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- [00:04]
A
What's interesting about the non technical people in your organization is that their careers are based on domain knowledge and then also soft skills, the ability to collaborate, emotional iq. All these skills that take a really long time to develop. AI is able to help you augment a technical knowledge base and an ability to execute a much wider breadth of technical skills than any of these people would have imagined to be able to do six months ago. If you're a business leader, the question is why is that important? What we're seeing over the last six months or so is a completely different way of delivering work. So for example, I have a colleague who knows everything about our industry and is super smart but is not a technical person. He had an idea two weeks ago that we should have a ROI calculator. He took the idea, put it in AI Google Studio, built a web application, showed it to our marketing team, they made a document with some feedback. He threw that feedback right into Google AI Studio and we pushed it live. So that's eight months, 20 people, however many hours between all of them to deliver something that has impact on the business. Down to 10 hours with three or four people.
- [01:12]
B
Welcome to the Work for Humans podcast. This is Dart Lindsley. A few years ago, Aaron Horwath was leading the L and D organization at Creative Force. In that role, he didn't want to do anything short of extraordinary. With the support of his open minded leaders, he started moving his organization toward a work as product approach. It's had a big effect, one of the more surprising of which is the organization's success in implementing AI. It's a success that most other companies have found to be elusive. Aaron is now the Director of AI Operations at CreativeForce. Aaron and his team achieve success by looking closely not just at what tasks require human judgment or could be automated, but also which tasks people in the organization love or hate. That question led them into AI, but not in the usual way. Instead of chasing tools, they started with people. They talked to teams about the work that drains them, mapped the tasks that create friction and identified the work that people find the most rewarding and then used that insight to guide where AI should be implemented. What emerged is a model where AI removes the repetitive low value work that gets in the way and employees get more time for the parts of the job they actually care about. But beyond that, Aaron and I talk about how his team approached this shift, why they focused on people before technology, and how new tools are helping people in the organization build their own bespoke software that meet their individual needs. More quickly so that they can learn faster, see the big picture in near real time, make better decisions, and understand their work in new ways. We also explore what it takes to prepare people for a future that is arriving and quickly, and how to invest in work as a product for employees, as customers. As always, subscribe so you never miss any of these conversations. And now here's my conversation with Aaron Horwath. Aaron Horwath, welcome to Work for Humans.
- [03:31]
A
Thanks, star, for having me.
- [03:33]
B
So you pointed out to me that I had avoided the topic of AI for a long time on the show, and that then I started doing shows on AI. And the reason that I avoided it originally is that I thought that most of the challenges that we face at work are not going to be solved by technology. But I realized, talking to you and talking to others, that actually AI can be a part of the solution. Somebody that I had on the show, Kentaro Toyama, who wrote Geek Heresies, he said technology is an amplifier, and so it amplifies the good in us, and it amplifies the not so good in us. And so the possibility that AI may be that amplifier of good is starting to be very compelling for me. That's our launching point, because I think that at Creative Force, you've been doing very interesting things very successfully with AI, and I want to go deeper into that.
- [04:34]
A
It's a good point. I think it's also fair to point out that there will be times where probably you and I both get excited about the topic that can age like milk in the cases where people misuse the technology. The reason that I was excited to talk to you about it was because it overlapped with this idea of work as a product, and we started to really see the opportunity for it to improve the work experience of employees. That doesn't mean that that's how everybody will use it, but that was the part that I got excited. So maybe it's a good disclaimer of, I think, talking about Glass Half Full, the best, most ethical use case of a technology that I think you and I both agree can have a really good impact and positive impact on the employee experience.
- [05:16]
B
Yeah, and that's the other thing. That's the other really big reason I wanted to have you on the show, to talk to you about how your reframing of work as a product has affected your implementation of AI and other things. AI is not the only thing. It's just we are going to talk a fair amount about that. So let's start with the context. What is Creative Force. And what is your role in relation to creative Force?
- [05:39]
A
We're an end to end content production platform for brands and retailers and commercial studios. And basically these companies, these photo studios previously were managed by taping together spreadsheets and other non purpose built softwares in order to manage really complex photo studios. And so our founders five years ago built a platform that was enterprise level and that managed every part of the content production process and not only eliminated all of that pain for the end users and creatives, but also gave studios access to data that they could use to improve their operational efficiency and address inefficiencies and get insights into their production. So that's pain. Creative force, part of it. The relevancy maybe for this conversation in particular, is that as a product we've been really pushing the integration of AI in our tools and we've also been trying to figure out how to promote or how to support our customers in leveraging AI in their content production. And that was the motivator for us to start to use it internally as well. Because one of the motivators was because we needed to make sure that our employees and our staff could talk to customers about leveraging AI from a first person perspective and make sure that they could speak about it as people who are using it themselves. So that's a little bit where the motivation for us began.
- [07:00]
B
Global team.
- [07:01]
A
Yes, global team all over Europe and the US and Vietnam as well.
- [07:07]
B
So here you are. You were leading L and D at the time, right? And along comes AI. How did you first get involved with it and what were some of the first opportunities that you went after?
- [07:17]
A
So, end of 2024, my boss, Astrid Raibropolsen, our VP of people, which is notable, I think, which we can talk about, was the one to actually, whatever you want to say, raise the flag on, like the AI thing is something that we're pushing in our product, but this is going to really change the way that we conceive and execute and deliver work. And we didn't really know exactly what that would look like, only that it was coming and we needed to start to get deeper into it in order to be ready for it. We didn't want to be in a position where it arrived and then we were playing catch up because we knew it was going to move really fast. So it was really Astrid who started by going to our leadership team and saying, here are a few reasons why this is worth the investment for us. One, like I said, is that our people need this first person experience with engaging with AI, because it's going to be part of our product, and we need them to be able to speak to that. But the second was that I had started to engage with your podcast also. We had already been speaking not exactly in your verbiage, but around this idea of being something much more than a traditional HR team, but much more like a business partner and a strategic partner for stakeholders around the business. And we saw an opportunity to improve the experience of work for people by leveraging AI and using that to eliminate all of the work that people don't tend to really enjoy about work. Right. So all of the data manipulation or translation, I have data in one form, I need to translate it into another. All of the admin that's required of knowledge workers, filling out your CRM, creating slide decks, all the things that people kind of like, ugh, I have to do this again. Anything that gets that kind of reaction, that really speaks to the work as a product philosophy. But we knew that that was super important for us. And then we also felt like we owed it to the organization that we needed to future proof our teams for what was coming and that we owed it to them to invest now in preparing for them for what the future of work was going to look like. And so that was really Astrid driving that as this needs to be something that we invest in.
- [09:24]
B
That's really interesting. I think that's very rare for leaders to really think about the future of the careers of their workforce when there's so much demand for whatever's going on. So thinking strategically like that is really great, really fantastic.
- [09:40]
A
We owed it to them. But also it's in the business best interest. Right. As well that we know that these things are going to change. And we were in a position to invest in the right ways early to make sure that people were in a position to excel when that change happened, which has happened over the last year. And thankfully, we have a really great adoption in the organization now around leveraging these tools. Obviously, it's an ongoing investment that we make and helping people upskill and figure out how to use these tools in the best way possible. Hopefully people are grateful for that, but not grateful but.
- [10:13]
B
Or just recognize it. I think you can say, hopefully people feel that we care. Feel that we're paying attention to them that way.
- [10:22]
A
No, but it's also a. There's a talent retention component to it. Right. Because if you wanted to make the business case for it, strictly, obviously, we care about investing in our people. But if you're a business leader, what is the business case for Investing in this type of upskill, especially at scale one on the people side is talent retention. We've changed the way that our employees work and we've also eliminated a lot of the things that people don't enjoy about those roles typically. And we've also empowered them, I will talk about later in the conversation probably. But we've also empowered them to execute skills at a different level and also a much more diverse set of skills than they would without these tools. And so I think it's also tough maybe to imagine, I hope, going somewhere else where you revert back to the old ways if you go somewhere that hasn't made this investment. Because all of a sudden now I don't have access to all the data and the cool tools that were built for me at Creative Force and I'm suddenly having to manually take notes again. I'm updating manually my CRM. I can't get the insights that I had before into my customers. I don't have the insights into my prospects that I used to have. I don't have the insights into how our products being used that I used to have. There's a loss there. And then also there's the talent acquisition part, the employer branding part also that, you know, the pitch for us is basically now we are going to allow you to spend so much more time on the parts of the job that you enjoy and that you became a customer success manager to do or that you became a salesperson to do and so much less time elsewhere spent on the things that you don't enjoy. It's a pretty good case. I mean that's all product of work, right? Or work is a product. But it is, I think, a selling point of working at Creative Force that we can say, you know, a typical CSM spends 60% of their time, 50% of their time on admin or what we would call non value generating activities. And at Creative Force we are very quickly eliminating a lot of the time spent on that and then we allow you to reinvest that time in collaborating, solving problems, interacting with customers, visiting customers, working on interesting projects. And that's a pretty good selling point I think for working at Creative Force as a company.
- [12:34]
B
Now you were already sort of on this path before you started hearing the formal language that we use on the show. What is the vocabulary or the positioning that's been most useful in the work as a product approach?
- [12:53]
A
Yeah, and that kind of goes into the development of this. I think it's all woven in there.
- [12:58]
B
So okay, let's then go into the AI approach that you took and how it's all woven together.
- [13:04]
A
It's interwoven in the narrative. So Astrid goes in and she says, this is something we need to invest in. And we get buy in from the leadership team because of everything that we just talked about. Right? We can improve the work experience they need, firsthand experience using and interacting with AI tools, et cetera. So then we form this AI enablement team with stakeholders from around the organization. And we don't really talk about AI in it at first. These are people, managers, but they're also individual contributors and we bring them from across the org together. They represent all different teams. And the first conversations that we have aren't related to AI at all. We talked about what are the current operational challenges that our teams are facing, what are the tasks and work that staff need to execute but isn't necessarily rewarding, or the things that they want to do, and what would be the business impact of us solving those things. This is where the bubble activity comes into play. And this is what I originally sent you on LinkedIn and said, hey, were doing this and it really worked.
- [14:08]
B
So for people who may have not heard about the bubble chart, how would you describe it and where did you come across it?
- [14:14]
A
I actually heard it on the podcast where you described it. I found the podcast early this year, and one of the first episodes was an episode where you and the guest were describing this really interesting activity where you take a team and you have basically a chart with two axes and on one axis, on the Y axis, you have a spectrum from high core to team mission to low core to team mission. And then on the X axis you have high business value to low business value. The activity that you guys were describing was basically to sit down with your team and start to map out the tasks and the responsibilities within the team along that spectrum. But you also map a few other dimensions as well. And you describe this idea of using different sized bubbles to describe how much attention the team focuses or uses on the individual tasks and responsibilities. And then there was a color dimension as well as a way to encode basically how much the team enjoyed doing those activities. So red was that they didn't like it at all, and then green was that it was something that they really enjoyed. So we actually used this activity. I took what I heard from you guys describing and I turned it into a Miro board, and then I shared it with our teams and they used it as the foundation for our quarterly reflection discussions that we do every quarter between our managers and Each of their direct reports or their team members. And so that was completely separate from our AI discussion. It was just a really great activity to start to map out the work in each of our teams and, and get a sense of were there tasks that had slipped into the teams that maybe weren't actually super high value, that the teams maybe wanted to start to evaluate in terms of potentially getting rid of where were people finding a lot of joy and motivation from their work. And so we did it as a one on one activity.
- [16:06]
B
And actually you did something different than what we've ever done in the past, which is that you did it as a one on one activity. And usually what we do is we identify the categories of work that are going to form the different bubbles and we do that for the whole team. And then the whole team uses the same categories of work. But you had individuals develop their own categories of work. And I think you got information that we don't normally get, which is, how do people think about breaking up their work in their own minds? So that was new, and we added.
- [16:40]
A
A color as well, which was our blue sticky, which I told you about later. And that was our future work sticky, which was, what work would you like to work on in the future so people could map and say, I think that this would be really impactful for the team in terms of business. I think it's core to our team mission and it's something that I would like to do in the future. And so that also gave our managers a sense of like, okay, this upcoming quarter, is there a way for us to fit that into what our team is already doing? Could we replace a red task and replace it with a blue task for somebody, for example?
- [17:12]
B
To me, that's an indication of really getting the philosophy, because that also is an innovation, but it's exactly aligned with the sort of thing that a manager's responsible for, which is winning the kind of team work that the team wants to do. So how do you know what that future possibility is? I think that's a great practice.
- [17:32]
A
And then as our AI efforts started to kick off, we started listing some operational challenges that we wanted to hopefully address with AI. But then I remembered that we had this database of this work that had been mapped out in Q1. So I took all of these bubble charts that we had created in Q1, put them into ChatGPT, and I started to analyze them in terms of, okay, what are some of the common trends of work that people are finding maybe valuable to the business, but not stuff that they necessarily Enjoy. That's kind of what we found, was the sweet spot. And so then we could start to evaluate is this work that we want to eliminate completely, is this work that we want to keep and keep it human executed, or is this work that fits really nicely with AI and something that we want to automate and take off the plate of our humans and deem non human work? And so we took all of those bubble charts or bubble activities and analyzed those at scale across the entire org and then we had this great database of insights around where it would make sense maybe to address some of these with AI or what stuff we wanted to keep as human and what stuff maybe we just wanted to get rid of entirely.
- [18:42]
B
And for the teams, did you tie it back to that exercise or was this sort of an independent thing, which is you used it in your head?
- [18:49]
A
It was great because even later on, as we started to roll out our first AI tools that we were developing, we could go back to our teams and say, do you remember in Q1 when you noted that this was a task that you felt was valuable for the company but that you didn't necessarily enjoy? That's been automated now and here's how we've done it. So it allowed us to tie back the efforts that we were doing in AI to really operational value and improvements in the work experience for individuals in our teams, which was, I think, really important around getting that early buy in, in what we were trying to do with AI internally.
- [19:25]
B
Just now I heard two ways, like two common patterns for how you've used it. One of them is data manipulation, which is we have multiple platforms, we have data in these different platforms. We need to get data from A to B. I've actually studied people whose job it is to go through LinkedIn and copy resumes over into a central database. They're unbelievably fast at it, but it's all they do. And they have these advanced degrees, but they're copying data. That's the sort of thing that people do all the time. So that's one. The second thing I heard was pulling together data again from different sources and giving people insight that they wouldn't have had otherwise. And that's very interesting to me. Those are different. Well, they might be related. You might have to manipulate the data to pull it together to get the insights. So characterize for me the kinds of drudgery that, you know, is there sort of a drudgery zoo? Like, are there types of drudgery?
- [20:37]
A
I started saying just because I thought it was catchy Ish. I called it the CAP framework. K A P. So we categorized use cases for AI internally into three categories. One was the K, which is knowledge at the point of need. I'm somebody who's in a role. I have a piece of information that I need. Let's try to keep it super practical. I'm a salesperson. I'm in a call with a prospect. They ask me about what integrations we offer six months ago, maybe that person says, not 100% sure. I'll talk to a colleague, I'll get back to you tomorrow. And then we start this loop of them going to ask a colleague and then they get that information and then they bring it back and they send it in an email 24 hours later. Now we have a bot that knows everything about our product. So as that prospect is talking, our salesperson can just say, what are all of our integrations? And in three seconds it pops out, these are all of the integrations that we offer. And then they say, oh, yeah, we offer this integration, this, et cetera. Right. That's a very small use case. But that is not having to go elsewhere, not having to wait for the information that you need, needing it right away.
- [21:41]
B
Right. And okay, so that's the K. And it's interesting because you used to go to a sales engineer who needed to be there at the table to be able to say, these are integrations and this is how it integrates. And now the front line doesn't need that whole loop of going to get that information.
- [22:03]
A
Yeah. Or imagine that you're a new employee at a new company. This is a really great use case for knowledge. At the point of need. There are policies that I need to know. I need to know our product and I also need to know our industry. And that usually requires that I sit down and I immediately hit a wall. And then I need to ping a colleague on Slack. And maybe that's not the right employee to ask, so I need to ask somebody else. And that's my learning loop, is I kind of run into a wall and then I have to ping a lot of people to get an answer. We don't want to entirely eliminate that. However, what we're noticing is that because we have bots trained on these different subject matter areas, our new employees, as they get onboarded, can onboard way faster because it allows their feedback loop to be way quicker. So all of a sudden I have a question about the product and I ping it and I learn. Or I can go in and ask. Tina is our product expert Bot. And so I can go in and ask Tina and say, I'm a new employee at Creative Force, could you give me a rundown of our product? Just high level, kind of explain what we do and how we impact customers. And she can provide an overview of that. I can ask her specific questions, I can ask her about a particular area of the product. But I have a colleague with me 24 hours a day who knows a lot about the product and the industry and our customers. And I can go back and forth with her super, super fast. Not to make her sound like a human, but she's the only bot that we've named. But Tina was our first bot, so she has like a special place in our hearts. But that feedback loop becomes very, very quick. There's obviously a role for humans in all of that, but we are noticing much faster uptimes or like getting people their feet under them much faster because we've closed that loop a little bit.
- [23:41]
B
Okay, what's the next letter in the acronym?
- [23:45]
A
A is automation. So anything that we recognize that we are doing over and over again that AI would be great for. And that's often the data manipulation part or transformation part. We just had a meeting with a customer. We analyze that call. The bots analyze that call. They push that data to our tool that we use for managing customers. It does an analysis for meeting notes and generates those. It also notes anything that changed in the relationship, pushes that up as well. That could be anything from transcripts to I need to create a business case for this customer. We have a bot that can create a business case for you. I need to create what we call a value pyramid, which is a document that during the sales process you create just to demonstrate your understanding of their business. We can generate that as well. Anything where there's particularly content creation that we need to do over and over again. The bots are very good at it because they have all of our interactions with the customers and so they're able to generate those super quickly. Also lots of use cases in our marketing team, but it's always about what is a repetitive process that's pretty structured. And that's where bots really thrive in the automation space.
- [24:55]
B
And that's really unfun work. What's the third thing?
- [25:01]
A
Proactive Insights is the P and this one is where we start to introduce what I would call V2 of AI adoption within an organization. So I would say V0 is what you see from a lot of organizations right now. An organization whose leadership team has said, use ChatGPT have not explained what the strategy is there. They haven't said what they expect their people to use it for. They've just said, we know AI is important, use it. And they use it mostly to polish emails and maybe do some basic tasks. And that's as far as their AI strategy goes. Version one is the knowledge at the point of need and a little bit of the automation. But I think the foundational difference is that in version one, you're as a human always starting the interaction with the AI. So we're always going to a bot and saying, I need this done. And then you do it. Of course, some automation in there. But mostly it's humans starting the conversation, asking a question. Maybe you have really rich data like we do, you can get really interesting insights back, it can do really interesting tasks for you. But you're always the one as the human who's going to the AI and starting the conversation. What I see as version two and what we're building right now is the easiest way to think about it is if you imagine that you are a salesperson or you're a customer success manager and you will have a team of bots. That team can be 5, 10, 15 bots. Each bot has a particular task that it's assigned to do and it is managed by an AI manager. Each individual bot is responsible for analyzing some unique aspect of the deal or the customer. So one is looking at deal health. Let's say one is looking at your latest calls and giving you feedback on how you performed. One is looking at how often you've contacted them. One of them is looking at the likelihood of this deal closing. One of them is doing an analysis based on your sales approach. If it's medpic or whatever. 10 of these bots, each with a specific use case and they're all processing every single night all of the new data that's come in around this deal. Their manager's job is to synthesize this data and then to do two things. One is to push it somewhere where you can see it. So a dashboard, pretty standard. So the AI team works all night, processes the data and then pushes it to where you can see it. And then the second thing is that team can then execute actions based on the changes in the data. So when you come in on Monday morning as a salesperson, not only do you have like a 360 degree view of your deal, for example, but your bots have also said, we've noticed these opportunities, we've also noticed these potential risks in the deal. One of them is that we haven't heard from this prospect in the last three days. So we've drafted an email for you to follow up with them and it's sitting in your email inbox waiting for you to approve and send out. Or we've made this calendar invite for you and it's waiting for your approval and then you can send it. This team works every night, always evaluating the data, surfacing insights, and then also making actions. For you as the human, your role is to come in and execute judgment. My bot team has told me that there's this opportunity, so I should maybe look into that. They've said that there's this risk, so I need to have maybe evaluated that a little bit. And then they've also said this is the action that they would suggest and I need to decide if that's the right action or not from a human perspective. And then also my job is to handle all the human to human interactions. To me, that AI operating layer below your humans in an organization is the next step where it's more like a. What are those suits? Exoskeleton for employees. You can basically be almost, if you wanted to, the same employee you were before. Your bots are just always filling in the gaps and boosting your performance, even if you didn't change anything because they have the ability to process way more data, notice things you may not have noticed, notice when you've missed things, notice insights you may not have caught. And so in some ways it's at the very least raising the floor of performance. And then maybe some of your other employees have the ability to take it to the next level and really like raise the ceiling on what's possible also.
- [29:14]
B
So to some extent, this is looking across events that are happening that would have been beyond your perception anyway. Either somebody else on the team has done something, there's been a contact in your CRM that you didn't know about because it's over here. And by the way, this is not the only deal that you're working on, so it's hard to keep track of all the deals that are happening. What this is doing is it's giving you, I want to say it's extrasensory perception, right? It's giving you perception beyond what is within view of you. So things that are around corners, behind walls that you wouldn't otherwise know about, and it's summarizing them in a way that you don't have to have the cognitive load of looking through all the information at once raw, it's got some intelligence to it and then it's helping you to transact. If that's V2, can you see V3 out there in the future? I mean, notice it.
- [30:15]
A
I will not venture a V3. The question is how soon it will be. This is going so fast that it's super hard to keep up. Of course, I mean, I think V2. Most companies right now are starting to look at V0. Maybe they're at that V1 level. It would seem like that's at least my sense of it. But what V3 will be, I mean, there are just certain things that we don't want humans not doing. The whole goal of this is just like you said, to remove the cognitive load, but also to give them the ability to see things that they maybe wouldn't have seen before. But also, as I said before, spend more time solving problems, interacting with people, collaborating, visiting people in person, making connections. All that human work.
- [31:06]
B
On work for humans. We've been exploring the principles of multi sided management, which is the belief that work is a product that every company designs, builds and delivers to employees. Along the way, people started asking how they could put these ideas into practice. So I founded the work design firm Elevenfold to help your company create the kind of work that makes teams feel alive and engaged in instead of dead and dull. So you can reduce turnover and build commitment. We're doing something revolutionary here. Learn more@elevenfold.com. that's 11F O L D dot com. So coming into this from an L and D perspective originally, is it kind of a natural step to say I used to be responsible for capabilities in the organization? It used to be that all of those capabilities were human capabilities, but now I'm thinking about capabilities and there's a combination of human and not human. So I want to talk about sort of whether that was natural and then I want to talk about the skills impact. Like what does it mean for the people working in the organization?
- [32:21]
A
Let's take that one first because I think that one is super interesting. Let's do a very crude categorization of employees at a in a knowledge work type of environment. And very crude. So all exceptions allowed here. You could split an organization into two halves. Generally speaking, your non technical employees, quote unquote. And then your technical employees, your non technical employees are your sales, your marketing, your people. Team finance sometimes has more technical skills, but let's say back office teams et cetera. And then you have your technical teams, the people who are building applications, building the website IT infrastructure, all of the hardcore IT related development work. What's interesting about the non technical people in your organization is that their careers are based on domain knowledge and then also soft skills, the ability, like we've been talking about, to collaborate, emotional iq. All these skills that take a really long time to develop and the best ones are really good at those things. Change management, communication, empathizing with other people, connecting with others. That skillset. What we've seen over the last six months is that that non technical group of people have all of a sudden added a technical skill set on top of that soft skill set. And that domain knowledge AI is able to help you augment a technical knowledge base and an ability to execute a much wider breadth of technical skills than any of these people would have imagined to be able to do six months ago. So you have these people who are great communicators, all that soft skill stuff, and then they add that skill set, at least an increasingly broad technical skill set. If you're a business leader, the question is, why is that important? And it's important because in knowledge work, as we all know it is kind of a relay race between stakeholders, leadership and technical people to get stuff done traditionally. I have an idea, I go to my boss, I say, is this a good idea? She says, or she says, yeah, that's a pretty good idea. Make a PowerPoint of it. And you're like, okay, I got to make a PowerPoint. I make a PowerPoint of it, then I take it around to stakeholders around the organization, they give some feedback. Yeah, that seems like a good idea. Eventually we get to the point where if needed, I need to take it to the technical team and say, hey, I have this idea for an app or a feature or a really intense visualization of data and a dashboard and can you help me build it? They say, we would love to help you build it, but we have other work to do. So it goes to the back of the queue. And so then you wait for that to come and eventually they work on it. Maybe over those weeks or months that you have to wait, your requirements change. So they bring you back something you need to give feedback to what they did, they go back, they continue to work on it, and that goes on and on until eventually work gets delivered. Right? What we're seeing over the last six months or so is a completely different way of delivering work. So now we have people who are saying, I have an idea for this thing and then they build it themselves and then they share it to one or two people to get some feedback. But it's the real thing. It's not a PowerPoint deck. It's not a technical documentation, it's not a product requirements document. It's the real thing. People can give feedback on it and then they can deploy it themselves. So for example, I have a colleague who knows everything about our industry and is super smart, but is not a technical person. He had an idea two weeks ago that we should have a ROI calculator that our sales team could show prospects and show the live ROI they purchased created for us. Pretty good idea, really good idea, and really impactful for the sales process. Six months ago, he would have followed that relay race between stakeholders, pitching the idea, taking it to the technical team, having them think about it, then eventually maybe developing it. In the last two weeks he took the idea, put it in AI Google Studio, built a web application, showed it to our marketing team, they said made a document with some feedback. He threw that feedback right into Google AI Studio and we pushed it live. So that's six months, eight months, 20 people, however many hours between all of them to deliver something that has impact on the business, down to 10 hours with three or four people involved in it, we're seeing that dynamic way more where the relay race between specialists and leadership is getting way condensed and we're able to move much faster. And I think that's the important part. If you're an organization leader right now, what is the big competitive advantage? If you're running even a very optimized relay race, you're probably not going to out compete somebody who's running it the way that maybe some organization like we are increasingly running it, where that relay race becomes way shorter and way more condensed and we can innovate way faster.
- [37:05]
B
Okay, I'm going to try to combine two ideas here and it's going to be awkward, but I'm going to do my best. First of all, it reminds me a lot of the OODA loop by John Boyd, which I'm not sure we've ever talked about on the podcast, at least not much, which is how quickly can you observe, orient, decide and act? And when you can execute that loop very quickly, you tend to out compete the fundamental difference. And the addition. Here I'm going to introduce another model. This is why this is going to be awkward. This is my own personal model, which is there's A work, B work and C work. So A work is transactions, it's the work we do to just run the business. B work is work you do to improve how you run the business and C work is work that you do to improve how you do B work. And so I can imagine an organization that's very quick and adaptive with how they execute A work, which is that the OODA loop for the A work is very quick. So what you're talking about is moving the speed, the reaction time to observe, orient, decide and act upon information and pushing it into B work very quickly. So that it's almost like using Colonel John Boyd, who was a fighter pilot. So you're out flying the mission and while you're flying this mission, you realize, you know, my wings would be better if they were a little longer or a little shorter or if I had more power in the engines. And so you modify them right there. And so now you're flying a different airplane. It's not a quantitative change. This is a qualitative change in how things happen. And I hadn't really thought that through. That's not a question, that's me discovering something new.
- [38:57]
A
I think it's an emerging thing, but it's also a. Whatever you want to call these employees. We've always had what we would call a super employee. It could be on the technical side also. You have somebody who has amazing domain knowledge in the technical area, is also a fantastic communicator, or you have somebody who is on the more non technical side, but for whatever reason also maybe has a hobby in something technical. So they kind of have both skill sets. What's interesting now is that we can augment this really easily. We can create these types of employees, particularly on the soft skill side of the organization or the non technical side of the organization. We can augment their skill sets almost overnight. There are levels to how fluid you are with AI at this point. People are still learning how to use it in the best way. But my job at Creative Force, to answer a question maybe you asked a while ago that I did not answer is is Director of AI Operations, which basically to me means we want to create as many of these people as possible. These people who can cut that loop all the way down. And I think that's super powerful, right? For the business, yes. But also a lot of the people on the non technical side I think have had this in them, that they've had really innovative ideas, they've seen potential solutions, but they were blocked by the technical knowledge and not being able to execute in that area. And all of a sudden there's. You could think of it as a new way for these people to express themselves within the organization. You know, they were either held back because maybe they weren't a great designer. They didn't have, like, an eye for design or they didn't know how to code something, and they weren't interested in learning necessarily, like going all the way down the rabbit hole. But now they don't need to. They can just execute and deliver. And what's coming out of those employees is super interesting. The way that people are using these tools is really interesting and really creative, and that's a very exciting thing. Just to be able to sit and watch and see what people do with these now that they have the freedom to do it.
- [40:58]
B
Yeah, and John Boyd would have talked about that. He would have said, you know, empower the edge. He referred to it as recon pull instead of command push, which is, let's have people fighting at the edge, be able to decide and act. And if we do that, the whole tempo speeds up. Super interesting.
- [41:18]
A
It introduces a question also for people of like, well, then, what should I be working on? If I can do anything, almost not anything, but if overnight, my skill set has doubled in this way, I have all of these new things that I could do, and then there becomes a priority work to do for every person also as well. There's so many things on the menu for me to choose from in terms of not only what I work on, how I execute it. Is it a bot? Is it a web application? Is it a traditional PowerPoint? Just in how you deliver knowledge and things, there's so many options that then it becomes an issue, not an issue, but you do have to start to decide what it is that's important to work on.
- [41:57]
B
Now, Yearbee 2, which is all about having essentially a congress of bots, you know, having whole hosts of bots with specialties. That sounds a lot like Anthea Roberts. Did you hear that episode? Yeah.
- [42:13]
A
She was working on the. It's very similar also to the guy from Google who has the. Is it cheese board or something?
- [42:19]
B
Right. I like that idea. Yeah, it's breadboard.
- [42:23]
A
Breadboard. Yeah, breadboard.
- [42:25]
B
Cheeseboard is the parallel product that goes with the bread. And in fact, those two things come together, and that was Dmitry Glazgov. So if someone's listening in and they're at step 0, v 0, or the beginning of v 1, which, by the way, there's no shame in that. I think there's real value to just waiting and watching other people for a minute before jumping into a new technology. Let's see how it works. And in fact, it doesn't work. And a lot of organizations have found that it's not being very effective for them, but it's been effective for you and for Creative Force. And so there's no shame in being at sort of V0 or V1 at this time. This is an opportunity to hear somebody who's gone farther and not have to figure it all out yourself. So what would you say to an organization that's there? How would they start and what are the actionable steps?
- [43:28]
A
The why is super important because it's also the why we want to invest in. This is really important. Organizations, I think, are, like you said, jumping in really fast but without a lot of purpose behind it, and then realizing that they bought a lot of tools and their people don't really know how to use them, or they're not really there addressing a business need and they're just existing. And I think that's where a lot of the trouble people get themselves in starts. So what is our interest in trying to help our organization leverage AI in the way that they work? Ours were very clear and I think it is worth investing time in to work with, particularly at the top, with the leadership team around what our objectives are for doing this. We found it very, very important to have that AI enablement group working together across the organization and to feel like not only that we had perspectives from around the organization of what was most important and to align them on why we were doing what we were doing, but it was also a chance for AI can be a scary topic for people for obvious reasons. And so feeling like even if they weren't in the group themselves, their leader or somebody they knew was part of it and could come back and say, this is what we're working on. And being super transparent around that and also sharing this is where things are going, this is how you're going to be using it. You're going to have an opportunity also to experiment with these tools themselves and provide feedback and share how you're using it in your own work. That part was really important. So the why? Absolutely. Focusing first on business objectives within your AI enablement team first, before you jump to tooling, what are our challenges, what do we want to achieve for our people, and what would be the business impact of doing that is super important to define. Because then later on you can tie all of those efforts back to what the impact was on the business and how it operates. And then four, we spent a lot of time working on enablement and going out to our teams, one on one, to teams as a group just going through use cases. This is what the technology is, this is what it can do. Here are the shortcomings right now? And what it's not as great at here are the ways that we could see you using it. But these tools are very open, so you can use them in lots of different ways and we want to hear how you're using them. But we spent a lot of time just demonstrating what was possible, how we envisioned it and trying to help people learn the skill because it is kind of a new way of thinking. Thinking AI first in your work there is a bit of a learning curve and I don't even know if we've necessarily defined what exactly that learning curve is, but you can see the people who really take to it and are using it to its absolute extreme. But it is a new way of thinking. Query first. And what would I ask the bots that we have and which data would I need in order to answer that question? And what are the different potential use cases that I come across each day where that would be useful? Thinking that way first takes some time and for people to understand where the value comes from. Some people get very excited by all of the insights they can get on their work. The customer, how people are using our product, et cetera. It's the insights part that they're like, I just want to know. These are things that I've always wondered about these areas of the business and now I can answer them and I can get really great insights into them. For other people, it's the automations part. They are so excited that I don't have to do X or Y or Z anymore. I didn't like doing that. It's so nice. AI takes care of that. And so we found that people's light bulb moment takes sometimes some exposure before they go, oh, now I see, I get it. And so the enablement part is really, really important. But for us it was really that roadmap around what our priorities were, what the impact was going to be on the organization, how we've thought that technology would address those and making that really, really clear for ourselves and everybody else. We've been very regular in our communication with the organization about what we're doing, why we're doing it and what we hope the impact would be.
- [47:29]
B
How did you decide which tools to bring in house and what are the categories of tools? That's way more specific than I ever talk about on the show, but I think you're in a unique position to talk about that.
- [47:44]
A
Our tech stack is pretty simple and we get a lot of range out of it. So we use Dust tt, not affiliated, just a Big fan. They changed my career, basically, which we could talk about. Maybe it's tied into the L and D part and I'm super thankful for them. But Dust TT is a French company and the platform allows you to build custom bots with a few clicks and very simply. And it allows you to chain multiple bots together. And it's the way that we do knowledge management now and it's the way that we access all of the data from all of our platforms at Creative Force.
- [48:14]
B
And so it sounds a lot like Breadboard or Opal that we talked about with Dmitry on the show. And when I talked to you, I had the feeling that that show with Dimitri didn't tell you anything new in our conversation afterwards because actually that's very similar to something that you're already working with. What else?
- [48:34]
A
N8N is our workflow engine or our workflow platform that we use. So N8N allows you to connect to a lot of different platforms and softwares, but it also allows you to chain multiple bots together to execute a task. So again, trying to be as practical as possible. Kind of a low hanging fruit example, but I hit that. There's an open job at Creative Force in our recruitment system that triggers via a web hook, three bots. One creates our job description, one of the bots creates the LinkedIn content that we're going to post with the job description, and one creates the internal documentation that we need to create for that job. Those all get delivered to a Google Drive folder after the bots are done working so that our recruitment team can go over them, polish them and use them. Right. So that's a multi agent, obviously it gets much more complex than that. But, but that's a simple example of you build that in N8N and it allows you to send data between bots and then other software platforms.
- [49:26]
B
I'm going to pitch a new use case for you. I'll tell you where this came from, which is that DeepMind and Stanford were putting together a competition for organizational effectiveness. How can AI make organizational effectiveness work better? So I reached out to David Aubsfeld, who'd been on the show, and I said, hey, you know, you were talking about how brokers work in social networks. Could we create an AI broker who would say, you know, hey, Aaron Dart's over in this other part of this organization and he's working on something that's similar to you and all it would have to do is for instance, look at your prompt history and to say your prompts are looking a lot. The Same maybe you actually would be interested in each other. So actually what it's doing is it's creating human connection inside the organization. And I haven't found anything that's been proposed to do.
- [50:19]
A
That thing is super interesting. It would require that it, I guess, has either your prompt history, like access to that, of course, or your internal chats in your Slack or whatever, which probably is a step farther than I think.
- [50:33]
B
It could be your prompt history. It also could be your team notes from something like an AI notetaker.
- [50:40]
A
I like it when you centralize data like this. It does give you insights into other teams, which is nice, right? So you have sales team that onboards brings a customer on, but then your onboarding team needs to know all about all the conversations that happen during the sales process, for example, and usually that is human to human. Sales tries to relay as much data back to the onboarding team as possible about what this customer is like, what they need, what they talked about during the sales process, et cetera. But our onboarding team can ask our bots, basically give me a rundown of all the conversations that sales had with this prospect during the sales process and then prepare the onboarding plan for us about what needs we need to address. And then that happens again for customer success as well at that handover. Let's say if you're working that way, we try to work a little bit more fluid through the entire process. But at those points where you have handoffs, it can be super nice because you're getting information from elsewhere around the organization to inform what you need to do, basically.
- [51:31]
B
I like that. So what kind of feedback are you getting from people at work about how they feel about this? How does it change their experience of work?
- [51:39]
A
Obviously we were learning at the beginning, and so I probably moved too fast and pushed one or two things a little bit too early. So there was the initial just learning curve of, ah, we need to really dial these in a little bit more before they release. That was more on me. I think people starting new, like new joiners, really powerful, like we talked about, because of the feedback loop and the ability to get that information right away. The second part particularly, and it kind of ties into the L and D part. People, why do you get into learning and development? And one of the reasons is to equip people with an increasingly more advanced skill set. And so people, I think, just feel very empowered to execute tasks and do work that they previously wouldn't necessarily have the domain knowledge to do. But they're definitely smart enough to evaluate if what they're getting back is accurate. But it's more like, how do I approach something like a financial analysis of something or whatever. And so we have that part where people just feel like, oh, there's so much more now that I can do. And that part I think is really rewarding. There's the expression part like we talked about, also where people feel like they don't have to wait so much for other people to execute that idea that they have. And it becomes more about collaborating on the idea. But there's not that sense of, I have an idea, but now I'm stuck. There's just a lot less getting stuck because of a lack of knowledge in one area or the other. And so I think that part has also been nice to hear and seeing what people are doing with that power. So I think overall it's been really positive. We also just have a lot of work to do to help people continue to see where the use cases lie and how they can leverage it in their work. And that's just a work in progress.
- [53:21]
B
That's really encouraging. I just gotta say, when I first imagined how AI would be used, I imagined it being used for pure cost cutting. It's not that I didn't think it could be used to make people more expressive, but I thought companies wouldn't use it to make people more expressive. And so just the idea that these tools are in the hands of people and that it's giving them the ability to just do what they want, it's freeing. It's freeing because all of a sudden I don't have to wait for technology. And by the way, many times in my life, I've been the person responsible for the list of ideas that come into technology organizations. And there's this incredibly long tail of good ideas that we just are never ever going to do. And I just had to say we're never going to do that. And the idea that the possibility that that tail could be, and I mean especially the tail could be brought in because the really big stuff that we would invest in, that's a bigger architectural play. Usually it's something really large, but the tail is. It has to be a tendency to be very person specific or very role specific. And it's not the sort of thing that's a big architectural play. That's one of those big investments. It's just not enough for the big engine to invest in.
- [54:45]
A
It's also why I think it was really important that it was Astrid and our people team that led the initiative. Because when you Listening to the topics that we're talking about, we're really talking about philosophy of work topics, right? What is human work? What is AI work? What is rewarding in the workplace? What does it mean to have a role that is inspiring and you feel expressive? And that's why it was important for us that it was the people team that led the effort. I think a lot of companies and when I try to talk to people in HR roles about this topic, I mostly just get, we're letting our IT team handle it and there's no bad intentions from it, but I think it asks much bigger questions about work that need to be considered by a different group of people. And for us, it was us who had perspective on how it was going to impact work and the things we needed to be mindful of. You know, what roles and what skills are we going to need in the future? What is work actually going to look like? What is going to be the role of a human in executing these different roles? How are skills going to change? You could imagine in a few years that people do these crazy skills taxonomies across all these different roles, right? In an organization. And they're always, you know, there's like three different, four different levels and you have 10 different skills for each role. But you can imagine that AI, except for very advanced and very niche areas, particularly very technical areas, that AI could turn people into kind of domain agnostic knowledge workers, where you more look at an organization in terms of the level of problem that you're solving, whether it's marketing, whether it's sales, we're seeing more fluidity between different domains where people are kind of able to float between these a little bit more and they fill in the gaps with the AI, but it really just becomes a list of problems that you're solving. And right now maybe AI is solving levels one through three and then everybody else is taking it from there. But what the specific domain is, I think is starting to matter slightly less. And it's more like, how good of a problem solver are you? And do people enjoy working with you? And maybe those are two of the bigger skills that you'll be looking at in the future or many roles.
- [56:56]
B
Okay, this is a weird question. You and I spoke ahead of time about the discussion in the episode with Matt Gazda and Isabel McCrum that was about how AI affects our experience of art. And the question was, if the identical piece of art or a product was created by a person versus the same exact product being created by an AI, would they be different what's your opinion on that?
- [57:26]
A
My opinion is, yes, it's different.
- [57:28]
B
Why?
- [57:29]
A
The point of art is to communicate something about the human experience and that the AI is just a probability machine. And that's the reason that I'm reading a novel or the reason I'm looking at a piece of art is to connect with the human experience experienced by someone else. And that doesn't come through from a computer.
- [57:47]
B
Okay, next question. Because I knew you were going to say that. Does that matter to business?
- [57:52]
A
I think it's unfair to say that there's no art in business or no art in work. I think that feels harsh. And I still haven't thought about it enough to say that. But I definitely think that you can see by people's eagerness to use these tools for certain things that it's very rare for someone to be writing emails and documentation that they feel like, say something really significant about their existence and place in the universe or something. Some one of the other topics that art covers, they would rather just not do that and focus during work on the more human tasks, which is usually interacting with other humans. So is a really nicely. And I know you're a fan of the like this would maybe be your thinking is like, there's a real beauty and an art to a beautifully formatted document.
- [58:44]
B
I would say that there's a real beauty and an art to systems. So for instance, service systems that make people experience the system, making them feel whole and alive. Right. And I think you've described one here today, which is really interesting. So there's two different parts to this. You've created a you can say service system. You can say it's an experience system. You've created an environment in which people have greater autonomy in the sense that they don't have to wait for other people to get stuff done as much. And so this is an environment that's going to create a completely different experience of work. And I think that's an artistic act, even if it wasn't set out to be that. But then there's this other question which is will your customers experience it as more or less human? And a lot of what you've described is AIs are supporting humans and humans talk to customers. The customer interface is not a robot. The customer interface is a more equipped human.
- [59:52]
A
I think that's super important just because it makes business sense. And it's also a more enjoyable place to work. And I think it allows people to perform even better. You get to reinvest in all of those things that make work human, we could say, I will tell you the truth.
- [60:11]
B
Everybody was saying that early on. I didn't believe that was going to happen. So it's exciting, it's great, it's really good. Another weird question, where does the IP reside? And really this question has been, who's going to own the AI capabilities? Is it going to be the company or is it going to be the people who work for the company? And that has to do with how much of what I create for myself as an employee is actually going to be portable with me. Is this going to be part of my capability set or do I leave it when I leave the company? Do we know yet?
- [60:49]
A
There is a skill in using these tools. There is a way of working with them that you will take with you because there will be data available, these tools are available. There's nothing specific about it. It's just the way that we're leveraging the technology. These are almost just curiosity machines. In some ways, the more curious that you are, the more you're going to leverage them. And of course, like I said, there is a way to think kind of AI first in the way that you're working. And people will learn that, but that will be the skill. The question is, who takes to that most aggressively right now? And it's people who already have an inherent curiosity about some of these things and get really excited by the opportunities that they present for them to indulge in that curiosity and learn more. And I think it's also, you learn, it's weird to say, but you learn from these systems also. There are things that they, of course they hallucinate and things, but they also raise insights and they raise ideas that you probably maybe haven't thought of before. And so by interacting with them, you also get better professionally in some ways and you learn from them in an interesting way. And that feedback loop is very fast. And all of that you take with you, right?
- [62:03]
B
Yes. And one of the things that I learned from you today is that a lot of the specific applications are very context specific. Like I'm going to build a bot that's going to pull from these three databases and it's going to give me an insight. Well, that doesn't make any sense outside of the context of that particular technical environment. What you did learn is how to do that. Here's another question, which is where are you finding inspiration? I'm asking that as a podcast that gave you some inspiration. Where are you getting inspiration beyond work for humans? Where do you get inspiration to do this work?
- [62:43]
A
It was super interesting to hear someone. This is related, I promise. But someone on your pod, on the pod said something about early indications that people with ADHD really take to these tools. And I've heard that since then from other people as well, that this seems to be something that people are noticing, whether it's like LinkedIn nonsense or it's real. The reason I don't think that that's the case is because we're noticing it also. And we have particularly colleagues who are really taking to these tools who also have that kind of real desire to get the feedback right away of realizing an idea, the ability to play and manipulate things in that tool. And you're dragging, you're dropping, you're pushing things around, you're connecting things. It's incredibly rewarding. Right. There is no I want to do this thing. So I need to wait three weeks to learn this very technical language in order to execute it. It's just drag and drop. And the reward is super nice for somebody who thrives on that. And then as soon as you build an idea, there's 10 more ideas you have or that you're inspired to do, and then you can actually execute them. And so for me, because it's really hard to find other companies. Exactly. That are doing what we're doing or not hard, but it's hard to get insights into what they're doing right now because it's so early. It's been my colleagues who have provided the most inspiration around what is possible. Because the tools are so open again that there's so many different ways to use them, leverage them. The different use cases people come up with that we're kind of just watching what people are doing and trying to see how different people use the tools. And it's mostly every day a bunch of us just sending things back and forth, saying, look what I built with this, look what this output is. Look at what we found here, look at this use case. And it's a lot of that right now. So there's a ton of discovery in the process because none of us has done this before. There's no LinkedIn learning course to be like, how do you roll out an AI, internal AI strategy and enable your organization to do it? So we are, I think, close. We feel pretty close to the edge. And that means that the only people, not the only people, but for us, it's everybody internally who are kind of pushing each other and trying to figure out what's really, really possible. So that would be like the first one, colleagues, for sure.
- [64:57]
B
Which is interesting because there's this moment in parts of industry where learning and innovation are the same thing. You can't know the thing unless you make it up. And that's super exciting.
- [65:10]
A
It's also, why, again, why would you go into learning and development? It's help people perform better, help them build their skill sets, improve their experience at work, and then hopefully you impact the business. And L and D, I think, has for a long time really struggled with a lot of those things. There's, of course, success stories, but I've never felt more like I'm impacting my colleagues experience at work and what they're able to do than now. And that part is super rewarding also and kind of inspiring even in itself.
- [65:40]
B
All right, Aaron, you know the closing questions. Aaron, what do you hire your job to do for you?
- [65:46]
A
You know, someone recently on the pod stole my thunder because I thought I had a really cool answer. But you had the podcast about fun in the workplace. And I really enjoy having fun at work. And that is what I hire my job to do. For me, it's when I perform my best. I think it's when my colleagues perform my best. But we have tried to maintain a sense of kind of working on a high school project. That energy and that fun and that excitement, and I want to have fun at work. Sometimes work is hard. Like, sometimes fun is hard, and it means solving complex problems, and that can be fun. It also means sometimes belly laughing, sometimes it means traveling together. But for me, it's super important that the 40, 50 hours I spend at work every week are not drudgery. I really want to enjoy the people I work with, the work I'm doing, the problems I'm solving. And to me, my work is all about trying to have as much fun as possible in every variation of fun.
- [66:44]
B
How do you make that happen?
- [66:47]
A
Culture is super hard. Our founders hired a really good foundation of people who feel the same way. And we've just tried our best to keep that. And we continue to hire people. It ties into the AI discussion a little bit. I think you're starting to see you don't necessarily need to hire the most experienced person because we can really help them now get up to speed in terms of their knowledge. It's really important for us that they're a good culture fit and that they can collaborate well together and they can have fun and we can joke around and that whatever you want to call it, the vibes are good. And that's been something that we have prioritized in the recruitment process and who we're looking for in terms of profiles. It's important to us that we have people who want to enjoy what they're doing and have that mindset going into work every day.
- [67:41]
B
What does your work cost you?
- [67:43]
A
Well, Dart, you will know as also a writer at some point. I think when you talk to people in tech companies, it's super interesting to hear all of the talents that people have. They're filmmakers, they're poets, writers, videographers, photographers. Everybody has, or many people have these sides, artistic ambitions or interests that they have on the side. But we live in a world where that's not really necessarily very viable for most people to explore. I would love a world where I get to write novels. It's very easy to say that not being able to do it and I don't know if I would like it as much as I think that I would. And so it's a bit. I would never take my situation for granted in that way, but it is, yeah, I think the artistic side of things.
- [68:34]
B
Yeah. Isabel McCrum on the show answered the question. It costs me different modes of thinking. In other words, getting hired to use your non artistic brain. There's a cost to that because you're going to spend a lot of your life using your non artistic brain. But I don't know because Wallace Stevens, in my opinion, one of the greatest American poets, was an insurance executive. He worked full time doing that and at the same time managed to use his artistic brain to create great poetry. And in his spare time he was the greatest poet of the 20th century, you know, or one of them, which is a hard thing to pull off. You'll notice he wasn't a novelist though.
- [69:19]
A
Yeah, we do benefit from it. I recognize also that the interest in writing is not the thing maybe that you're going to be explicitly paid for. But I have definitely benefited my career from the ability to write. And it's also a thing now working with AI that I'm very protective of. And I now put extra effort into maintaining because the more that you use these tools, you feel how quickly your brain starts to switch over to thinking query first versus how I would write that first. And so I do think that the more that you use these tools in your work, there is a, in your private time effort that needs to be made to protect them by reading a real book, writing whatever your analog activity is in order to protect that part of your brain. Because your brain, it seems, at least in my case, is very susceptible to wanting to slip into straight AI. Takes care of it all the time mode.
- [70:19]
B
I have a hypothesis about companies that are doing great work, by the way, which I formulated partially because of watching you and your work at Creative Force. It's that companies that do great work, they don't ever do it because they're trying to not fail. They do great work because they're reaching for something extraordinary. And a part of the reason, and this is a note that I wrote to myself while I was preparing for this, is that trying not to fail is all about being risk averse. But trying to be extraordinary actually takes some risk. And so you set off into this unknown without knowing for sure that it was going to work. And did you feel at the time that you were taking some risk and reaching for something?
- [71:12]
A
Reaching for something, yes. But our leadership team would not punish us for reaching far. I mean, I think what you're talking about, and this is probably well known, but early on in a company, and that's the interesting thing of being part of a company early and growing with it, is that in the early days there's basically no risk to taking some big chances and there's lots of reward, right? But the bigger that the company goes, the marginal benefit of taking a big risk increases and the benefit starts to decrease. And we see that with companies, their innovation basically just stalls out. Because why would anybody with a sweet salary package and benefits package, why would they take a risk when they could just not take a risk and pretty much be safe? Our leadership team has done a very good job in making sure that there is a sense of safety around really reaching and, and then falling short and then pivoting. And that sounds like very startup y, but there is a real sense of safety net there of if you're reaching for something, it's the only way for us to innovate. So that's not going to be something that we punish people for. It's doing something that's a little bit outside the norm.
- [72:20]
B
Where can people learn more about you and where can people learn more about Creative Force?
- [72:25]
A
Oh, you can learn about creative force at Creativeforce IO. For me, I don't really have anything to push, but I would be really excited if people connected with me on LinkedIn and just wanted to chat about AI stuff. I'm very interested to hear what people's thoughts are about it and make connections with people who are just interested in it. So if you want to find me on LinkedIn, send me a DM and we can get on a call and just chat about it. I love, I think the topic is super interesting. You and I have talked about it a lot. I would love to talk to more people, just what their thoughts are on it and where they're at and what they think is ahead for them in their organization as they start to think about integrating it further.
- [73:03]
B
That's fantastic. This is a really encouraging conversation to me about what the future can hold. So thank you very much for being a guest on the show today.
- [73:15]
A
Thank you. Doug.
- [73:18]
B
Thanks for joining me for another episode of Work for Humans. If you enjoyed this episode, please please give us a five star rating. Wherever you listen to podcasts and share the show with one person you think would get value from it, believe it or not, this really helps us grow the show and reach more people who want to build the kind of work that people really want. As always, thank you to my producer Jason Ames at ninthpath Audio for his insights into content and his high standard for quality. Final note, the opinions shared here are my own and not the views of Google or Cisco Systems. Thanks again for listening. See you next time.