Friday, September 19, 2025
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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. Sound Waves: Podcast Highlights
Next week’s podcast guest solved a million-dollar problem from his hotel room the night before his wedding. Sammy Greenwall, founder of Henry.AI, joins me to share how customer obsession beats automation every time – and why his Mexico debugging session landed them a deal over seven competitors. Hit one of the below links to check it out:
Subscribe for free today on your listening platform of choice to ensure you never miss a beat. New episodes release every two weeks.
2. Algorithmic Musings. Prompt Packs, Incompetent Hacks & Meaningless “Facts”: The 3 Under-Hyped Skills You Actually Need to Master AI
The following is excerpted from my latest course on AI fluency: Mindset Mastery: How Smart Professionals Really Work with AI. Because there’s a difference between using AI and mastering it.
You can’t log onto LinkedIn these days without someone breathlessly trying to sell you the ultimate AI mastery course. (Hopefully that someone isn’t me.) From my least favorite term “prompt engineer” to magical incantations promising to unlock “game-changing” AI abilities, it’s all a bit hyperbolic.
And completely underwhelming.
Candidly, it reminds me of that scene in Fletch where Chevy Chase’s character confidently declares to an airplane mechanic, “It’s all ball bearings these days.” Is it??? To mix metaphors, an awful lot of folks are selling magic AI beans. And unlike the ones that fairytale Jack bought, most of these will grow a cute little plant that’ll likely die with the next update to the AI model it’s based upon.
So what do you actually need to succeed with AI?
Here are three skills that I believe are critical to mastering AI. Skills that are frequently misunderstood, often misapplied, and typically overlooked in discussions about artificial intelligence. They are: Coaching, Mentoring, and Editing.
Let’s dig in.
Coaching: Stop Asking AI to Read Your Mind
Most people treat AI like a magic 8-ball. Shake it with a vague question and hope for wisdom.
Stop it.
The best AI interactions happen when you approach it like a coach would. Ask better questions, dig deeper, and help both yourself and the AI understand what you’re really trying to accomplish. Think less “give me a marketing plan” and more “help me think through what questions I should be asking about my target market.”
When you coach AI effectively, you’re not just getting answers. You’re getting better answers because you’ve invested in the process of discovery.
Mentoring: It’s Not Just About Getting Answers
Here’s where most people get it backwards. They think mentoring means the AI teaches them.
But the real magic happens when you mentor the AI first. Feed it context, examples, and your expertise, then let it mentor you back with insights you might have missed. It’s a two-way relationship, not a one-way information dump.
The professionals who master this create AI interactions that feel less like using a tool and more like collaborating with a really smart colleague. And isn’t that exactly what you want?
Editing: Where Good Enough Goes to Die
This is the skill that separates the pros from the pretenders.
Anyone can get AI to spit out a decent first draft. But can you transform that draft into something you’d stake your professional reputation on? Real AI mastery isn’t about generating perfect outputs. It’s about knowing how to shape, refine, and elevate what AI gives you until it meets your standards, not the AI’s.
Think of it this way: AI gives you raw material. Your editing skills turn that material into something meaningful.
The Real Secret? Knowing When to Switch
Here’s what the prompt pack peddlers won’t tell you: the skill isn’t in having the perfect technique. It’s in knowing which approach to use when.
Sometimes you need to coach the AI through a complex problem. Sometimes you need to mentor it with your expertise. Sometimes you need to edit ruthlessly. The magic happens when you can fluidly move between these roles based on what the situation demands.
Most AI training focuses on the mechanics. Better prompts, clever tricks, technical features. But the professionals I work with who truly excel with AI have mastered something more fundamental: they’ve learned to think relationally about their AI interactions.
They’re not just users. They’re partners in a process.
And that’s a skill worth developing, because unlike those magic beans everyone else is selling, it’s the kind of capability that actually grows stronger with practice.
What questions do you have about building real AI fluency? If you’re ready to move beyond prompt hacks and develop these three critical skills in your professional context, drop me a line. My newest course dives deep into these three critical skills plus a foundational fourth skill showing you exactly how to apply this model and mindset in your professional context. I’d love to help you figure out how to make AI work better for you, not the other way around.
3. Research Roundup: What the Data Tells Us
Physical AI in Manufacturing: Finally, Robots That Think Like Your Best Operators
Every middle-market manufacturer I talk to faces the same squeeze: labor shortages, supply chain chaos, and customers demanding faster delivery. Now, a World Economic Forum study with Boston Consulting Group just quantified what smart automation can actually deliver.
The numbers that matter: Amazon’s AI-powered fulfillment centers show 25% faster delivery and 25% efficiency gains while creating 30% more skilled jobs onsite. Foxconn cut deployment time by 40% and operational costs by 15% using intelligent robotics for precision tasks.
What this means for your Monday morning: We’re past the age of robots that only handle repetitive tasks. Physical AI combines vision, reasoning, and real-time decision-making, letting robots adapt to variable conditions without reprogramming. Your operators can focus on problem-solving while robots handle the routine complexity.
The catch: This isn’t plug-and-play yet. Success requires layered automation strategy—rule-based robots for repetitive work, training-based systems for variable tasks, and emerging context-based robots for unpredictable processes. Plus, your workforce needs reskilling for robot supervision and maintenance roles.
Action item: This week: Walk your factory floor and identify three processes where operators make judgment calls that slow things down. Those variable bottlenecks are your Physical AI sweet spots. Then call one robotics-as-a-service provider for a pilot conversation – no capital commitment required.
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
Foundation models are becoming the “coffee beans to Starbucks” of AI. Your AI vendor costs are about to plummet as the competitive landscape is shifting faster than expected with OpenAI, Anthropic, and Google at risk of becoming low-margin commodity suppliers while application-layer companies capture the real value. If you’re building AI products or choosing vendors, consider betting on the interface and customization, not the underlying model. Switching costs are dropping fast.
OpenAI ramps up robotics hiring for “high volume” manufacturing. Manufacturing just got a 5-year acceleration as OpenAI has begun recruiting humanoid robotics experts and posting jobs for mechanical engineers with “1M+ unit” manufacturing experience. This isn’t research—it’s preparation for mass production. If you’re in manufacturing, logistics, or any industry with repetitive physical tasks, start scenario planning now. The timeline just accelerated.
You should be rewriting your prompts when switching AI models. Each AI model has different formatting preferences and biases – your GPT-4 prompts won’t work optimally with Claude or vice versa. If your team is evaluating new AI vendors, budget time for prompt optimization, not just API swapping. Working with model defaults instead of against them cuts costs and improves performance.
5. Elevate Your Leadership with AI for the C Suite
Want to move beyond AI prompt tricks to real business results? My new course “Mindset Mastery: How Smart Professionals Really Work with AI” teaches you the coaching, mentoring, and editing skills that actually matter. (Plus the fourth foundational skill most people miss entirely.)
If you’re ready to stop collecting AI hacks and start building genuine AI fluency for your team, let’s talk. Reply to this email or give me a call – I’d love to help you figure out how to make AI work better for you, not the other way around.
Until next Friday, Chad
P.S. If this newsletter provided value for your week, forward it to a colleague who’s wrestling with AI strategy. They’ll thank you, and so will I.