comfortable with what AI is is going to be able to do for them.

Daniel: 38:54

Yeah. I I love that fact of the way that you're tying into things that people are already using. And also I've seen, of course, I actually really I do really like these systems where for me personally, that like, oh, I can drag and drop and create this like DAG pipeline to automate and like create these things and make custom API calls and all this stuff. And I love that. It's a great user experience for me.

Daniel: 39:20

But putting that in front of a different audience, it's so overwhelming and terrifying. And I think one of the things that you showed me at one point is this kind of, just like describe your process in words, right? Yeah. Upload that in words, not as a prompt. So you don't have to learn how to prompt.

Daniel: 39:42

You don't have to learn how to like build this DAG pipeline. You essentially describe like you would to maybe an intern or a new hire or something like that. So I'm wondering, was that initially kind of part of things or was that another one of these like, hey, we tried to tell people how to use this DSL or something and they like had no idea how to do it. How did that perspective come out, I guess?

Jason: 40:11

So as with any SaaS product, we're on our third version. Yeah. The second version was all drag and drop, like what you talked about. And it was like a and we would show it to business leaders, you know, CEOs, presidents, operations officers. And immediately, every time they're like, oh, go talk to IT.

Jason: 40:30

Like, we're not even gonna look at it. It's like, well, I I appreciate IT. I actually love IT. We do IT. Like like, I love all of that.

Jason: 40:38

But if you're gonna get AI in your business, the leadership's gotta be doing it. So the conversation was, what does it need to look like for you to actually engage what your AI agents are gonna be doing? And it basically came back as I need to be able to read it kinda like it's a standard operating procedure because I understand that. That's my world. So if I can read it and it's like, oh, this is the order it's gonna get done.

Jason: 41:01

Here's what's gonna get done along the way. I can comprehend and be like, no. I don't want that to happen. Okay. Well, how would you change that?

Jason: 41:10

Tell me in plain English, and they can rewrite it. And suddenly now it's accessible. So when you start to give tools and we don't have this tool yet. It's on our road map, but I'm really excited about it coming. Because the processes are managed by AI, we can do AB testing of processes.

Jason: 41:25

Like, that's pretty unique. And if you can, as a small or mid sized business, say, here's my process now, I'm gonna make some changes and I'm going to basically say, take version a, send 50% of my runs there, take version b, send 50% there, and now compare the efficiency and what's actually happening, the amount of time we're spending on it, like, that allows you to be scientific in how you start to scale your process. So I I get pretty excited about that kind of stuff, but people aren't gonna do that if it feels too technical. It's gotta feel extremely accessible, which is why we have to go with with, like, plain English, to get them to engage it.

Chris: 42:03

Works. So Jason is as we start winding up, and you're kind of looking out at you as you're kind of pioneering, you know, process change and, and how to make this work with these new tools and how people can reimagine it in a in a way that's different from what they've done before. As you're looking forward at this field and kind of the the possibilities, you know, so not necessarily what you're doing now or what's on the immediate roadmap, but kind of out, Like, what how do you see this evolving over time? How do you see the future looking in terms of how companies as they move forward and and adoption becomes widespread, but you're pushing the limits a little bit on what's possible at each point in time. How do you see this unfolding for companies?

Chris: 42:49

And what are some of the things that people might look forward to in in the, you know, over the next few years?

Jason: 42:56

Yeah. The the thing that I'm starting to imagine is happening, I kind of I'm not sure that business leaders are really gonna be interacting with computers. I think it's gonna all come down to their phone, and there's gonna be some kind of just voice conversation that's happening. And behind the scenes, if you're able to upload your processes into a, again, a external team member, and it knows how to execute them, and you can kinda manage from there, It's a whole lot easier for me just to pick up my phone and have a conversation with an external team member that can do that work for me than it is for me to log on and try and figure out what to do. And I see that coming pretty like, in the next year, I kinda feel like that's gonna be have to be a major thing.

Jason: 43:37

We're not imagining it how to do it right now. I'm just now kind of envisioning that this is gonna be, like, probably the main use case. Because I just think the way that with with the power of AI and the way that it understands that the intent of conversation and it's able to logically break down what it is we're actually trying to say, the computer itself is gonna become less and less of a focal point of business and is gonna become more of just an expected conversation that's happening behind the scenes. And I I feel like we're gonna have to embrace that really quickly. And I don't know what that looks like.

Jason: 44:14

I that's really hard to dream about. Like, I mean, my whole life, I've had a computer. Right? Like like, I can't imagine what it looks like to basically say, I am going to not have one. I tried to do, like, an iPad for, like, six months, and I couldn't handle it.

Jason: 44:26

Right? Like, so what does that world look like? I that's hard for me to to to imagine how we could get to that kind of a place. I mean, it's kind of the Star Trek button. Right?

Jason: 44:36

Like, hey, computer. Go do this. Like, that kind of idea. But I think that's real. I think it's coming.

Jason: 44:43

If it's I mean, it's probably already here. I'm just not caught up yet.

Daniel: 44:46

So Well, hopefully, I I get my pen sometime in 2026. I'm I would I would look forward to trying it for sure. Really appreciate your insights today, Jason. It's been a real pleasure. I appreciate the way that that you and the RoboSource team are innovating.

Daniel: 45:02

Thanks for taking time to talk to us. Really appreciate it.

Jason: 45:05

Well, I had a lot of fun. Thank you both, and it was just it was a fun conversation.

Jerod: 45:16

Alright, that's our show for this week. If you haven't checked out our website, head to practicalai.fm and be sure to connect with us on LinkedIn, X, or Blue Sky. You'll see us posting insights related to the latest AI developments, and we would love for you to join the conversation. Thanks to our partner Prediction Guard for providing operational support for the show. Check them out at predictionguard.com.

Jerod: 45:38

Also, thanks to Breakmaster Cylinder for the Beats and to you for listening. That's all for now, but you'll hear from us again next week.