Making Space for AI in the Enterprise

This transcript was created using AI tools and is not a verbatim, word-for-word transcript of the episode. Please forgive any errors or omissions from the finished product.

Courtney: [00:00:00] VR, the Metaverse, Google Glass, and AI? I don’t think AI belongs in that list, but what needs to happen to make sure it’s not just another flash in the pan. And how do we make time for AI and all the new initiatives in the midst of everything else we already have on our plates?

Courtney: Hi, I’m Courtney Baker and this is AI Knowhow from Knownwell, helping you reimagine your business in the AI era. As always, I’m joined by Knownwell CEO David DeWolf, Chief Product Officer Mohan Rao, and Chief Strategy Officer Pete Buer.

We also have a discussion with Katie Taylor of Narratize about tangible ways Generative AI can actually give you more time back in your day.

But first, buckle up for a brand new segment we’re calling AI MythBusters.

Courtney: Pete Buer joins us as always, but this week [00:01:00] instead of breaking down some of the latest AI news, we’re gonna try something a little different today. Pete, you know, I have a degree in sports marketing and you know I love a good sports analogy.

So are you ready for this curve ball?

Pete: Hey, Courtney. Yes, by all means, let us score a touchdown.

Courtney: Okay, great. This week we wanna do a segment called AI Mythbusters. Every now and then we’ll be talking through some of the biggest misconceptions out there around AI and why it’s important to cut through all the stuff and just get to what’s really going on. So today, Pete, I’m gonna stick with this baseball analogy and kind of throw you a little bit of a softball. Uh, but this first AI myth I’d like for you to dispel because I have heard it, is that AI is only for technology companies or for large corporations.

Pete: Okay, so let’s bust this myth on both [00:02:00] dimensions, tech companies, and large corporations. And let’s start with large corporations. I think the assumption there comes from the fact that large companies have deep pockets to plow into development. And objectively this is true. But the beautiful thing about AI is that there are many low code, no code, low cost and AI as a service applications that don’t break the bank.

And that absolutely enables small companies to compete against. ones at a reasonable cost. Um, we see countless examples where small businesses are using AI to gain competitive advantage. And I know that from my debate club past, the way that you’re supposed to down an argument or, or refute an argument is to provide evidence to, um, disagree with the statement.

So, found some data in an article in Forbes from the Small Business Entrepreneurship Council. Uh, as of November of last year, 41% of small [00:03:00] businesses were already using AI to leverage their leaders and their uh, operators. 40% had invested in AI tools to improve customer service. Customer engagement, customer retention, which of course is near and dear to our heart.

Uh, and one in four had used AI to compete in the labor market, upskilling their staff rather than competing for higher cost talent. Uh, and the summary stat, 93% of small business owners said AI is a great way to save costs and improve profitability. So I think probably 93 out of 100 small business owners would disagree with the, with our starting statement.

But then there’s the tech company half, and here I would just turn this one a little bit on. Its here. The tech company distinction these days is just simply lost on me. Every company is a tech company. Every company has an opportunity to improve its production. Its customer sensing, its customer service, its delivery, its employee management with technology.

And [00:04:00] so we are all technology companies and therefore AI should benefit us all. So in a weird and upside down way, I guess I’m agreeing with the second half of this statement.

Courtney: Yes. I like that. Uh, So you busted half the myth. Uh, I like that here, here. I love it being turned on its head.  And just a great reminder for everyone that this technology, it’s here. It’s now, now is the moment to get engaged if you’re not already, Pete, thank you as always.

Pete: Thank you, Courtney.

Let’s be honest. What you’re probably not hearing from your team is that they have too much time on their hands. If anything, it is the other extreme, and you’re probably included in that camp. So how can you make space to master a brand new complex technology like AI in a way that makes strategic sense?

Courtney: I was excited to dive into that very [00:05:00] question with David DeWolf and Mohan Rao.

Courtney: David, Mohan, do you have a concept of like corporate or business curse words? You know, they’re like words you like hate to hear in a company. I.

David: Oh gosh, that’s an interesting question. I’d have to think about that.

Courtney: Yeah, you may not have any. Uh, I have one. I hate hearing this word. Uh, it starts with a C…I hate hearing it. Capacity.

David: Capacity.

Mohan: Ah,

David: Okay.

Mohan: it’s

Courtney: it’s like,

David: the word

capacity? Courtney?

Courtney: you know, I feel like sometimes it’s a cop out.

David: Mm-Hmm.

Courtney: It’s, it feels very uncreative and sometimes just like the default,

you know, we don’t have the bandwidth or like don’t have enough capacity. But I feel like, is that just me? Am I the only one? You know, I, always [00:06:00] just love to come at a different angle.

Don’t tell me capacity.

David: I can tell you it would make my blood boil at 3Pillar. Right? The, the bigger and bigger we got and the less entrepreneurial like I think we were in your entrepreneurial mode.

Courtney: Mm-Hmm.

David: you know, people are just used to picking up new things and going and focus is good, but you don’t want to take it too far ’cause you have to be able to react and move and navigate.

The bigger you get, the more, oh, I don’t have time for that. And

Courtney: Yeah.

David: much time and that takes that much time. And, um, I, I agree with you.

Courtney: Yeah.

David: us it as an excuse. Um, I also think the other is true, right? Which is leaders don’t recognize what they’re asking, um, and try to push too much through the system.

And so it’s, it’s fraught with peril.

Courtney: Good point. Um, but that’s not where we’re gonna go today. Uh,

David: I’m just trying to be balanced, Courtney. Hmm

Courtney: that’s right. I, I don’t like it. No, I’m just kidding. so capacity, I wanna bring up that word today because [00:07:00] I as. We’ve engaged with more and more companies as they’re kind of pressing forward in AI. I’ve heard this come up and where I’ve heard it come up from is not from the people on the execution level.

It’s actually from the people. You don’t hear it from very much the executives, and so I wanna throw this out to you as we have this new transformational technology. Coming on board, how do we actually grapple and make space for our organizations to deploy AI in our organizations? We’ve talked over and over again about the opportunity, especially if you’re in mid-market or a smaller business, but there is, at the end of the day, there is a finite amount of.

Resources in an organization. So I would love to get you two to help provide some advice on how you prioritize these things. How do you make the space in the organization to really take advantage of this moment in time?

Mohan: [00:08:00] I have a lot of sympathy for the business leaders who say that I. most businesses are consumed by the hurricanes around them, right? There is operations, there’s customers to manage. There is the, um, uh, revenue to be maintained, right? So, especially if you’re in a recurring revenue business, right?

So you can’t, we all know that keeping existing customers and revenues are more very, very, very, very important. Uh, right? So I have a lot of sympathy for, um, these hurricanes that happen in a company and it’s very hard to make space. But then it fundamentally boils down to, are you ambitious or not?

David: Hmm.

Mohan: you, right. If you are ambitious, uh, you have to one way or the other, create a 70 20. 10 formula or a four DX type, uh, you know, uh, wildly important goals. However you do it, you’ve gotta make space for it. Um, and, uh, you know, you don’t have to, it doesn’t have to dominate your company [00:09:00] because the operations of the company, the business as usual, stuff that produces the revenue trace, super important, but, uh, have to make space for this.

And it depends on the depth of the ambition you have.

David: I’ll say that in a similar way. I, I was thinking the word shortsighted, I, I totally get being overwhelmed, but I have found over and over whether we’re talking about these leaders or we’re talking about the. folks on the line, uh, that are doing the work that a lot of people really struggle to understand that the decision you make today to not improve your environment will undermine your ability to actually ever get ahead.

Right? It’s, it’s like that cartoon where, um, you know, the, the caveman is using the wheelbarrow with the square wheels. And he doesn’t have time to put the circle wheels on. Right. And and it’s like so often I see that mentality [00:10:00] and I would apply that to ai. Right now, one of the things AI is great at is saving us time.

So if you’re overwhelmed and you are telling me that you don’t have time to take that first experiment, I think you’re shooting yourself in your foot. You’re, you’re actually costing yourself more time. Go find a pragmatic example where you can use it to save. Time and get that 30% savings and give 15% back to the line and take 15% for the next AI initiative.

Right? And keep doing that. And that’s how you create momentum. And I think too many organizations do exactly what you said, Mohan. They get stuck in the whirlwind, in the hurricane of the things going on. And it’s just react, react, react. The way you get ahead is that you get proactive and oftentimes if you, if you bite the bullet once.

To get proactive, you can turn the momentum and all of a sudden it starts to cascade because you reinvest the [00:11:00] return back into the next and the next and the next.

Courtney: Yeah. I feel like what you’re really saying is how do you prioritize the type of initiatives or goals or again, whatever framework you’re using to deploy this into. Your organization, how do you pick the things that actually have the domino effect, that make later initiatives even easier? And I think AI almost always fits that bill of, Hey, once we have this, it’s gonna make maybe even the things you have ahead of AI in this moment easier if you flipped those around.

Mohan, how does that sound to you?

Mohan: Uh, that sounds right on to me. Um, you know, if you think, uh, if you. Reflect back on the theory of disruption, right? So what you’re trying to create, um, uh, this is sort of my, my paraphrasing of it. you gonna disrupt in place or are you gonna disrupt in a way you disrupt yourself in a way that [00:12:00] is sort of set up through a slightly different structure that you’re gonna set up, Uh, so you just need to figure out which one is the right thing for you, uh, in place disruption, uh, changing, uh, the, uh, wheels from square wheels to circular wheels. Uh. Would be in place disruption. It makes sense. You just have to bring all the wheel battles to the garage or maybe 20% of the garage, uh, each week and just change them and put it back into production.

Uh, right. You can do that in place, but on the other hand, if you’re building an entirely new set of product and service that’s going to. Disrupt the company itself because you are thinking of this in a totally different way. You have to set up a completely different organization, um, uh, a team within your company and make space for it.

Otherwise, the um, internal, uh, uh, you know, there is gonna be, uh, uh, organizational resistance. Uh, to these sorts of stuff, and that’s when you start hearing about bandwidth issue, capacity issues, Mm-Hmm. so [00:13:00] forth. You might have to just set up a different org within your org.

David: I, I’m gonna give you a very real, tangible example of where I have failed with this and then recovered from it. Okay? So, um, the two of you are incredible fans of the tool Grammarly Pro. Now going back to your hot take from several episodes ago, Courtney, you said that all of these AI tools are gonna actually slow us down more than they help because we gotta weed through all these tools.

Courtney: Mm-Hmm. you guys first challenged me to use Grammarly Pro and we’re we’re raving about it, like I was just like, handle. Another tool. I don’t have time to go install it. I don’t have a good time to go look at it. That’s stupid. I’m a good writer already. Right. Well, eventually I realized that. I actually spend a lot of time writing, uh, in what I do.

David: And, um, one day something actually caused me to just go take a peek at it and I installed a trial version and it actually [00:14:00] cost me time to do that, but because you guys raved about it so much, I decided to. Take it to try it, and in that short little, probably 15 minute or less investment that I had been saying, I don’t have time to do because I’m overwhelmed and I’m too busy, I probably have saved myself hours.

Since then, right? Because I made that investment. And if we can take that small example and apply it on an enterprise level and just say, yeah, just carve out the time to do the little thing and push it forward, that’s a real AI use case. Like that’s a tool that we can think of of, oh, it’s just gonna slow me down ’cause there are too many of these.

But I can listen to those around me, see how they’re using it and apply it. And now my editorial process after I write is a lot shorter than it used to be. And. Every single week, I’m saving those hours.

Courtney: Yeah, that’s a great example. I wanna flip that to the other extreme because I wonder if maybe [00:15:00] what is hard in this situation is you have to, as a leader, make the decision to cut something that you’ve invested heavily in.

David: Hmm

Courtney: And there’s a lot on the table there. And I keep thinking about Apple Apple cut the Triton, the apple car completely. Something we’ve been working on for 10 years. Titan, Titan, there’s already a Titan car. It’s a Nissan truck. Right. Is it,

David: Well, uh, doesn’t matter.

Oh, sorry.

Courtney: Get that right.

Mohan: it was a naming problem.

David: They just

Courtney: I could have helped.

David: I mean, come on.

Courtney: I could helped. They killed that entire project actually to make room to, to lean in heavier to ai. And I think, I wonder if there are things like that, things that companies have been working on pursuing that. It’s just hard to say, we’re not, we’re gonna kill this even though we’ve put a lot of, [00:16:00] you know, sweat equity at time and effort and resources into it, but it’s because we see something bigger on the horizon.

David: I’m, I’m gonna get real here. All right. Here’s one. You’re talking about the fallacy of sunk costs. You know, where I think there’s a ton of, of fallacy of sunk costs lying around people building their modern data stacks and their business intelligence tools that they’re never getting value from. As new AI tools start to come out, as new platforms start to come out that look at data differently, I would challenge leaders to go look at, yeah, the millions of dollars that you spend on your modern data stack. Is it really paying off? Is it giving you the value you thought or are there new approaches that allow you to rethink that and maybe pull the plug on that? David, I’m so glad you said that ’cause I’ve heard you say it before and I was hoping you were gonna say it here on the podcast. So thank you.

that?

Mohan: So one of, one of my favorite bus business books is this book called [00:17:00] The Dip by Seth Godin. Um, and it’s the whole art of, uh, when do you quit versus when do you stick, uh, right. And it is a super important leadership attribute to know whether you’re gonna invest in something that’s not working, to make it better, and then be successful eventually.

Or make something that’s successful, even more successful, uh, right. So, and obviously there’s a spectrum in the middle, uh, and that art of leadership is still the art of leadership and where you apply this to is, uh, super important. Uh, whether you extend the franchise or you just gonna quit and apply, I think of new approaches.

Courtney: Well, for everybody listening, if you found yourself trying to figure out how are we gonna find time? How are we gonna find the capacity? That a word I don’t like in our organization to deploy ai. Hopefully this has given you some fodder to think about and maybe to execute in your organization how you make room for these new AI [00:18:00] initiatives.

David Mohan, thank you as always.

Courtney: One of the worst feelings in business is being surprised, especially when you’re surprised by a client walking out the door. Fortunately, we’re building an AI powered platform that helps you keep your finger on the pulse of your portfolio health and really understand what’s happening with your clients. You can go to Knownwell dot com right now to sign up for our beta wait list.

Katie Taylor is the founder of Narratize, an AI powered storytelling platform that helps companies use generative AI to create impactful content that scales. She sat down with Pete Buer recently to talk about how leaders can ensure they create enough space for ai.

Pete: Katie, welcome. It’s so great to have you on the show.

Katie: Thank you, Pete. So excited to be here.

Pete: If, if [00:19:00] we could, let’s give our listeners a little bit of context for the conversation. Uh, could you talk a little about, uh, ize in your role? I.

Katie: Absolutely. So I’m Katie Taylor, CEO Co-founder of Narratize. We’ve been building in stealth for the last three years and just launched in April of 2023. So we’re coming up on, uh, about a year in the market and

Pete: Happy birthday.

Katie: Thank you. Thank you. Yeah. Happy work anniversary as, as LinkedIn likes to say.

Um, yeah, and we, we have designed a trusted generative ai co-author that’s specifically designed to help innovative enterprises accelerate their message market fit.

Pete: I noticed on, on the website there’s a, um, description that ize is where innovators create high impact stories in 20 minutes or less. This is the dream of ai, right? Like taking something that would otherwise take hours or days and, you know, driving it down to 20 minutes or less. Four.

Leaders [00:20:00] listening who may not have all the technical background to understand the fine points of how AI is doing the job. Can you describe kind of the how though? How it works?

Katie: if you think about the components of AI on the backend, there are large sets of training data. In the case of open ai, it’s the entire web.

There are, those are the most large, large, large, large language models.

Pete: Right.

Katie: there are interesting smaller, large language models that are starting to pop up and they’re starting to get higher accuracy for certain use cases. A lot of those are. Being developed in partnership with, with large capability companies like nvidia.

We are actually an Nvidia startup and it gives us access to creating smaller, large language models alongside our customers. Those are typically, again, very high costs, very, very expensive undertakings, and so

Pete: Yep.

Katie: are not building large language models. Most companies are asking, how do we safely and securely call into [00:21:00] APIs for the large language models that exist today?

challenge is, it’s actually really, it’s, it’s not, it’s not hard to call into the API. What’s hard is getting it to do the work you, your people need to get done. And so that’s where as leaders, as you evaluate and you think through, I build this? Do I buy this? How do we continue to innovate and, and deliver to our customers?

You have this, you have this sort of crossroads of deciding where you’re going to build and where you’re going to to buy.

Pete: Yep.

Katie: there’s so much available to buy. This marketplace is so noisy, but it’s also nascent in many ways. I think the things to look out for probably three key things. Number one, does this solution, if you’re going to buy, and most mid-market CEOs are, are, or c-suite off, uh, are thinking about buying.

If we’re going to buy, how do we find solutions that help us call into the most [00:22:00] available large language models? How do we ensure that they, that the solution is going to train on our unique knowledge in a secure and private way?

Pete: Yep.

Katie: how do we prevent bias? But I think on top of all of those things, so those are kind of, to me, those are table stakes.

top of all of it, Pete is the critical. The critical evaluation of, is this going to transform how our people work,

Pete: Yep.

Katie: how we do business? And so, I, I know at least at ize, that has been our North star, how do we really get AI transformation for teams? And one of our five, we, we came up with and, and adapted from the DOD and, and Gartner and other resources around responsible ai.

We, we identified five pillars. And the number one for us is human led

Pete: Mm-Hmm.

Katie: because we strongly believe that if you put a chat bot in front of every professional around the world, it won’t necessarily help ’em do their work better, and they won’t necessarily know how to use it.

Pete: Mm-Hmm.

Katie: [00:23:00] most of the large language model companies that everybody’s excited about, like OpenAI don’t have a playbook for how on earth to do it.

And so people, it’s exciting, people wanna use it, but the assistant AI, assistant design the chat bot design. Sort of leaves a lot of people grasping in the dark for how it’s supposed to be relevant to their work.

Pete: mm-Hmm.

Katie: So, so we believe in human led ai. I think as you think about how to make it really transform your culture, choosing those solutions that embed the AI into your team’s workflows to get a better output and to to help them.

You know, it’s the, the same way that that AI for the last 10 years has existed. It’s all behind the scenes

Pete: Nope.

Katie: we haven’t had to necessarily become, uh, experts and AI engineers in order to use it. It was just behind the scenes. And so, um, one quick example I’ll share. We partnered

Pete: Thank you.

Katie: Housekeeping Institute.

They, um, if you’re familiar, they create lists essentially

Pete: Yes.

Katie: every kind of appliance beauty product, and they rate it. But it’s very [00:24:00] scientifically driven. They have laboratories set up there’s, they have full-time scientists testing products in every which way, and they have massive amounts of testing data.

But it would typically take them about 200 hours to write one research report for one E-commerce article. with ize, now they can write that report in 20 minutes or less.

Pete: Amazing.

Katie: exciting and, and it’s because we didn’t just create a core product with different content templates, but we also trained it on their unique knowledge, and then we embedded the AI into workflows that already existed among their scientists.

So that’s an example, hopefully of outside of marketing, although it does become a marketing deliverable and a transformation after that research report. But hopefully that becomes an example of how to look for solutions that. That sort of have the core capabilities that are actually gonna help you transform your culture.

Pete: we’ve used the word trust several times in the conversation already, and, um, I know that it, it’s, uh, an active ingredient in the work that you do. Can you sort of share how [00:25:00] trust factors in and how you manage to it?

Katie: Some of the ways that we work to reduce hallucinations or the. Propagation of misinformation that can come inside of a chat bot we, we use guardrails around industry, best practice domain, best practice, and we also then call it into APIs across academic peer reviewed databases.

So for ize, we’re supporting clients like nasa, Boeing, um, under Armour, right? These deeply innovative enterprises. And oftentimes they’re highly reliable. EN enterprises, meaning lives are at stake if you get that content wrong. If you, if you, uh, say the wrong fact or you could actually, uh, really harm someone.

And so we, we believe that the strongest. Data architectures the strongest guardrails against what our output, um, really they have to include some kind of filter through what is peer reviewed or, or at least through the most authorized and, and credible sources. And so [00:26:00] that’s, that’s the key piece of how we are sort of guard railing against large language models.

But I think trust is a bigger issue. Trust is something that the more we demand trust as users, as buyers of this technology, then the more that those, uh, foundational model companies will have to adhere to and, and, and, live up to that. And so trust is earned through explainability. It’s earned through transparency and, and the, the data training sets, it’s earned through a commitment to diversity, equity, inclusion, and accessibility.

Almost every venture company in ai, um, that’s received. Again, venture funding is led by teams that are not diverse. Um, and for us at ize, we’re one of. 0.3%. Basically 0.3% of the venture and AI goes to women founders and my co-founders and I are all three women. So we’re sort of this wild, I wanna say [00:27:00] unicorn.

We’re not necessarily a unicorn yet, technically and tech speak, but we’re getting there. But, a, a unicorn in the sense of, um, again, I think we need to buy from vendors who are, have diverse leadership, who are taking diversity, equity, and inclusion seriously, or will continue to see. Massive challenges around the bias of, of what’s output

Pete: Yeah.

Katie: chatbots.

Pete: it’s been fascinating on a number of levels, like seeing how AI is deployed to enable professionals make us better, you know, on the one hand, but also the story that you’re telling.

About evolving the business model in professional services. I, I think that’s at least half of what will be mo interesting, you know, to everyone who’s listening, uh, in our audience. So thank you on all those levels. Uh, it’s been a, a pleasure to have you here.

Katie: Thank you so much, Pete. Can’t wait to talk more.

Courtney: Thanks as always for listening and watching and hey Courtney here. Legit. [00:28:00] It would be awesome if you would take a minute and give us a review on our show. It is the number one way that other people find this show. So listen, we don’t ask for sponsorship. We don’t even make you listen to bad ads.

All we ask is that you give us a review. That’s it. All right, thanks. At the end of every episode, we like to get one of our AI friends to weigh in on the topic at hand. So today, Hey perplexity. Welcome back. This episode we’re talking about how leaders can make space for AI in the enterprise. So what do you think?

Courtney: and now you are in the know. Thanks as always for listening or watching. We’ll be back next week with more headlines, discussions, and interviews [00:29:00] with AI experts. Hey, if you made it this far in the episode, first of all, thank you. And second, we know that AI is changing all the time, and so we’ve got you covered. Here is another great episode for you to watch, to stay in the loop on what’s happening in AI and business, and second of all, we’d love for you to subscribe.

You can do that right here. I’ve always wanted to do that, by the way, so do that now. That would be great.