Everything Executives Need to Know About AI

This transcript was created using AI tools and is not a verbatim transcript of the episode. Please forgive any spelling and grammar errors that may be included. 

[00:00:00]

Courtney: What are the most important things for executives to know about AI and how can leaders stay current on all the most important things when the space is moving at the speed of light?

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 Sharon McCarthy about the connection between behavioral science and AI. [00:01:00]

Courtney: F. Scott Fitzgerald famously wrote that the test of a first-rate intelligence is the ability to hold two opposed ideas in the mind at the same time and still retain the ability to function. If you’ve been listening to AI Knowhow for a while, you know that our Chief Strategy Officer Pete Buer’s intelligence… second to none. So, we’re kicking off the show today with a new segment that we’re calling Pete/CounterPete. Pete, are you ready for this?

Pete: Well, keeping with your frame, Courtney, I am totally ready, and I am not ready in the least.

Courtney: That is amazing. So I’m so glad you showed up with your game faces on today. Love to see it. So the statement that I’d like to hear you debate against yourself today is AI will disrupt and [00:02:00] transform entire industries, much like the digital revolution of the proceeding decades. So, Pete, take it away.

Pete: So I don’t know where the heck CounterPete could be coming from on this one, but I find it very hard not to take a stance that AI will disrupt and transform entire industries. We’ve talked about it on the podcast many times, and you read about it every day. The possibilities of AI and business transformation are staggering and endless.

Disease, diagnosis and drug discovery in health, translation of ancient languages and communicating with whales, other species in science, wholesale elimination of entire departments in companies like Google advertising and marketing, algorithmic trade and banking. The list just goes on and on and with each

use case in each industry, you can see utility and you can [00:03:00] see how the economics of the business will be rewritten.

The technology itself isn’t new, but access to it is democratized. Understanding is on the rise thanks to all the consumer applications that we have access to as individuals. And comfort with the role of AI in business is getting stronger and stronger with each passing day.

With just a little imagination, and we’ll have to see if CounterPete has any, it’s safe to say that AI can turn pretty much every job, every department, every business, and every industry on its ear. Like, I should have Groucho marks glasses or color of my beard, a different color or something. Um, thank you Courtney. Uh, in the spirit of maintaining our close relationship, uh, my counter Pete response is gonna be a yes. And um, sure AI has the potential of fundamentally [00:04:00] transforming all industries. Um, but that, does that mean it actually will?

I don’t think so. And so here’s why. First off. It’s a very well understood determination of how AI will impact different industries differently as a function of the inherent human or social component, uh, and util uh, in the way value is provided in each. So think therapy, intensive counseling, social work on one end of the spectrum, and software development, accounting research on the other.

It’s more a story of degrees of impact than it is one of wholesale rewriting in each industry. The second, it’s useful to remember that just because something can be, uh, transformed doesn’t mean it should be. I suppose Haircuttery could access the upward facing camera in my rabbit, R one ha ha. And keep a, an eye monitor.

My hair growth might be a Slowmo video that [00:05:00] they’re working with. Um, send me a calendar prompted when they notice change a calendar invite that sets up my next visit. Maybe mail me my, my lollipop in advance, um, as an incentive. But an awful lot of people couldn’t care less about getting their haircut.

Some people go to get their hair in order to chew the fat rather than cut the mop. And somehow I just don’t see investors lining up. For an in, uh, uh, a business possibility of a use case, uh, like this one. So my feeling is that although we as human beings can dream our way to fantastic possibilities at every corner of industry diverse human behaviors and preferences and needs.

Certain inherent and natural types of resistance to change and economic realities of business all intervene, uh, to slow down our dreams and sometimes head them off at the past. So, final answer. AI will transform some industries. It will transform some more than others, all [00:06:00] according to where the real opportunity exists.

And maybe back to your Fitzgerald referenced, Courtney, where the In the Great Gatsby, he reminds us. Of our struggle to create a future that matches our vision, like boats moving against the current, often backwards.

Courtney: Oh, Pete, thank you so much. Uh, thank you for fulfilling the test of first rate intelligence. Uh, as always, I would like to thank both Pete and Counter Pete for joining us today.

Pete: We’re gonna go take it outside and take up fist to cuffs.

Speaker 5: being an executive in the age of AI is no easy task. Technology change is happening faster than ever. Obviously, 300,000 kilometers per second to be exact customer loyalty is dropping off a cliff and the competition is stiff.

I talked with two of our favorite [00:07:00] executives, David De Wolff and Mohan Rao about everything executives need to know about ai.

Courtney: David Mohan, our very first episode ever was about building an AI literate leadership team, and we recently came back to that topic, but today, I wanna expand that a little bit beyond just your leadership team, but how do you start thinking about building an AI ready? Culture of your company, what is an approach that will work for that, and how should we as executives be thinking about how we’re we’re building that AI ready organization?

David: You know, it’s funny ’cause we did, we started with. This top down approach of the leadership team and how do you build that culture in your leadership team? I actually think that’s a common mistake, and recently I’ve been doing a lot of thinking about the [00:08:00] way true transformation is gonna happen, and I actually think it’s gonna happen from both directions.

Um, typically, I. Change does happen from both directions. Um, but especially for big monumental changes like we’re going through right now. Um, I think you need the top down sponsorship when it comes to large enterprise transformation for major processes and cross collaboration and the operational things we’ve been talking about.

That will be essential and you’ve gotta have leaders that understand this. But I also think to actually drive the cultural day-to-Day usage of this technology. absolutely essential that leaders create an environment where the innovation can happen from the bottoms up, and that you create an environment where it’s safe to experiment and to figure out where can this be the most impactful?

And so looking at it from a company-wide perspective, I think is essential and may be a place where a lot of folks have gotten it wrong [00:09:00] so far.

Courtney: I love that idea of how do we make it easy for this to be experimental on an individual? level, I feel like right now there are some very big obstacles in the way of that happening. It’s almost like what you said of everything has to be top down right now because there are so many concerns about, Hey, do we have the right policies in place?

Do we, you know, privacy? All these different things that I think some of the hype cycle around. AI has put in place that we haven’t had more recently with other technologies.

David: Right. Y you know, I was speaking at an event, gosh, two weeks ago, and Ethan Mollick one of the leading, um, thought leaders, uh, in academia around AI and its adoption, was speaking at the same event. And I, I snuck in to listen, uh, and, and participate, not just be a speaker. And as Ethan was talking, he actually started to address one of the biggest issues he [00:10:00] sees in adoption.

Which is the concern over security and privacy in particular. Um, obviously everybody recognizes that we’re talking about data here and information and, and security and privacy is essential. But Ethan made a really interesting point that struck me, and I’d never thought of it this way. He said, I believe that right now people have started to think of AI as a.

Thing like an entity, uh, was the word he used. Almost like it has a personality. And because of that, it has become a lot scarier than just the reality that this is software running in the cloud. And we have been leveraging software in the cloud forever. And so people are scared to death about copying and pasting or uploading a document.

Into these tools and he, he made the point, this is just software in a cloud. It’s not a person even though it feels like one, and unless the companies are all out [00:11:00] lying to us or something really, really, really severe happens in error that could happen in any cloud software. We are blowing this out of proportion and I thought that was just a fascinating argument.

And there it’s so true when you step back and think about it, right? These are just algorithms. They’re just running on scopes of data that we can’t comprehend and in a, in a new way. And I think you are right to bring this back to your point about adoption. We have personalized these things because of what they do.

These artificial intelligence, um, large language models have become this big scary entity that has us not really experimenting the way we have traditionally with new technologies.

Mohan: And, and similarly, uh, you know, taking very, uh, uh, more easier use cases and starting to work with those, uh, would be, would be the way to get started. [00:12:00] we ourselves ran a hackathon a few weeks ago and we got fantastic ideas. We are an AI company, but it was just the mobilization of the team and getting everybody to work together and thinking about the possibilities sparked so many ideas.

And I know of other companies as well who’ve, uh, conducted AI hackathons and it’s been a tremendous success because the cost of these tools are not prohibitively expensive. You’re talking $20 a month sort of thing for, uh, any of the, uh, GPT type technologies out there. Uh, it’s not expensive, it’s just question of.

Uh, giving permission to think outside of everyday work and think about how can you increase efficiency? How can you increase productivity? What is like an enhanced customer experience that can be offered through these technologies And just parking this, parking the moment and, uh, getting going.

Courtney: I recently was talking to the host of the No-Brainer [00:13:00] podcast, and we’ve had them over on our show, and they were talking about so many people that they’re talking to on the marketing side of organizations, they can’t actually implement.

A lot of the technology that they’ve always been able to do previously that they’re having to funnel everything through the CTO. So I think that’s just like more proof of AI is being treated completely different than any other technology that we’ve had in the past. So, okay. Right now what I’m hearing you say, David and Mohan is one.

Yes. You have the top down. Approach because that’s important with this type of technology, but also creating a structure that allows for bottom up. What do you think are the other obstacles in making these two things become reality?

Mohan: I think it’s not to overcomplicate things, right? There are always simple use cases. where you can [00:14:00] build momentum and finding those simple use cases and just giving permission to people to go ahead and do it, uh, is gonna pay off dividends that are immeasurable in this journey.

Courtney: David, you recently wrote a post that I loved, Mohan, loved it too. Uh, gold stars all around,

David: You.

Courtney: but you had several recommendations in that post that I think would be great to share with our audience.

Help us out. you, know, I, I was contrasting this, this top down versus bottoms up, right? I think there has been a lot of executive intention on we need to do things now. There there’s been parts of the organization. I think your IT organization, your software engineering organization has naturally taken it upon themselves.

David: To adopt some of these tools. But I do think largely they’ve, they’ve also been pushed, uh, from the top down. And I, I was arguing that we needed to foster this type of experimentation and usage in order to figure [00:15:00] out the real use cases that are really gonna impact the work on the ground. Um, to me, one of the biggest obstacles for doing that is that.

This is a brand new field and we don’t have real guidelines around what is acceptable use. And in a world where we have personalized AI to be an entity, to be a thing, to be the scary robot in the sky, um, I think people are a little bit paralyzed. And so I’d encourage leaders, one of the most important things they can do is actually create a very simple set of guidelines that are the acceptable use.

How in your enterprise do you want to, um, research has. Continually shown over the years, the more you give people actual parameters, what are the boundaries? The more they flex their muscles and go all the way to the edges of those, boundaries and can actually be empowered with them in them. And so one of the best ways to empower your team to actually move is to provide clear parameters of this is where we can, and this is where we can’t use this new technology.

Mohan: And you know, by, [00:16:00] by giving those parameters or guidelines, I think it’s possible for employees to take, uh, little bets, right? So, uh, one of, uh, that’s one of my favorite books, uh, little Bets where breakthrough ideas come from, these little bets that you take and you just give permission to your employees to start working from which the breakthrough ideas will emerge.

David: And then I love the idea of taking those little ones and celebrating ’em. Celebrate the wins. You know, celebrate the accountant who uses AI to cut 30% of their time off of a task that they’re always doing right. Really going in and saying, it’s not just okay, it’s expected, it’s rewarded, is how you fuel behavior.

Courtney: I think that rewarded word is really. Important because I do think when it comes to change, and again, we all feel. Everybody feels like their workload is full. You know, I’ve yet to meet anybody that’s like, no, I have tons of time [00:17:00] just hanging out here. You know? And so it’s how do we take the time? If you think back to my hot take, it was that AI might take more time this year, not less, because we’ve got to figure out how to use these tools, where they actually work.

Which platform is the best one, yada, yada. I think rewarding, really incentivizing how we, the, the behavior we wanna see to adopt these tools because we know it, it takes extra effort, it takes more than, uh, the daily grind of what we’re accomplishing and working on.

David Mohan, any last words here for. Our listeners on how to think through

this culture change.

Mohan: Yeah. Just to recap, um, David’s blog, uh, you know, almost everything that, uh, everything good that comes is a w process, right? So it starts at the top. Uh, you gotta set some guidelines. Then it goes down, and then it’s gotta be bottom up in terms of initiative and [00:18:00] taking little bets and small experimentation, because small experimentation leads to small failures, which you can afford.

Um, and then it comes back up. It’s, uh, w and you just keep going in those cycles. Right? So that’s the essence of that article.

Courtney: Yes. I love that. That’s so good. What is that from somewhere? Does David say that in the article?

David: No, that, that’s Mohan’s brilliance on top of my article. I should have inquired with it before I published

Courtney: So good, so good.

Courtney: We’ve got a great resource for professional service leaders to ensure you are keeping the most valuable resource. Your clients. We recently held a roundtable discussion called Client Retention in the age of AI, and you won’t wanna miss it. If you wanna get access to the recording, you can go to Knownwell dot com slash roundtable. You’ll get to hear how innovative executives are utilizing AI to drive client retention, customer satisfaction, and more.

[00:19:00] Download right now at Knownwell dot com slash roundtable.

 

Speaker 5: Sharon McCarthy is an expert in behavioral science and marketing who’s worked with brands like Taco Bell, craft Discovery Channel, and many more to drive behavioral change and business growth.

She sat down with Pete Viewer recently to talk about the intersection of behavioral science and ai

Pete: Hi, Sharon. Welcome to the podcast. We’re so grateful that you’ve joined.

Sharon: I’m honored to be here.

Pete: so the website for your business, I was doing a little snooping around. I hope That’s okay. Uh, references nearly a thousand hours of training in behavioral economics. Um, as you know, this is a podcast that talks about, uh, AI and its role, its emerging role in the business. So I’d love to start there. Um, how is AI showing up and being used in the world of behavioral economics, behavioral science?

Sharon: Great question. So let’s take a step back for a [00:20:00] second and just. Talk a little bit about behavioral science. It’s the science of how we make decisions and change behavior. a research backed decision. Principles that we as a species developed over the course of the millennia to reliably steer us in the right direction when facing uncertainty.

if you think about ai, what’s more uncertain than ai? Am I gonna lose my job? What model do I use? Do I build or buy? Do we wait for a vendor to do this? Um, all of that uncertainty tends to do two things. One is it tends to stall decision making, and two, it tends to silo efforts. And behavioral science can help accelerate adoption of AI because it’s how we’ve naturally evolved to make decisions when facing uncertainty.

Pete: So you reference, um, stalling of decisions and, [00:21:00] uh, siloing of efforts. Uh, where in, in the business today are you seeing that happening the most?

Sharon: You know, it’s all across the board. In some cases, I’ll have a very large organization that will say, I’ve got 700 use cases. I’ll have another that will say, you know, a committee is looking into it. that says, uh, yeah, I think we’re using it. just marketing is using it. if you think about AI and trying to create an AI culture transformation, don’t wanna think about it as another typical deployment.

You wanna think about it in terms of the broad cultural transformation that it really is. And so you don’t wanna use the same tools that you’ve used to, you know, conduct a digital transformation, which by the way, McKinsey says 65% of them fail Right. because they don’t change behavior. want to lean [00:22:00] into the way people naturally have evolved to make decisions, and that’s where behavioral science can help.

Pete: Wow. Can, can you share a little more color on how to think about managing the change differently with AI versus digital?

Sharon: So one of the things that I hear is that, um, I lose my job? will. AI take over my job and I no longer have any decision making, and so all of this fear and sense that I’m going to lose my autonomy can be remedied a couple of ways. One is just understanding that this is a tool of disruption that we haven’t had before, and for the first time, that tool of disruption can be put in your team’s hands to not just mitigate the disruption.

But actually capitalize it on it and exploit it and make it really work for them. [00:23:00] And so what I would do is intentionally put those paid apps into the hands of your team, give them a choice of three to five different AI tools, and that’s gonna do a couple things. One, it’s going to give them a greater sense of agency.

And confidence. The second is they’re more likely to embrace AI since they had a hand in selecting the tool. The third thing that it does is that it ensures that these models are not being trained on your data. The paid version like Tetchy, BT teams, um, has enterprise, grade security.

Pete: where does the system break down most often as companies are trying to drive change? ’cause I think about the work of, of leaders, um, trying to bring, for instance, as you referenced, 700 use cases to life. There’s so much going on across the organization. Um, in your experience working with companies is, are there [00:24:00] patterns in, in the fail points.

Sharon: Yes, they tend to think of change as being, just having information that with information everyone’s gonna know the right thing to do, and what behavioral science says actually information doesn’t change behavior. And what behavioral science tends to do is to rely on three, well, I call them three core principles, herd.

We look to the behavior of others as to what to do, habit. We do what we’re used to doing and hassle-free. We look to conserve our resources and do what’s easiest. And so the breakdown tends to be using more traditional ways of just providing information and hoping people use it. Because it’s rational.

Here’s the information.

Pete: And so we’ve been talking, uh, through to here about, Application of behavioral science thinking and [00:25:00] methods to driving change within the organization. The opportunity for AI, of course, is inside our four walls as well as outside with customers. what are some of the challenges you’re seeing in, um, changing customer ways of behaving?

Sharon: For solution providers, the sheer volume tools that are out there, the number of GPTs, the number of models that all kind of seem similar. the challenge for them actually is how do they become more salient stick, and that’s the challenge I see with them. I think there are some opportunities from a UX perspective to help guide the customer better, uh, to help them deal with the uncertainty of some of the outcomes.

Um, so yeah, I think how do you break through and stand out is a major challenge facing any business. [00:26:00] Launching an AI solution.

Pete: And, and do the, the same sort of three Hs. Uh, the principles stand in the customer case also.

Sharon: Yes. And so what I tend to do is I, first, I think of it, behavioral science is kind of like driving a car. There are, there’s a driver, like a key behavior that you wanna change. are accelerators to that behavior. are breaks, Nice. and some of those breaks might be, it’s too difficult, I don’t remember it.

It’s psychologically painful. Um, there’s too much logistical friction, and then there are mental shortcuts that you take. And so typically what I do is identify what the key driver is, the accelerators are, what the brakes are, and then what are the mental shortcuts to ultimately achieve your destination faster.

a framework I use.

Pete: Let’s, let’s say [00:27:00] I’m a leader in a business and I’m on the exec team and I need to drive transformation. I’ve got some, some methods, um, thanks to you for how to think about doing it. You know, give, give tools to the, uh, to the team, get ’em comfortable with it, have him start, um, exploring.

As a leader in that setting, set aside the individual sort of change management steps that we take. Am I acting differently? Am I a different person as a leader with different, I don’t know, skills, capabilities, or mandates? Uh, as I’m driving change going forward?

Sharon: That’s a great question. And so we’re all human we all bring our past experiences, and so the first thing I would do, Is if you are not an AI expert, as a leader, bring in a thought leader and have them talk to your organization about AI and [00:28:00] then think about communicating. know, so you, in other words, you borrow the expertise to borrow the credibility from an expert to convince the organization that you have the authority, you are the rightful leader of this process. The second thing is I really believe in a both ends to the middle adoption style. So having the CEO their vision and put it in their own words, and not in the sanitized memo that a marketing department or a communications department will put together because employees will be able to tell this is a phony voice or not.

And what that does is. Then the CEO, once they make this public commitment in their own voice, they’re, it’s called a commitment device, by the way. Behavioral scientists would call it Okay. It commits them to acting in a certain way, [00:29:00] and then we ask organization to then translate the C-suite to translate that CEO’s vision into their own words, and then that gets cascaded down through the organization and then at the ground level.

have, you employees to adopt these apps. You give them a choice. You have actually thought leaders in the on work teams recommend the tools, and then at a standup you have them describe how they’re using it. And so you take an existing behavior, like a standup, and you attach a new behavior associated with it.

And that new behavior is more likely to stick. That new behavior is describing how you use this new AI tool and that provides social proof to the rest of the team that everybody is doing

Pete: Mm-Hmm.

Sharon: and that is that herd [00:30:00] component.

The other thing I would do is I would focus the efforts first, not on a client facing situation or a Right.

But have work teams decide what that internal productivity issue they wanna solve through AI and have it be just a small win. Yep. what that does is it engages, you know, your team a solution that they’re more likely to wanna pitch and recommend and adopt.

It also is lower risk for the organization because. They’re just using it for their Great. They don’t have to worry about it being a very expensive cost unknown for a client or outcome unknown for a client, the people working on that solution are close to it and are better able to identify what the ROI is and having work teams develop these [00:31:00] small quick wins.

Does what Dr. Robert Cialdini. The well-known behavioral scientist would say it creates unity. It creates a sense of I am part of this team. I am one with this other group of people that I used to think as being different and other than me, we’re all part of this. And when you feel part, when you share that identity, a couple of things happened.

Well, one is you’re more likely to get things done faster. You make decisions faster, you’re more likely to be generous in giving help, and you’re more likely to be forgiving in mistakes. The science says that small quick wins are the best way to galvanize a team’s culture precisely because of that unity principle that Dr. Chelini talks about.

Pete: Sharon, it’s been, uh, a privilege to have you on the show. [00:32:00] Thank you for sharing your insights and your experience, uh, and your guidance for executives as they try to navigate these, uh, turbulent waters. We’re grateful.

Sharon: Thank you. I really appreciate your great questions.

 

Courtney: Thanks as always for listening and watching. Don’t forget to give us a five star review. Again, we don’t make you listen to ads, but we do ask for a review. It’s really helpful in helping more people find out about the show.

At the end of every episode, we like to ask one of our AI friends to weigh in on the topic at hand. So, hey Perplexity, good to see you again. This episode we’re talking about everything executives need to know about ai. So what do you think?

[00:33:00]

Courtney: And now, you’re in the know. Thanks as always for listening, and we’ll be back next week with more headlines, round table discussions and interviews with AI experts.