Friday, July 25, 2025
Read time: 5-6 min
Read this article online
Hi, it’s Chad. Every Friday, I serve as your AI guide to help you navigate a rapidly evolving landscape, discern signals from noise and transform cutting-edge insights into practical leadership wisdom. Here’s what you need to know:
1. Algorithmic Musings. The Great AI Model Chase: Why You’re Probably Doing It Wrong
I had a perfectly good article queued up for you today. It opened with a zinger: “What’s your level of model anxiety?” Then it proceeded to dissect a growing fear I keep hearing from leaders – the paralyzing worry that they’ll “choose the wrong AI model” and waste precious time, money, and effort in the process.
My conclusion in that piece was rock solid: do your homework, nail down the fundamentals, and make a damn choice.
And then Thursday afternoon happened.
Reports started trickling out about ChatGPT-5’s impending release. Oh, the delicious irony. Here I was, writing about model anxiety, when my very own insecurity about providing outdated information surfaced faster than you could say “artificial intelligence.” Time for a complete rewrite.
But here’s the thing with this last-minute contortion – it actually proves my original point. We’re all getting caught up in the AI model merry-go-round, constantly looking over our shoulders for the next big thing instead of mastering what’s already in front of us.
So let’s talk about five things worth considering as ChatGPT-5 looms on the horizon.
1. The Latest and Greatest Isn’t Always the Latest and Greatest-est
Stop chasing shiny objects.
I know, I know. Your LinkedIn feed is probably buzzing with “experts” breathlessly declaring that GPT-5 will revolutionize everything from your morning coffee routine to quarterly earnings calls. But here’s a reality check: the most powerful AI tool is the one you actually use consistently and effectively.
Think about it this way – how many features in your current software do you actually use? If you’re like most people, you’re probably operating at about 20% capacity with the tools you already have. Before you start salivating over GPT-5’s capabilities, ask yourself: Are you squeezing every ounce of value from GPT-4?
2. Model Switching Isn’t Musical Chairs—It’s Strategic
Here’s what I see in my consulting work: leaders treating AI models like marriage – you pick one and stick with it forever. But what if I told you that switching models within a project could dramatically improve your results?
Different models excel at different tasks. Think of it like having a toolbox instead of just a hammer. GPT-4 might nail your initial brainstorming session, while Claude could excel at refining your strategy, and another model might be perfect for final implementation planning.
The key is understanding each model’s strengths and knowing when to make the switch. It’s not about being promiscuous with your AI choices – it’s about being strategic.
3. Context Is King, and GPT-5’s Million-Token Window Changes the Game
Let me translate this from tech-speak into real business language.
Imagine being able to upload your entire annual report, strategic plan, last quarter’s board presentation, and your latest market research into a single conversation. Then imagine having the AI remember every detail, connection, and nuance throughout your entire working session. No more breaking complex projects into bite-sized chunks. No more losing the thread when you’re knee-deep in analysis.
That’s what GPT-5’s extended context window promises.
For leaders wrestling with multi-faceted challenges (and honestly, which business problems aren’t multi-faceted these days?), this represents a fundamental shift. You can finally tackle those sprawling, interconnected issues the way they actually exist—as complete, messy, beautiful ecosystems rather than isolated fragments.
Think about your last major strategic initiative. How much time did you waste re-explaining context, rehashing background information, or bridging gaps between different pieces of analysis? GPT-5’s expanded memory could eliminate much of that friction.
4. The Reasoning Upgrade Isn’t Technical Jargon—It’s Your New Strategic Thinking Partner
Remember those moments when you’re working through a complex decision and desperately wish you had someone to help you think through all the angles? Someone who never gets tired, never has a bad day, and never brings their own agenda to the table?
GPT-5’s integrated reasoning capabilities promise exactly that.
The model incorporates what developers call “chain-of-thought” processing. Sounds fancy, but it boils down to this: instead of jumping straight to conclusions, it shows its work. It walks through problems step-by-step, weighs alternatives, explores scenarios, and explains its reasoning process—much like that brilliant colleague who never seems to miss the crucial detail everyone else overlooked.
This isn’t about replacing your strategic thinking. You’re still the CEO of your decisions. But imagine having a thinking partner that can help you identify blind spots, test assumptions, and explore possibilities you might not have considered. That’s the real promise here.
5. Multimodal Integration Means Your Data Finally Speaks the Same Language
Now let’s talk business applications.
GPT-5 processes text, images, audio, and video within a single conversation. No more digital hopscotch – uploading a chart to one tool, transcribing meeting audio in another, and copying text between platforms like some sort of data-shuffling dance.
Picture this scenario: You drop in last week’s presentation slides, include the audio from your client call, reference your latest market research report, and ask for comprehensive analysis—all in one place. The model doesn’t just see these as separate inputs; it understands the connections between them.
For leaders drowning in multimedia information streams (and let’s be honest, that’s all of us), this integration could be transformative. Your data becomes truly collaborative instead of siloed in digital islands.
The Bottom Line Up Front
Will GPT-5 be revolutionary? Probably.
Should you panic about your current AI strategy? Absolutely not.
Should you put all your AI initiatives on hold while you wait for the next big thing? Hell no.
The fundamentals remain unchanged: understand your actual needs, start with clear objectives, and remember that the best AI tool is the one you consistently use to solve real problems. Whether you’re working with today’s models or tomorrow’s latest releases, the magic happens when you move beyond the hype and focus on getting stuff done.
Here’s what I want you to do right now: Before you start planning your GPT-5 migration strategy, honestly assess how you’re using your current AI tools. Are you maximizing their potential? Have you integrated them into your actual workflow, or are they just expensive digital paperweights?
The leaders who will thrive in the AI era aren’t the ones who chase every new release. They’re the ones who master the fundamentals, understand their real business needs, and use whatever tools are available to solve meaningful problems.
If you’re struggling to cut through the AI noise and develop a practical approach that works for your organization, drop me a line. Sometimes the best way forward is a conversation with someone who’s not trying to sell you the latest shiny object—just someone who’s interested in helping you figure out what actually works.
Now, if you’ll excuse me, I need to go update my own AI strategy. Again.
2. Sound Waves: Podcast Highlights
Dave Trier just dropped a bombshell in our latest episode: 86% of enterprises spending billions on AI can’t even tell you what they’re getting for their money. As VP of Product at ModelOp and former Accenture AI transformation leader, Dave reveals findings from their brand-new 2025 AI Governance Benchmark Report surveying over 100 senior executives.
Key takeaway for executives: If you can’t measure your AI investments, you’re flying blind with your budget. Dave breaks down the three governance frameworks that separate successful AI adopters from those burning cash on pilot projects that never scale.
Episode drops Monday, July 28. Subscribe for free today on your listening platform of choice to ensure you never miss a beat.
New episodes release every two weeks.
3. Research Roundup: What the Data Tells Us
AI-Driven Visual Monitoring: Your Answer to Assembly Line Safety & Costs
Your assembly line mistakes cost more than you think. New AI monitoring just proved it can catch procedural errors before they shut down your production line for days.
Here’s what matters: A manufacturing AI system called ViMAT just completed real-world testing on hydraulic press operations. While it achieved 73% accuracy in controlled conditions, the real story is the 43% accuracy in actual messy factory environments—because that’s still catching critical errors your current systems miss entirely.
What this means for your Monday morning: Picture your most expensive assembly mistake from last year. If it involved someone installing a component incorrectly, positioning a mold wrong, or missing a safety step, this AI would have flagged it in real-time. We’re talking immediate alerts before mistakes become downtime, not discovering problems after equipment damage.
The business case: One hydraulic press shutdown costs most manufacturers $50,000-$200,000 in lost production time, not counting repair costs. If this AI prevents just one major incident per year, it pays for itself.
Your next move: Review your three costliest assembly errors from the past 12 months. If any involved procedural mistakes that visual monitoring could catch, start conversations with your operations team about pilot implementation. You’ll need multiple cameras per workstation and someone to document your procedures, but the ROI math works if you’re dealing with high-stakes assembly operations.
Read our full analysis of this and all other analyzed research papers at AI for the C Suite.
4. Radar Hits: What’s Worth Your Attention
CEO survey reveals only 13% have clear AI strategies, while 42% are still just “exploring”. This matters because your competitors aren’t waiting for perfect clarity – companies like Engine doubled sales productivity in five months using agentic AI. If you’re still in exploration mode, start with one specific use case where AI can automate repetitive tasks your team actually hates doing.
Anthropic launches Claude for Financial Services, with early adopters cutting underwriting time by 80%. Translation for middle-market leaders: sector-specific AI tools are here, and they’re delivering measurable ROI. If you’re in financial services or work with complex data analysis, these aren’t just productivity gains—they’re competitive advantages your industry peers are already capturing.
5. Elevate Your Leadership with AI for the C Suite
Here’s what I’m seeing: leaders who wait for AI clarity get lapped by those who start with focused experiments. If you’re tired of AI pilots that never scale or strategies that sound good in meetings but fall apart in practice, let’s talk. I help middle-market leaders cut through the hype and build AI capabilities that actually move the revenue needle.
As we navigate this unprecedented fusion of human and machine intelligence, remember: the best leaders aren’t just adapting to change – they’re actively shaping it. Until next week, keep pushing boundaries.
Chad