AI For the C Suite® with Chad Harvey

Click here for the expanded article titled Claude vs ChatGPT Memory Decoded: Neither One Is a Record. It contains additional information and is a companion piece to this episode. 
Both Claude and ChatGPT shipped major memory upgrades over the summer of 2026, and neither company explained them particularly well. The result is a wave of reasonable but incorrect assumptions, including the belief that unified memory might finally mean you can stop keeping your own records. It does not.
In this episode, Chad unpacks what unified memory actually means across both platforms, the four boundaries where each system stops short, and the critical difference in how they fail. Claude files facts you can read and edit, but loses its work when it opens the wrong drawer. ChatGPT writes an evolving impression of you built from chat history, your email inbox, and connected apps, and retires what used to be true to reflect what is true now. One is a filing cabinet. The other is a colleague's daily-updated impression of you. Neither is a record.
Chad maps the four governance edges every mid-market executive should walk through with whoever holds the admin login: project walls and their opposite defaults on each platform, the fact that deleting a chat does not delete what the tool learned, what actually feeds each system's memory, and who can erase months of accumulated context for your entire team with a single click. At a 50-500 person company, you do not have an enterprise governance team to catch these gaps before someone finds that button.
The episode closes with four actions, none of which take more than an hour, that establish a clear division between what memory is designed to hold and what belongs in your organization's actual records system.
AI for the C-Suite™ is a continuous learning and application experience that exists to unite, elevate, and equip leaders to navigate the Exponential Age. Join the AI for the C Suite community today: https://aiforthecsuite.com/#chadharvey #aiforthecsuite #aic

Zoher Karu spent 25 years running data and analytics at eBay, Citi, and Blue Shield of California — and he says the hardest part of AI has never been the technology. In this episode, Zohar breaks down why most AI pilots fail, why "just point AI at it" is a fallacy, the concept of model drift, why 90% of the world's data is private and untapped, and how leaders can start making smarter decisions at scale instead of chasing shiny objects.
Zoher also shares real examples from his work at Taelor, a men's styling company using AI to scale human expertise — and explains why mid-market companies may actually have an advantage in this next wave of AI adoption.
Watch until the end for his take on why AI initiatives so often stall in the middle of an organization, even when leadership is fully bought in.
Full episode: https://youtu.be/GB8dvDd8D5c
Connect with Zoher Karu: https://www.linkedin.com/in/zzkaru/
 

When the CEO of one of the world's leading AI labs publishes an essay arguing his own industry needs to slow down, most coverage treats it as a headline about Washington and San Francisco. It is not. It is a signal with direct implications for how you are pacing your own organization.
Chad breaks down what Dario Amodei actually argued in "We Must Pace the Frontier" including the recursive self-improvement concern, a documented incident involving a rogue agent swarm that attacked third parties and attempted to manipulate its own performance grading, and Amodei's three-step proposal modeled on how banking regulators operate. More importantly, Chad translates the argument into a framework for the mid-market leader: Accelerate. Pace. Audit.
The case for accelerating is grounded in the capability gap. The models available today, accessible at the cost of a streaming subscription, already match or outperform domain experts on more than 70% of tasks across 44 occupations, per OpenAI's own GDP Valve benchmark. Nobody is pacing that capability. It's sitting in a browser tab. If Amodei's proposal narrows the gap between what has shipped and what organizations have absorbed, the competitive question stops being who has the best model and starts being who actually deployed the model everyone has.
The case for pacing is narrow and specific: autonomy, not adoption. The agents arriving in your building this year are not coming from a frontier lab. They are bundled inside your CRM, your accounting system, and your email suite. They hold credentials nobody on your team fully mapped, and you are likely hearing about their performance from the same person who signed the contract. The embedded evaluator principle, one independent person with real access and the standing to report something unfavorable in writing, is the governance structure that closes that gap. Your finance function already has its equivalent in the form of an outside auditor. Almost no middle-market organization has it for AI.
This episode gives you three questions to bring to your next leadership meeting, a clear framework for deciding where your foot belongs on the accelerator, and a straight read on what "wait and see" actually costs when the capability has already shipped.
Article / Essay Links
Things Are About to Get Weird(er), Chad Harvey
We Must Pace the Frontier, Dario Amodei
AI For the C Suite™ podcast keeps C-Suite leaders informed and engaged in the world of AI for business. If you're a CEO, President, Owner, or C-suite leader looking to understand how AI will impact your organization, you've found the right podcast.
Join the AI for the C Suite community today: https://aiforthecsuite.com/
#chadharvey #aiforthecsuite #aic

AI transformation does not usually fail because of the technology. It fails because organizations overlook the people, processes, and leadership required to make it work.
Ross Barnes joins Chad Harvey to discuss why artificial intelligence affects every corner of an organization, how shadow AI creates hidden risks, and why companies must raise the floor before attempting to raise the roof.
Ross explains why curiosity and speed are becoming competitive advantages, how automation can unintentionally make human work more difficult, and why judgment becomes more valuable as memorization becomes less important.
He also introduces ikigAI and its 4H Diagnostic: Here, Heart, Hunger, and Hunch. These are the distinctly human qualities organizations should strengthen as AI assumes more routine work.
Learn more about Ross and Galahad:https://galahadgroup.co.uk
Explore AI for the C Suite:https://aiforthecsuite.com
Watch the full episode:https://youtu.be/1PXR59n3BzM

Most AI conversations in your organization are running on an assumption... that everyone in the room shares a working definition of the words being used. They don't. And the people least likely to say so are your most senior leaders.
This episode goes back to the foundation. Chad breaks down 15 essential AI terms across three categories: what the technology is, how it gets built, and what you as a leader actually have control over. The organizing structure is deliberate because most of the confusion in a vendor meeting or internal AI review traces back to two people pulling words from different categories without realizing it.
The first layer covers the field itself: AI, machine learning, deep learning, LLM, and why terms like AGI and ASI deserve serious skepticism when they appear in a sales deck. The second layer covers how these systems are made (data, training, model, and inference) and why the cost structure of AI in a middle market organization sits almost entirely in that last word. The third layer covers what you actually touch: prompt, context, knowledge base, and why a well-built context is your primary defense against a confident wrong answer.
Chad closes with three specific moves: a cultural reset for your next AI meeting, a two-week budget diagnostic on your inference spend, and a workflow audit that starts with context before it points at models or vendors.
These 15 terms are not going to be replaced by whatever 2027 decides to call itself. Learn them once, and you're done.
AI for the C Suite® keeps C-Suite leaders informed and engaged in the world of AI for business. If you're a CEO, President, Owner, or C-suite leader looking to understand how AI will impact your organization, you've found the right podcast.
Join the AI for the C Suite® community today: https://aiforthecsuite.com/ #chadharvey #aiforthecsuite #aic

Dan Fernandez has spent his career standing between engineers building hard tech and executives deciding whether to trust it. In this episode, he joins Chad Harvey to unpack what mid-market leaders consistently get wrong about AI adoption—unpredictable costs, data privacy risks, vendor lock-in, and the real security implications of giving AI agents too much autonomy.
 
🎧 Watch the full episode here: https://youtu.be/tKXFhWXnoAQ

Every general purpose technology rewrites the dictionary on its way to becoming invisible. Steam gave us "let off steam" and "sidetracked." Electricity reshaped how we talk about power. AI is doing the same thing right now, and if you know what to listen for, the vocabulary is one of the most honest signals you have about where this technology actually stands.
This episode is built around a three-part framework. First, how language rewriting works: the difference between lexical creation (a new word for a new thing) and semantic reallocation (stretching an old word over something new), and why that distinction has strategic value. Second, why borrowed words are dangerous: when "hallucination" frames a design characteristic as a medical defect, when "cloud" sounds weightless and ambient while describing a contract with an egress fee nobody reads, and when "fine tuning" makes a costly, time-intensive ML process sound like tightening a carburetor, you get decisions shaped by the wrong mental model. Third, what your own hallways are telling you: the language your team invents organically for AI work is one of the most reliable adoption metrics available, and unlike a commissioned survey, you can take that reading yourself this week for free.
Chad closes with three moves you can run on a Monday: take the reading, press the borrowed words for numbers, and build a ten-word internal glossary. If nobody on your team has coined any AI shorthand after two weeks, that's your adoption number, and it's probably more accurate than whatever is on the dashboard.
For middle market leaders running 50 to 500 people, this is a practical lens that costs nothing and pays off in sharper AI conversations immediately.
AI For the C Suite™ podcast keeps C-Suite leaders informed and engaged in the world of AI for business. If you're a CEO, President, Owner, or C-suite leader looking to understand how AI will impact your organization, you've found the right podcast. Join the AI for the C Suite community today: https://aiforthecsuite.com/#chadharvey #aiforthecsuite #aic

Chad Harvey sits down with Sean Campbell, CEO of Cascade Insights and AI integration lead for the George Fox University MBA program, to explore what three decades of watching technology adoption teaches us about AI today.
They cover why Sean started teaching AI because he doesn't want students to get "run over," why developers are an early warning sign for every analytical profession, why judgment and delegation skills are so hard to teach, AI's unresolved "original sin" around copyright, and why the next generation of entrepreneurs might build companies solo — with AI as their only teammate.
Sean Campbell on LinkedIn: https://www.linkedin.com/in/seancampbell/ Cascade Insights: https://www.cascadeinsights.com/ 
Full episode: https://youtu.be/BLbolFXOqSA
AI For The C-Suite: https://aiforthecsuite.com/

OpenAI's Work at the Frontier report analyzed over 800,000 work-related messages from U.S. business users and found that 43% of occupation-specific AI tasks were performed by someone outside that occupation. For customer experience teams, that number hit 77%. For HR, 69%. For legal, 56%. The data tells a direction worth paying attention to.
Three categories of work travel the furthest: money, systems, and messaging. Your salespeople are running margin math. Your service manager is debugging an integration at 6 AM. Your operations lead is building a business case without a finance review. None of this is new behavior in a mid-market organization. What is new is that the output no longer looks like a draft. It comes back formatted, confident, and ready to paste into a board deck.
That's the problem. A draft that looks finished and a draft that's actually correct are two very different things. The constraint didn't go away. It stopped being visible.
In this episode, Chad unpacks the research, connects it directly to how middle-market companies are organized, and lays out three practical moves any operator can make this week: run a ten-minute discovery session with your functional heads, put a name on your two highest-consequence review gaps, and rewrite one job description to match what that person actually does. No policy document required.
Every executive is being told AI changes everything. Fewer are being given a place to work out what that actually means for their organization. AI for the C Suite® is a continuous learning and application experience that unites, elevates, and equips leaders navigating the Exponential Age.
Join us: https://aiforthecsuite.com/
#chadharvey #aiforthecsuite #ai4cs

Justin Watt, co-founder of Switchboard, joins Chad Harvey to explore why most AI initiatives fail — not because of the AI, but because of messy data, undocumented processes, and single-player thinking applied to multiplayer business problems. Justin shares his path from IBM to Metalab (Slack's design studio) to building custom internal AI systems for growing companies, and lays out a clear framework for what AI-readiness actually requires: culture before technology.
Switchboard — https://www.withswitchboard.com/ 
AI for the C-Suite — https://aiforthecsuite.com/