Transcript: Removing Bottlenecks, Not People with Stephanie Sylvestre
0:00 Stephanie What we do is we look at processes,
0:02 Stephanie we run them manually,
0:04 Stephanie and then after a while we automate them.
0:24 Scott Hello, everybody.
0:25 Scott Welcome to another episode of the How Many CTOs Does It Take podcast.
0:29 Scott My name is Scott Porad,
0:30 Scott my trusty co-host.
0:32 Scott Brad is on vacation this week, and I have a special guest here today,
0:37 Scott someone who I'm very interested in hearing about what she is working on,
0:40 Scott Stephanie Silvestri.
0:41 Scott So welcome to the pod,
0:43 Scott Stephanie.
0:43 Stephanie Thank you for having me.
0:44 Stephanie I love the title.
0:46 Stephanie How many CTOs does it take? And looking forward to a great conversation.
0:51 Stephanie And I'm with Avatar Buddy.
0:53 Stephanie I started Avatar Buddy 10 years ago.
0:55 Stephanie So I was in AI before AI was a thing. In fact, people are like, I don't know what you're talking about.
1:01 Stephanie And I still think people now just go,
1:05 Stephanie whatever.
1:05 Stephanie I still don't know what you're talking about.
1:09 Stephanie And
1:11 Stephanie I have been super excited with the evolution because 10 years ago I said we can we can use AI to help people be the best version of themselves and people taught that's
1:24 Stephanie insane that's ridiculous um it's too big and I'm like it's supposed to be that big because that is how big AI can be and so to be able to see how big AI has become um is kind of validating and we have been
1:44 Stephanie doubling down,
1:45 Stephanie tripling down, quadrupling down on the fact that AI plus human takes us to a whole different level.
1:55 Stephanie And I have been taking people to task whenever they talk about AI to eliminate jobs or
2:03 Stephanie eliminate people.
2:05 Stephanie And so that is that has been like my latest thing.
2:08 Stephanie In fact, we just launched our remove bottlenecks, not people campaign.
2:14 Stephanie That's all around.
2:15 Stephanie Let me tell you how you can identify bottlenecks in your organization. And then let me show you how you can remove them while keeping the people and increase throughput,
2:28 Stephanie increase profitability.
2:30 Stephanie Increase impact.
2:31 Scott Okay,
2:32 Scott there's a lot. I have a lot of questions. Just in like one minute, I have a lot of questions. So can we go back to the very beginning?
2:39 Stephanie Yes.
2:40 Scott So 10 years ago,
2:42 Scott I feel like
2:45 Scott I was just a baby in diapers when AI wasn't even on my radar 10 years ago.
2:50 Scott I mean, it was a thing maybe we heard about we saw in science fiction movies.
2:54 Scott How did it come to be that 10 years ago, you were already thinking about this, that you already knew about it? Like, what was that? How did you get to the starting line a decade ago?
3:04 Stephanie So I've been in tech for over 30 years and one of the things that I saw when I first started
3:10 Stephanie In tech was people would learn a language,
3:13 Stephanie focus on that language,
3:15 Stephanie the world would evolve around them, they'd get laid off,
3:19 Stephanie they'd be angry,
3:20 Stephanie and then blame other people. I was like, you should blame yourself.
3:26 Stephanie And I said, I don't want to be like that.
3:29 Stephanie And secondly,
3:30 Stephanie I always like to learn new things. I like to keep my skills up and come in. I like to think of myself as the oldest Gen Z.
3:40 Stephanie And soon I'm going to be like,
3:41 Stephanie I like to think of myself as the oldest alpha.
3:45 Stephanie And so I was at the point in my career where I was like,
3:49 Stephanie I need to learn something new.
3:51 Stephanie What am I going to learn?
3:53 Stephanie VR.
3:55 Stephanie AR or AI and VR is virtual reality AR is augmented reality and then AI artificial intelligence so I didn't want to do VR because I I was like this makes me nauseous augmented reality I just could not see why people would use it it just did not have a practical implementation in my mind you know
4:22 Stephanie I come from the school of if the solution takes more than two clicks,
4:25 Stephanie it's high friction and people are not going to use it.
4:29 Stephanie It's just not,
4:31 Stephanie you're not going to be able to deploy it.
4:32 Stephanie And then I said AI.
4:35 Stephanie AI covers everything anyway,
4:38 Stephanie so I got into AI.
4:39 Stephanie And I was at dinner one night and the restaurant was crowded,
4:44 Stephanie it was loud.
4:45 Stephanie And my friend said I should create a digital mentor.
4:49 Stephanie And I was like, oh my God, that's so great.
4:51 Stephanie That's such a beautiful,
4:52 Stephanie that's a wonderful idea.
4:54 Stephanie We should definitely do that.
4:55 Stephanie And then she was like, that's not what I said. I said, well, it doesn't matter what you said.
4:59 Scott Ha,
4:59 Stephanie That's
5:00 Scott ha, ha,
5:00 Stephanie what we're going with.
5:01 Stephanie And that's how Avatar Buddy was born by mistake.
5:04 Stephanie I was just telling some of my employees that you have to fall into success,
5:10 Stephanie right?
5:10 Stephanie Or
5:11 Scott ha.
5:11 Stephanie fail forward.
5:12 Stephanie And so I want them to go faster.
5:16 Stephanie I'm like, you need to move faster.
5:18 Stephanie They're like, well, we got to take time.
5:19 Stephanie I'm like, no,
5:20 Stephanie no, no.
5:20 Stephanie make all the mistakes like the faster you move the more mistakes you you make and the more faster we find the what's not working so we can fix it like I don't want to take five months to figure out something we could figure out in five weeks no and they're looking at me I'm like yeah fail forward
5:37 Scott what did the first version of avatar buddy look like i mean 10 years ago you didn't have you know now we've got all these tools the frontier models are available to everybody
5:46 Scott What were
5:47 Stephanie I mean,
5:48 Scott you doing back then?
5:49 Stephanie it took we did lots of research.
5:52 Stephanie We didn't we and then we got some.
5:57 Stephanie Teenagers from after school programs around Miami to write out requirements for us. We taught them how to write user stories.
6:05 Stephanie That's how agile methodology uses to capture requirements.
6:10 Stephanie And then that was in 2019.
6:13 Stephanie So I mean, it took a lot of time to research,
6:15 Stephanie to find, to figure out, to find anybody that even understood what I was talking about.
6:22 Scott Yeah.
6:22 Stephanie It's like.
6:23 Stephanie and and um people were talking about you know crypto and i was like no it's not crypto it's not blockchain it's it's different so we got all our requirements and then covid hit but the good thing about it is my current developer he had been telling me that that like this is just insane you can't that can't be done it just can't be done stephanie and i was like yes it can't be done i can think about it it can be done i'm not that smart
6:49 Stephanie he's like no and so he started doing some research i said oh this is what stephanie is talking about and
6:58 Scott Mm-hmm.
6:59 Stephanie so
7:00 Stephanie um he built out this really cloozy uh prototype on that we deployed on the martin luther king day on 2021 and
7:11 Scott Hmm
7:12 Stephanie then we were just kind of like building this stuff and he's like stephanie i don't think you can build this stuff by yourself and i was like we'll try so we're you know we're there we're trying it's not really doing what i want it to do i was like nah it's not
7:26 Stephanie We've got some teenagers to test it.
7:28 Stephanie They're giving us feedback where we're rewriting the software.
7:31 Stephanie It was just crazy, crazy, crazy, right?
7:34 Stephanie And then we got into open AI and back then you had to apply to be a developer and write justification and it was like competitive and they would say no or yes.
7:46 Stephanie So we got into open AI developer community in 2021 and that really helped us.
7:54 Stephanie But, you know, back then.
7:58 Stephanie The GPT was just a mess.
8:01 Stephanie It was like a hot
8:01 Scott Yeah.
8:02 Stephanie mess.
8:02 Stephanie Oh my god.
8:04 Stephanie In the summer 2022, we got 145 teenagers and we taught them how to do software testing and how to validate our requirements.
8:18 Stephanie And we had like three pages of defects that we sent off to OpenAI.
8:23 Stephanie and so I tell them I said we were part you were part of creating chat GPT because OpenAI would do is give the stuff out the developer community would find all the defects and then we would send it back to them they would fix it we test it and and so when they went out in November I was like this is cool I'd already planned to go live in January because I was like
8:47 Stephanie I'm at a point where we need to put it into production and finish testing in production.
8:53 Stephanie I know people don't like when you say that,
8:55 Stephanie but we all know if you're a good CTO that you have to test in production.
9:00 Stephanie Otherwise,
9:00 Scott Right.
9:00 Stephanie I mean, no matter how pristine your test environment is,
9:06 Stephanie you have to test in production because your testers and your developers.
9:13 Stephanie do not think the way the end users think and
9:16 Scott Right.
9:16 Stephanie and so we had um our first first customer I still I have the check I took the check I put it in a little frame put it on a shelf in in my in my live in my living room and it's just kind of like um where I look at it ever so often remind me of where we've come
9:36 Stephanie And, and that's that that was it,
9:39 Stephanie right?
9:39 Stephanie We just started refining,
9:40 Stephanie we realized like, you know what?
9:43 Stephanie This LLM stuff is by itself is just crazy.
9:47 Stephanie And I remember back my day when I worked at Burger King and remember portals, remember when portals first came out and people
9:55 Scott Yeah, of course.
9:56 Stephanie were like,
9:56 Stephanie what,
9:57 Stephanie what is this? And it was just so complicated and difficult to implement.
10:01 Stephanie We said,
10:02 Stephanie you need a data vault.
10:04 Stephanie We called it a small language model.
10:06 Stephanie Now people call it rag.
10:08 Stephanie I don't like that term.
10:09 Stephanie um and so now we call it data vault because small language model people like what
10:14 Stephanie so data vault and so that's like one of our differentiators because we figured out data vault and
10:21 Stephanie What are people now talking about?
10:23 Stephanie Data vaults. Like you got to give the AI just a small subset of curated data.
10:28 Stephanie That's when it's best.
10:30 Stephanie We figured that out in 23.
10:32 Scott Can we skip ahead for a second?
10:34 Scott Because I realize I know something that maybe people who are listening don't know. We kind of need to skip to the end of the story for a second.
10:41 Scott What is Avatar Buddy?
10:44 Scott Because I realize we talked before and I looked at your website before the podcast,
10:47 Scott but we haven't actually said for people who are listening.
10:48 Scott People are listening.
10:48 Scott What is AvatarBuddy?
10:50 Stephanie Avatar Buddy is a managed AI as a service company that builds AI agents on your behalf and manage manages them and then provides AI advisory services to ensure that your AI investment is maximized. So basically, we're your AI team.
11:09 Scott Mm
11:09 Stephanie But
11:10 Scott -hmm.
11:10 Stephanie most places you'll find are either doing product or they're doing service.
11:14 Stephanie We're doing both.
11:16 Stephanie And if you're keeping up with the space, you just saw that OpenAI just made some deals with the large consulting companies.
11:27 Stephanie Anderson and or is it Mackenzie Mackenzie and BCG group we've been doing that since 2023 as well that we're helping you figure out how to implement AI configure it for you
11:44 Stephanie And we've maintained tight control over configuring the AI.
11:48 Stephanie We don't let people configure AI on our platform because,
11:51 Stephanie in my opinion,
11:53 Stephanie the way you configure AI is the most dangerous part of
11:57 Scott Hmm.
11:57 Stephanie AI.
11:59 Stephanie And you see with Metos now,
12:02 Stephanie the way they've configured that.
12:04 Stephanie And now they're trying to figure out what they're going to do.
12:07 Scott Talk about a genie you can't put back in the bottle.
12:10 Stephanie One of the things that people like to ask about when I go talk, they're like, what's your background?
12:19 Stephanie Like what's your background?
12:20 Stephanie Do you know anything about AI?
12:21 Scott Mm
12:22 Stephanie And
12:22 Scott -hmm.
12:22 Stephanie I will say that people that just know about AI,
12:25 Stephanie they are the most dangerous people walking this planet right now. Because if you don't understand how software works,
12:32 Stephanie even though AI is not really a software,
12:35 Stephanie but it's adjacent to software.
12:38 Stephanie If you understand how software works,
12:40 Stephanie you're going to build AI products that are just horrible for the society.
12:46 Stephanie So like I think about Mythos or Claude Code or any of those things and I think to myself,
12:53 Stephanie these guys don't even understand how software works because if they did, they would not even think that this was a good idea to build. Because all they did was.
13:03 Stephanie made it easier for everybody to be hacked and it's in such an out of scale and level it's so unsafe right like you we have we literally have no safety anymore it's like everybody's house even though you locked your door the bad guys have the keys the master keys
13:24 Scott Mm
13:24 Stephanie Even
13:24 Scott -hmm.
13:25 Stephanie though you have an alarm set,
13:26 Stephanie the bad guys have the master key to override your alarm.
13:30 Stephanie And just for your people listening out there, just let me break down why I have an issue with this.
13:38 Stephanie So when you build software,
13:40 Stephanie you never have a defect-free software.
13:43 Stephanie What you have is a piece of software that has the least amount of defects. If you're a really good QA person.
13:51 Stephanie You're going to have, you're going to build software,
13:54 Stephanie you're going to test it, you're going to test it in a way that 95% of the bugs have been shaken out.
14:01 Stephanie Mm But
14:02 Scott -hmm.
14:02 Stephanie we all know there's a 5% chance.
14:04 Stephanie And, you know, I remember we used to say this thing that it's possible,
14:08 Stephanie but not probable.
14:10 Stephanie So.
14:11 Stephanie It's possible that somebody will find this defect,
14:14 Stephanie but it's not probable.
14:17 Scott Right.
14:17 Stephanie And you look at probability and then that's how you determine whether or not you should release the software.
14:24 Stephanie Now, we know that lots of large...
14:30 Stephanie Fortune 50 companies still run on mainframe. That's the dirty little secret that we rarely ever talk about.
14:39 Stephanie And over the years, what they've done is they bolted the latest software on top of the mainframe.
14:47 Stephanie And then they jimmy-rigged. I mean, jimmy-rigged, that is a valid IT term.
14:57 Stephanie Right?
14:58 Stephanie We're
14:58 Scott Yeah.
14:59 Stephanie just going to generate this.
15:01 Stephanie There's lots of hard coding happening in the back end.
15:05 Stephanie And then those developers leave,
15:07 Stephanie they retire.
15:08 Stephanie And how can it help you if you're no longer friends with them?
15:13 Stephanie Because now when you go try to fix that code,
15:15 Stephanie you can't because you don't know what else you're going to break. Because is there any comments in those codes?
15:24 Stephanie Probably not.
15:26 Stephanie Documentation can't find it.
15:30 Stephanie So that's the state of our software globally.
15:34 Stephanie Nobody has clean,
15:36 Stephanie pretty,
15:37 Stephanie pristine, defect-free software.
15:39 Stephanie Now you're going to build a solution that can find all the defects in under nine seconds.
15:45 Stephanie How is that okay?
15:47 Scott Can it fix them in under nine seconds?
15:49 Scott That's the question. If they can find
15:50 Stephanie No,
15:50 Scott them,
15:50 Stephanie they can't.
15:51 Scott can they fix them?
15:53 Stephanie No, they can't.
15:55 Stephanie Because one,
15:56 Stephanie remember AI doesn't have state the way we have state, where I can be like,
16:03 Stephanie Scott,
16:03 Stephanie remember that piece of code you wrote 10 years ago?
16:06 Stephanie Can you remember?
16:07 Stephanie And if I probe you enough and...
16:11 Stephanie shake you enough you'll be oh yes this is why I did it this way AI can never get to that so how can you fix it does
16:20 Scott Yeah, it doesn't know well to use the buzzword. It doesn't know the context.
16:24 Stephanie not know the context and and then um we all know you write a piece of code
16:32 Stephanie And you fix it in one place and it breaks 15 other things that your
16:36 Scott Mm hmm.
16:37 Stephanie logic going like,
16:39 Stephanie how did it break that?
16:40 Stephanie That couldn't have worked.
16:42 Stephanie Like, how are those two things connected?
16:44 Scott Yeah.
16:45 Stephanie Because in the developer's mind, it made sense.
16:48 Scott Mm
16:48 Stephanie And
16:48 Scott hmm.
16:49 Stephanie that developer is not there fixing that code.
16:51 Scott That's right.
16:52 Scott Okay.
16:53 Scott So if let's say that my company wants to work with Avatar Buddy,
16:57 Scott like on the services side,
16:59 Scott let's start there.
17:00 Scott What's that kind of engagement look like? How's that go?
17:04 Stephanie So, first of all, we have a scoping call.
17:07 Stephanie It's like the first date.
17:09 Stephanie Like, do I like you?
17:10 Stephanie Do I like your energy?
17:14 Stephanie And so...
17:16 Stephanie I'm very quickly moved through the state of any customer is a good customer like no to if the customer does not align with our value system does not bring us joy I don't have any enjoyment speaking with them I don't want them as a customer and and people will be like well you can't do that I was like yes you can because
17:39 Stephanie This is about making the world a better place.
17:41 Stephanie Why would I want to have a bunch of customers that doesn't bring me joy?
17:46 Stephanie That just means that I'm going to be a miserable person and anybody around me will be miserable by extension.
17:50 Scott Mm-hmm. Mm
17:51 Stephanie So
17:51 Scott -hmm.
17:52 Stephanie that's the first thing we do.
17:54 Stephanie Then the second thing we do is we do an AI opportunity mapping session,
17:58 Stephanie which is I just sit down and I say,
17:59 Stephanie Scott,
18:00 Stephanie what problem are you trying to solve with AI?
18:02 Scott Mm-hmm.
18:02 Stephanie If you can't articulate what problem you're trying to solve,
18:05 Stephanie I can't help you.
18:07 Scott Yeah.
18:08 Stephanie And some people get tested with that and people are like.
18:13 Stephanie Why do you do that on the front end and part of your sales cycle and you do it for free?
18:18 Stephanie You could charge two,
18:19 Stephanie three thousand, five thousand dollars for that.
18:21 Stephanie And I go, if I understand why they want to solve what problem they want to solve for and we get an alignment and agreement,
18:32 Stephanie I will more than make up for that five thousand dollars on the back end.
18:37 Scott Mm-hmm.
18:38 Stephanie But if I don't, I will spend it will cost me more than the $5,000 in the back end.
18:44 Scott Okay, so can you offer like a little bit of advice?
18:49 Stephanie Yep.
18:49 Scott what is what's an example or two of clients you've had who have come like where good problems like a good like you've had a you've had a client that's come prepared and they've they've said x this is it and i'm sure you've had clients that have come unprepared and they've said why like what does prepared look like and what does unprepared look like
19:10 Stephanie So prepared looks like.
19:12 Stephanie um you understand your business process you brought people that do the work to the meet-in so
19:18 Scott Mm-hmm.
19:19 Stephanie like i just one person goes i don't need to talk to the team i was like yes you do they're like no we don't need to talk to the team i was like no this is not a good customer because
19:29 Scott Okay.
19:29 Stephanie if the team doesn't have buy-in and the team's not helping solve the problem then
19:36 Stephanie You know, I mean, we've both been leaders in organization and somebody comes and says this is the problem.
19:44 Stephanie And then you might have been busy and you say,
19:46 Stephanie OK,
19:47 Stephanie let's solve that problem.
19:48 Stephanie And then halfway through solving the problem,
19:50 Stephanie you go and you you finally go talk to the people that are dealing with the problem. They're like, no,
19:56 Stephanie like that's not the problem.
19:57 Scott Yeah.
19:58 Stephanie I'll give you an example.
20:00 Stephanie This company said, we have an email problem.
20:02 Stephanie Can you help us put in an AI solution to help with our emails?
20:07 Stephanie And I said, well,
20:08 Stephanie no,
20:09 Stephanie I'm not the company. I don't do that.
20:10 Stephanie Don't automate emails.
20:13 Scott Mm
20:13 Stephanie I
20:13 Scott -hmm.
20:13 Stephanie said, you have an email problem?
20:17 Stephanie I said, tell me why you have an email problem.
20:20 Stephanie Well,
20:20 Stephanie it turns out they did not have an email problem. They have an email.
20:24 Stephanie problem email the email is a symptom of a bigger problem so
20:28 Scott Mm-hmm.
20:29 Stephanie there's two things one is they didn't have a systematic way to collect all the requirements so what people would do is they'd send these emails back and forth back and forth to CYA because
20:45 Stephanie I don't want to like Scott didn't give me all the information. I want to black and white. It's Scott's fault if this
20:50 Scott Mm-hmm.
20:51 Stephanie deal goes through because he's so I said, look,
20:56 Stephanie we'll sit with your person that prices out the orders and we'll ask them all the different parameters that are needed based on whatever type of orders you get.
21:10 Stephanie Always give it to the AI and we'll tell the AI that if they don't give you all the information you asked it for all the information.
21:18 Stephanie When I send the email to Scott.
21:21 Stephanie for the order fulfillment he has all the thing one email as opposed to seven did you have an email problem so
21:29 Stephanie No.
21:29 Stephanie Well,
21:30 Stephanie you did. There was an email problem of too much emails, but that was really the symptom of the deeper problem.
21:36 Stephanie that's what we do in an AI opportunity mapping session just go deep and understand and find the bottlenecks
21:43 Stephanie and look at the processes and say,
21:45 Stephanie we're going to just automate this piece of the process, we're going to capture this piece of the process,
21:50 Stephanie we're going to document this piece of the process, and the rest of it you just carry on, and that automizes and saves.
21:55 Stephanie Okay. So part one is, Hey, the right people have to be in the room.
22:00 Stephanie And part two is you have to have an awareness of what.
22:07 Stephanie root causes are the problems you can't just say if if you call Stephanie up you call avatar buddy up you can't just say make my people more productive I mean you could and I suppose you could spend a bunch of time with the team trying to figure it out but um that's not there's a little bit of awareness that has to happen by the client about what's going on
22:28 Stephanie Yeah, everybody knows where the problems are.
22:31 Stephanie So I say to the leadership,
22:33 Stephanie like, we have to create a safe environment where
22:35 Stephanie Mm-hmm,
22:36 Stephanie people can tell you the truth.
22:38 Stephanie So
22:38 Stephanie mm-hmm.
22:38 Stephanie I was actually very refreshed.
22:41 Stephanie One of the sessions I was at,
22:44 Stephanie these people were like,
22:45 Stephanie the CEO is a bottleneck.
22:47 Stephanie Everything has to go through him.
22:48 Stephanie He's never here.
22:49 Stephanie And then he changes stuff.
22:52 Stephanie And so, you know, I get my work done on Monday and then on Thursday night.
22:58 Stephanie he's sending me all of these revisions and now friday i'm scrambling to get it done and
23:05 Stephanie Mm-hmm.
23:05 Stephanie you know it that somehow it's my fault that it doesn't get done so
23:11 Stephanie we're like okay well let's let's capture how how he works run run the the your finished product through the ai
23:22 Stephanie That is his digital twin and then get all the edits done so when you send it to him, if he reviews it on Thursday,
23:31 Stephanie he has no edits or maybe one little edit so you're not scrambling on Friday.
23:35 Stephanie Interesting.
23:36 Stephanie So what have you found in the people that you've worked with, the clients you work with, what are the types of things or even an example that's been just a tremendous success?
23:46 Stephanie Like what should...
23:48 Stephanie What should we all be looking out for in our organizations as the kinds of things that seem to work really well?
23:55 Stephanie We know in the engineering space,
23:57 Stephanie we're seeing it starting to work really well, the writing software.
24:00 Stephanie There's still pitfalls and we need guardrails and things like that.
24:03 Stephanie What other parts of these businesses,
24:05 Stephanie not these businesses,
24:06 Stephanie of businesses are you seeing where this stuff works really well?
24:10 Stephanie So I think that's um
24:12 Stephanie workers spend a lot of time staring at a blank screen trying to like get their thoughts out if
24:22 Stephanie Oh, yeah.
24:23 Stephanie the AI knows how to do your job the way you do your job and has the data it can help you get those thoughts out faster like
24:33 Stephanie Yeah,
24:34 Stephanie that makes a lot of sense.
24:36 Stephanie like I'll give you an example
24:38 Stephanie I wanted to do the remove bottlenecks, not people campaign.
24:43 Stephanie And I don't know how to run a marketing campaign.
24:47 Stephanie I don't know how to write up a marketing campaign. I don't know how to do any of that stuff. Right.
24:50 Stephanie But I did know that everything needed to be aligned.
24:54 Stephanie And I just said to the AI,
24:55 Stephanie like, this is the campaign I want to run.
24:57 Stephanie This is the emotional hook that we want to ground it on.
25:00 Stephanie And it needs to go across all of these. These are all these channels that I'm using. And it wrote.
25:06 Stephanie a pretty slick marketing campaign for
25:09 Stephanie Mm
25:09 Stephanie me and
25:10 Stephanie -hmm.
25:10 Stephanie then I went through and I just tweaked a little things like it used some terminology I didn't like so I had to go back and I had to fix it then I had to go through the document make sure it's all and then then then I just played it through right I talked to each person on the team
25:27 Stephanie Like, hey,
25:28 Stephanie how does this work for your thing?
25:30 Stephanie This is what we want to do.
25:31 Stephanie And so like that's an example, right?
25:34 Stephanie That took me about six hours to do.
25:39 Stephanie If I had hired a marketing consultant and I brought somebody in,
25:45 Stephanie like that would have been like a six week effort or maybe even longer.
25:49 Stephanie Yes.
25:51 Stephanie And.
25:52 Stephanie And that's the beauty.
25:55 Stephanie But why was I able to do that? One,
25:58 Stephanie I had an AI agent that's configured to be a marketing guru.
26:02 Stephanie Mm And-hmm.
26:03 Stephanie we configure that based on a marketing guru,
26:06 Stephanie like the AI is based on an actual human person.
26:11 Stephanie And two,
26:12 Stephanie we gave the AI all the context and data for our company.
26:18 Stephanie So it took our existing campaign,
26:22 Stephanie everyone needs a buddy,
26:24 Stephanie safe,
26:25 Stephanie simple,
26:26 Stephanie fast,
26:26 Stephanie human digital handshake, and it pulled all of that in along with remove bottlenecks, not people.
26:37 Stephanie Mm
26:37 Stephanie And
26:37 Stephanie -hmm.
26:37 Stephanie that was the level of connectiveness that I've been trying to get and I was not able to get.
26:43 Stephanie That's an example of like,
26:46 Stephanie you know, what a human have been able to do that? Probably not.
26:52 Stephanie And if they had, it would have taken so long that I would have lost interest and wandered away.
26:57 Stephanie Mm-hmm. Mm-hmm.
26:59 Stephanie And that's that's where.
27:02 Stephanie You see AI really having a true impact.
27:05 Stephanie So now I say remove bottlenecks, not people,
27:08 Stephanie or eliminate bottlenecks, not people,
27:10 Stephanie remove,
27:11 Stephanie eliminate their interchangeable, you know, you tell the AI every, every, we got a social media buddy,
27:21 Stephanie every social media post has to have.
27:24 Stephanie Save simple fast,
27:25 Stephanie manage AI as a service,
27:27 Stephanie eliminate bottlenecks, not people as a hashtag.
27:31 Stephanie I was thinking, as you were talking,
27:33 Stephanie I was thinking about how are you going to scale your business?
27:41 Stephanie And because meeting with each client one-on-one is time-consuming. I was thinking.
27:51 Stephanie Is it the case that there could be an AI agent? You just talked about the introductory call or maybe a few calls you might have with a potential client to try and understand the real root causes of what they have going on.
28:02 Stephanie And I'm wondering,
28:03 Stephanie could that itself be an agent?
28:06 Stephanie No.
28:07 Stephanie Now you think that needs to be a human?
28:09 Stephanie I mean, you could obviously be a human, but based on our value system,
28:15 Stephanie it's not going to be a human.
28:16 Stephanie What we do is we look at processes,
28:20 Stephanie we run them manually,
28:21 Stephanie and then after a while we automate them.
28:24 Stephanie Yeah.
28:25 Stephanie So every time something becomes too much.
28:29 Stephanie we automate them like i just had a conversation with my cfo yesterday i said let's fix the ai agent so you can close in one day end of month close should be one day she's she's like man like the agent is great uh something that would have taken me a few days i took just a couple i took um four or five hours and and she's like i don't like how it writes
28:53 Stephanie And I was like,
28:54 Stephanie well,
28:54 Stephanie okay,
28:55 Stephanie we'll fix that.
28:56 Stephanie teach it that's very interesting the close in one day because where you can you can say it's taking us n days to close the books and you can look at all the different parts that cause that time to add up and it's a very objective measure of is it getting better and you can look at that piece by piece i'm reminded of a time that just reminded me of a time
29:19 Stephanie Hi, this is a long time ago. When was this? This is about 2010.
29:24 Stephanie And I was at this company and it was taking forever to deploy our software.
29:30 Stephanie And we set a goal for ourselves that we wanted to take 10 minutes to deploy.
29:35 Stephanie And the first thing we had to do was measure how long does it take to deploy now.
29:41 Stephanie And it took I remember this, it took 174 minutes to deploy.
29:45 Stephanie And so then it was a matter of shipping away.
29:48 Stephanie And so it's interesting that the kinds of processes that have a very measurable, like closing the books for a month, the kind of processes that have a very objective measurement of like,
29:59 Stephanie is it getting better?
30:03 Stephanie Are well suited for this kind of work.
30:06 Stephanie Exactly.
30:07 Stephanie So now when I meet with her, we're
30:11 Stephanie Mm-hmm.
30:11 Stephanie having real conversations about the business.
30:16 Stephanie She's saying,
30:18 Stephanie oh, by the way, I think once we get down to we can close in a day.
30:25 Stephanie I said,
30:26 Stephanie once you, we're in this woman's group together, I said, once you come in, once you get to get it to close in, close in one day,
30:34 Stephanie we're going to host a meeting, we're going to host a pop up with this with the women from,
30:40 Stephanie and you're going to talk about how you're using AI to close the books in one day.
30:44 Stephanie And that will, that's how we're going to sell it.
30:47 Stephanie And people are going to be like, whoa.
30:50 Stephanie And so if I take the time, right,
30:53 Stephanie like on the front end, if I take four hours on the front end to get to know a customer,
31:01 Stephanie learn what they're trying to do,
31:03 Stephanie onboarding them becomes easier, managing them becomes easier,
31:09 Stephanie building out their solution becomes easier. We have some AI.
31:14 Stephanie agents that we give other AI agents to create right not all our AI agents are created by hand it's
31:21 Stephanie Turtles all the way down.
31:23 Stephanie it's exactly um and and so it's like kind of like the same thing right um we have a marketing company and I was just like I am tired of reviewing the same thing every month and making the same freaking edits every month
31:41 Stephanie So I was like, if you don't use Marketing Buddy to help you write it, I'm just going to send it back.
31:48 Stephanie Mm-hmm.
31:49 Stephanie And they started using Marketing Buddy and they're like, yeah,
31:52 Stephanie like it took five, it used to take five hours to create the content calendar. It takes an hour and a half.
31:59 Stephanie I was like, it's in your best interest because I pay you a fat rate.
32:03 Stephanie So if it takes you 20 hours,
32:04 Stephanie it takes you an hour and a half,
32:06 Stephanie Yeah.
32:06 Stephanie you're still getting paid the same amount of money. So you want to reduce it.
32:11 Stephanie And that's where.
32:13 Stephanie the service and the product comes in right it cannot divorce the two and when people try to divorce the two you don't have the impact it's why Anthropik is winning right now Anthropik Enterprise is winning because they figured out that they have to give consult with the product it's why OpenAI just went and bought a little boutique consulting firm out of London and is making all these deals with these companies because they they
32:40 Stephanie They realize that like I like to tongue in cheek say Sam Altman has like a wire in my bug in my house and whatever I think about like three to six months later,
32:52 Stephanie he comes up with the same thing. So he's following, he's following me.
32:56 Stephanie The whole point is, is that
32:59 Stephanie Because I understand software delivery,
33:02 Stephanie because I understand how business works,
33:04 Stephanie because I did IT quality,
33:07 Stephanie because I also ran,
33:09 Stephanie helped run a small government, a local government entity. It was only $184 million.
33:19 Stephanie Pretty substantial.
33:20 Stephanie I understand both sides of the table.
33:23 Stephanie I have a master's in economic development,
33:27 Stephanie so I have the social skills.
33:29 Stephanie So when you come to Avatar,
33:32 Stephanie buddy,
33:32 Stephanie what you're buying is my IP.
33:36 Stephanie And as AI becomes everywhere and so it's nowhere,
33:43 Stephanie what will differentiate people is do you have IP that other people want?
33:51 Stephanie that's not in the AI or if it's in the AI it's in an AI that's behind a firewall so you so you can't get to it it's the IP it's the absolute
34:00 Stephanie Yeah.
34:00 Stephanie IP so so that's that's that's how that's how we're working that's what makes us different and once I realized that um
34:10 Stephanie It made us unlock so many things, right?
34:13 Stephanie And I was talking to a company, we just entered into a partnership with a company out of South Africa.
34:21 Stephanie And, you know, I just got back from South Africa beginning of this week and I came back with a signed contract with an exclusive partnership.
34:30 Stephanie um to do work for them right and i said to her like we were presented at one client this morning and i said what we have to do is the client wants our ip and you can't you can't be at all the clients unless we create this solution this solution captures your ip
34:55 Stephanie And then we're just going to sell it to the client and a client will push a button and two, three hours later,
35:01 Stephanie we'll have the reports they need.
35:02 Stephanie We don't need to we don't need to do that work and we just interact with the client to make sure that, you know, their AI investment is where it needs to be.
35:14 Stephanie You've touched on something that I've thought about recently,
35:16 Stephanie which is.
35:19 Stephanie Right now, folks like you understand how these tools work,
35:25 Stephanie how to create agents that can do things for people.
35:29 Stephanie You've got, as you said,
35:31 Stephanie IP or insight that others don't have.
35:34 Stephanie I was talking to a friend of mine.
35:37 Stephanie I guess it was two weeks ago we were having dinner.
35:40 Stephanie He owns a coffee shop, actually has a few coffee shops, but he has some coffee shops.
35:45 Stephanie And he has been using, I don't know if he uses Clot or ChatGPT or whatever,
35:50 Stephanie to sort of summarize his financial reports and help him understand what's happening with his business.
35:57 Stephanie he has sort of a CFO type person who would give him all this stack of reports and he could, it was always, they were always having to have these long conversations. And so he just fed it into, into the chat and said,
36:08 Stephanie you know, help me understand this.
36:10 Stephanie And I thought to myself,
36:11 Stephanie well,
36:12 Stephanie someone could create an agent or a skill to do that.
36:15 Stephanie And then being who I am, I thought, and that could be a software product that you could sell to people, right?
36:23 Stephanie Yep.
36:24 Stephanie Which is what we're
36:25 Stephanie So
36:25 Stephanie doing.
36:25 Stephanie we can sew that same thing over and over again.
36:27 Stephanie But then I thought to myself,
36:29 Stephanie what kind of, you know, moat or protection is there around that?
36:36 Stephanie I guess what I'm asking is, it seems like maybe in five or 10 years,
36:41 Stephanie probably closer to five, like a lot of these things are a lot of this knowledge is going to be out there, so to speak, kind of like open source software.
36:50 Stephanie And I guess we keep,
36:53 Stephanie now I'm going to answer, this is a terrible.
36:55 Stephanie Question, Stephanie,
36:56 Stephanie because now I want to propose an answer to it.
36:58 Stephanie Please answer it because I want to hear your answer because I have an answer too, but I want to hear your answer.
37:03 Stephanie I guess what's going to happen is the humans are always going to be learning a little bit ahead of the robots.
37:09 Stephanie We're going to teach the robots a bunch of stuff that we know,
37:12 Stephanie and then we're going to go out and learn more stuff.
37:14 Stephanie Original learning is going to happen by the humans,
37:17 Stephanie and we're going to feed it to the robots. So every company,
37:20 Stephanie look.
37:21 Stephanie QuickBooks is going to build their report summary agent into QuickBooks, right?
37:25 Stephanie And then that's all going to be not that valuable of intellectual property anymore. But we're going to learn more as humans and we're going to then,
37:32 Stephanie that's always,
37:32 Stephanie the humans are always going to be the cutting edge. That's my answer.
37:35 Stephanie You're in your team with your friend that's using chat GPT to summarize,
37:40 Stephanie right?
37:41 Stephanie He looks at his financials his way.
37:46 Stephanie Yeah.
37:46 Stephanie You look at your financials your way.
37:48 Stephanie I look at my financials my way. You can't have the same agent.
37:55 Stephanie It's what you
37:56 Stephanie That's
37:57 Stephanie have is you have the same base of an agent,
38:01 Stephanie but you have three different agents behaving slightly different.
38:09 Stephanie The AI that I keep telling people is because it does not take that much effort.
38:16 Stephanie Once you create the base agent,
38:17 Stephanie it's just an incremental amount of effort to create the agent to work the way you want it, the way your friend wants it, and the way I want it.
38:27 Stephanie And we're now all happy.
38:29 Stephanie Yeah, that's interesting.
38:30 Stephanie Not everybody works the same way.
38:33 Stephanie That makes sense too.
38:35 Stephanie I mean, and that's that's been the biggest friction in corporate America from beginning of time.
38:40 Stephanie You
38:40 Stephanie Mm-hmm.
38:40 Stephanie must fit into this box. If you don't fit into this box,
38:44 Stephanie then you are a bad employee.
38:45 Stephanie And if you're a bad employee,
38:47 Stephanie depending on our level of tolerance will either.
38:51 Stephanie keep you and marginalize you and then just make you a miserable person then do you just then in turn makes everybody a miserable person or we just fire you and we come up with and if we if we have no spine we'll just reorg your your um your position out the door if we have some spine we'll come back and we'll give you constructive feedback
39:12 Stephanie Yeah, that's a very vicious way of thinking about it. I even think about I've worked at.
39:16 Stephanie work with three different companies that all kind of do the same thing but they do it you know they do it different ways um and and you can sort of say well that's not that's not how we did it at this last company but i guess i can see why you guys do it that way so i guess we'll do it that way even though it's the same same business you know people choose to do it their own way
39:36 Stephanie Exactly. So now all of a sudden, I mean, like, that's like,
39:39 Stephanie that's where.
39:41 Stephanie It come back to helping people be the best version of themselves because I no longer have to work the way Scott works because Scott's my boss.
39:52 Stephanie Mm
39:52 Stephanie I
39:52 Stephanie -hmm.
39:52 Stephanie can work the way Stephanie works because I have an AI agent that's helping me work the way Stephanie works.
39:59 Stephanie That makes me be the best version of myself.
40:02 Stephanie It builds self-agency. And because I am feeling good about myself,
40:06 Stephanie I am loving Scott and everybody else on the floor and I'm getting my mom to bring cookies in for Scott and Scott's like,
40:13 Stephanie oh my God, this is great.
40:15 Scott Well,
40:15 Scott that sounds wonderful.
40:17 Scott And I feel like that's the note we should end on with one other small addendum. If people want to find you or want to find AI Buddy,
40:25 Scott where should they go?
40:26 Stephanie AvatarBuddy.ai,
40:28 Stephanie or you can look us up on LinkedIn.
40:31 Stephanie If you look for me,
40:33 Stephanie Stephanie Sylvestre, T-R-E,
40:35 Stephanie AvatarBuddy.
40:38 Stephanie And if you mentioned that you saw me on this podcast,
40:42 Stephanie I will happily give you a half an hour to chat about anything.
40:49 Stephanie You want to learn about Africa burn?
40:51 Stephanie Sure,
40:52 Stephanie we'll talk about Africa burn.
40:55 Scott Well, thank you so much for coming on today.
40:57 Scott This has been really fascinating and frankly a little bit inspiring to me, which is good.
41:01 Scott So I'm glad it's always inspiring to talk to you, but I'm a little bit motivated now to go try and figure out where there are some bottlenecks in our world so we can transfer tech on them.
41:10 Scott Thank
41:10 Stephanie Yeah.
41:10 Scott you, Stephanie.
41:11 Scott Appreciate it.
41:11 Stephanie Thank you.
41:12 Scott This episode of the How Many CTOs Does It Take podcast was produced and edited by Safran Heptakow.
41:18 Scott Our theme song was AI generated by Brad.
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