SPEAKER_01

We spend more than half of our time in managing the work that's being produced, not producing the work itself. Fundamentally, with any sort of these technological events, what we're seeing is it's elevating the job people are doing. So what is being replaced? It is partially some of that manual labor, that's repeated labor.

SPEAKER_02

What new skills should they be thinking about developing that are going to become more valuable as AI handles more of the execution side of things?

SPEAKER_01

You need a good tracking system. You have to have good prioritization skills, you need to have good communication skills. And then you need to have good accountability. In order to be a good leader, you have to be a good manager of yourself first. If you love being a project manager, this is not for you. And if you love just getting things done and connecting with people, and if you want to offload all the project management BS, then this is for you.

SPEAKER_02

Hey everybody, welcome to another episode of Using AI at Work. My name is Chris Daigle and I'm the host. And uh just to prove that I'm one of the hardest working men in show business, here I am doing this from a uh a study room at a library because I'm on the road and I don't have access to our studio. But that is not gonna impact the quality of content that we're sharing with you today. Okay, today our guest is Ken Zhang. And Ken's a hands-on kind of guy with product, data, AI, cybersecurity, the whole deal, with background that includes leadership roles at Cisco, IBM, Fidelity, where he actually ran large global teams. Now, during the pandemic, he founded Jupyter One, which was a cloud native cybersecurity platform and hit uh, you know, the vaunted unicorn status uh during that with clients such as Databricks and HSBC. So this is a guy who knows what's going on, not just with AI, but certainly like the people side of things, the product side of things, the whole deal. Now he's building a tool that's very interesting to me because uh as a listener, you've probably heard me talk about this concept of the single brain or the intelligence layer, the company brain, and not just me. You're starting to see that over and over again in uh AI conversations about this concept, which we're really gonna dive into today with somebody who's building software for that. And that company is called, am I gonna pronounce it right, Ken? Oriso?

SPEAKER_00

Oriso, that's right.

SPEAKER_02

Oriso, yeah. So it's essentially, and we're using it here at Chief AI Officer. Uh, we're we're testing it out right now to see if this can actually be a faster solution for us when it comes to that resource. It's an AI-powered intelligence layer that's designed to help individuals and teams uh improve coordination, judgment, communication, decision making, the whole deal. So um, Ken, before we get started, I know you've got a book that you're working on called Everyone is a Manager Now, and I want to make sure that we we do dip into that topic today. But anything maybe that I've missed that you think might be um helpful for our audience to kind of understand the context from which you're approaching this.

SPEAKER_01

Yeah, that that is a great setup. And uh, Chris, I very much appreciate the opportunity to come in here and talk to your audience about this, right? So I think this is fundamentally, I believe, the biggest challenge that I've had throughout my professional career. And I continue to see this challenge in many organizations. And it's something that's not going away. As long as we have people working, that is not going away, right? So I'm glad that we're talking about this today.

SPEAKER_02

Um, and uh, for those of you in the Austin area, I know we've got a lot of AI um uh experts and uh enthusiasts in the Austin area. Ken is a transplant from the Bay Area to Austin. So welcome him if you see him. Ken, one of the things that I found was pretty interesting was this idea of everyone is a manager now. And it's uh as I understood it, it's your position that AI is kind of changing the fundamental job of what a knowledge worker is. Can we kind of jump into what that means for someone without direct reports, maybe?

SPEAKER_01

Yeah, yeah. And thanks for the plug for my upcoming book. And that do look forward to that. And um so let's just think about it, right? So so we have so many different, several different uh revolutions in you know, that in in the in the in the history of mankind. And um industrial revolution that changed uh human labor to machine, and um you no longer have to manually produce things, right? So so how does that like translate and apply today? And uh you know, remember that you know, back in the days when calculators were first introduced in classrooms and people were freaking out, right? And I think we we felt a little bit of that with AI recently, and you know, AI is writing code, right? AI is generating content. People are freaking out and saying, oh my God, I'm gonna lose my jobs, right? But I think you know, fundamentally, with any sort of these um technological events, what we're seeing is it's elevating the job people are doing. Okay. And not necessarily, I mean, it certainly is replacing some aspect of that. Sure. Right. So what is being replaced, it is it is it is partially some of that manual labor, that's repeated labor, right? And for things like writing code, right? So the the difficult part is not the syntax of the code that you're writing. It's not producing the number of slides, right? And that's what AI is replacing. But what then elevated the job too is to decide what to build, to make better judgment, to make better architecture, and so on and so forth, right? And we've we've always been afraid and say, hey, you know, AI is gonna replace software engineers, but hey, guess what? We're still here. Yeah. And I think fundamentally, if you look at how knowledge work is being done, it's really we spend more than half of our time in managing the work that's being produced, not producing the work itself. Right. And I think that's what the book is about. And that's also what I'm passionate about and what I want to make build platforms and products for AI to help is in the managing part of it, not just the producing.

SPEAKER_02

So, okay, help me understand that concept a little bit better. So, as a as a knowledge worker who's listening to this podcast, the management that you're talking about, like what would fall under that? Is that like finding the information to work with, getting the report from Nancy so that I can take the next step? Like what all is involved in that management description?

SPEAKER_01

So, so I'll I'll tell you uh one of my personal stories, right? So early days in my career, I had uh an advisor, a mentor, and who at the time was also my boss, right? So he he told me something uh that he fundamentally believes in that uh accelerated his career. He said, Hey, do these three simple things. That's that's how it works. That's his system. He says, um, tell people what you're gonna do, do it, and then tell people you did it. Yeah. Okay. Simple, simple. But most of us, what do what do we do in most of the day today? We just do the middle part, we just do the execution, we just do it. Right. So we we are not very good at telling people what we're gonna do ahead of time, right? So getting alignment and preparing the preparation ahead of that. Yeah, and then we're not very good at telling people that we did it, right? Is is that tracking, that reporting, and the and then I would extend that to reflecting on measuring the work that's being done and how good was it, and how can I improve, right? So if you think about this whole system, and I I break that into kind of these four parts, right? So so track, report, uh, analyze and measure, right? So so all those things. And the doing the work is probably a third or a quarter of this entire flywheel that you call work.

SPEAKER_02

Yeah.

SPEAKER_01

And the the rest of it is managing the work, is communicating the work. So that's what I mean. Got it. And I think that's the part that well we still have to do regardless of how much AI replaces the doing the work part itself.

SPEAKER_02

Well, then let's talk about that. Like what skills do you see knowledge workers at all levels, whether it's senior decision maker, leadership, all the way down to you know, staff level, what new skills should they be thinking about developing that are going to become more valuable as AI handles more of the execution side of things?

SPEAKER_01

Yeah. So um I think a lot of this is the routines and the first principles and the good practices of management. Right. So so let me take a step back. And you know, I I actually when I pre-share some of the my my book with others, right? So I get a lot of these questions and say, hey, uh, why why do you use the word manager? Right. And you know, you you you say that, hey, everyone is a manager now. Wouldn't it better to say that everyone is a leader? Okay. And I say that you, you know what, I I specifically chose the word manager because look, at the end of the day, that is what it is. And in order to be a good leader, you have to be a good manager of yourself first. Yeah, yeah. So I think I think the industry as a whole look at the word management as a bad word because we have so many bad managers at their jobs and they don't know what the best way approach is, and it drives people crazy, it drives their team crazy. And also fundamentally, if the teams themselves are not what I call self-managing teams, then of course, then you're gonna have some people managing you, and then you're gonna feel bad. And then, you know, it's so so that's overall, we're just in this kind of vicious circle of management becomes a bad thing, which it should not be. That goes back to what you were saying, right? So what what does that mean? I I think it's just the what skills do you need? You you need a good tracking system. So I think that's that's first and foremost. You need to be able to remember and track the things that you plan to do, then you need to do so the good tracking system. You have to have that first, right? So that you know things don't get lost. And then you have to have good prioritization skills. So you need to know like, you know, what's most important, so you're not just like going over the place and doing things that don't matter, right? And then you need to have good communication skills, right? And then then, of course, last, then you have to have good accountability to actually follow up and do those things. And then I would add just one more is the learning and the reflection. And these are the things that people don't think about, right? Is if you want to get better, the best way to get better is by looking at the things that you've done and analyze and understand how can I improve a little at a time, right? All those things, right? So the tracking, the prioritization, the communication, the reporting and the reflection and analysis and accountability, right? So all those are good management practices that we want every individual, whether you're a people manager or not, this is not about people management. It's just about doing work the right way.

SPEAKER_02

Okay. So I'm seeing these principles that we're discussing now. First, a number of those things you talked about, AI can most definitely at least augment my ability to do that. So that was great to hear. And, you know, it reminds me of that quote, uh maybe Peter Drucker, but that which is measured improves. So I totally agree with that, with that perspective there. So that kind of leads us into what I really want to talk about, which is this idea of this intelligence layer, this company brain. And just for those maybe who haven't listened to some of the past uh episodes where I've talked about that, do you mind explaining kind of what your definition is of this, you know, this context layer or this company brain concept?

SPEAKER_01

Yeah, and and uh I hear a lot about that too, and and uh I get into conversations and and sometimes debates on you know uh whether we should have a company brain and how it should be built and what's shape and what's the access control and so on and so forth, right? So I I think a lot of people think about company brain as in a simple view of I got a bunch of information and data sources across the organization. Let me just kind of dump that into one place so that I can have um an AI agent that sits on top of it so that I can ask questions, I can get insights, and so on and so forth, right?

SPEAKER_02

Which sounds like it brings a lot of value to anybody who has access to that agent who's got the ability to surface intel from all of the context about my business. So the idea is sound. I like it.

SPEAKER_01

The idea is sound. The idea is sound, but I think it's the architecture and the approach that is different.

SPEAKER_02

And you guys are doing something that I haven't heard others doing.

SPEAKER_01

Yeah, and I I I believe fundamentally there are some challenge with that, right? So, of course, the most obvious is access control. Like, you know, when once you dump everything in there, like who gets access to it, then how do you control it, right? And and also, so that's one. And then the second thing is then privacy, right? So you say that, you know, even though it is all business data and company data and and and whatnot, there is still a level of privacy concerns when it comes to employee-specific conversations and things that are working on and so on and so forth, right? And especially when those two are coupled together, when you have a challenge in access control, and then a challenge in what data are you supposed to or not supposed to put in there, right? That becomes a very complicated way of approaching it. So again, the idea is sound. So that's one. And I would also challenge the company brain uh with the following. Who benefits the most from the company brain? Now, in the most, for the most part, I think the people who want the company brain is the leadership team. You say that, hey, you know, I already have access to those things, you know, I I I want to make better decisions, I want to aggregate all my company information to that, right? So that's I I believe that is a very that is a somewhat one-sided view to that. So my my my thinking is really that how do you benefit not just the leadership team, but every individual, down to the individual contributors.

SPEAKER_02

And still address the access control, still address the security and that sort of thing.

SPEAKER_01

Yeah. Okay. Exactly. Exactly. Right. So I actually believe that it is a combination of a company brain and a distributed individual brain or like a replication of that individual brain. So think about the things that I just said, right? So you want to remember everything, you want to track, you want to report, you want to analyze, you want to prioritize, you want to learn from those things. In order for the whole company to improve and get better, and in order to have the whole company to roll and get aligned in the same direction, my belief is you have to start at the smallest fundamental element, which is one person one day. That is the smallest element. If you can improve one person every day and then replicate that and scale that and compound that, then you solve both step problems, right? So then you build that brain for those individuals, but then that gets replicated and that's cat that's compounded to a company brain, and you you kind of gets the best of those worlds.

SPEAKER_02

Okay. So let me just make sure that I'm I'm following this. So with most of these companies that are talking about the concept of single brain, it is exactly what you mentioned. It is, hey, let's get all of our Slack messages, Microsoft Teams messages, emails, calendar events, call transcripts from Fathom or Fireflies or whatever you're using, throw those into some data environment so that an executive as they're going through the data might say, Oh, I wonder what this is. And they're able to go to that and get very like accurate information, pretty much on demand, that might not necessarily be like a normal report that they'd run, but it's intelligence that they need in order to say, like, what is the next step for the business? But you're suggesting that rather than then throw everything into one big pool, we start at the individual level because collectively, at the individual level, if we get all of the pieces, we'll have the sum of the whole.

SPEAKER_01

That's right. And you would have to sum of it hold with the correct access controlling place, with the correct privacy guardrails in place, as information are being rolled up. Right? So that's the difference. You you still you still get your company brain because the reason that you get your company brain is you want those answers. You want those answers, that doesn't necessarily mean that you have to have access to the raw data in order to get those answers. The the answers can be synthesized and analyzed along the way to derive and come and get to the conclusions that you're trying to get. Okay. And you can still protect the access and privacy along the way.

SPEAKER_02

And so, for context for the listeners, like this is something that I learned about originally from uh some comments that Jack Dorsey made about what he did at Block. Seemed very interesting, the concept. I mean, anybody listening to this is gonna go, damn, that sounds like a good idea. If I was able to just like tap into the big brain of my business and ask questions and have the data set, kind of like using Chat GPT, except the data set is all of my information in my business, sounds great. But the the issue that we ran into early on was exactly what you've addressed. How do we limit somebody can ask the question, but how do we limit the answers they get back? Yeah, so um very big concern. So by what your framework is, we're addressing it at the individual level, so that's where security is held.

SPEAKER_01

That's right. And and I think I think that's that is one, right? So I I also believe fundamentally, right? So if we are only giving 10% of the company access to this company brain, you're only benefiting 10% of the company. And I think a lot of the challenge that we have today is, you know, we focus on top performers, right? So we focus on the executives and helping, you know, them do better at their jobs and do the leadership trainings for them and so on and so forth, right? But then what happens to the 80 and 90 percent of the organization? You how they get left behind? They don't have access to the resources that to make them better, right? So I think I think fundamentally, right, so to make a dramatic shift, we have to start thinking about how do we make the masses of the organization better, not the selective few. Yeah, yeah, yeah.

SPEAKER_02

That makes perfect sense. So I got a question. So for the listeners that we've got, we've got really across the spectrum. We've got individuals who are uh decision makers at large companies, we've got solopreneurs, we've got the whole thing. Because this is this isn't something that it's not like, oh, let me just have Chat GPT do this. There's a, there's like, there's effort required to do this. What size business does this start to make sense? Or what size team should I be thinking about before this effort starts to really make sense or pay off?

SPEAKER_01

Yeah, so we actually work with uh companies as small as a team of five or ten to um you know as large as you know hundreds and a couple thousands, right? And uh I I think that it starts to make sense when you start having a team of at least five or ten people, right? So there is that coordination, even with a resource ourselves, we're still a small team of less than 10, you know, we utilize this day in and day out. And I use this as my own personal tracking system. I look at my own weekly reflection reports to just to see that where did I spend time? You know, how can I be better? Right. So even as an individual improvement and tracking system, it is already very beneficial. And um I myself and many of our customers have told me the same, right? Is that uh I've stopped using ChatGPT, you know, and I've stopped using Cloud, right? I mean, I still do use Cloud Code, and by the way, this is not a NLB or the only AI agent that you ever interact with, right? But because it has all of my memory and contextual data, that has my digital brain, and anything that I do, I interact with that, you know, I feel like I'm working with a partner and not just a tool. Right? So I think even at the individual level, it is beneficial. And when it becomes more apparent is when you start to have a even a small team. You know, I'll give you one example, right? So there are a couple of people on my team, you know, had some communication conflicts, right? So they're very different style, different background. Okay. And uh I would go to RE and say, hey, you know, these two team members are having some challenge and difficulties and some frictions of working together. How do I best help them resolve? Right, because my partner has access and understands not just the knowledge and the data, but also how we work, our profile, our personality, our communication style. Right. So she can then make recommendations on how to resolve interpersonal conflicts.

SPEAKER_00

So fascinating. Even small teams.

SPEAKER_02

Yeah, okay, great. So let's talk about the the software that you've been in development with and kind of walk me through like when did you start this idea? Because I know that you guys got started before this idea really hit the scene. I mean, uh you know, upon reflection, the concept when somebody tells it to me, I'm like, Oh, yeah, well that's obvious, but but I hadn't thought about it until it was presented to me. How did you come up with this? Um, because you've been working on this for a couple of years.

SPEAKER_01

Yeah. Or uh yeah, yeah. A year and a half. I think this is like come over time. I took some uh break to reflect on my previous journeys of my um previous startup and my time at the large organizations and so on. I I I started reflecting on what worked, what didn't work. And and then I I realized that what what didn't work a lot of times are all of these kind of per interpersonal relationship things, communication things. And what did work are all the good management practices, right? So that we practice. So I combined the two, and then I realized that um all the things that I wish I had known before, I wish I had known sooner, are things on that the blind spots that I've had can a lot of times be uncovered with better communications, with better clarity and transparency within the organization. So that's when it started hitting for me. Right. And then of course, you know, we started thinking, you know, we we we were thinking about different approaches of doing this, right? So we thought about the overall company brain, we thought about having a personal coach for everyone, you know, we thought about the uh AI assistant chief of staff and all of those, right? And then what I realized is that all these are pieces that actually belong together. You know, what what what I what I really wanted at the end of the day is that chief of staff who can help me connect all these dots.

SPEAKER_02

Yeah. So define this role that I I'm I'm very bullish on this concept of the chief of staff. So for the listener, what does the chief of staff do? Like what is whether it's let's let's focus on the AI chief of staff. What do you think that should be doing for the the user?

SPEAKER_01

Yeah, I look, I think this uh AI chief of staff term is uh quickly getting overloaded with just AI assistance. There are many products out there that um claim to be an AI or chief of staff, but they are more of a glorified AI executive assistant that helps with emails and calendars and so on and so forth, right? But I view a chief of staff being a business partner who is a director VP level person, right? So on the team, who is at least it depends on where you're role, right? Maybe you're a manager, then the chief of staff for you should be at least at your level, right? And um it's like an HR business partner type of chief of staff. And and someone who can make you better and make your team better, not just the tactical execution of tasks. Yeah, right? So the chief of staff is the glue, right? So think about you know, the chief of staff or any uh elected public officials, right? That's not an EA function. That is understand the organization, understand the team, understand everything that's happening, and being the glue that holds that organization together.

SPEAKER_02

Okay, so that was what the original concept was that you came up with. And now, kind of fast forward, what are we looking at? Because I know that our company, Chief AI officer, um, after I had the conversation with you, I was like, hey, development team, because we've got our own small team, but I was like, guys, like let's stop doing that and let's take a look and see if Ari or Ariso uh is able to satisfy that. Now we've just kind of got gotten started. So walk me through as let's say, quote unquote, a new customer, let's say. What should we be expecting over the next days, weeks, and months of using the tool?

SPEAKER_01

I I think what you would expect is at um three different layers. Okay. So starting at the individual layer, what you should start um experiencing is a proactive partner who sits in your corner, understands your day, start remembering and tracking all the things that happen, writes down the journal quietly for you on the uh on an everyday basis so that you can always go back and ask, hey, um, for example, like, hey, I met with Chris two weeks ago. What do we what do we talk about? You know, you can easily ask that, right? So um, or something like, hey, I have an upcoming discussion with a partner, with an investor, or with so and so. Um, how should I best prepare for that? What are the topics? Right? Or um I can say that, hey, you know, my uh new quarter is coming up, right? So how should I think about the priorities for myself and for the team for the next quarter? You know, it's that interactive partner that can that that is your brainstorm partner that helps you um make better decisions and make better analysis. And part of this is you can also delegate work to that partner, right? So um Ari joins all my meetings and captures everything. And even in the meetings, I can I can um tell Ari to say, hey, just after meeting, you know, make sure that you drop that up for me and you know, create a status report for me or um set up that project for me or something. Right. So she's your thought partner and execution partner.

SPEAKER_02

Oh, nice. So let me ask you. Um one of the things when we go on site with clients is we help them with some some pretty lightweight uh solutions, but we help them create skills based on their role and things like that. The same functionality is is Ari able to like leverage the skills that I've built in my cloud or my Chat GPT account? Or am I able to bring the skills that I've been using in those models into Ari to be able to uh train Ari on my writing style or our brand guide and that sort of thing?

SPEAKER_01

Yeah, the answer is absolutely right. So there's there's actually a couple of things that you can do. Uh you can actually directly connect Ari to your cloud or cloud code, right? And just um have Cloud Code continue to execute for some of the things, and you know, Ari becomes a um a conduit to those things. And also Ari becomes aware of all the things that's happening though, so she can help keep track of you know all the things that happen, um, that becomes an input to it as well. And you know, we also provide an MCP server, right? So for you to connect uh to those solutions. On the other hand, you could you could also bring those, right? So you could also bring the skills uh into Ari herself and uh just train Ari on, you know, and she would actually do some of the research, right? So by connecting your emails and whatnot, right? So she would also automatically do the research on how you write emails, how do you communicate, build up this profile, you know, and you can interact with her to have her have her learn about your communication style, your uh strengths and weakness, and you know, how do you best receive feedback and all those things?

SPEAKER_02

So, okay, we've got listeners that have been interested. Some of the most listened to episodes that we've done have been on the concept of of agents, right? I'm talking about like since early 2025 or whatever. And so I know that a lot of our listeners have maybe they've they're at least familiar with open claw, maybe even Hermes. Maybe they've tried to build their own, maybe they have one functioning. How is what you're talking about here different than uh like, well, couldn't I just build it in the open claw or couldn't I just build it with Hermes? Individually, yes.

SPEAKER_01

I uh our architecture, yeah, our architecturally uh architecture is not built uh on open cloud or Hermes, but it has uh some striking similarities to it, right? Because I think at the end of the day, open cloud and Hermes is is also an infrastructure that connects into different tools and provides a contextual memory system, right? And provides an infrastructure for you to interact throughout different channels, right? So we do the same, right? So you can interact with the agents in Slack, in Teams, on the web, in uh text message or iMessage, you know, all those things. So from that individual perspective, it is similar. Now, there's there are two key differences I want to call out, right? So one is what we've built into it is a lot of kind of pre-built proactive capabilities. You know, things like um, you know, joining meetings that replaces meeting note takers, right? So that's one example. And background processes that are already predefined of capturing a lot of work that's being done, write your journal, write your status update, write your performance report, you know, and um do the research for your meetings, right? So a lot of those things are pre-built. So essentially you can think of this as an open cloud that is already pre-built with all these proactive things that you don't have to then start from scratch and redefine, right? Because a lot of this is the best practices that come from the book I'm writing, that comes from the 20 plus years of leadership experiences that I've personally had, right? So that's difference number one. And difference number two is then think about this on a team. Once you scale this to a team, you have to have everyone's open cloud interact with each other's open cloud. And you have to have them all perform and track things and do things in a consistent way, right? Yeah. It's that consistency and what to share, what not to share. And then this becomes then exponentially complex. Because it's not just one person and another, right? It's this exponentially number of different dots and connections that you have to make to make all everybody's systems all interact, everybody's agents all work together, everybody's context and knowledge all share in a consistent way. So that, for example, I, as the manager, I can go ask my RE and say, how is my team doing? And you know, simple things like that. How is my team doing? You know, I can get things like what are they spending time on, how are they prioritizing things, what's the team morale, and you know, what are they focused on? Are they aligned? Do they have challenges and blockers? All those things. Okay. So imagine that for that to happen, if you were to use OpenCloud, or if you were to build that yourself, right? So then that means everybody would have to do it. Number one, that that already is not an easy thing to say, hey, everybody's have to do it. And two, everybody has to do it consistently.

SPEAKER_00

Yeah.

SPEAKER_01

So right. So, Chris, you you cannot measure things differently than I do, because then otherwise I cannot aggregate a two. Okay.

SPEAKER_02

Yeah. This makes a lot of sense. So could a listener do this? Yeah. Would it take them a long time to get it everything synced up? No quite if the if it ever got synced up. So basically, what you're doing is I can have that functionality of um uh that a chief of staff, but also a bit a bit proactive agent without having to spend all of the time on the calibration, not just for my own personal agent, but for each person in my organization who I would want their context to be able to be included in the considerations that my agent made. That sounds like a nightmare trying to build that. And I think that unfortunately, I think that that's the approach that we were taking initially internally. And that's why when your team reached out about being on the show, I was ex I was especially intrigued about, okay, well, maybe this is the shortcut. So um the tool is ready. I know that we're using it. Uh what are the what are kind of like the the plans for growth? Who who is this? What industries is this ideal for? What size organization would you say that your your ARIA specifically would be uh the best match, the best fit?

SPEAKER_01

Yeah, uh misssized organizations, right? And and uh we have uh customers that are ranging from 10 to 500 people, right? So I think miss size organizations. So I I I think I ideally, you know, if you have 50 or 100 people, so it's probably the the best sweet spot. But you know, again, right, so the wrench is fairly big, right? So but we we purposely today are not focused on the Fortune 500, um, larger enterprises, right? Maybe a a subset, maybe a a team within those organizations, maybe a a business unit, right, within that organization. But I I would say, you know, up to 500 or up to 500-ish um teams, right? So it's it's the the size that we sort. And okay.

SPEAKER_02

Yeah. And the primary communication, at least in our experience, as we've been using it, and again, it's been about a week that we've been uh testing RE, has been through Slack. Is that the primary um channel that clients would be communicating with their internal Ari agent?

SPEAKER_01

Yeah, Slack is one what we get started with, but we we but we also do Teams and uh text messaging, right? So I I think uh for collaboration, Slack and Teams are the primary. And for um individual communications, lately I've been using just iMessage with Ari quite a bit and just for quick things. Like I would just uh ask her to say, hey, remind me to do X, Y, and Z and you know in text. And sometimes I would just uh use my um iPhone and say and say, hey Siri, and tell RE to remind me to do something, you know. So uh that that is actually really cool, just from a you know voice standpoint. Sure. Um, yeah. And and back to your question, so like, you know, which part of the team people you know most get started with, right? So I think really it's kind of twofold. One is um make well just one thing, in fact, right? So it's I think it's making the managers less strongly in project management and status updates and so on and so forth, or team leads, right? Because I think that's mostly what we spend the time on, right? Is to chase after people. And you know, once once you have everybody self-managing and you know, all the status updates all self-write themselves and you have clear communication lines and clear alignment, then you know, a lot of things just somehow just happen more magically.

SPEAKER_02

Yeah, yeah. No, we're very excited about using the tool. For those who are listening who have been either this is a new idea for them, like this whole agentic chief of staff that's you know global to my organization at the individual level, which again is uh I haven't heard others approaching it. Uh every other solution that I've seen out there tends to approach it, or at least I understand they're approaching it at the business level, not the individual level that collectively makes up the business, which I think is much smarter, easier to control, uh easier to source the information, um, rather than going through the whole database. I know, oh, the marketing team or oh, a sales conversation. It's just much easier. For those that are are like they like the concept and they wanted to test this, how long would you say somebody would need to be on one of these um solutions before they started to see value? Like let's say Ari specifically.

SPEAKER_01

I I will say that the level values are um progressively unlocked. And of course, um you you you get as much as you put in. Okay. So uh you can have a fast start and just go click the sign up button, it's free to sign up, by the way, and and go directly to start chatting with Ari. Then at that point, it's not very different than a chat to BT or cloud, right? But if you put in some effort of connecting your data and resources and working with uh Ari to just get Ari to understand a little bit of your world and start tracking some of those things, then I would say after a couple of days, you start to feel the difference. And then when it's when they really to start to see the difference is when you start working with the team over the course of maybe two to four weeks. You start to see that yeah, you see that collaboration impact coming.

SPEAKER_02

Okay. So that's the good news, like you know, um, in general, especially decision makers, are looking for uh quick uh satisfaction or gratification when it comes to uh whatever the question is or the the approach they're taking. And two to four weeks, I would say in the age of AI, considering all of the information and insights it could give you, is as a decision maker in an organization, I would certainly consider that an acceptable timeline. So for those of you listening, um you would know pretty quickly whether or not this concept of a chief of staff that had access to uh individual brain that made up the company brain, that sort of, you'd know pretty quickly, is this worth rolling out to the organization? Is it worth the investment of time to set it up and all those sorts of things? And I can tell you for the impact that this is going to have on a business, now as a user, so I'm talking to the listeners now, as a user, if this exists, but you're not thinking in a manner that allows you to get the most out of it, eh, it, you know, that's not the tech's problem, that's your problem. So I would make sure that you understood that it's not just a matter of uh collecting the data and the architecture and can the configuration, but it's also you becoming somebody who knows the questions to ask, who's thinking in AI about how do I leverage this uh pretty powerful uh agent for sure that now has access to what's going on in the business at a level that outside of me doing meetings with everybody every day, I'd never get that kind of access. So it's fantastic. Um again, very bullish on on our uh test so far. It's been early, but I'm expecting uh positive things simply because of the way you architect it, which again, that's the distinction for me. But for individuals who maybe want to um like have you guys do you do webinars? I know you probably do demos and stuff at the individual level, but do you have anything that people can consume or where are you sharing kind of like your breakthroughs in this? Is it LinkedIn? Is it YouTube? Is it company blog?

SPEAKER_01

Yeah, company blog, right? Company blog and LinkedIn. So those are the primary sources. And um yeah, follow us on LinkedIn, right? Is Ariso-ai on LinkedIn. And the blog is on ariso.ai slash blog, right? So, you know, um the blog is not vendor talk, right? So I promise. And a lot of the blog is really, you know, how we're using it. And and by the way, you you don't have to use our product. And it really is more of a mindset and a practice. Yeah. Like how do we how do our engineer team use it so that we remove the process bottleneck, right? How do we do stand ups so that we do everything faster and we get to the solutions and get to the pull requests like immediately, right? You know, things like that. How do how do I manage the team and so on and so forth? So I think those are the two best places. And of course, the upcoming book uh that I will be uh getting out, and that has uh a tremendous amount of resources in there as well.

SPEAKER_02

Yeah. Okay, great. So we're gonna have for the listener, we're gonna have all of those um listed in the show notes. We do a good job of documenting everything that gets talked about in the show notes. So it's a great place for you to find it. You can find it obviously at the you know the iTunes uh page for our blog or the Spotify page for our blog. So, Ken, in closing, I guess, is there anything in particular that as a maybe somebody's like, okay, I get it, but like what's the urgency? Or is there any kind of like you know, message you want to make sure, guys, you need to be thinking about it this way?

SPEAKER_01

Yes, I I will leave this final thought, right? If you love being a project manager, this is not for you. And if you love just getting things done and connecting with people and doing the higher value stuff, and if you want to offline offload all the project management BS, then this is for you.

SPEAKER_02

Nice. Okay, well, that was uh certainly um very enlightening when it comes to this because this is a concept that for the listeners, especially, if you're not familiar with this, like it's going to be a big topic now, a big topic in 2027, because this seems to me to be kind of the next evolution of well, how do I use it? I'd use AI more, but I don't know where to use it. Well, if it's got access to everything in your business, essentially real time, this will become part of how you run a business, run a team, make decisions for you know the deliverables you're responsible for. So I'm excited to continue the conversation and I'm excited to uh continue digging in with Ari. So, Ken, thank you so much. Uh stay out of the heat there in Austin. And uh I look forward to seeing you in our Slack. Yeah.

SPEAKER_01

Likewise. And go go get yourself your partner today.

SPEAKER_02

And we'll have that link for everybody. And for the listeners, listen, we've got some bandwidth um at Chief AI Officer. I don't know if you know, like we do the podcast, sure, but a big part of what we do uh, especially over the past year and a half, is work with companies, go on site, teach their teams how to use this stuff, help them identify pilots and skills and custom GPTs and agents that they should be building, and then helping them build it or building it for them. So if your company is, if you're listening to this because like your company is exploring, answering the question, hey guys, what are we gonna do about AI? Chief AI officer may have the answer. So you can reach out to us uh and we'll put that information in the show notes as well. But it's pretty easy. My email is docdoc. They call me Dr. Dagle, doc at chiefaiofficer.com and just say, hey, I heard the show. Um, we want to find out more about what that would look like. We'd be happy to help you. So everybody, thank you so much for being a listener of the show. Um, if you got other folks, peers, that sort of thing who uh would benefit from like listening in on conversations like this to better understand what's happening, not only at the macro, but also at the tactical level as a knowledge worker who's wants to be an AI leader, this is the show forum. So I'd appreciate if you'd um pass along an episode that you found particularly helpful and let them know that we're doing this every week. So with that, thanks everybody. Um, we will see you on the next show for another fascinating guest here on Using AI at Work. Thanks for tuning in to Using AI at Work. Don't forget to subscribe for more conversations about how to use AI at work. And a special thank you to our sponsor, Chief AI Officer, for empowering businesses with AI education and training. Visit their website for a free AI readiness assessment and AI strategy guide to help you get started using AI at work. That's www.chiefaiofficer.com. Follow us on Twitter at the handle UsingAI at work and visit www.usingai at work.com for free resources to help you harness AI in your role.