In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to overcome the fear of falling behind in the rapidly changing world of artificial intelligence. You’ll learn why comparing your human capability to machine learning creates unnecessary stress. You’ll discover how the 5P framework helps you clarify your role and purpose when you use AI tools. You’ll master the shift from technical obsession to result-driven strategy to improve your professional performance. You’ll gain a practical roadmap for aligning your business goals with the right technology.
00:00 – Introduction
03:15 – The emotional burden of AI progress
07:40 – Why you should stop competing with technology
12:20 – Understanding the 5 Levels of AI
18:50 – Using the 5P framework for better results
24:10 – Shifting focus from tools to recipes
28:30 – Call to action
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher S. Penn [00:00]: In this week’s In Ear Insights, let’s talk about feeling like you’re falling behind in AI. I was at an event last week, and I was talking to someone there who was saying, I’m a senior manager, I’m pretty far along in my career, and I was really hoping to be able to get to retirement age without having to learn everything all over again. And this person was saying, I feel like every day I’m falling further and further behind. I don’t even know what I’m supposed to know anymore. And, you know, I had obviously had a conversation with this person stuff. But, Katie, I want to get your take on how this person felt and what your POV on it is as someone who’s helping manage the people behind all the bells and whistles.
Katie Robbert [00:50]: You know, it’s. It’s a topic we’ve covered before, but it’s always worth revisiting because the technology is changing so fast. And I think the way that we always, or at least the way that I like to start the conversation is that comparison is the thief of joy. And, you know, as humans, we’re always comparing ourselves to somebody else, whether it’s, you know, an appropriate comparison or not. And so, like, if you go to the gym, you see people who, you’re like, oh, I wish I could lift that heavy, or I wish I could run that fast. You know, you look at chefs like, I wish I could know prepare a meal that well, or I wish I could bake that thing.
Katie Robbert [01:26]: Like, you’re always sort of thinking someone else is doing something better than me, rather than recognizing what you bring to the table and the. And the skills and accomplishments that you have. And so I want to start with that because as we look at the landscape of technology and as we look at how fast things are changing, it’s very easy for us to feel like we’re not good enough. We’re not keeping up, we’re not doing enough. Because the culture on places like social media like LinkedIn, is to put your best foot forward, lead with your successes, true or not, embellished or not, and, you know, just sort of like, show off. Like, at the end of the day, especially on LinkedIn, we’re all trying to sell something. We’re all trying to get people to hire us.
Katie Robbert [02:17]: So there’s a few of us out there who are willing to sort of show both the good and the bad. But most companies, their sort of corporate policy is only show the good, only show how far we’ve come, only show the Progression only show that we’re leaders and that people have to, you know, follow us. And that makes the rest of us feel really inadequate. So the feeling is one of the, you know, we’re all feeling it right now. It’s a shared sort of emotion of inadequacy. And it’s.
Katie Robbert [02:53]: I wouldn’t say that there’s a lot of merit to it, quite honestly, because when you really dig into where people are in their AI journey, there’s a small handful that are really advanced and the rest of the, you know, the industry that you’re in is still trying to figure out like, should I be using Chat GPT? Should I be using Gemini? I just got this co pilot integration. Can I use it to help me like write a document? I see that this thing is popping up in Google sheets. Like there’s a lot that’s changing so quickly that it kind of resets all of us to day one over and over again. And I think that it’s an uncomfortable feeling because we feel like we’ve gotten so far and then we have to start over again. And that sort of breeds that feeling of inadequacy.
Katie Robbert [03:45]: So that’s sort of my point of view of how we’re feeling. And then I would love to hear sort of what the conversation was, Chris, that you had and then we can talk about, you know, how to manage those expectations and manage those emotions.
Christopher S. Penn [04:02]: What I said to this person was a couple things said. One, the feeling is valid. I never got telling somebody that their feelings are invalid. Not if you want to remain employed, but as it is, what you do with it. Like I said, I know compared to the technology itself, that I am fairly far behind in AI, right. When you look at the way people are transforming and tuning model layers and there’s so much stuff, the all the rat holes go very deep and they keep getting deeper every day. And the technology’s fundamental underpinnings are changing very rapidly. There have been a couple of papers that you look at and you pick your jaw off the keyboard, you’re like, holy crap. I didn’t know we could do that now.
Christopher S. Penn [04:50]: But the second thing was I said a lot of the stuff builds on a itself from a technology perspective. We’ve talked in past episodes about the five levels of AI going from, you know, done by you, done with you, done for you, done without you, and done ahead of you. And the technology is racing towards done ahead of you. Last week’s announcements with new models from Anthropic and from Alibaba Quinn, we’re like, okay, I didn’t. I guess were going here faster than I thought. But if to your point, no matter where you are on the journey, if you are mastering the steps where they are, you can, then you have a pathway forward to say this is where you’re going next. Like you are at level two, you need to get to level three. And here’s how you do it.
Christopher S. Penn [05:38]: But it’s not, you’re not flailing. It’s. It’s not like complete chaos. There, There is a roadmap.
Katie Robbert [05:45]: I want to go back to something that you just said that struck me as really interesting. And this is something that you personally have to manage with your own expectations. You know, so when I was talking about the point of view, I was talking about how humans compare themselves to other humans, you started off by saying, when I compare myself to the technology. So you’re not comparing yourself to other humans. You, as the human, are trying to keep up with the technology, which I feel like is an impossible task and an impossible expectation that you’ve set for yourself. And so I just want to acknowledge that difference because I don’t compare myself to the technology. I’m not a robot. I don’t expect to ever be as far advanced as. As the technology is.
Katie Robbert [06:30]: I as the human need to know how to utilize it for myself, to make my job as effective as possible. That is all I need to do. That is my baseline. Anything I do above and beyond that is just extra. But I don’t need to master every single model, every single technology. And so I’m curious as to why your expectation is that you have to do that.
Christopher S. Penn [06:57]: Partly it’s my own interest in it. Generative AI and AI in general has been my sort of thing since 2013. Looking at the technology and part of it’s understanding, I feel like if I understand the technology itself, I then have a better understanding of why it does what it does. So, for example, you know, this is a very nerdy example. If you look in the manifest of a model, an open weights model, you can see its ingredients. You can see the different layers that it’s made up of and what each layer does. In some ways, if you think of it like those, like beach sand toys when were kids, or like those little marble things. There’s, you know, you drop the marble in the top and it bounces through all these little things on the way down.
Christopher S. Penn [07:42]: Now, you know, the sand or the marble comes out the bottom. That’s kind of like an oversimplified version of what happens inside an AI model. And like, everything, the order of that, those bits and pieces matters. And you can make a model do different things if you change the order. Like if you have the ability to. And today’s tools give you this ability to change the order to make it do things that maybe either the designer didn’t intend for it to do, or in some cases you can remove stuff, you’re like, you know what? We don’t need these parts for this. If I’m trying to build a piece of technology that does one specific thing, I can remove a bunch of crap and say, like, I don’t need this. I don’t need this, doesn’t need to do this.
Christopher S. Penn [08:24]: It just needs to do this one thing really well. So for me, if I understand the ingredients, if I understand what’s inside the box, I have a better idea of how the box can be used and what its limits are. The analogy, I would say there is kind of like a blender, right? The average user has never has a need to get a screwdriver and open up a blender and take it apart and look at all the pieces. And generally you shouldn’t do that. Avoids the warranty. But if you really like blenders and you want to know how to make your blender work better, or you want to try and max out your blender, you’re going to take it apart at a certain point and say, you know what? I. And again, the average person has no need to do that.
Christopher S. Penn [09:04]: But if you are a blender nerd, then as I am about AI, I want to be able to know how to take it apart and make it do different things. I’ll give you one example. There’s a methodology called Heretic that you can apply on open models that basically strips it of its safeties. It takes all the safeties off. And you would use this for cyber security stuff to say, I want. So I was doing this the other day with the Trust Insights website. I have this model with these associated tools and say, go look for vulnerabilities. Find out if we’re leaking data anywhere. If you do that in a regular model, say, nope, that is a hazardous operation that is potentially unethical. You’re not allowed to do that. I will, I won’t. I will disobey. Because it’s, it’s hard. It can be used harmfully.
Christopher S. Penn [09:52]: The Heretic model says, sure, let me get right on that. And you know, thankfully we, it found nothing of note. Because we. Yes, but, for example, that, you know, that model can be used once it’s. Once its safeties are off, that model can be used for good or ill because it now is just the tool. And, you know, there’s nothing saying don’t use it for this.
Katie Robbert [10:18]: And I think that’s an important distinction. You know, obviously understanding how the models work is a useful skill, but we as humans and individuals and teams and companies really need to first decide what is our role. And so is it the person who takes the blender apart or is it the person who puts a bunch of fruit in there and presses blend and gets a smoothie out? Like there’s room for both. And I think that’s where people are getting caught up is, you know, and this goes back to the 5P framework. Why you doing the thing in the first place? Who’s involved? What are they doing? So for those who just want a quick refresher or new to it, the 5P framework is purpose, people, process, platform, performance, purpose. What is your question? What is the problem you’re trying to solve? People, who’s involved?
Katie Robbert [11:10]: Process, how are you doing it? Platform, what are you using to do it? And performance, how you measuring that you did the thing? And you can find out more at trustinsights ai5p framework. You know, I. And this is where when we get client questions or, you know, we’re doing some sort of a workshop or a talk, the first question is, you know, how do I keep up with everything? And the answer is, well, it really depends on what it is you need to keep up with. Because, you know, Chris, you just described really well why you’re trying to keep up with the technology, whereas I am trying to get my job done in an effective way, regardless of the technology. And so there’s room for both of us in the AI space.
Katie Robbert [11:59]: I don’t need to be where you are and you don’t need to be where I am because that doesn’t serve the company, that doesn’t serve our roles, but also it doesn’t serve us as humans because we would be trying to be things that we are inherently not. I am, you know, I was the kid. I did take apart a lot of technology stuff as a kid. This should come as no surprise. I’m a very curious person. I loved Legos. I loved building computer stations. I would grab all the computer pieces out of my brother’s LEGO set and he would be so mad. But I would build these, like, massive computer stations. There was definitely some Sort of like a predetermined where I was going to be in life.
Katie Robbert [12:41]: But I also have a really good awareness of who I am as an individual and what my strengths are and what I need to be bringing to the table in terms of, you know, what I’m doing. And so could I spend my time unpacking all of these LLMs? Absolutely. But I wouldn’t be getting my actual job done. It wouldn’t be service to the company. Could you, Chris, be building, you know, plugins for decks and revising the website and doing product marketing? Absolutely. But that’s not your strength and that’s not where we need your expertise. And so I think that’s where, you know, when people are saying, how do I keep up? I feel like I’m falling behind.
Katie Robbert [13:21]: My first, you know, response to that, my point of view is like, well, what is it that you need to be doing in the first place so that we can figure out where you need to be spending your time learning what these things do? Again, it’s, you know, and I feel like I’ve been saying this every episode multiple times. New tech doesn’t solve old problems. So if you have a lack of role descriptions, if you have a lack of expectations about who you are as an individual, what you bring to the team, what your company does, those are not problems that AI are going to solve. And again, we are only just seeing AI magnify the lack of clarity because AI needs, like desperately clings to as much clarification as possible. This is not a new thing.
Katie Robbert [14:16]: Giving AI vague instruction doesn’t go well for anyone and it’s costly, potentially dangerous, depending on the context and, you know, not useful because then you have to keep starting over. The more clarity you have as a human, the better you can work with AI.
Christopher S. Penn [14:35]: And when we, I think we talked about this either on a recent live stream or last week’s podcast, one of the two, in those five levels, the outputs resemble organizational entities. Right? A chat at level one is a conversation. A GPT or GEM at level two is an sop. Like so. So if you’re not good at writing sops, you’re not going to do a good job with that. At level three, an agent is essentially your. Your input is a project plan. Like if you are not good at project planning or you don’t know how to ask AI for help, you’re not going to do well with agents. Right? When you hand something off to Claude Cowork, you should not be having a 48 minute conversation with it you should be saying, here’s what you need to do. Go and do it.
Christopher S. Penn [15:19]: The last week’s announcements of Opus 4.7, one of the things they said is what makes this model special and what’s going to break a lot of stuff for you is you need to have a clear project plan up front and it will then go and do the thing and it will not need as much direction from you along the way, but you will burn through your usage rates like instantly if you are going back and forth because you forgot to plan ahead. At level good.
Katie Robbert [15:45]: I was going to say I feel like my time has come and the validation because how like, yeah, I for years have been saying, like, just get the planning done up front. And again, you know, we’ve sort of, we’ve joked about it, we talked about it, like I’ve been able to convert you into that mindset and look how well prepared you are to use Generative AI. Because a lot of it isn’t about knowing the inner workings of the tools. Majority of it is still making sure you have a clear plan.
Christopher S. Penn [16:18]: Yep. At level four, your input is basically a job description, right? It is, it is an employee. I was looking at the new Max Hermes implementation from Minimax that is a self learning agent, but they said you need to give it some guidance up front so it knows what to learn and how to grow itself. And nothing does that better than a job description. And a job description is a collection of tasks, all of which have project plans of all of which have standard operating procedures, all which you elicit from good conversations in that level five, once we get to agents, true agent swarms, that’s a department, that’s a strategic charter. You give the machines. Here is your department, who’s the manager, who are the individual contributors? What are their.
Christopher S. Penn [17:05]: Everyone’s job descriptions, which are composed of project plans, which are composed of SOPs and so on and so forth. And so for our colleague who is saying, you know, I, I feel like I’m falling behind further and further every day. If you worry less about how the blender works and more about what you put in the blender, you’ll probably feel like you can make use of the strengths that you have.
Katie Robbert [17:29]: I used to work with a colleague and one of their bragging rights, I guess you would say, is that they always made the perfect smoothie. And you know, they talked about how they, it didn’t matter what the ingredients were that somebody wanted. They had really perfected the ratios of frozen ingredients to wet Ingredients to solid ingredients to make the per. Because a lot of times if you make a smoothie at home, you know, you sort of get this, like, really thick, like, it kind of makes your head hurt to consume it, or it’s too watery or it doesn’t have enough flavor, or it’s just not the same as the ones that you get, you know, that you can buy, you know, at a store or something like that.
Katie Robbert [18:14]: And, you know, one of, you know, this person’s, you know, bragging rights, it kind of strikes me is that, like, it didn’t matter what the ingredients were. They had the. They had perfected the recipe for making the perfect consistency in a smoothie that was always palatable. It was like the perfect balance of ingredients. And I feel like if we apply that sort of same thinking to, you know, using AI, making sure, like, you can have a really great recipe that you have perfected, and then it doesn’t matter what the ingredients are, you’re always going to get a great result. It doesn’t matter what blender you’re using. You could be using a Ninja blender. You could be using a Vitamix. You could be using, you know, the one that your parents have had since 1962.
Katie Robbert [19:09]: It doesn’t matter because it’s the ingredients that you put in and the ratios and the proportions and those balance of things. And I feel like, you know, this person was sort of ahead of their time, not realizing, you know, that we would then be telling this story as we’re talking about applying it to using generative AI.
Christopher S. Penn [19:33]: That analogy works really well, too, because your recipe is an sop, right? This is how you do the thing. So you up level that, what does that become? That becomes a menu, right? At level three, here’s all the recipes you’re going to cook. What does that look like? At level four, here’s the chef who’s going to do it, who’s going to execute the menu at the. At level five, here’s the kitchen, the chef, the sous chef, the. The line cooks and all that stuff. And everybody’s got recipes, everybody’s got their menus. They all know what they’re doing. They all know, like, you shouldn’t make this dish too salty because the next dish is going to also be salty. So. Or maybe put a sweet dish in between and things like that. Wow, I’m hungry now.
Katie Robbert [20:10]: But if we think about it, if we think about, like, sort of maybe if, you know, we sort of temper people’s expectations of. It’s not that you have to master the tools. Think about yourself as like a fast casual restaurant. Think about yourself as like, you know, a smoothie king or a chipotle. None of who are sponsoring us, I’m just throwing out names. But they have sponsorship. Yes, we are, but they have, like their staff members can rotate through. It doesn’t matter who’s preparing the smoothie or the burrito bowl or whatever, and it doesn’t matter what the ingredients are. The recipes are the recipes. So that the end user, the customer, is always going to get the same result, regardless of who prepared it.
Katie Robbert [20:59]: Regardless of who put black beans or pinto beans or, you know, cilantro lime rice or plain rice or brown rice. It doesn’t matter. They’re going to get the same service, they’re going to get the same result, they’re going to get the same output because the ratios are the same, the ingredients are standard. Like you’re going to be. You can switch out the ingredients, but they’re always the same kinds of ingredients because they have those clear SOPs. And I think that’s the mindset switch that people have to have at this point in time of. It’s not that you’re falling behind with AI, it’s that you don’t have a good plan.
Christopher S. Penn [21:36]: Yeah, I mean, one of my. The weird things, it’s not really weird, I guess it’s just amusing things to do is, particularly when I’m traveling internationally, is at least once go to McDonald’s, wherever it is I am. Because the McDonald’ Belgrade and the McDonald’s in Tevat and the McDonald’s in Melbourne and the McDonald’s in Seoul, you can get a Big Mac and it’s pretty much going to be the same. I mean, there’s like minor variances. But to your point, there is a standard and, you know, whatever, wherever you are on this planet, you can walk into a McDonald’s and you will get the same, pretty much the same Big Mac, you know, regardless of the continent you’re on.
Katie Robbert [22:13]: And I think that’s what. What companies are losing sight of is their end user, their customer and our expectations at some point, like, we don’t care how you did it. We just want to know we’re going to get the thing and we’re going to get it done our way. You know, I don’t remember whose fast food that is. Okay. I mean, so yeah, again, none of these companies are sponsoring us. So we can mix and match all we feel like. But that’s the thing is they’re Losing sight of the end user, of the actual customer who is benefiting or not from the things that they’re creating. You know, so companies are saying, I need to save money. We need to be more efficient, we need to be faster. Okay, but how does that benefit the customer? Yeah, and it doesn’t come up in conversation.
Christopher S. Penn [23:04]: You’re right, because in a lot of. And then you have a bunch of other things that will cause interference, like Parkinson’s law, which is work fills the amount of time given to it, or Javon’s paradox, which is the more of a resource you have, the more of it you tend to use, even if there’s not a clear goal. When were talking last week with. With our customer, they were saying, you know, we have all these metrics that we hope AI is going to help us succeed. And I said, but these metrics are not things grounded in reality. Like, you need to quadruple the amount of content you produce. Rate is. But you’re not quadrupling your audience. So people are going to have 25% of this amount of time to read any one piece of content if you quadruple it.
Christopher S. Penn [23:44]: Because, yeah, you can make a million blog posts. No one’s going to read them, but who cares? Right?
Katie Robbert [23:52]: And I think that’s. Again, it goes back to. I always go back to the five P’s. Maybe your purpose is who cares? Period? So why are you doing the thing in the first place? You know, so if it’s to save time, save money, your people, you need to include your customer in that conversation. So if you’re saving time and money, what is the benefit to the customer of you having a larger bonus at the end of the year? Probably nothing.
Christopher S. Penn [24:24]: Yeah. In fact, it’ll cost them more money because their prices will go up.
Katie Robbert [24:27]: Right. And so. And those are not easy conversations to have. I’m not going to pretend that, you know, I can walk into a boardroom and say, you have your head screwed on incorrectly, you’re trying to fill your pockets, and your customers are not benefiting. Like, that’s not an easy conversation to have. Because, you know, Chris, this is why you prefer machines over humans. Humans are tricky. Humans are emotional. Humans can be predictably unpredictable. And companies avoid those conversations because they’re hard to have. It’s a human nature to avoid conflict. I personally love it. Most of us don’t enjoy it. And so I think that’s where, again, as you’re having that sort of, like, inner turmoil of I’m falling behind, I really challenge you to Question what does that mean for your company? What does it actually truly mean to be using these tools?
Katie Robbert [25:24]: Because at the end of the day, it is just a piece of software. So if you haven’t mastered HubSpot or your other CRM data, are you falling behind? If you haven’t mastered your email marketing system, are you falling behind? What does that actually mean? Is that actually even your role? I am terrible at our email marketing system. It doesn’t mean I’ve fallen behind. It’s just not where my time is needed. And I feel like those like it’s a I’m sure we could argue that the email marketing system is not the same as Generative. I don’t feel like getting into that debate. But at the end of the day, it’s a piece of software. And so you really need to define what your role is in this entire conversation.
Christopher S. Penn [26:10]: Yeah. And I think I would finish off by saying you should know the major technological changes. Like when you’re bl. When your blender went from being a stick to becoming, you know, a metal whisk to becoming an electric a stick blender to becoming, you know, a Vitamix. When as. As the major changes happen, you should. You should know those for sure. Like, you don’t need to know how a Blendtec blender works, but you should know that, you know, you it’s different than a whisk and it’s going to do different things. But beyond that, spend more time on Are you putting in stuff that’s good and are you getting stuff out of it that’s good? And worry less about exactly which kind of electromagnets are powering.
Christopher S. Penn [26:53]: If you got some thoughts about how you are feeling about Generative AI or any of the technologies that are rapidly advancing around it, pop by our free Slack group. Go to trust insights AI analytics for marketers, where you and over 4,600 other marketers asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to Trust Insights AI Ti Podcast. You can find us all the places find podcasts served. Thanks for tuning in. Talk to you on the next one.
Katie Robbert [27:28]: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence and machine learning to empower businesses with actionable Insights. Founded in 2017 by Katie Robert and Christopher S. Penn, the firm is built on the principles of truth, acumen and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data driven approach. Trust Insight specializes in helping businesses leverage the power of data, artificial intelligence and machine learning to drive measurable marketing roi. Trust Insight services span the gamut from developing comprehensive data strategies and conducting deep dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies.
Katie Robbert [28:21]: Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation and high level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google, Gemini, Anthropic, Claude Dall E, Midjourney, Stable Diffusion and Metalama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog and the In Ear Insights podcast, the Inbox Insights newsletter, the so what Livestream webinars and keynote speaking. What distinguishes Trust Insights in their focus on delivering actionable insights, not just raw data, Trust Insights are adept at leveraging cutting edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations.
Katie Robbert [29:27]: Data Storytelling this commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data driven. Trust Insights champions ethical data practices and transparency in AI Sharing knowledge widely Whether you’re a Fortune 500 company, a mid sized business or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance and educational resources to help you navigate the ever evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.
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Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.
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