How to Sell AI to the Rest of Your Company

The Cranky Bean Counter over in Finance, the bubbly Gen Z Snapchatter over in HR.

How can you sell AI to the rest of your company when the rest of your company is so unique?

And let’s face it, maybe just a little resistant to change.

And I may be looking at you, Finance Team.

All the love though, all the love.

Hi, I’m Courtney Baker and this is AI Knowhow from Knownwell, helping you re-imagine your business in the AI era.

As always, I’m joined by Knownwell’s CEO, David DeWolf, Chief Technology Officer and Chief Product Officer, Mohan Rao and Chief Strategy Officer, Pete Buer.

We also have a discussion with Philipp Mueller of Outsell about how to bring along those in your organization who may not naturally think of themselves as innovators or early adopters.

But first, get ready for a little inspiration and a feel-good story on this edition of AI in the Wild.

Jennifer Wexton is a member of the US House of Representatives, who was diagnosed with progressive supranuclear palsy in 2023.

It’s a rare neurological disorder that affects mobility, vision, and speech.

PSP has taken away Representative Wexton’s ability to speak, but thanks to AI, it hasn’t taken away her voice.

So Pete, hey, how are you?

I’m good, Courtney.

How are you?

I’m good.

So today, what I thought we could do is actually roll the tape of Representative Wexton that she recently posted to Twitter, and get our responses.

Hi, it’s Congresswoman Jennifer Wexton.

As many of you know, last year, I was diagnosed with Progressive Supernuclear Palsy, or PSP.

It’s like Parkinson’s disease on steroids, and it’s affected my mobility and ability to speak clearly.

For those of you who heard me speak before PSP robbed me of my full voice, you may think your ears are deceiving you right now.

I assure you, they are not.

I’m very pleased to debut my new AI voice today, and share how this remarkable technology has helped empower me to keep living my life and doing the job I love.

Over the past couple weeks, my team and I have compiled old clips of my public speeches like this one.

I rise in support of House Resolution 79, which is my resolution expressing the sense of the House of Representatives that government shutdowns are detrimental to the nation and should not occur.

To generate an AI model of what my voice sounded like before PSP began to affect the volume and clarity of my speech.

I hope that this new step of adopting an AI voice model can also be a moment to start a conversation about new and creative ways we can continue to empower people facing the kinds of health and accessibility issues as I have and to show that our abilities do not define who we are.

AI technology can be a scary new frontier, especially if used in the wrong ways by people with malicious intentions.

And it’s clear that there’s work to be done to properly protect against potential dangers it poses.

But it can also provide new, unimaginable and life-changing opportunities for Americans with disabilities.

That’s why I cried happy tears when I first heard my new, old AI voice.

Pete, were you familiar with the story at all?

You know, I’m familiar with her name for sure, though I wasn’t familiar with the story.

It’s pretty, pretty fantastic.

The voice and the ability to express oneself is one of the greatest tools that a politician has at their disposal.

And to be compromised there has got to be tough.

It must be limiting to your impact, but also deeply, deeply frustrating as it would be to any human.

I think about the people who are able to listen and hear now and who benefit from her word, including your family.

I bet you there’s a pretty fun set of first experiments with the technology at home and if that didn’t bring a tear to an eye for a few folks, I’d be surprised.

Yeah, I love this story so much.

I many times on this podcast even have talked about, you know, obviously, there are risk with AI and we have to walk this transition carefully.

And she even talks a little bit about that in the clip.

But the like doom and gloom seems so large in comparison to some of the even just health benefits that can happen for all of us.

And it gets so little play that it was really encouraging to me to see this story.

And somebody that is using the technology firsthand in a spot where can actually impact stewarding it well, safeguarding us, but using it, you know, leveraging it to its full potential.

I’ll tell you, with every passing day, the stories I hear out of the security community make me more and more comfortable with the fact that we will have AI also at our disposal to determine what’s fake and what’s not fake when it comes to video and voice.

And so, you know, I think that will work itself out.

And yeah, it’s nice to be able to put one in the win column for making the world a better place.

Yep.

Pretty awesome.

You know, Pete, I’m reminded of how our company and Knownwell were all about how do we leverage AI to help humans be better humans.

And this feels like such a wonderful example of that exact thing.

So thank you, as always.

Beautifully put.

Thank you, Courtney.

Not everyone is an innovator or early adopter.

So how can you get those who may be hesitant to experiment with AI to cross the chasm and come along for the ride?

I talked with David and Mohan about just that recently.

Every once in a while, I have an experience where I remember that not everyone is accustomed to change the way that I am.

I think I’m just more naturally, if there’s a new technology or just something, it could be anything needs to be changed.

I’m usually really apt to be good with pursuing it.

I think you two are both that way as well.

Am I right?

Potentially.

We can dissect that.

Okay.

I would be shocked if you said no.

I’m going to say yes for you.

Let’s just pretend you said yes.

Mohan, I feel like you’re pretty good with change as well.

I think so.

Yeah.

I love how you-all are just totally going with me here.

Well, I think it’s easy when you are someone that kind of rolls with change pretty easily, is used to kind of new technologies or just experimenting with new things.

I think it becomes very easy to forget that a lot of people are not that way.

And several episodes back, we talked about how to be an innovator in your business.

But today, I really want to flip the script on that and talk about how do you bring other people along and get excited about this new technology because it is a change.

So David, Mohan, what’s your initial take on how do you bring people, especially those maybe pulling the other way or in the middle, along with you to start experimenting and figuring out how AI can move your business forward?

Courtney, I almost want to start at the intersection of your assumption and your question, right?

So your assumption was about Mohan and I embracing change.

The question was about how you bring others along.

What comes to my mind when I think about those two things is actually jeffrey Moore’s book Crossing the Chasm, where he lays out the technology adoption lifecycle.

And in that book, before the Chasm, he talks about the difference between innovators and early adopters.

And I think the reason for my pause in answering your question is, I don’t think I’m actually an innovator.

I think I’m much more of an early adopter.

Innovators tend to want to be on the bleeding edge for the sake of being on the bleeding edge, whereas early adopters tend to move quickly, but they’re looking for some early validation already to be done, some hot reviews, those types of things.

I didn’t buy a rabbit.

I wasn’t the first one to have an iPhone or an iWatch, but I’m pretty good at embracing it once it’s proven to at least meet a certain bar.

I say that because I think it’s a key to your question.

I think it’s important that we understand where we are in the technology adoption lifecycle both as a society, but also within our businesses, potentially, so that you know who and how you’re talking to folks and you understand the unique psychographics of each phase.

And I think really identifying who those innovators are, who want to be on the bleeding edge, who want to be experimenting, who want the thrill of technology for its own sake versus the early adapters that want to move quickly, but not necessarily just for the pure technology itself, can be a helpful frame for thinking about how you engage the audience.

I think this is a fascinating thing in figuring out the behavioral economics within an organization, right?

So just thinking about what incentives people respond to, and how do you get the incentives in such a way that more people will respond to in an organization.

So it’s really, there is just an understanding of who your audience is.

Just like David said, it’s not possible to convert everybody in one go.

There’s always going to be these innovators and early adapters.

I’ll combine those together.

And it’s understand who they are, how you can pre-sell this to them, how do you tip the incentives out of just staring at the problem versus doing something about it, and then just building the momentum around it.

I really think of these as incentives, and how do you incentivize people to do the things that you want them to do, in terms of adoption of new technologies, in this case AI.

Sometimes people overcomplicate the problem with ethical, moral arguments, which are all real, but those tend to be much more of, hey, listen, we need to worry about that in maybe month 13, not in month one, even before you got the basics going.

So that’s how I think of it.

Yeah, it’s really interesting.

And I think what my, probably for most people listening to this podcast, they probably are in that innovator or early adopter sector.

And I think what my point is, is I think it’s when you are one of those people, it is sometimes easy to forget that the vast majority of people are not.

David, you may know the percentages off the top of your head, but there’s not actually very many people in those categories.

And so, I think it’s again, helpful to reframe some ways that you can bring people that are further back in the adoption life cycle up to speed more quickly.

Because I think without some real intentional engagement in these areas, you may find yourself another year from now still feeling like, oh my gosh, we can’t get everybody trying these things or experimenting, still kind of fighting that battle.

So with that said, what’s your top three ways you would advise organizations to get people up to speed and bring them along in this process?

Yeah.

So I think if I carry that forward and think about like moving an organization, Courtney, one of the distinct differences between an innovator and an early adopter is that the innovators tend to be, you know, for the sake of the technology, the risk taking, they get a thrill out of that.

They don’t necessarily see the downside.

The early adopters actually tend to be more of social beings that are looked at as those individuals that are pushing things forward and others seek advice from.

They tend to have more connections into social fabric.

One of the reasons I think it’s important to distinguish this is not only to understand the sequence with which they’re going to engage, but also to make sure that you understand that it’s essential that you don’t just get those extreme risk takers that are trying the technology for itself, but also the early adopters who want to be seen as at the forefront, that are taking the cues from the innovators, and then they’re actually espousing it.

They’re your champions in the organization.

I think really making sure that you take the time to look at both of these buckets and work through it deliberately to make sure you have those champions who are connected to the social fabric of your organization and can influence others is a really important thing.

I love that.

So the first one is identify your champions.

And to your point, it may not be the first person that tried ChatGPT and maybe the people right after that.

That’s really great.

Yeah.

David, as you were saying that, I was reminded of the old Malcolm Gladwell’s book, The Tipping Point.

I don’t know if you guys have read it.

I mean, there are the concepts of connectors and mavens, right?

I mean, connectors are social butterflies who occupy multiple worlds.

They connect different parts of the organization, and the mavens are essentially specialists in whatever we’re talking about, right?

So it’s really just putting labels to what you just said.

I totally love how this podcast has now become a book club, and crossing the chasm and the tipping point of this Utah recommendation.

Hopefully, this is an ally drinking game.

You know, as these early efforts start coming to fruition, it’s really important to showcase these results and show it to everybody in the company that builds the momentum for this.

The showcasing, celebrating, showing early events are really important.

It’s a momentum game and you got to just build it up within the company as and when there are wins.

Mohan, I love that point.

I think momentum is an underrated thing in business and other small wins which breed more wins, right?

And it’s like the domino effect, right?

The small one and ultimately you knock down the whole set and they get bigger and bigger as you go along, right?

It’s like a flywheel.

And I think that is such a critical concept when we’re talking about AI.

AI has proven that it can make significant changes, right?

But really small changes in a broad set of things, right?

And really assist us in a lot of different areas.

If we gain 7% across a multitude of different use cases, that’s a huge lift.

And just identifying one of those and putting it into action can actually lead to somebody else understanding it, embracing it and trying it and something that is adjacent to that, right?

And I really think that that concept of simply using ChatGPT or simply using Grammarly or simply using a tool that is AI based that gives us a small little win that people can say, yeah, I saved 3% or 4% of my effort on that.

And that starts to cascade, I think, is huge.

I think part of what may be the problem, even some of the examples you gave, is it’s hard at an organizational level because so much of it is bottom up to actually surface a lot of those things that are wins, but to cascade it in a way that really shows, hey, this is moving the needle for the company.

It’s just happening on an individual level right now.

I think that’s so good, showcasing results.

So we’ve got identifying champions and then we’ve got showcasing results.

I think really what y’all are saying or what you’re alluding to, which is our third point, could be frame things as experiments.

A great way to bring people that are a little further back in the life cycle is just to bring the temperature down of, hey, these are an experiment.

We’re not making wholesale change.

We’re not, frame it as an experiment so that more people are willing to take it on as an endeavor.

Yeah, lowering the risk, right, can really help people push in.

I think that’s a great point, Courtney.

I also think with that, one of the other ways we can lower the risk is not just saying it, but actually pointing to other organizations and pointing to what is the risk if we don’t act, right?

Showing how other businesses are transforming.

And I think there is a very real element in AI of, yes, there is the risk of embracing it, but the risk of not is 10 times bigger, right?

Just like you don’t want to be Sears Roebuck these days because Sears went out of business because of Amazon, right?

Just like you don’t want to be Blockbuster because now there’s Netflix.

I think the same wave is going to hit us, and people should lower the risk by realizing what the alternative is, right?

Not moving is a decision, and it is just as if not more risky.

And so, yes, frame it as experiments, but also make sure people have their hands around how not embracing it is also go incredibly risky.

Awesome.

Well, I think this is a really great conversation.

Just as a recap, if you are looking at your organization and figuring out, okay, how do we continue to bring people in the organization up the curve and really embrace AI in a new way that really moves your business forward?

Three things to think about.

The first is identifying champions.

The second is showcasing results.

At this point, hopefully, you have a few of those, even if they’re not as obvious as other tools or technologies you’ve used in the past.

Then last, you frame things as experiments, bring down the risk level.

David, Mohan, thank you as always.

Thanks a ton, Courtney.

It was fun, Mohan.

Thanks, David.

Thanks, Courtney.

You know, one way to sell AI to the rest of your company is to put AI to use solving a really big problem.

For professional service leaders, we know that client retention and stopping churn, especially surprise churn, is one of those big problems.

That’s why we’re building an AI platform that can stop churn in its tracks.

Go to knownwell.com today to learn more and sign up for a demo.

Philipp Mueller is Chief Analyst and Product Officer at Outsell.

He recently chatted with Pete Buer about how to get those in your organization who may be hesitant all aboard the AI train.

So nice to see you, second time in as many months.

Welcome to the show.

Yeah, thank you for having me.

Great to see you too.

Could we start with a little bit of background?

Could you tell us about your role at Outsell?

Yes, I’m Chief Analyst and Product Officer.

And so that means I oversee pretty much anything we put out, both in our syndicated coverage, our syndicated research that we publish, but also most of the consulting work that we do, especially the work that we do on market-focused competitive dynamics, anything to do with strategic advisory.

We operate a digital platform that our clients can use to not just access our research, but also information about the companies that we cover.

We cover around 15,000 companies representing around a trillion dollars of spend, and or trillion dollars of revenue, if you will.

And so we have that.

We also publish every day a very widely read summary of the previous day’s industry headlines.

As I think you know, our listeners, our leaders, especially in middle market companies who are trying to make sense of AI in the business.

And I wonder, as you’ve been watching the Deal Tracker, any high level trends worth mentioning that would be useful for people to be aware of?

Yes.

So the Deal Tracker is an interesting thing, because of course it captures almost entirely publicized deals.

Sure.

So there’s a lot more happening that isn’t public.

But what you will glean from the Deal Tracker is that the bulk of deals that have been announced have been done by OpenAI.

That is mostly because as part of their preferred publisher program, which is the umbrella program under which these deals are struck, tends to have at least on our understanding a requirement for press releases.

So they get published and they get talked about and they’re visible versus the more private ones.

But the trend that you can very clearly see there is that more and more rights holders, more and more publishers, are thinking about licensing their content.

The ones that have been first out of the gate have been mostly news and entertainment publishers, right?

So very well publicized, Axel Springer, FT, and so on.

The laggards, if you will, have been more the book publishers, educational publishers, publishers of what I would call reference content.

And there’s a very good reason for that, which is that the value, the economics of news and entertainment content is very different than the value of reference content.

News content tends to be much more short lived.

So the archives have a different value profile than the archives of reference content.

Reference content is very long lived.

And so thinking about that, maybe from a sort of competitive dynamic, right?

What risks do I take by licensing those archives into a generative AI setting?

For news and entertainment publishers, licensing their archives does not create a threat to their business models.

And I think it’s fair to say that historically, those archives have been under monetized.

You know, there have been use cases, for example, financial services, right?

Lots of data mining, hedge funds, that kind of thing.

But it’s been an easier decision for them to make, to license those archives, than it is for publishers of reference content.

And by reference, I should include in that, there’s also, of course, fiction publishers and that kind of thing.

But short-lived versus long-lived is the lens that we’re looking at this through at Outsell.

Yeah, that’s fascinating.

And as to the use case in those deals, so we’ve been seeing some reports recently, Goldman Sachs and others sort of talking about how all of the investment in AI is going to be, you know, waiting years to get to some reasonable amount of return, especially because a lot of the early use cases are about efficiency and productivity as opposed to breakthrough growth.

Do you see patterns that would support or refute that assertion?

Yes, absolutely.

You know, at Outsell, we talk to CEOs all day, every day.

We have a pretty good sense of tangible value creation, right?

What’s working and what’s not.

And so we’re seeing that both in the technology and information industry, and we’re also seeing it sort of in the end markets that they serve.

And it is absolutely fair to say that the primary value driver has been productivity and efficiency.

We haven’t seen all that many revenue growth cases.

There are some, but they are few and far between.

It is true that, especially in the information space, many vendors, if not most vendors, are baking generative AI capabilities into their products in one way or another, especially as an augmentation to search, enhanced search, conversational interactions with content.

That’s happening across the board, to the point where it becomes table stakes.

And so the question there now is, is that allowing vendors to either create new price points or increase price, or is it just being priced in because everybody now expects those kinds of features?

And what we also don’t yet know is, once those features are deployed, are users actually using them and do they like them, right?

So the jury is still out on some of these product related capabilities.

I think retention rates are kind of proving to be on the low side too, right?

And so that’s exactly that’s a separate whole, there’s a whole separate thread around the current market dynamics.

So we do a quarterly sales benchmark among our member base, and we’re actually publishing the current one, the Q2 survey quite soon, and the results of that.

So I can’t announce just yet what it says.

But the pattern is absolutely that.

And we’ve seen this happening over the, it’s a trend that’s been building over the last few quarters.

Sales cycles are getting longer.

Customers are taking longer to make those purchasing decisions.

They’re going deeper and more granular in their assessment of purchasing decisions.

So it’s not an easy market out there, and one gets the sense across the board that where this kind of is going to go is that vendors will be adding these features in order to justify price points, not introduce new ones.

But again, we’ll see.

We’re starting now to see products being launched with new pricing.

And so we’ll have to wait a couple of quarters to see if that’s going to stick or not.

Are you able to share any of your favorite or most promising ideas that you’re seeing in terms of interesting new products?

We…

It’s okay.

No’s and no can’ts.

I’ll be very, very general.

I’ll be very, very general.

I do think that there is mileage in the conversational interaction model in certain settings.

So there are some use cases, some industries, some professions where that’s really valuable.

So, I think we’re going to get…

I don’t want to get too specific, but certainly in healthcare, for example, we think it’s going to be a needle mover.

There are…

I think, actually, frankly, on the productivity side, both within end markets, but also within the information industry, I don’t think we’ve yet reached…

We’re not saturated yet.

There’s a lot more headroom for value capture.

So organizations are still learning, right?

We’re still having conversations every day about hypotheses to test things to try out.

So we haven’t…

It’s still early, I guess, is the mean way to say that.

I know we don’t have you forever.

So one last question for you, if I may.

Elsewhere in this episode, there’s a roundtable discussion about how to sell AI to the rest of your company, sort of the part of the change management process, I guess.

And I’m sure that in engagements that you’ve been doing with customers, or you’ve been working on something innovative, whether it be for cost savings, productivity, or developing something new as a growth platform, you’ve seen leaders running into this challenge.

What’s coming up in terms of resistance?

I guess you just shared one example, but what else are you seeing in terms of resistance and how are leaders overcoming it?

You mean resistance to adoption in general?

Yes, adoption in general, but also even in the case of specific initiatives within the organization.

Resistance is mostly about professional and business risk, I think.

Companies are worried about making missteps.

They’re worried about leaking personal information, so they’re worried about leaking proprietary content, internal data, that kind of thing, right?

So data privacy goes beyond, the concerns about data privacy extend beyond sort of personal information, but also corporate internal business information, right?

Lots of concern about that still, lots of concern about hallucinations, the trustworthiness of what comes out of the models.

And, you know, most companies that we work with have some form of sort of internal policy, but many, many actually still don’t and they’re still figuring that out.

And even the ones who have policies tend, you know, that there are efforts to try and sort of contain perceived risks.

We’re starting to see that soften a little bit as people become more familiar with technology, and especially these topics around the licensing, sort of IP controls, and what’s the difference between training versus inference, and all of these kinds of things.

So we’re starting to see some maturing there, and with it, an increasing openness and willingness to engage and to experiment.

But it’s going to take time.

It’s going to take time.

No slick transformation management hot knives through butter.

It’s really about education, policies in place, getting the technology right.

In part, why there are no slick transformation hot knives through butter is because there are so few use cases that are truly proven, right, where there are scalable solutions that, you know, you plug them in ready to go.

And because so much of it is specific.

Again, I’m looking at this from a sort of internal corporate, you know, business process optimization standpoint, efficiency play primarily.

Every company is different.

Every company has its own idiosyncrasies.

And so that often comes a level of customization and adaptation.

And so actually the other elephant in the room is cost.

It costs money to do all of these things, not just the by now fairly well documented operating cost, right?

The fact that every query I throw at, ChatGPT or open AIs API costs money.

But the capital investment of even building tools or customizing solutions is obviously quite high.

And then, as I mentioned, you know, off the shelf, again, in our conversations, we haven’t, we’ve not heard many instances of people shouting from the rooftops that they found something that truly transforms, you know, found a tool or a capability that truly transforms their business.

There’s increasing adoption, yes, but it’s still seeing lots of experimentation, lots of trials, figuring out if things are going to work, and very gradual sort of adoption in business use sort of on the fringes as companies gain insights.

It’s a tempering but hopeful assessment, you know, on the one hand, it’s going to be a gradual process and it requires good business policy and approach.

And on the other hand, the challenges, while not simple, are at least surmountable with time and hard work.

So we’ll get there, but maybe just not on the path or with the slope of the growth line quite as steep as we were hoping originally.

Yeah, that’s right.

And I’m not trying to say, I’ll be very clear, right?

I’m not saying that AI is not going to continue growing.

I’m not negative on the AI outlook.

I’m actually quite positive.

I think it’s more that it’s clearly taking time and effort and sort of learning to get off the ground.

In many ways, it’s a product market fit question as well, right?

We’re still figuring out how to harness the technology in the most effective ways.

Great place to end.

Philipp, thank you so much.

It’s been a pleasure to see you and an honor to have you on the show.

Yeah, likewise, my pleasure.

Thanks as always for listening and watching.

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At the end of every episode, we like to ask one of the large language models to give us their take on the topic at hand.

So hey, Gemini, what’s happening?

Today, we’re talking about how to sell AI to the rest of the company at large.

So what do you think?

Selling AI isn’t about the tech.

It’s about solving problems.

Show how AI can be their secret weapon for saving time, cutting costs or boosting sales.

Give them concrete examples, not just buzzwords.

And now you’re in the know.

Thanks as always for listening.

We’ll see you next week with more AI applications, discussions and experts.