Best of Director’s Cuts: Unedited Interviews with AI Experts

We are officially, at least in the United States, in the dog days of summer.

Matter of fact, we might be in a heat advisory at this very moment.

And if you tune in to your actual TV station any longer, I know no one does, but maybe you remember those days.

You know that this means reruns.

Fortunately, we’re much more dedicated to our craft than all of those network executives out there.

Maybe why Netflix is taking over.

We’re not making you watch or listen to a show you’ve already seen before.

Today, we’re bringing you two guest interviews that we’ve never aired before in their entirety.

That’s right, we usually get far more tape on a guest interview than we actually have time to air.

And for these two particular guests, it was just such a rich conversation that we wanted to get them in front of you today.

First, let’s tune in to Pete Buer’s conversation with Katie Taylor of Narratize.

Narratize is a company that was recently accepted into Nvidia’s Inception Accelerator program.

And I absolutely loved listening to Katie’s conversation with Pete Buer.

If you wanna hear more about the episode that this was part of, it was episode 31, Making Space for AI in the Enterprise.

Enjoy.

Katie, welcome.

It’s so great to have you on the show.

Thank you, Pete.

So excited to be here.

If we could, let’s give our listeners a little bit of context for the conversation.

Could you talk a little about Narratize and your role?

Absolutely.

So I’m Katie Trouff-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 about a year in the market.

And we’re a generative AI.

Thank you.

Thank you.

Yeah, happy work-aversary as LinkedIn likes to say.

Yeah, and we have designed a trusted generative AI co-author that’s specifically designed to help innovative enterprises.

In the world of possibilities is much broader than that.

Can you sort of describe your vision for how generative AI can be used to create compelling content?

I think that’s true.

I think you’re spot on that the emphasis over the last maybe couple of years and the market leaders have been marketing led content platforms.

And when we recognize that as we were starting to concept narratize, we knew there was a use case, a strong use case for marketing when it comes to having a trusted co-author.

But we also, because I came up out of CPG, out of research and development, out of innovation and product teams, I saw all of the types of communications and content that they have to create and the disconnect often that there is between those sort of front end innovation, commercialization teams, and those who in marketing have to go get traction and personalize the messaging at scale.

And I thought our team essentially unearthed this, this challenge, and this is true.

Hopefully listeners are resonating with this fact that marketing sometimes speaks a different language than product or engineering.

And so how might we create a platform where that communication channel stays open, where marketing can ping a subject matter expert in a certain technical area to pull their insight into a piece of thought leadership that accelerates conversion for a particular B2B market?

So we really wanted to look at the problem from a different way because ultimately, Pete, what it came down to from our point of view was how does a company get from the seed of a great idea all the way through traction in the marketplace and think of all the communication that has to go right for that to happen across so many different functional areas?

That’s really the problem that we focus on solving.

Also, I noticed on the website, there’s a description, Narratize 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 driving it down to 20 minutes or less.

For leaders 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?

Sure, yeah, I’m happy to.

I have a unique sort of journey in AI.

I, for the last decade, have been CEO of a professional services organization.

And so five years ago, I said to our team, we’re passionate about the problem that we’re solving, but we’re solving it through consulting and through training and through content, strategy and creation.

In five years, we have to be a technology company, and we have to get this capability that’s in our heads as knowledge workers and put it into the hands of our users.

And we, from there, sort of started on this journey to do customer discovery and to listen and understand what is it that customers would want out of that experience if the consultant didn’t do the work for them, how would they go about their work differently?

What’s the superpower that we were bringing to the table?

And right at the end of that, because our team has this really unique blend, my PhD is in narrative science, which is essentially narrative algorithms, the way that words make meaning, how you identify them and building foundational AI models, we got tapped by OpenAI.

This was two years ago now, to be one of seven developer ambassadors.

So this is far before ChatGPT sort of took the world by storm.

And so we were in on these conversations around, right, what these foundational models are looking like, how they’re trained, and we concepted Narratize from that.

So I think that to break it down, I share that journey, right?

Because I think most of your listeners can hopefully relate to the journey that I’ve been on.

And so if you think about the components of AI on the back end, there are large sets of training data.

In the case of OpenAI, it’s the entire web.

Those are the most large, large, large language models.

And then 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 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 cost, very, very expensive undertakings.

And so most companies are not building large language models.

Most companies are asking, how do we safely and securely call into APIs for the large language models that exist today?

The challenge is it’s actually really, it’s not hard to call into the API.

What’s hard is getting it to do the work your people need to get done.

And so that’s where as leaders, as you evaluate and you think through, do I build this?

Do I buy this?

How do we continue to innovate and deliver to our customers?

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

And there’s so much available to buy.

This marketplace is so noisy, but it’s also nascent in many ways.

And 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 or C-Suite are thinking about mostly buying, if we’re going to buy, how do we find solutions that help us call into the most available large language models?

How do we ensure that the solution is going to train on our unique knowledge in a secure and private way?

And how do we prevent bias?

But I think on top of all of those things, those are kind of, to me, those are table stakes.

On top of all of it, Pete, is the critical evaluation of, is this going to transform how our people work, how we do business?

And so I know at least at Narratize, that has been our North Star.

How do we really get AI transformation for teams?

And one of our five, we came up with and adapted from the DoD and Gartner and other resources around responsible AI.

We identified five pillars.

And the number one for us is human-led AI.

Because we strongly believe that if you put a chat bot in front of every professional around the world, it won’t necessarily help them do their work better.

They won’t necessarily know how to use it.

And most of the large language model companies that everybody is excited about, like OpenAI, don’t have a playbook for how on earth to do it.

And so people, it’s exciting.

People want to 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.

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 help them.

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

It’s all behind the scenes.

We haven’t had to necessarily become experts and AI engineers in order to use it.

It was just behind the scenes.

And so one quick example I’ll share.

We partnered with the Good Housekeeping Institute.

They, if you’re familiar, they create lists, essentially, of every kind of appliance, beauty product, and they rate it.

But it’s very scientifically driven.

They have laboratories set up.

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.

And with Narratize, now they can write that report in 20 minutes or less.

It’s so exciting.

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 in a transformation after that research report.

But hopefully that becomes an example of how to look for solutions that sort of have the core capabilities that are actually going to help you transform your culture.

So fascinating.

Thank you.

At the level of the affected individual, the worker, the employee, the member of the team, do you track how happy they are with the solution?

Or do you get that kind of feedback?

Because I’m thinking, on the one hand, I could do so much more or so much more quickly.

But on the other hand, there’s something about the dark night of the soul in writing that’s satisfying.

And if some of that’s being done for you, you’re like, how do I feel as the human?

Yes, Pete, I’m a former English professor.

You’re singing to the choir.

Yeah, absolutely, 75% of the work of writing is thinking.

So again, I’m speaking mostly to the methodology we use at Narratize.

And hopefully it’s helpful as you think about what to embed into your team’s workflows, what to engage with.

But for us, we are hyper focused on preserving that thinking work and keeping the human, again, human led AI and everything you’re doing.

So what our users say are things like, it helped me compile my thoughts.

It helped me know what questions to be answering.

It felt like sitting down next to a colleague who has that great dialogue with me and going back and forth just like we are right now.

And we’re preserving that dynamic in everything we’re doing.

And that’s one of the beauties of that reverse chat bot design.

You’re not in an isolated chat with an anonymous robot.

You are being asked the right questions that help you pull the ideas out of you.

And then it’s getting mapped to domain and industry based data architectures on the back end of Narratize.

It’s calling across multiple large language models, not just GPT.

And from there, it’s also mapping to the right narrative algorithm.

So if you’re a writer, then you intuitively understand narrative algorithm.

Things like there are certain story frameworks that we all know and love, like the Pixar story arc.

Once upon a time, there was this character, and then something happened along the way.

He met a guide and so on.

And we know the hero’s journey, most of us, if we’re familiar with branding.

And there are others in scientific communications that are even more popular, like the ABT, which stands for and, but, therefore.

And the storyline goes like this.

I’ll share it.

Tell me if this sounds familiar.

There’s an ordinary world and there’s something at stake, but there’s some sort of problem or challenge.

Therefore, here’s our proposed solution.

Nice.

So we’ve spent, you know, because we were in professional services, we were boots on the ground doing this content work alongside Fortune 500s for a decade.

We built this fascinating database of narrative algorithms like the ABT, and we started sort of mapping them like constellations in the night sky.

And so that enables people who are not necessarily calling writing their superpower, folks like scientists, technologists, engineers, and so many other professionals who maybe didn’t get a PhD in English, who don’t see the world through narrative algorithm, to be able to leverage those tools to compile their thoughts in a way that makes them go, oh, my goodness, that’s how I would say it if I said it in front of a leader.

We’ve used the word trust several times in the conversation already, and I know that it’s an active ingredient in the work that you do.

Can you sort of share how trust factors in and how you manage to it?

Yeah, I think if you’ve had a bad experience with a chatbot or if you’ve seen some of the offensive, racist, sexist content that’s come out of it or inaccurate or misleading or just truly factually wrong content that’s come out of them, then you feel this pain point.

In the moment, they say trust is earned in drops and it’s lost in buckets.

Again, I think as you’re evaluating how to engage with AI, it’s critical to look to those solutions that are building really strong guardrails against what the large language models output.

Large language models are scraped from the internet, which is full of contention.

It’s full of competing ideas, and some of it is authorized and some of it’s not.

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

For Narratize, we’re supporting clients like NASA, Boeing, Under Armour, these deeply innovative enterprises, and oftentimes they’re highly reliable enterprises, meaning lives are at stake if you get that content wrong, if you say the wrong fact or you could actually really harm someone.

And so we believe that the strongest data architectures, the strongest guardrails against what our output, really they have to include some kind of filter through what is peer-reviewed or at least through the most authorized and credible sources.

And so that’s the key piece of how we’re sort of guardrailing 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 foundational model companies will have to adhere to and live up to that.

And so trust is earned through explainability.

It’s earned through transparency in the data training sets.

It’s earned through a commitment to diversity, equity, inclusion and accessibility.

Almost every venture company in AI that’s received, again, venture funding is led by teams that are not diverse.

And for us at Narratize, we’re one of 0.3%, basically, 0.3% of the venture in AI goes to women founders.

And my co-founders and I are all three women.

So we’re sort of this wild, I want to say unicorn, we’re not necessarily a unicorn yet, technically, and tech speak, but we’re getting there.

But a unicorn in the sense of, again, I think we need to buy from vendors who have diverse leadership, who are taking diversity, equity, and inclusion seriously, or will continue to see massive challenges around the bias of what’s output from these chatbots.

That’s so incredibly helpful.

And you’ve described managing trust is probably the wrong word, but instilling trust or driving work processes and parameters around the product platform to ensure that trust is maintained.

I have to imagine it’s in your culture, too, though.

Is there anything about how you’re running the business that kind of feeds the trust machine, too?

Yeah, we probably have an outsized number of Ph.D.s.

And that sounds really…

I’m not being boastful or being too saying that academic is always the right lens, but what that means is half of our team have Ph.D.s in data science or narrative science and also in the humanities.

And what that means is we’ve been trained in mixed methods research, qualitative and quantitative.

We’ve been taught to see design through the lens of community-based understanding.

And I think the more that we put that type of talent at the forefront of AI, the stronger that the outputs will be, the more human the outputs will be, and the more we’ll feel comfortable recognizing that these models can actually learn at the cultural level, at the language level, and propagate in a way that is inclusive and strong.

So I think those are differentiators, and I think it’s a great opportunity for those who came up out of the humanities but perhaps have been overlooked when it comes to what gets invested in in tech.

But today, my strong hypothesis and what I see working beautifully at Narratize is the combination of data science, narrative science, and computer engineering.

You bring it together, and you help them speak the same language, and suddenly you can create products that are rich with evidence-based, you know, human-led design that actually solve problems for people.

And that’s what it’s all about at the end of the day, is hopefully keeping that as your North Star, keeping evidence and humanity at the North Star of all that we do in AI.

What a wonderful poetic vision for the work that we’re all up against.

Thank you for that.

And thank you for your time.

It’s been fascinating on a number of levels, like seeing how AI is deployed to enable professionals, make us better, on the one hand, but also the story that you’re telling about evolving the business model in professional services.

I think that’s at least half of what will be interesting to everyone who’s listening in our audience.

So thank you on all those levels.

It’s been a pleasure to have you here.

Thank you so much, Pete.

Can’t wait to talk more.

Next up, here’s my full conversation with Greg Alexander of Collective 54, a mastermind group for professional services leaders that Greg founded.

One of his quotes from the episode resonated so deeply with me that I cited it at least once on the show and many more times in real life.

Listen to the interview to see if you can pick it out and go back and listen to episode 20 on redrawing the map of business success for more on the topic.

Greg, it’s so great to have you on AI Knowhow today.

Thanks so much for being with us.

My pleasure, Courtney.

We always like to start by giving listeners really just a short version of your company’s background, where AI fits in and what Collective 54 is all about and why are you vested in AI?

What brings you here?

Well, Collective 54 is the first mastermind community dedicated exclusively to the needs of boutique professional services firms.

Your listeners might be familiar with mastermind communities.

Others like it are Vistage, YPO and EO.

We’re very similar to that with three distinctions.

We focus on one industry, professional services, one segment, which is the boutique, which we define as 10 to 250 billable employees, and one person within that segment, which is the founder or the co-founder, the owner of the firm.

So the reason why I’m interested in AI is because I think that AI is going to result in a boom for the boutique professional services firms.

I think the profit implications of AI, for those that embrace it, are enormous.

And I’m doing my best to help our members by learning as much as I can about it and spreading the word.

And that’s really interesting.

We’ve been talking a lot on our show about the intersection of AI and professional services.

And the reason for that is we, along with a lot of other people, think it’s going to fundamentally change the space.

And there’s a lot of data that reflects that.

And obviously it sounds like you agree with that.

You also had a recent blog post that kind of, I think you said this is a wake up call, a reality check for people in professional services.

And you are speaking specifically to boutique firms.

But can you give us a little insight into what you shared?

Yeah.

So the title of the post, which was meant to be provocative, was the AI Revolution and Urgent Wake Up Call for Boutique Professional Services Firms.

And I present four kind of yes-no checklist style questions.

And I do so to grab everyone’s attention within the first paragraph of the post, because I do think people have their head in the sand a little bit.

I think AI and machine learning in particular, large language models, it’s moving much faster than people realize.

And I think my members, if they’re not careful, they’re going to get left behind and it could have devastating effects.

So let me read the four yes-no checklist questions and tell you why I started with those, and then maybe that will guide us for the rest of the conversation.

So the first question is, are you up to date with how AI can and will replace traditional jobs and professional services?

So I remember when I came up, I would attend client meetings with a partner and my job was to take notes so that the partner could be engaged with the client.

And then at the end, I had to synthesize my notes.

Well, that’s not happening anymore.

The partner can go in that meeting by himself and turn on a recording device on his phone.

And before he gets back to his home office, the transcript has been transcribed.

And the key points of the meeting has been summarized for him via an AI assistant.

So why do you need the associate?

You know, the associate is going to go away.

If you’re a founder of a firm, you own the firm, 85% of your cost structure is labor cost.

And you just eliminated labor.

So your profits have gone up exponentially.

So that’s one thing to keep in mind.

Now, that’s a rather draconian thing.

And some people might be listening to that, saying, oh my gosh, all these jobs are going to be eliminated.

Fundamentally, AI is going to eliminate some jobs, but it’s also going to increase the productivity of those that remain.

I mean, in professional services, everything comes down to the billable hour, regardless of how you package that.

It could be a retainer, it could be a performance contract, it could be charging time and materials, it could be a fixed bid, it could be a subscription or membership.

It does come back down to the billable hour.

So therefore, output per hour is what drives productivity.

So imagine all of a sudden you’re a 50-person firm, which would be a boutique, and you’re growing at 30% a year.

Well, you don’t have to add 30% headcount anymore.

You can get 30% productivity left by bracing AI in the same 50 people, right?

So all that drops to the bottom line.

And the second question is, do you understand the profit expansion opportunity that this presents you?

Well, I think I just did a pretty good job of explaining that.

Questions three and four, a little bit more difficult to talk about, and that is have you started your firm on the steep AI learning curve?

You know, we’ve got a few hundred members in our collective, and I would tell you that, you know, like anything else, it’s probably the top 20% that are down this path and are embracing it.

These are the pioneers, those willing to go first and go through the difficult, messy learning process.

There’s another 20% that think this is a fad, it’s going to come and go, and they’re hoping it just goes away.

Those people are going to get run over.

And then there’s that middle group, that 60% if you will, and they’re taking a wait and see approach.

Hopefully, those pioneers, the 20% that are embracing this will have enough success that that middle 60% will say, let’s get on board with it.

And then just to round out the four questions, the last one is, do you understand the imminent risk of ignoring AI in your industry?

And we’re seeing that, for example, we have a lot of marketing agencies in Collective 54.

Some of them are in the social media monitoring space.

They’re getting decimated.

We used to pay human beings to post things on social media, and now that’s all automated through artificial intelligence.

So those firms are in real trouble unless they reinvent themselves.

So those are the four questions in the article.

Those that are listening to this, want to read the article, go to collective54.com and hopefully it serves as a wake up call for you as well.

I’m curious.

I know you wrote this for boutique firms.

How do you think these would apply for maybe even larger professional service companies?

Do you think they’re the same?

What is the correlation when it comes to the size of the service?

It used to be the big eat the small.

Now it’s the fast eat the slow.

Yeah.

So I think the big firms are in serious trouble.

They would win the day because they had massive scope and massive scale.

So a big global 2000 company would hire a big firm because one stop shopping that big firm can do a lot of things for them.

That advantage of size is going away because let’s say you are a boutique and you’re competing with a giant firm now.

I mean, the giant firm to justify their fee is going to say, hey, this project is going to take six months and a team of five.

A boutique can say, I’m going to get that done in a month with a team of two.

How am I doing that?

AI assistance.

So the advantage is going to the boutique, not the big firm, in my opinion.

Secondly, with ChatGPT and everything else, knowledge is becoming a commodity.

And therefore, general knowledge is toast.

What’s going to be required is hyper hyper specialization.

For example, if I’m a prospective client of a consulting firm, and I go to ChatGPT for it, I issue a query, who’s showing up?

Is it the big generalist firm?

No.

Because the machine intelligence algorithm is going to ignore that because it’s not as detailed or as specific.

Or is that boutique firm that’s hyper hyper niched into a segment?

Is that firm going to be able to show up?

The answer is yes.

So if you are a boutique, you now no longer have to pay Google, Facebook, LinkedIn, a fortune to get your ads placed and to get your search rankings up.

You’re showing up at ChatGPT for free.

So the advantages of size, access to capital, people, scope, et cetera, are gone.

So I think it’s going to be the golden era of the boutique professional services firm.

Yeah, they’re really primed to, for this moment, to really take advantage of not having maybe as much bureaucracy or having to turn the ship that some of these larger professional services agencies have in place.

I am curious, if you go back to when you were talking about, hey, you have the 20% that are all in, you’ve got the other end of the spectrum, the group in the middle.

I’m curious what advice you would give them to get on board.

What are you telling your members to get them maybe leaning?

Again, you said, hey, hopefully these other people have the traction and can kind of show it, but are there other things that you’re saying to them right now to just lean into the technology to try to get them there faster?

Yeah, well, I think it starts with the business strategy adjustment before the technology.

So what I mean by that is I think everybody needs to take a fresh look at the fundamental assumptions that are sitting in their P&L.

For example, how much revenue per head can we produce?

I think that fundamental assumption is going away because if everyone’s going to be able to produce so much more per hour because of the AI assistance, whatever they are, technology-wise, I think the staffing plans are going to change dramatically, and that has a huge impact on professional services.

I also think the pace upon which firms can enter new service lines is going to accelerate dramatically.

I mean, if you have the world’s knowledge at your fingertips at a PhD level, are you now limited by your own expertise?

I don’t think so.

I mean, you basically have the world’s expertise at your fingertips, so if you can be an effective prompter, what is it that you can’t figure out?

And clients, particularly large clients, those are the big consumers of consulting services and IT services, etc.

What are they really looking for?

In many cases, they’re looking for a curator.

They probably can do what you can do as well as you can do it, but guess what?

They got 15 things to do in their priority list and only enough time to get 10 of them done.

So they’re going to throw some work to third parties, services firms, as added capacity to get some things done in their to-do list.

And they’re looking for people that can get it done quickly, can get it done accurately, can get it done on time, on spec, on budget.

And now a lot of these small firms have the ability to do that.

Well, previously, they didn’t.

If you were a consulting firm, as an example, and you were going to 30% a year, consuming 30% more headcount per year, if you just think about the recruiting, the interviewing, the hiring, the onboarding, the making productive, that was a massive amount of work.

Your growth was constrained based on not demand, but based on supply.

How many people culturally could you consume as a company every year?

Well, that’s gone now.

That constraint has been eliminated.

So I think the opportunity in front of us is dramatic.

So those are kind of fundamental strategic decisions and adjustments that need to be made.

And you can make the same case on the pricing.

I mean, if all of a sudden it costs you one tenth of what it used to cost you to produce a deliverable for a client, is there elasticity of demand?

Could you take your pricing down 30, 40, 50%?

And if so, do you get a corresponding increase in demand?

So all these strategic decisions have to be rethought.

Now, once you make those, then of course you have to think about the tech.

What technology can I use to allow me to make this happen?

And, you know, for 20 bucks a month, Chad GPT-4 is a pretty good place to start.

What is there?

I think there’s 173 hours or something like that.

Divide that by 20 bucks, and you’re talking about pennies per hour versus hundreds of dollars per hour.

So we’re on the front end of a really exciting opportunity.

Yeah, I absolutely agree.

And it seems like you have a really interesting spot to be looking at lots of different firms and seeing what they’re doing.

I’d love to find out, are there specific examples you’ve seen of how professional services are already using AI in their businesses that you could share?

I think right now, there’s so much to metabolize right now.

Some of the best way is just to hear, oh my gosh, they’re doing that with that and it’s working.

What would you share with the audience?

You know, we have a lot of fractional executive firms in the community.

So, for example, the office of the CFO, and I’ll use this as one example.

When a company decides to outsource finance, you know, that’s a high risk, high stakes decision.

And for the longest time, the finance department was not outsourced by mid-sized companies or even big companies because the risk was perceived to be too great.

The only consumers of outsourced CFO services tended to be small businesses.

And what we’ve seen in the last six months is a dramatic change.

You know, these fractional CFO companies are moving up market dramatically.

And here’s why.

Most of those services, if you think about a finance department, there’s really four roles within that.

So there’s the CFO who’s doing strategy.

There’s the controller who’s doing today’s project.

There’s the accountant that’s worrying about the taxes.

And then there’s the general bookkeeper who’s worried about the chart of accounts and the transactions and all that.

These fractional CFO firms now are now consolidating four jobs into one because the tech automation opportunity there is dramatic in the vendors that are serving that space.

I don’t know if you’ve seen the machine learning capability of QuickBooks now, but I mean, it’s approaching like, sophisticated ERP like stuff used by the global 2000.

So now all of a sudden you’re a billion dollar company and you get a proposal on the table to outsource finance.

Normally you would say, no, it’s too risky.

Now you can take 90% of the cost out of the department.

I mean, maybe you give it a shot.

Yeah.

So I think the market opportunity that’s now available to these once perceived risky outsource opportunities is going away because the risk reward profile has been changed dramatically.

I mean, if I can take the cost of running my finance department down by 90%, I mean, how do I not at least take a hard look at it?

That’s one example.

And we’re seeing examples across all the spectrums, consulting companies, market research firms, I mentioned marketing agencies, IT services.

I mean, I don’t think it’s a coincidence that the managed service provider, the MSP, is doing so well right now because IT, similar to finance, is an outsourceable function.

And now if you’re a highly-nitched MSP who has tech enabled through AI, your value proposition is better, faster, cheaper than doing it in-house.

And it’s so dramatic now, this spread.

I mean, it’s not like a 10% improvement.

It’s so great that people are willing to take a chance on it.

So those are some examples.

That’s awesome.

Well, Greg, thank you so much for joining us.

It’s been a pleasure.

All right, well, thanks for having me.

It used to be the big eat the small, now it’s the fast eat the slow.

That was the quote.

It probably jumped out to you because it certainly did to me.

I think it’s the perfect encapsulation of where we are in this moment in time.

And I hope that in some small way, or maybe in a large way, we’re helping you move faster when it comes to AI.

Thanks as always for listening and watching.

Don’t forget to give us a rating on your podcast player of choice.

And we really appreciate it if you can leave a review or share this episode on social media.

Be sure to tune in next week for another something new.

We’re going to be trading places with Jeff Livingston’s No Brainer podcast and airing an episode of their show in our feed where Jeff and his co-host, Greg Verdino interview me.

I’m sorry, guys, you’re going to get more of me, but it’s going to be great.

We have an incredible conversation about creativity, relationships and AI.

You’re definitely not going to want to miss it.