Michael Davern with Jason Barnard on Fastlane Founders And Legacy. End-to-End AI Marketing Enablement

Michael Davern talks with Jason Barnard about end-to-end AI marketing enablement.

Michael Davern, CEO and Co-Founder of Incept, talks with Jason Barnard about how AI becomes exponentially more valuable with training, the shift from time-based billing to outcome-focused work, and why reps—just like in marketing—are key to getting reliable results.

Michael shares how Incept uses AI to speed up research, streamline client deliverables, and enable teams to focus on the 20% of work they truly excel at. He compares AI to an intern that, when trained well, becomes a high-performing digital team member capable of scaling capacity without hiring new full-time staff.

Michael Davern and Jason Barnard also discuss the risk of relying blindly on automation, the importance of internal policy and oversight, and the human-centered future of work. For Michael, AI does not replace people—it empowers them to be more creative, strategic, and human.

What you’ll learn from Michael Davern

00:00 Michael Davern and Jason Barnard
03:04 Why is the Brand SERP Result for Michael Davern Not Showing up in Google According to Jason Barnard?
03:19 Why, According to Jason Barnard, Does Google Think the Scientist is More Famous Than Michael Davern?
03:42 What Did Jason Barnard Do to Make the Name Michael Davern Appear in the Search Results?
03:52 What Does Michael Davern Need to Make Himself Appear as a Super Expert in AI Marketing?
04:18 What Does AI Marketing Mean?
04:55 What Qualifies as AI Versus Simple Automation or Workflow?
05:29 Why Did the Tech World Used to Be Strict About Distinguishing AI, Machine Learning, and Automation?
05:56 What Should a Business Leader Do When They Do Not Have Time to Implement Marketing Tasks Themselves?
06:17 Where Should a Business Leader Start to Guide Their Team Toward Strategies That Help the Business?
06:29 What Role Can AI Play in Helping Users Improve Workflows?
06:56 Why is it Helpful to Regularly Explore and Experiment With Evolving AI Tools?
07:16 What Tasks Cannot Be Automated but Could Be Handled by an Intern or Entry-Level Employee?
07:37 What is the First Step in Training Your AI Assistant to Handle Entry-Level Tasks Effectively?
07:45 Why is Research a Good Starting Point When Delegating Tasks to Your AI Assistant?
07:54 What is a Smart Way for CEOs to Begin Integrating AI Into Their Team Workflow?
08:10 What Makes Comparing AI to an Entry-Level Intern a Helpful Mindset for Business Leaders Adopting AI?
08:30 Why Do Many Businesses Hesitate to Adopt AI Despite Its Long-Standing Presence in Everyday Life?
08:59 What Can Business Leaders Learn by Creating a Custom GPT?
09:26 What Happens When You Customize AI Settings to Match Your Communication Style?
09:47 How Do AI Interactions Improve Over Time as the Agent Learns Your Preferences?
10:22 Why is it Easier to Accept Criticism From a Machine Than From a Person?
11:20 What Are Some Practical Ways to Start Using AI in Marketing?
12:20 What Does Effective Research Look Like When Working With AI Tools?
13:13 What Happens When You Interact With AI in a More Conversational Way?
14:30 What Are the Different Ways to Use AI for Research and Competitive Analysis in Business Strategy?
15:31 Why is AI Considered a Time-Saver in Fast-Paced Business Environments?
16:11 How Can You Make AI-Generated Results More Reliable?
17:27 What Steps Are Involved in Effectively Training an AI Model to Deliver Helpful Results?
18:14 What Are the Risks of Relying Entirely on AI Without Incorporating Human Input?
19:01 Why Might There Be Pushback on AI From People Who Fear Losing Their Jobs?
21:13 How is AI Enabling Businesses to Shift From Hourly Billing to Outcome-Based Pricing Models?

This episode was recorded live on video May 27th 2025

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Michael Davern

Transcript from Michael Davern with Jason Barnard on Fastlane Founders And Legacy. End-to-End AI Marketing Enablement

[00:00:00] Jason Barnard: There may be pushback on AI because people are frightened of losing their jobs because they don’t see that as an option. They think if I automate all of this with AI, I lose my job.

[00:00:14] Michael Davern: And some people will. There’s just some things that happen that are going to be automated to a point where it’s done as well or better than a person can do it.

[00:00:23] Michael Davern: I think I might have already mentioned, like what I told you yesterday is different than an hour. So we’ve gotta make sure we’re on top of that as well. But that is more about still the 80 20 rule. and one of our core values is always be on the better side of 20. So it’s really easy for me to talk in those 80 20, but if 20% of your job is what you love doing, and a lot of it, the rest of it is either monotonous or just something I have to do well, you don’t have to do that.

[00:00:49] Michael Davern: There’s automation. There’s AI learning that’s going to get better. And better for us to be able to have that more monotonous part of your position, not be something you have to do, and it’s going help us scale

[00:00:59] Narrator: Fastlane Founders and Legacy with Jason Barnard. Each week, Jason sits down with successful entrepreneurs, CEOs and executives, and get them to share how they mastered the delicate balance between rapid growth and enduring success in the business world.

[00:01:16] Narrator: How can we quickly build a profitable business that stands the test of time and becomes their legacy? A legacy we’re proud of. Fastlane Founders and Legacy with Jason Barnard.

[00:01:28] Jason Barnard: Hi everybody, and welcome to another edition of Fastlane Founders and Legacy. I’m Jason Barnard, and a quick hello and we’re good to go.

[00:01:37] Jason Barnard: Welcome to the show, Michael Davern.

[00:01:43] Michael Davern: Thank you, Jason. I can promise you this is the first time I’ve ever been serenaded as an intro into a podcast.

[00:01:50] Jason Barnard: Yeah. I think that a lot of people say that to me. And the nice thing is that I started doing this in 2019, so that’s six years I’ve been singing the intro and I recently became the lead singer in a band, and I’ve got better at singing.

[00:02:05] Jason Barnard: So if anybody listens to the very first episode and then this one, even in that short piece of singing, I think I’ve got better.

[00:02:13] Michael Davern: So we don’t need to go into chat and start a custom GPT for voice coaching.

[00:02:17] Jason Barnard: No thank you.

[00:02:18] Michael Davern: Okay. Okay.

[00:02:19] Jason Barnard: I hope not, but some people may disagree. They might say, actually you sing in tune, but, ah, I don’t like the style and I could learn some style.

[00:02:28] Jason Barnard: But that’s not the point of the show today. The point of the show is end-to-end AI marketing enablement. How to use AI for your marketing efforts within your company. And Michael, you were saying that you use your own AI on your own company and then help your clients with that. So this is processes that you’ve created, that work. And it’s what we do at Kalicube is that we apply to ourselves what we’re advising our clients to do. So when we’re optimizing a brand for Google and AI, it’s things that we’ve done either for myself, Jason Barnard, or the company Kalicube. But before we get into that, I’ll show you some Brand SERP.

[00:03:06] Jason Barnard: This is your Brand SERP. When we search Michael Davern in Google you don’t come up.

[00:03:14] Michael Davern: No, there’s far more important people with the same name out there, man.

[00:03:19] Jason Barnard: That’s it. The guy who does come up as a public figure is a scientist of some sort. So he’s on Google Scholar. That’s very powerful. Relatively speaking, Google is going to think he’s more famous than you are out of the box because he’s on Google Scholar and it isn’t that he is more famous, it’s that Google understands him better, recognized in his field. And so I then added your company name, Incept. And there I get you. And I think that’s what people do is if I don’t get you the first time, I’ll add your company name if I know it, and then I do get the result.

[00:03:52] Jason Barnard: But if I may, I really think you need a Knowledge Panel there on the right hand side to make yourself look the way you deserve, which is a super expert in AI marketing.

[00:04:03] Michael Davern: I don’t disagree with you at all, Jason.

[00:04:07] Jason Barnard: So we’re going to pick your brains on AI marketing. The first thing is, what do you mean by AI marketing?

[00:04:12] Jason Barnard: Because my initial research, I actually misunderstood, so please go ahead. Define yourself.

[00:04:17] Michael Davern: It goes everything from process to all the way, from automating tasks that we no longer should be worrying about to really going through getting deeper into insight, research, building strategy, content planning, supporting tactics, execution optimization, measurement, reporting, all the things we do in marketing. But where can we get some enablement to help us along. That’s really where it is. It’s to help us along. There’s a lot of back and forth on the AI piece that, especially since we were allowed to talk about it officially, what was it, last? January? January one of 24. Suddenly we got to talk AI, we didn’t have to say automation or machine learning.

[00:05:09] Michael Davern: We could start saying, oh yeah, we’ve been doing this forever and this was an automated task that we could take care of. And that’s I think going back what’s AI and what’s not automation and workflow is not necessarily AI. You can add AI elements into the mix.

[00:05:26] Jason Barnard: Right. No. We’d stop there so everyone can breathe.

[00:05:29] Jason Barnard: And say, what you were saying though is that we weren’t allowed to say AI, and there was a whole kind of debate about what’s machine learning, what’s automation, what’s AI? And there were lots of picky people getting really annoyed about using the wrong term in the wrong place for whatever reason. And now we can just say, AI encompasses the whole lot.

[00:05:46] Jason Barnard: We don’t need to worry about that distinction between automation, Machine Learning and AI. And I think that’s liberating. I think you’re right, and I hadn’t really thought about it like that. You’ve just given me a huge, long list of all the things that I could do, and I’m the boss of a company. I don’t do these things all day long.

[00:06:02] Jason Barnard: I’ve got a head of marketing and I’ve got a marketing team. Where do I start as the boss? Where do I start to look into this? Say, where? How can I guide my team towards something that’s going to help the business?

[00:06:16] Michael Davern: Start really easy with some workflow and some things that you’re doing routinely that you know that you don’t have the time or should be spending the time to do explore where you can get some help.

[00:06:29] Michael Davern: That doesn’t mean that we just put everything necessarily on autopilot. It might mean that we’re going to add some AI sidekick, I might call it, into the enablement of the workflow so that it’s learning more and helping get more efficient and even getting to mastering how you’re speaking with your AI and your prompts, letting your AI train you on some things as well, and giving you notifications when there’s a kink in the workflow or something that you can improve. And that really is getting some reps will come quick because what most people who start even just playing around for the first time fine.

[00:07:05] Michael Davern: Beyond all the things we do, like just asking questions and doing research and every week there’s a new update to every platform. So that could be video creation or image generation. So we all go and play around with those kinds of things. But it comes back to, I might have needed a year to hone in this process.

[00:07:21] Michael Davern: Now maybe I need a couple of weeks or a month. I’ve saved a lot of time. And then I’m going to move on into that piece about what tasks can’t I put on autopilot, what I could really use an intern for, or an entry level team. And then it’s about enabling training your AI agent of choice about who they are, what you want from them, and what kind of those more entry level tasks they could be helping with.

[00:07:49] Michael Davern: Research is really a good place to start.

[00:07:52] Michael Davern: And it’s the building blocks.

[00:07:54] Jason Barnard: You are saying building blocks. So you start with identifying the things that it can do. And then you build a block so it can do it. And you start with the simplest things. And a good way of thinking about it from a CEO’s perspective is entry level intern.

[00:08:11] Michael Davern: I know that a lot of us in the game, were talking about our AI being an entry level intern, more like 18 months ago, maybe a little bit longer, but it’s more comfortable to start with something that’s familiar and that is oftentimes what would I have an intern or an entry level employee doing?

[00:08:30] Jason Barnard: Okay. Which is brilliant. And I think one thing that we tend to forget is AI has been in our lives for years and we are very used to it. And we have the impression that if we’re not doing the cutting edge stuff, we are losing out.

[00:08:44] Jason Barnard: But in fact, 6% of corporations in America are actually using AI in any meaningful manner in the business. So 94% of people are still saying, they’re thinking, how do I use this? And they can’t make that leap. And I think, I found one of the nicest things I did was set up my own custom GPT on ChatGPT. I gave it instructions. One of those instructions was, don’t be nice to me. Be critical when I’m saying idiotic things and tell me how to help you. Help me. So give me instructions when I prompt you badly. And that taught me a lot about how to talk to the machines and what the machines are capable of.

[00:09:22] Jason Barnard: Would that be a good starting spot, is for me to understand better?

[00:09:26] Michael Davern: Absolutely. And that’s where when you get into the settings of whatever platform that you’re using is they’re going to vary in terms of how specific you can make those hard settings. And then as conversation goes and learning goes, you can instruct it. I would say I’ve had the skepticism filter turned on in ChatGPT for as long as I could. But as my agent gets more and more familiar with my style and the interactions that we have, even just yesterday I got back, can I really be forward with this? Can I actually take the gloves off and throw down with you?

[00:10:01] Michael Davern: I’m like, absolutely. And that’s the conversational level that I have with my chat agent right now. And then I get some real good feedback. And just anything, we don’t all love to hear about things that we could improve upon, but we learn a hell of a lot more from things we should improve than things that we’re winning in doing.

[00:10:21] Jason Barnard: Yeah. And being told by a machine that doesn’t have a soul or a personality and we don’t have to like it and it doesn’t have to like us, is easier than if you told me I was doing everything well.

[00:10:31] Michael Davern: We’re not talking to a person, so we don’t get all of the body language and things that could actually elevate our reaction.

[00:10:37] Michael Davern: Too constructive criticism either.

[00:10:40] Jason Barnard: Yeah. Which is really nice. So I think once we as entrepreneurs, CEOs, top level in the company understand how it can help us just with a conversation and researching and saying to the machine, find this out, research this. And I’ve been using deep research with Google Gemini, where it goes and crawls the web and tries to find an answer.

[00:11:01] Jason Barnard: And it gives this huge answer. It’s way too long. Once we get used to that ourselves, it’s easier for them to help the marketing team. So give me some pragmatic examples of starting points where people can implement AI in marketing that you’ve typically seen and been able to implement for your clients.

[00:11:20] Michael Davern: So we’ve talked about research already. It’s a incredible time saver. So whether utilizing deep research within Gemini, whether you’re utilizing deep research within ChatGPT, and again, a quick aside is that you asked about our starting point. My also really strong recommendation is pick one and go with it.

[00:11:41] Michael Davern: Just spend some time and get some reps within one platform. So not only is it learning and continuing the conversation and just building on the knowledge and getting to know you and your style or a caveat to that, your counter style, which I’ll maybe we’ll get back to. But when we look at that and then know that’s where we’re heading, pick the one. So then I’m pretty, pretty solid in, and we talked about this in the pre-call, in ChatGPT. You’re very dedicated and really committed to Gemini, which is great. We’re both getting very similar to very similar places with what we’re comfortable with and what we’ve built. But that’s the part is the research is testing. Test and see what it can do.

[00:12:25] Michael Davern: Then test the sources, look at everything and say, are you actually being accurate? Then once you get enough reps to know, okay, cool, you’re accurate and now I’ve created maybe a project. So now all of the learning and memory sits here in this place with this topic that we’re talking about. We can build on that and add to the research.

[00:12:43] Michael Davern: But when I talk about research, I’m talking about what we might need to do to get to know a client or what a CEO might need to go to know to about, but maybe that’s a prospective big client or even just their, marketplace. Maybe you’re in your position for the first time, you’ve got a lot of reps running an organization, but you maybe might be a little bit late on the industry knowledge.

[00:13:04] Michael Davern: Those are the places that will really shine to help build it up, and then you can parse it down into talking points. And really get it boiled down. And one thing I can say, my chat agent’s probably pretty verbose, but it pointed out to me that it’s verbose because I was, because it called me out on a 34 minute talking to it a few weeks ago.

[00:13:25] Michael Davern: And it was, Mike, but to be fair, our last interaction was five and a half. But your longest with me was like 34 minutes in. Some amount of seconds, but sometimes it’s that part of it is the conversational aspect, so that’s the number two is don’t forget to be human and conversational with the AI that you’re using because you’re going to get more human conversational back. So you’re going to get the work that maybe it would’ve taken 30 hours to do the research that a deep research project say when chat first launched, it might have taken 15, 16, 20 minutes to come up with.

[00:14:01] Michael Davern: Now it’s taking two minutes to come up with something as in depth. But part of that is those building blocks, like I talked about. You’re not just going to walk in to an experience with AI and get everything you want from it. And as probably you’re in the same position, we learned that because we’ve gotten so much learning and education in that building block. Now we get to the point where we get to say less and still get good results.

[00:14:27] Michael Davern: Because it’s learned what it is that we’re expecting from it. But that part of it, the research is a huge piece. Competitive analysis, a part of the research, and tying it in, building comparison chart. I like to see everything lined up for me so that when I ask, I want you to check this and these sources about this particular topic or problem, but I also want to do a comparison about how my competitors are doing it, what they’re focusing on, what a tool that I’m exploring, what are all the options. So it really takes what we might have been doing for even an internet search and having to silo everything and then bring it all together.

[00:15:05] Michael Davern: It’s taking all of those data points, bringing it all together in the way you ask it to, and you are so much further ahead from insight. And at least from our perspective at Incept, we’re sharing that information with clients as we’re talking through a project. I actually wouldn’t have been able to have this to you for two weeks, but because we did this and this, I got it to you and we’re meeting about it three days later. So not only is it topical and pertinent, so whether it is getting a process in place, whether it’s doing research for a deal to happen, whether it’s trying to close a new client, if you’re in our world, time kills everything and this is an incredible time saver.

[00:15:47] Michael Davern: So that is that next building block, is that proof of concept about reliable results in a faster period of time so we can do more with what we’re offering out to the world.

[00:15:58] Jason Barnard: The word reliable result, that’s key because hallucination, which is when the machines make stuff up or get it wrong, how do you make it more reliable?

[00:16:11] Michael Davern: I think it’s got to do really Jason with the learning. So if we go back to the analogy of the intern or entry level employee, if we have a tight job description and really set expectations, and then we allow that intern or employee, or in this case the AI agent you’re using to get more and more reps.

[00:16:33] Michael Davern: Reps is what gets us to where we want to be. No different than marketing in life, really. It’s about the number of times that we need to do something to perfect something. We just have a tool that can help us get that to scale at a much more rapid pace. And as we get those reps, the reliability’s there, because we’ve both been in a situation I’m sure where, no, that’s not everything. And I know it’s not. You missed something here and then that is a trainable moment where, again, ’cause it’s not human, we don’t have to be as nice as we might need to be for interns or employees. I didn’t say that, but it can be like, no, you missed the mark. I went and checked your work and you missed X, Y, and Z.

[00:17:09] Michael Davern: That might have been me five or six months ago on some topics. I don’t get that anymore because we’re at the point where it’s not there. But it is making sure you’ve got your checkpoints because if you let your AI take everything over without getting it where it needs to be, you will be wrong 80% of the time.

[00:17:27] Jason Barnard: What I’m hearing is there’s a lot of work training the model. And so you look at that and you say, okay, I need to treat this like training my dog. It takes time. It has to go back. We have to keep doing it. We have to check that it’s right. And the training takes time. But once the dog, ChatGPT, AI is trained, it’s incredibly helpful. And I think one mistake people make is to leap in and expect those brilliant results right after saying create your LinkedIn profile or LinkedIn Post. And the AI isn’t particularly inventive, it sounds like AI. And that problem comes from lack of training.

[00:18:04] Michael Davern: And competitors of ours who are doing that right now will take care of the market themselves ’cause they’re not going to be in business anymore.

[00:18:12] Michael Davern: And just if we go into a client and anyone, any entity, any person that’s full on using AI for everything that they do is in trouble because they’re not going to get everything they need. You’re going to lose the human touch. I almost look at it as if you’ve got the reps, you’ve actually got your AI letting you be more human.

[00:18:34] Michael Davern: Because the 20% of what we shine at, we shine doing and love doing, we get to do more of. And that really is where we want to get people is that, why would you not want to love almost a hundred percent of what you’re doing and only versus only 20? Let’s just amplify that 20 that makes you. And this is an incredible tool, regardless of the platform to be able to do that.

[00:18:59] Jason Barnard: Yeah, which is a brilliant point, but it actually brings me to a rather negative point, which is there may be pushback on AI because people are frightened of losing their jobs because they don’t see that as an option. They think if I automate all of this with AI, I lose my job.

[00:19:16] Michael Davern: And some people will. There’s just some things that happen that are going to be automated to a point where it’s done as well or better than a person can do it. But when we look at, I’m going to pick on my world and I look at my team. And everyone is in our ChatGPT Team account. Everyone has expectations we’re we have policy, which is always evolving because the technology we’re using is evolving.

[00:19:41] Michael Davern: I think I might have already mentioned, like what I told you yesterday is different in an hour, so we’ve got to make sure we’re on top of that as well. But that is more about still the 80 20 rule. and one of our core values is always be on the better side of 20. So it’s really easy for me to talk in those 80 20, but if 20% of your job is what you love doing, and a lot of it, the rest of it is either monotonous or just something I have to do like I don’t know any proper digital, let’s say media strategist who loves doing reporting. They like strategizing and executing and seeing what happens, but putting it all together and having to hit that deadline, that’s challenging. We don’t have to, you don’t have to do that. There’s automation, there’s AI learning that’s going to get better and better for us to be able to have that more monotonous part of your position, not be something you have to do.

[00:20:31] Michael Davern: And it’s going to help us scale. So what I see more of, and I’m just being real about this, is it’s going to slow down our need to add FTEs. In my world, if we get a brand new account, because my chat agent, for instance, is a team of seven people wrapped into one. We’ve gone from intern to I’ve got a team of seven people, and the lead is a reflection of me, which is great.

[00:20:56] Michael Davern: It won’t be great for everybody, but that’s what works for me. And as we, then help enable our team to use it, they’re building out a sub-team. So then it’s going to be a very weird, like how many people are in your agency? it’s 15, but it’s really more like 50. What do you mean by that? Because of the enablement, I can come to market, I can now offer you this kind of cool pricing plan.

[00:21:18] Michael Davern: That’s a flat rate because we’re going to really focus on outcomes and not on ours because none of us want to do our time sheets anyway. That’s another thing that no one in an agency or an attorney or anyone who’s working in the hours of time against projects, we’d love to all get away from that.

[00:21:33] Michael Davern: That’s where we’re headed. And I think it’s more about the adaption and not everyone’s going to be on the same page, but Jason, as we all know, but looking at my business, I don’t need a tiny little sliver of everyone to be successful for myself, my company, and my employees.

[00:21:49] Jason Barnard: That’s a brilliant way to end it.

[00:21:51] Jason Barnard: Thank you so much, Michael. That was great. Thank you everyone for watching. I’m taking a lot away from this and definitely AI feels to me like something I’m increasingly comfortable with from an execution perspective because at Kalicube we’re actually educating the AI from the outside, which is a completely different angle on it.

[00:22:13] Jason Barnard: And ironically, I’ve actually been quite ill at ease using the AI to create content and to create reports and so on and so forth. But that was absolutely brilliant. Thank you so much. You get the outro song as well.

[00:22:24] Michael Davern: Oh, awesome. Thank you.

[00:22:25] Jason Barnard: A quick goodbye to and the show. Thank you, Michael.

[00:22:32] Michael Davern: Thank you, Jason.

[00:22:33] Jason Barnard: That was brilliant.

[00:22:34] Michael Davern: Take care.

[00:22:35] Narrator: Your corporate and personal brands are what Google and AI say they are. We can give you back control. Kalicube.