On this week's Inside Outside Innovation, Robyn and Brian sit down to talk about the AI question that no one wants to answer, the power of a good value proposition, and why industry veterans are building tomorrow's billion-dollar startups. Let's get started.

Inside Outside Innovation is the podcast to help innovation leaders navigate what's next. Each week we'll give you a front row seat into what it takes to grow and thrive in a world of hyper uncertainty and accelerating change. Join me, Brian Ardinger and Miles Zero's Robyn Bolton. As we discuss the latest tools, tactics, and trends for creating innovations with impact. Let's get started.

Podcast Transcript with Brian Ardinger and Robyn Bolton

Opening Reflections on Innovation and the Year Ahead

[00:00:30] Brian Ardinger: Welcome to another episode of Inside Outside Innovation. I'm your host, Brian Ardinger, and we have Robyn Bolton, our co-host from Mile Zero. Welcome, Robyn.

[00:00:53] Robyn Bolton: Thank you. Great to be here as always.

[00:00:55] Brian Ardinger: We've got the number of different articles we're going to talk about today. As everyone knows, this podcast is about giving the real insights of what's going on in the world when it comes to innovation. What's going on in your world? 

[00:01:07] Robyn Bolton: It's funny, it's time of year, so kind of feels like everyone is both wrapping up and gearing up, trying to bring things to a close. So, we can all effortlessly and go on the holidays, but January we'll be here before you know it. And so people are already starting to think about what's going on with AI in 2026, and what does the new world of work look like?

[00:01:29] Brian Ardinger: I'm looking forward to my inbox being filled with the best things that happened in 2025 and what to look forward to in 2026, and like kind of year-end wrap stuff that you get. It's interesting times, especially like on the investment front, you know, a lot of things slow down at the end of the year as people start planning for it. I kind of love and hate this time of the year from the standpoint of, gives you some time sometimes to do that stuff that you don't always have time to do and remap what you're going to do for 2026. 

[00:01:55] Robyn Bolton: Yes. And speaking of the emails, wrapping things up. Spotify's Yearend rap came out I think a couple days ago, so also getting a lot of those in the old inbox.

[00:02:07] Brian Ardinger: Alright, well let's get into it. We've got a couple of articles to talk to today. The first one that we came upon was from KP Ready. It is called the AI Question. Nobody wants to Answer and KP does a good analysis. He basically says, is the juice worth the squeeze when it comes to AI. And I think a lot of people are asking that question right now.

You know, as more and more enterprises, you're hearing about more and more experiments, more and more people using the technology, and you're getting conflicting results and feedback on is this really paying off. All the money that's being spent into ai, all the things that we're doing around it. Are we seeing the returns and when will we see the returns?

You know, from my understanding and what I've seen, the question is not like, will we receive returns, but when and how do we get through this exploration phase so that we can be effective with using the dollars and the time and the resources around this to actually find the value that's created. And so let's start with that particular article. What was your thought on it?

Is the Juice Worth the Squeeze? AI ROI and Experimentation

[00:03:04] Robyn Bolton: There's always this level of uncertainty around new technologies of is the juice worth the squeeze? Are we gonna get ROI? When are we going to get ROI? Running lots of experiments, but it definitely seems like AI has kind of amplified that. I actually just wrote a blog post asking, like, did your AI strategy, was it developed by the underpants gnomes?

And just in case there, we have listeners who don't know who the underpants gnomes are, they're from South Park, and basically their business plan is phase one, collect underpants, phase two, question mark, phase three profit. And it just seems like there are so many AI startups, companies, experts, consultants, et cetera out there who have become underpants gnomes. And kind of just have this like, hi, put it on top of everything, and profit, and no one's kind of slowing down to kind of like, well, do we even need AI? How do we need it? Like, what makes sense here? 

[00:04:04] Brian Ardinger: I think a lot of people are not necessarily thinking. They feel the pressure to start doing something with AI, and so they start immediately deploying and doing things without looking at, well, is this a particular area that really would benefit? Or could we create real value if we can get this right? And they oftentimes overlook some of the other kind of hidden costs when you talk about it, deploying technology that's new or different.

And I think more importantly, how it affects the culture of the people deploying it. So, you've got the, you know, the data infrastructure costs, you've got the integration complexity, you've got the change management, ongoing maintenance, all these kind of hidden costs when you're dealing with a brand new technology that you don't necessarily know.

And some of the things I saw in the article that were interesting and I've seen in real life too, is how you can kind of maybe think through this process of, you know, which particular project should we deploy and that? Don't be afraid to kill experiments quickly if you're not seeing ROI in a particular area, you know, maybe shelve that particular idea and focus on one that is showing some value to it.

Don't focus on having to deploy it everywhere, all at once. Again, try to find the particular areas or the particular people that are more willing and able to make those steps. And then thinking about that, you have to build everything yourself.

I think that's another place where I'm seeing a lot of brand new tools and folks out there that are trying things that have already built some things that maybe you can go out and purchase and buy and experiment rather than having to come up with your own team to do and make all the mistakes that they're probably already going through it and made that tool in the first place.

[00:05:00] Robyn Bolton: So, all great advice, and I just want to underscore the people aspect is you have people who are going to use this, people that you hope will benefit from it. Some people who will be resistant, and so don't underestimate the people, the human. AI interaction and all of those dynamics as part of the rollout, the implementation, the change management, all of that.

Sharp Value Propositions in the AI Era

[00:05:30] Brian Ardinger: All right. The second article is from our good friend Ben Yoskovitz. He always puts out some great stuff. His article on his substack called Focus Chaos is called the Real Differentiator in the AI era, A sharp specific Value proposition. Ben talks about how a lot of folks are falling into particular traps of, again, thinking that, well, I've got AI so hot, I'm going to be deploying AI, and let's go out there and build a startup around that, and without thinking about, well, what are we actually building? 

Why are we building it? What's the value proposition? And it's led to a lot of generic types of workflow automation tools, things that aren't necessarily differentiated from a customer perspective. And so, his bet is that, you know, the sharper, more precise, the value proposition, the higher likelihood that you're going to survive and thrive. And so, I'd love to hear your thoughts on that. 

[00:06:52] Robyn Bolton: Yeah, it was interesting because I've been reading about how AI is going to usher in a new age of generalists. And then so to see Ben come out and say, oh no, it's about the specialists.

As I read more about it and you know, Ben's a great writer, I realized that there's absolutely the need for both and the case that Ben is talking about of creating the AI tools. I do agree with him that like the specialist of creating for a very specific use case and that person who has lived it, you know, some of the examples that they gave of a nurse who created an AI for a specific context.

That makes a ton of sense because then you know, as a user, your odds of getting higher quality output are probably much, much higher because an expert has created this. And especially, I think in corporations, you're going to need generalists who can see the value and can integrate all of these specialists. Sort of AI agents and perspectives into something that works at scale. So, it was a very, very interesting, provocative article to read. 

[00:07:58] Brian Ardinger: One of the things I liked about Ben is he kind of encapsulated like, how do you talk about your value proposition and make it into concise, quick bullet point kind of thing where if you're talking about to somebody, they can quickly understand that.

And so, his kind of Mad Libs approach, he had a sentence that basically said for your specific audience who whatever specific context or pain they're having, our product delivers this outcome by some unique mechanism or approach so that you can actually create some specific business result and you can put your mad lib into that particular sentence.

And if you can do that, articulate that quickly and repeatedly, you can probably have a chance to have better conversations when you're going about it and hopefully hit the mark faster than if you're just out there doing whatever it is from a general perspective.

Specialists, Domain Expertise, and the Future of Startup Creation

[00:08:44] Robyn Bolton: And it's good advice, I'd say, for any business owner because I tried to do that with my business and failed miserably, but took that as a learning and was like, all right, this is work I got to do. 

[00:08:53] Brian Ardinger: I think that's a great point because a lot of times I think we think about, well, we have to have that perfect mad lib. We have to have that, and rather than thinking of it, it's got to be perfect the first time out, and this is what the company is, and this is where we're going to be.

Think about it as a starting point and or this is our best guess at this point. And then test it. Try it. The more you talk to people and realize, oh, that's not the right specific niche or specific domain expertise I should be focused on. But by having that conversation with a more predefined, easily understandable force has a better chance of having those conversations that points you in the right direction if you are off track.

[00:9:30] Robyn Bolton: Absolutely, a hundred percent.

[00:9:45] Brian Ardinger: And the third article actually kind of overlapped some of the stuff that Ben was writing about, but Wildfire Labs has an article about the domain expert revolution and why industry veterans are building tomorrow's billion-dollar startups.

And again, this shift from the generalist to folks who maybe have been in business for 15, 20, 25 years who understand the problems, things that are going on, that can actually then use that domain expertise to create something of value and create a startup or innovation that really has an impact.

[00:10:01] Robyn Bolton: And just to illustrate how much overlap and compatibility there was between Ben's article and the one we're talking about now, I confused the two. It was the Wildfire article that talked about a 45-year-old nurse who spent 20 years watching hospital workflows break. Another example in there of wildlife fighters who built a resource coordination platform.

So, it just reinforces what Ben was talking about of the domain expertise that is going to become more and more important as AI platforms, tools, agents, et cetera, become easier and easier to make. You're going to need to get more and more specific on what your value truly is.

New Entrepreneurship Paths and Corporate Innovation Ripples

[00:10:43] Brian Ardinger: Well, and I think it opens up a lot of different new entrepreneurship opportunities for folks who may not have thought of themselves as entrepreneurs. The person who's worked in a company for 20 years, who knows the regulations and how to navigate that. They know which particular solutions are out there, the competitors, et cetera.

Maybe they have an opportunity to become an entrepreneur and start a startup with these new tools and tactics out there to leverage up the domain expertise and actually solve the problems that are really important in the marketplace.

[00:11:12] Robyn Bolton: And it may completely change a lot of things, but even corporate innovation because now the people within companies or people within organizations like this nurse or the firefighters who have been frustrated for a while, but trying to get traction from the bosses and funding.

And they don't need to wait for all of that to go create a solution, and that could have some really interesting ripple effects on how employees start viewing themselves as entrepreneurs. How corporations start viewing open innovation. What open innovation starts to look like in terms of what people have to show and produce. It's not just about the idea. It's easier to create a prototype. So, there's some interesting ripple effects that could come from this as well. 

[00:11:58] Brian Ardinger: And the last thing that he talks about in this article, I guess from a investor perspective, he said stop recruiting Stanford dropouts. Start recruiting industry lifers. You know, look for folks who can actually, you know, rather than just build the app, understand what it means to create the value that the app will provide out there. Another way to think about it.

Tactics to Try: Real-World AI Use During the Holidays

Excellent. Well, we're now in our section of tactics to try and I'll throw out a short one. So we're heading into the holiday party season and I had one last night and I've got I think two or three in the next week or two. Industry parties and gatherings. And so I thought it might be an interesting tactic to try just to start tracking my conversations around these topics of AI.

What are the real tools and tactics that people are actually using, you know, outside of ChatGPT for example, what are people using this for? You know, maybe talk to your colleagues talk to your friends and say, Hey, are you using this stuff? We're hearing a lot about it in the press and that, but like fundamentally, how are you using it?

Are you using it for work? Obviously, I've seen a lot of new stuff coming out of the Black Friday sales, where AI shopping research is on the rise. Have any of your friends used AI for shopping, travel, et cetera? So, I'm going to be taking the holiday season and sprinkling in my conversation to find out how are people actually using these new technologies out there.

[00:13:11] Robyn Bolton: So great minds think alike. I've been doing the same thing. Asking people about how they're using AI. Are they using AI? How are they using it in their companies? And all the press, we hear you, and I talk all the time about AI, so I'm always amazed if people are like, yeah, no, I'm not touching that thing.

And it's for good reasons. You know, they're afraid of being replaced by AI, but there's also so many other uses of like, I have these five ingredients in my kitchen. What can I make for dinner tonight? So, it's just interesting to ask the question as you said, and find out the real everyday reality of where AI is versus what we're hearing and seeing in the media.

Closing Remarks

[00:13:51] Brian Ardinger: Absolutely. Well, that's another episode of Inside Outside Innovation. We'll be talking about AI I'm sure in coming days and weeks. Always appreciate everyone who comes out and listens to this, and we look forward to seeing you next time on Inside Outside Innovation. 

[00:14:04] Robyn Bolton: See you next time. 

[00:14:07] Brian Ardinger: That's it for another episode of Inside Outside Innovation. Today's episode was produced and engineered by Susan Stibal. If you want to learn more about our teams, our content, our services, check out insideoutside.io or if you want to connect with Robyn Bolton, go to MileZero.io, and until next time, go out and innovate.


Articles Discussed

The AI Question Nobody Wants to Answer - KP Reddy