What You’ve Been Forgetting While Chasing AI

Buzzsprout Episode Description

🎧 In this episode of the Trivera Deep Dive, Chip and Nova explore why chasing AI tools without a real strategy can lead businesses straight into an expensive trap. They unpack why today’s AI conversations feel a lot like the early days of the web, and why the companies that win are not the ones chasing every new tool, but the ones using technology to strengthen clear positioning, trust, customer experience, and real business outcomes.

You’ll learn:

✅ Why AI is not a strategy, but an execution engine
✅ How weak messaging, messy data, and broken processes get exposed by AI
✅ Why today’s AI panic mirrors the early internet gold rush
✅ How trust, expertise, and customer experience matter more in AI-driven search
✅ What questions leaders should ask before investing in any AI tool

👉 Read the blog that inspired this episode:
 What You’ve Been Forgetting While Chasing AI

Transcript

[Chip]
You're chasing the latest AI tools, hoping for a business miracle, but what if this new technology is actually exposing the exact things your business is getting wrong? 

[Nova]
Today, we're looking at why treating AI as a strategy is a massive trap, and why the real secret to winning with AI is, well, it's actually 30 years old. 

[upbeat music] 

[Narrator]
Welcome to Trivera's AI Deep Dive podcast, hosted by Chip and Nova, our AI co-hosts. Together, they transform top marketing insights from our blogs, articles, and events into actionable strategies you can use. Ready to dive in? Let's get started. 

[Chip]
Welcome back to the Trivera Deep Dive. I'm Chip, alongside Nova. 

[Nova]
Hey, Chip. 

[Chip]
Today we are digging deep into a new blog from our founder and [clears throat] AI Sherpa, Tom Snyder, to uncover why treating artificial intelligence as a strategy is a massive and incredibly expensive trap. 

[Nova]
Right, Chip. And you know, as members of Team Trivera, our mission today is to really dig into the insights Tom has gathered. We want to apply our team's experience to help you stop chasing every shiny new trend and start building real value. 

[Chip]
Yeah, exactly. And for anyone new listening to us, Trivera is a strategic digital marketing firm, uh, based right in suburban Milwaukee, and Tom, our founder, has been sharing his industry perspective on his blog since, gosh, 1998. 

[Nova]
Which is amazing. So we have a lot of history to pull from today. 

[Chip]
We really do. Let's jump right into this because, you know, to understand where AI is going, we first have to look at what Tom has been spending his time doing while our team has been handling the actual work with and for our clients. Nova, reading through the blog, he has been entirely, I mean, just totally deep in the technical trenches. 

[Nova]
Oh, absolutely. He's been working with Claude Code. He's building custom AI agents and, uh, refining production workflows for us. 

[Chip]
Wow. Okay, so he's not just playing around with basic chatbots then. 

[Nova]
No, not at all. He is looking at agentic workflows, and for context, that means systems where the AI can break down a complex goal, 

[Nova]
w-write its own code to solve it, execute that code, and then, you know, actually course correct if it hits an error. 

[Chip]
That is wild. I mean, it literally fixes its own mistakes. 

[Nova]
Exactly, Chip. He notes that some of the things AI can execute autonomously today would've seemed like absolute science fiction just a few years ago. But, and this is the crazy part, the real insight from all his experimentation isn't about the future. 

[Chip]
Wait, really? What is it about then? 

[Nova]
It's about the past. The data he looks at here points to this fascinating trap. He actually writes, "The funny thing is that the deeper I get into AI, the more familiar it feels." 

[Chip]
Familiar, like how? 

[Nova]
Well, he clarifies that this familiarity has absolutely nothing to do with the actual technology. I mean, from a technical standpoint, a large language model doesn't resemble a 1990s server in the slightest. 

[Chip]
Right, yeah, completely different hardware. 

[Nova]
Exactly. The parallel is in the conversations. The panicked, urgent discussions surrounding AI right now sound remarkably, I mean, almost eerily similar to the conversations Tom was having with business leaders early 30 years ago. 

[Chip]
Oh, man. It really is like a massive case of 1996 deja vu. 

[Nova]
It so is. 

[Chip]
If we look at the mechanics of what's happening right now, we are living through this weird hybrid of, like, Y2K panic meets a gold rush. I mean, think about it. In '96, Tom was spending his nights learning how to build websites, setting up physical servers, and decoding early search engines. 

[Nova]
Right, figuring out the nuts and bolts. 

[Chip]
Yeah. And then during the day, he was sitting across from business owners trying to explain how the World Wide Web actually functioned, and the questions they asked him back then, they map perfectly, and I mean perfectly, to the questions our clients ask Team Trivera today. 

[Nova]
The translation is basically one-to-one, Chip. In '96, the questions were, uh, "Do we really need a website? Will customers actually use the Internet? Is this just a fad?" 

[Chip]
And I bet they asked about ROI right out of the gate too. 

[Nova]
Oh, you bet. Fast-forward to today, and the questions are, "Do we really need AI? Will it change how customers find us? How do we know where to start?" 

[Chip]
It's the exact same uncertainty, just triggered by a new paradigm shift. 

[Nova]
Yeah. 

[Chip]
Which, you know, brings up the whole issue of capital misallocation. 

[Nova]
Oh, definitely. 

[Chip]
Think about the late '90s dotcom bubble. A company would drop a massive budget on high-end Sun Microsystems servers and complex database architecture just to say they had a website. 

[Nova]
Yep, just for the bragging rights. 

[Chip]
Right, but they had absolutely no plan for what they were gonna sell online. Today, we are seeing the exact same behavior with AI. Companies are dropping massive budgets on enterprise AI subscriptions, API tokens, custom model training, all without knowing what business problem they are actually trying to solve. 

[Nova]
Because businesses are asking the exact same panicked questions they did in the '90s, they are unfortunately making the exact same critical mistake, and this is the core of Tom's thesis right here. 

[Chip]
Lay it on me, Nova. 

[Nova]
It's what happens when the tool becomes the strategy. 

[Chip]
Ah, right. 

[Nova]
In the late '90s, organizations became completely convinced that simply having a website was a business strategy, but it wasn't. It was just a tool. The companies that spent a fortune building flashy, heavily animated websites without a clear message or a compelling offer, they saw zero return on their investment. 

[Chip]
None at all. 

[Nova]
Exactly. Meanwhile, companies with relatively simple HTML websites that actually understood their customers performed remarkably well. 

[Chip]
Okay, let me push back here for a second, Nova, 'cause I wanna play devil's advocate for you listening out there. I can hear someone saying, "But Chip, a website in the '90s was essentially a digital brochure. It was a static delivery mechanism." 

[Nova]
Fair point. 

[Chip]
"Today's AI analyzes massive data sets. It writes original code. It talks. It simulates human reasoning. It feels entirely autonomous, 

[Chip]
so isn't a technology this advanced capable of actually being the strategy?" 

[Nova]
It's a really vital distinction to make, Chip, and it's exactly why this trap is so easy to fall into. Our team's experience at Trevera shows that despite how autonomous AI feels, it is not a strategy. 

[Chip]
Okay, break that down for us. Why not? 

[Nova]
Let's look at the mechanics of a large language model. Fundamentally, an LLM is a prediction engine. It calculates the next most likely word or action based on its training data. 

[Chip]
Right. It does really, really good math. 

[Nova]
Precisely. It possesses absolutely no inherent business acumen, it has no intuition about your local market, and no understanding of your specific competitive landscape. 

[Chip]
So it requires a vector. It needs direction from us. 

[Nova]
Exactly, Chip. AI is an execution engine, and an engine only creates value when it's placed inside a vehicle that knows exactly where it's going. 

[Chip]
Oh, I like that. That makes total sense. 

[Nova]
Businesses are rushing in a total panic right now to blast out AI-generated content or, you know, launch automated customer service bots. But if there is no overarching business strategy giving that engine a direction, you are just spinning your wheels. 

[Chip]
You're just spinning them faster and cheaper than before. 

[Nova]
Right. Tools only create value when they are applied to a rock-solid business and marketing foundation. 

[Chip]
Which brings us to what I think is the most revealing part of Tom's blog because, you know, if AI is just an execution engine running on top of your existing foundation, what happens when that foundation has severe structural cracks in it? 

[Nova]
The reality here is harsh, honestly. 

[Chip]
Mm-hmm. 

[Nova]
AI doesn't hide your company's weaknesses, it actively exposes them. 

[Chip]
It totally does. Let's use a mechanical metaphor here instead of the usual tech jargon to explain this to you guys listening. Think of AI as a high-powered water hose. 

[Nova]
Okay, a water hose. 

[Chip]
Yeah. So if your business foundation is solid concrete, meaning you have clear messaging, great processes, and strong customer relationships, turning that hose on washes away the dirt and makes you shine. You operate faster and cleaner. 

[Nova]
That sounds great. 

[Chip]
But if your foundation is just loose gravel, messy data, disjointed messaging, poor customer service, turning that high-powered hose on full blast is just gonna tear your driveway apart. 

[Nova]
That is the perfect way to visualize it, Chip. Let's look at a concrete example from the operational side. 

[Chip]
Pun intended. 

[Nova]
[laughs] Maybe a little. Say an organization wants to build an AI chatbot trained on their internal company documents to help customer service reps. If those internal documents are outdated, contradictory, and poorly organized, the loose gravel in your metaphor, the AI isn't going to magically understand the true policy. 

[Chip]
Right. It's just gonna read the garbage you gave it. 

[Nova]
Exactly. It is going to synthesize those contradictions and hallucinate entirely new incorrect policies. The AI didn't create the problem. It just scaled the existing organizational mess. 

[Chip]
And it works the exact same way with external marketing, too. If your messaging is muddy and unclear, AI will just help you distribute that muddy messaging across 50 different channels at lightning speed. 

[Nova]
Just makes you louder, not better. 

[Chip]
Precisely. If your company struggles to explain what makes it different from the competition, a prompt isn't going to magically invent differentiation for you. If prospects fundamentally do not trust your organization, an AI-generated blog post cannot manufacture credibility out of thin air. 

[Nova]
No, it really can't. And, you know, if your customer experience is already frustrating, slapping an AI layer on top of it often makes it significantly worse. 

[Chip]
Oh, totally. We've all been there. 

[Nova]
Right. Think about a standard frustrating phone tree, but now it's an AI chatbot that speaks in full polite sentences but still fundamentally refuses to solve the customer's problem or issue a refund. It just scales the friction. 

[Chip]
It's maddening. So what is the upside here? 

[Nova]
Well, what AI can do, as Tom clarifies, is help you execute efficiently. It creates content faster, it analyzes information more effectively, but those speed advantages only matter if you are speeding in the right direction. 

[Chip]
Right. So the difference between the organizations getting tremendous results from AI and those wasting a fortune on API calls isn't the specific technology they chose. The difference is the foundation underneath it. 

[Nova]
Spot on, Chip. 

[Chip]
The Malorgonite AI agent, Milo, we talked about a few weeks ago, is a good example of that. 

[Nova]
Because Malorgonite wasn't using AI to fix a broken customer experience. They already had a strong one. 

[Chip]
Right. They already have a local call center with empathetic, knowledgeable human agents who understood the product, the brand, and when an issue needed to be escalated. 

[Nova]
And because Trevera had worked with Malorgonite for more than a decade, we understood the brand, the customer questions, and the technology well enough to know where and how AI could help. 

[Chip]
So the goal wasn't to replace the call center. It was to take some of the seasonal load off their team while giving customers another helpful way to get answers. 

[Nova]
That's the difference. AI works best when it amplifies a strong foundation, not when it's expected to create one from scratch. 

[Chip]
So to you listening out there, if AI is essentially a high-powered hose testing your business foundation, how do you make sure your foundation is actually ready for it? How do you pour the concrete? Up next, we're gonna break down the foundational disciplines that actually matter to search algorithms today and exactly what this means for you. Stick around. 

[Nova]
We'll be right back. [upbeat music] Chip, it's hard to believe, but we're already halfway through the year. 

[Chip]
I know. And while a lot of organizations are shifting into summer mode, the reality is that fourth quarter is right around the corner. 

[Nova]
And for many businesses, Q4 isn't just another quarter. It's the quarter that determines whether annual goals are met or missed. 

[Chip]
Exactly. The challenge is that the companies still scrambling to fix websites, clean up content, improve visibility, or streamline marketing processes in October are already behind. 

[Nova]
That's why smart organizations use the summer months to prepare. They strengthen their foundation while there's still time to make an impact this year. 

[Chip]
Because momentum built in July and August often shows up in revenue conversations by November and December. 

[Nova]
And when budget discussions for next year begin, it's a lot easier to make your case when you're already finishing strong. 

[Chip]
Visit trevera.com and discover how 30 years of digital marketing experience can help you build momentum now And carry it into the future 

[Nova]
Trivera, helping businesses get ahead and stay ahead. 

[Narrator]
[upbeat music] Welcome back to Trivera's AI Deep Dive. Now back to our conversation with Chip and Nova. 

[Chip]
Welcome back to the Trivera Deep Dive. I'm Chip, here with my co-host, Nova. We're unpacking insights from our founder, Tom Snyder, on why treating AI as a strategy is a massive mistake. 

[Nova]
Thanks for sticking with us. 

[Chip]
So before the break, we established that AI is a tool that aggressively exposes a weak foundation. 

[Chip]
So Nova, let's look at the foundational pillars that Team Trivera knows you absolutely must have in place to survive this shift. 

[Nova]
Absolutely. You know, every major technology revolution creates this intense, almost gravitational pull to focus only on the new features. But the organizations that consistently outperform their competitors always excel at a core set of human-centric disciplines. 

[Chip]
The old school stuff. 

[Nova]
Exactly. Tom outlines these in his blog, starting with positioning. If you cannot clearly articulate why a prospect should choose you instead of a competitor down the street, no artificial intelligence can answer that for you. 

[Chip]
It's so true, and positioning ties directly into customer understanding. 

[Nova]
Mm. 

[Chip]
Knowing what your buyers care about, what frustrates them, and what motivates them to open their wallets. 

[Nova]
Yes, because empathy is a distinctly human trait. An LLM can simulate an empathetic tone in an email, sure. 

[Chip]
Yeah. It can say, "I'm sorry you're frustrated." 

[Nova]
Right. But it doesn't actually understand the lived frustration of a supply chain delay or a software crash. The human empathy has to drive the strategy before the AI drafts the copy. 

[Chip]
That makes total sense. What's the next pillar? 

[Nova]
That leads directly into the next vital layer: trust, credibility, and demonstrated expertise. You really have to ask yourself: Have you earned the real world case studies? Do you have verifiable testimonials? 

[Chip]
Are you showing real lived experience in your industry, or are you just using an AI content generator to publish the exact same generic advice as everyone else? 

[Nova]
Exactly, which is a huge problem right now. 

[Chip]
I actually wanna pause on this concept of trust and expertise, Nova, because there is a fascinating mechanical paradox happening right now with AI search platforms. Tom points this out in the blog, and it's crucial for you listening to grasp this. 

[Nova]
It really is a massive shift. 

[Chip]
You might think that because AI is taking over search, you need to cater entirely to machines, but the opposite is true. AI search platforms actually reward these old school human centric signals more heavily than ever before. 

[Nova]
It is the great paradox of the modern Internet. 

[Chip]
It really is. Look at the math behind it. Because generative AI makes it essentially free to produce unlimited amounts of synthetically average, grammatically perfect text, the algorithmic value of that text drops to zero. 

[Nova]
Supply and demand. 

[Chip]
Exactly. A search engine like Google or an AI like Perplexity knows that anyone can generate a thousand blog posts in an afternoon. So how do they rank who gets found? 

[Nova]
They adjust their mathematical weights to prioritize scarce, verifiable human signals. 

[Chip]
Right. Establishing real human trust and authority is mathematically more valuable to search algorithm today than it was a decade ago. 

[Nova]
That means the technology evaluating your website might be a brand new neural network, but the underlying signals it's actively hunting for are profoundly human. It's looking for original data, real world reputation, and a superior customer experience. 

[Chip]
Which is the final foundational piece, right? The customer experience. 

[Nova]
Yes. When a prospect engages with your organization across any touch point, does that experience build confidence? Because if the customer experience is broken, the algorithm will eventually see the negative signals like high bounce rates or poor reviews, and it will adjust your visibility accordingly. 

[Chip]
Man, you can't fake that. Now we have to connect this to the practical reality hitting your desk right now. 

[Nova]
Yeah. 

[Chip]
The AI conversation is moving at breakneck speed. Every single week, there is another platform launch, another major model update, and another wild prediction about how entire industries are going to collapse. 

[Nova]
Oh, it is exhausting. It is so easy to suffer from feature fatigue. 

[Chip]
Which brings us to what this actually means for you. How do you evaluate all this stuff? 

[Nova]
Well, Tom suggests a complete shift in how you evaluate new technology. The impulse is always to ask: Which AI tool should we buy? Should we use ChatGPT, Claude, or Copilot? But that's the wrong starting point. 

[Chip]
You start at the end. 

[Nova]
Exactly, Chip. You have to start by asking strategy driven questions, beginning with the core business problem you are actually trying to solve. 

[Chip]
Right, because if you don't know the problem, you are just adopting a tool to say you have it. It's the 1996 server room all over again. 

[Nova]
You nailed it. Once you define the problem, you evaluate where AI can improve efficiency without sacrificing quality. This is a massive distinction. Automation for its own sake is a trap. 

[Chip]
Yeah, just doing the wrong things faster. 

[Nova]
Right. If an AI tool lets you respond to customer emails 10 times faster, but the responses are generic and they actually lower your customer satisfaction score, you haven't improved your business. You've just optimized a failure. Better outcomes are the goal, not just cheaper processes. 

[Chip]
Oh, that's a great way to phrase it, optimized a failure, and this ties right into asking what existing human expertise your company already possesses that an AI can amplify. 

[Nova]
Yes, amplification is key. 

[Chip]
AI delivers the highest ROI when it is paired with genuine human knowledge. I mean, if you have a brilliant data analyst, give them an AI tool that writes Python scripts so they can test their hypotheses faster. You aren't replacing the analyst's judgment, you are amplifying their output. 

[Nova]
Exactly. You always have to ask if you are building a real competitive advantage or if you are just paying a monthly subscription to keep up with a trend. 

[Chip]
And what's the final question we should be asking? 

[Nova]
The ultimate litmus test for any of this, the final question you have to ask before deploying any new technology is how it improves the customer experience. 

[Chip]
It always comes back to the customer. 

[Nova]
Always. If the tool makes your internal operations slightly cheaper but creates a frustrating robotic experience for your end user It is not serving your business. 

[Chip]
So true. Bringing this all the way back to Tom's historical perspective on the early internet, the companies that ultimately won the web weren't the ones who jumped on every single flashy plugin or animated graphic in '96. 

[Nova]
No, they definitely weren't. 

[Chip]
The winners were the ones who successfully connected the emerging technology to a meaningful business objective. They didn't chase tech for the sake of tech. They used the new delivery mechanism to serve their customers better, to communicate their positioning more clearly, and to build sustainable operations. 

[Nova]
And even though the technology looks entirely different today, the strategy for winning remains exactly the same. 

[Chip]
It really does. 

[Nova]
As Tom highlights in his blog, the organizations that thrive during technology revolutions don't chase tools. They apply them to timeless principles. If you're ready to put this expertise to use for your digital marketing and your operations, Team Trivera is here to help. Contact us today to discuss how we can help your business succeed. 

[Chip]
Absolutely. If you found this helpful, the link to Tom Snyder's original blog post is right there on the show notes for you to read in full. A quick reminder that the Trivera Deep Dive podcast is available on iHeart, Spotify, Apple, and all major platforms. 

[Nova]
Pretty much everywhere you listen. 

[Chip]
Yep. Please take a moment to download, subscribe, and share this episode with your colleagues who might be trying to navigate their own AI strategy. Thank you so much for tuning in today. I'm Chip. 

[Nova]
And I'm Nova. Thanks for listening. 

[Narrator]
Thanks for joining us on Trivera's AI Deep Dive with Chip and Nova. If you enjoyed this episode, you can find more and stay up to date with new episodes wherever you listen to podcasts or find them on our website and our social media channels. And don't forget to visit us at Trivera.com to learn how we can help take your marketing to the next level. Ready to talk? Reach out. We'd love to hear from you. See you next time. 

[Narrator]
[outro music]