Friday, October 31, 2025
Read time: 5-6 min
Read this article online
Hi, it’s Chad. Here’s what you need to know about AI this week:
1. Sound Waves: Podcast Highlights
This Monday my episode with Nick Damoulakis, CEO at Orases drops. You know those Linkedin posts with ChatGPT Agent “cheat sheets” and elaborate 10-minute builds that promise to revolutionize your business? Nick hired the creators to test them. Spoiler: They. Don’t. Work. Nick and I unpack how to separate real AI value from viral hype plus much, much more. 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. When Strategy Becomes Code
What if your next strategic plan was written by an algorithm, refined by machine learning, and presented to you as a series of confidence-weighted scenarios?
Most leaders would balk at that question. Yet larger consulting firms are already using AI to automate pieces of their traditional strategy work. Within a few years, what I just described won’t be science fiction. It’ll be standard practice.
The question isn’t whether this shift is coming. It’s how you’ll lead when strategy becomes code.
What This Actually Looks Like
Picture Monday morning. Before you’ve finished your coffee, your strategy engine has run 10,000 simulations of your Q3 market entry plan. It analyzed competitor moves from last quarter, incorporated the latest market data, and adjusted for three dozen variables you haven’t considered yet.
By 9 AM, you’re not staring at a static PowerPoint deck. You’re looking at a dynamic dashboard: “87% confidence: Accelerate product launch by three weeks. Key risk factors: supply chain variables in Southeast Asia.”
The algorithm learns from every quarterly cycle. Your decisions, your team’s execution, your market’s response. Each choice you make becomes training data for the next round of recommendations.
This isn’t some distant future. Pricing optimization? Resource allocation? Workforce planning? It’s happening now in these domains. The full strategic planning suite is just a matter of time.
Your Role Fundamentally Changes
Here’s what most leaders miss: You’re no longer the sole architect of strategy. You become the chief calibrator of machine-generated possibility.
Your job isn’t to generate all the answers. It’s to know when the algorithm is right, when it’s missing context, and when your human judgment should override its recommendations.
That means asking different questions. When my intuition conflicts with the model, what does that signal? Which decisions benefit most from algorithmic input versus human judgment? How do I preserve the essential human elements (creativity, moral judgment, relationship dynamics) that algorithms can’t capture?
You’ll need what I call “calibration literacy.” Understanding not just what the model recommends, but how it arrived there. Which variables weighted most heavily? What assumptions underpinned the simulation? Where are the blind spots?
Here’s the uncomfortable part: Your failures become data points. When you override the algorithm and it works? Visible. When you follow it and it fails? Also visible. There’s nowhere to hide behind “strategic judgment” or your “gut” when the data shows otherwise.
The Window to Prepare is Now
Start asking yourself: Where in my decision-making would algorithmic input be most valuable? What decisions am I making on intuition that I could validate with data? How would I explain my strategic choices to a machine learning model?
The leaders who thrive won’t be those who resist algorithmic strategy. They’ll be those who learn to calibrate it through sharp questions and knowing when to trust the code and when to trust their gut.
Want to explore what “chief calibrator” leadership might look like in your organization? Drop me a line. I’m always up for a conversation about the future of strategic decision-making.
3. Research Roundup: What the Data Tells Us
ROI & GENAI: The Numbers are in… and They’re Bigger Than You Think
Every middle-market CEO asks me the same question: “Is GenAI actually worth the investment, or is this just hype?” I usually respond with case studies and anecdotes. But now I can point to something better: hard numbers from testing with millions of real customers and the ROI is bigger than most executives expect.
The numbers that matter: Sales increased between 2% and 16.3% across seven different GenAI deployments. Run that through a middle-market lens – a retailer with 1 million customers and $50 million in revenue could see an additional $1-8 million annually. But here’s what really matters: the smallest sellers saw the biggest gains. GenAI actually levels the playing field against larger competitors with bigger tech budgets.
What this means for your Monday morning: Stop thinking about GenAI as a cost-cutting tool. The revenue lift came from conversion rate improvements—turning more browsers into buyers through better customer service, smarter search, and richer product content. The pre-sale chatbot alone drove a 16.3% sales increase by providing 24/7 multilingual support that smaller companies couldn’t afford with human staff. (One of my clients just discovered 40% of their weekend web traffic was from Spanish-speaking customers. Guess what we’re implementing?)
The reality check you need to hear: Generic, out-of-the-box AI models flopped. When the company in question tried using an untuned model for Google ad optimization, it failed completely. Success required domain-specific customization: training the AI on actual product catalogs, customer service scenarios, and industry terminology. Bottom line: Anyone selling you ‘plug-and-play GenAI ROI’ is overselling. Budget for customization time and expertise, or you’ll end up with expensive shelfware.
Your next move: Identify your biggest customer experience gap where you’re losing sales right now – product descriptions that don’t answer questions, after-hours inquiries going unanswered, or non-English speakers bouncing from your site. That’s where GenAI will deliver fastest ROI. Start with one pilot, measure conversion rates rigorously for 90-180 days, then scale what works. (Shameless plug: If you need help identifying which gap will deliver the biggest return, that’s exactly what I help clients figure out.)
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
WPP’s chief AI officer says SEO is dying—and GEO is the future. If your brand isn’t in AI training data, you don’t exist to the 25% of searches Gartner predicts will bypass Google next year. If you haven’t already, ask ChatGPT, Claude, and Gemini what they know about your company. The gaps in their answers tell you exactly where your brand discoverability is broken.
Research shows AI-literate users are MORE overconfident about their work quality. This reverses everything we thought we knew about competence. Your team members who brag about their ChatGPT skills? They’re the ones most likely to ship garbage without checking it. The Dunning-Kruger effect just got an AI upgrade.
Microsoft is rebuilding Outlook from scratch as an AI assistant. New leadership is promising to reimagine Outlook as your “body double” that reads emails, drafts replies, and manages your calendar autonomously. If your organization runs on M365, this isn’t a feature update. It’s Microsoft rewriting how 400 million people do email. Start your change management planning before they ship it and your team revolts.
5. Elevate Your Leadership with AI for the C Suite
Want to figure out your AI strategy before it becomes code?
The shift to algorithmic decision-making isn’t waiting for you to be ready. If you’re wondering where GenAI will actually move the revenue needle in your organization, or how to lead when strategy becomes a confidence-weighted dashboard, let’s talk. I work with middle-market leaders who need practical AI strategy, not vendor pitches. Drop me a line or connect with me on LinkedIn. I’m always up for a conversation about what “chief calibrator” leadership looks like in the real world. — Chad
P.S. Share this newsletter with another executive who’s tired of AI hype and wants actionable insights.