AI For the C Suite with Chad Harvey™

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/

The FBI's Internet Crime Complaint Center logged more than one million complaints in 2025 for the first time in the program's 25-year history. Reported losses came in just under $21 billion, up roughly 25% year over year. Business email compromise alone accounted for over $3 billion — making it the most financially destructive threat category aimed specifically at organizations. And for the first time, the IC3 report carved out a dedicated section for AI-related crime.
This isn't a story about Fortune 500 targets. A large company has a fraud team, a security operations center, and dedicated identity verification vendors. Your 150-person company has a controller, an office manager who also handles HR, and an outside IT provider you call when something breaks. The attackers are the same. The tools are the same. The asymmetry is the point.
In this episode, Chad walks through three operational controls built for the middle market: verifying any payment or account-change request outside the channel it arrived on, replacing one-time authentication codes with passkeys and FIDO2 authentication, and shifting your team's training from spotting a fake to following a procedure — regardless of how convincing the request looks. The third control is the one most organizations skip, and it's the one that matters most now that AI has made every signal your team was trained to trust trivially easy to fake.
You'll leave with three concrete actions you can assign tomorrow afternoon: a one-page callback rule, one question for your IT provider, and a call to your bank's fraud desk before you ever need it.
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.
https://aiforthecsuite.com/#chadharvey #aiforthecsuite #aic

What happens to your personal brand when AI can write it better — and faster — than you can?
In Episode 1 of AI for the C-Suite, I sat down with Elizabeth Rosenberg, founder of The Good Advice Company and former Global Communications lead for Apple's creative agency and 72andSunny, to dig into one of the most pressing questions for senior leaders right now: how do you stay unmistakably you in a world where AI is flattening every voice into the same optimized, agreeable, forgettable noise?
We covered a lot of ground — here are a few things that stuck with me:
🔹 AI is building brands where everyone sounds like each other. The posts that break through right now are the ones that say what everyone's thinking but nobody is posting.
🔹 The old playbook for executive brand-building doesn't work anymore. Vulnerability and authentic storytelling aren't optional extras — they're the thing.
🔹 AI will tell founders their plan is brilliant and they're going to the Today Show. A good strategist will tell them the truth.
🔹 Crisis comms has never mattered more — and the lag between the news cycle and LLM crawls is a contradiction nobody's solved yet.
🔹 Intuitive intelligence, EQ, and critical thinking are the skills that will define the next generation of leaders. We need to start teaching them now.
Elizabeth is equal parts strategist and straight talker, and this conversation went places I didn't expect. Worth your time.
Watch Here: https://youtu.be/Fg6-qQAYMOg
 
🌐 The Good Advice Company: https://www.thegoodadvicecompany.com 
🔗 Elizabeth Rosenberg: https://www.linkedin.com/in/elizabethrosenberg 
 
🌐 AI for the C-Suite: https://www.aiforthecsuite.com 
🌐 Chad Harvey: https://www.chadharvey.com
🔗 Chad Harvey: https://www.linkedin.com/in/chadcharvey

Most leaders in middle-market organizations have already been exposed to AI. They've read the articles, seen the product launches, and typed something into a chatbot. The information has been there, night after night. The gap is not awareness. It's the refusal to turn that awareness on yourself and your organization specifically.
In this episode, Chad uses the premise of a family film — a flock of sheep who understand death intellectually but have built a comfortable story to keep it at arm's length — to name the exact mechanism by which capable, informed leaders stay stuck on AI. The sheep believe they will simply turn into clouds. You may be doing something similar. Chad calls this "the cloud story," and he argues that the most dangerous version of it is not ignorance — it is exposure without application.
Yet the more important argument comes after the wake-up. Chad draws a sharp distinction between two separate events that organizations routinely conflate: the moment of recognition (finally seeing that AI is real, relevant, and already affecting your field) and the work of becoming competent (following clues, getting it wrong, building actual capability over time). One is a moment. The other is a disciplined practice. Confusing them is where most AI initiatives stall.
This episode gives you a framework for two honest questions: Where are you still treating AI as someone else's story? And where have you mistaken waking up for getting good? Those two questions are a starting point for leaders who are ready to close the gap between awareness and execution in their organizations.
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. 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

Alan Rambam, founder of EO4.AI, breaks down one of the biggest shifts in digital marketing right now — the rise of AI-driven search and what it means for your business visibility. Mid-market companies have a rare, time-sensitive opportunity to become the go-to source AI models cite in their category.
The window is closing.
We cover:
- Why Google's shift from "10 blue links" to AI-synthesized answers changes everything
- How AI agents are already making purchases on behalf of consumers
- The Anthropic experiment that revealed how smarter AI models find better prices - Why your website may be completely invisible to AI — and how to fix it
- Generative Engine Optimization (GEO) and why a16z is betting big on it
- The most common mistakes companies are making right now
- Why Reddit dominates AI results — and what that tells us about how AI learns
- How to structure content so AI can see it, trust it, and cite you If you're a business owner, marketer, or founder trying to understand where search is going — this episode is essential listening.
 
Connect with Alan:
LinkedIn: linkedin.com/in/alanrambam
Website: EO4.AI
 
Full episode: https://youtu.be/_XCaZPyC_FE

On June 12, 2026, the U.S. government issued an export control directive that forced Anthropic to take its two most advanced models offline — not just for foreign nationals, but for everyone. Within days, the most capable products that company makes had gone dark.
This episode unpacks what that event actually revealed about the AI stack most middle-market organizations are running today. The core issue isn't the specific directive — it's the asymmetry it exposed. Closed frontier models run through checkpoints: identity, billing, region. Pull the right thread and the model goes dark. Open-weight models, once downloaded, cannot be recalled by anyone. That gap has always existed. The Anthropic directive is the first time it cost real organizations real access.
Chad frames this as a risk category that most organizations haven't added to their risk register yet: regulatory shutoff. An outage, you can wait out. A price hike, you can negotiate. A government directive can pull your access overnight, sometimes without explanation. If your customer-facing product or internal workflow runs on a single closed frontier model with no fallback, that's not just a vendor dependency — it's a permission you don't control.
The practical framework is three steps: inventory every workload running on a single closed provider without a backup, flag the ones where downtime is genuinely unacceptable, and test an alternate for each. Chad calls the decision structure Cut, Coast, or Redeploy — some dependencies you hold, some you hedge, and some you redeploy around before the choice gets made for you. The move is not to abandon closed frontier models. It's to treat your model choice as an architecture decision with a political risk line item, not just a comparison of capability and price.
This episode gives you a practical starting point for building that fallback — while it's still calm.
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 convenes the first-ever AI Super Friends roundtable — a live jam session with five practitioners who are building, deploying, and governing AI at the enterprise level right now.
In this episode: Mike Gadsby (Co-Founder & Chief Innovation Officer, O3 World), Arjun Raj Jain (Co-Founder, Pre.dev), Diego Morales (VP of AI, O3 World), Mike Urban (Chief Tech Ops Officer, Besteg), and Josh Friedman (Staff Engineer, O3 World) go deep on agentic AI, governance frameworks, the double standard we hold AI to versus humans, and what all of them are most excited about on the horizon.
Topics covered: → Can existing frameworks actually support AI adoption at scale? → Well-governed agents vs. fully autonomous agents — where's the line? → Hyper-specialized models and the death of the general-purpose approach → Implicit trust: what it means to treat AI like a coworker → The AI harness explained (non-technical breakdown) → Cybersecurity, open-source models, and the attack surface nobody's ready for → Why AI is democratizing entrepreneurship — and what that means for the next generation
Whether you're a senior leader trying to figure out where to start or a practitioner already in the trenches, this one goes to places you won't want to miss.
 
🎙️ Full Episode: https://youtu.be/QlVCp8cwc2M 
🌐 Chad Harvey: www.chadharvey.com 
🤖 AI for the C-Suite: www.aiforthecsuite.com
 

Four out of five companies in this country have not yet started using AI — not falling behind, not experimenting, not started. Meanwhile, AI capability is doubling at roughly the interval of a business quarter. That gap between two clocks on the same wall is not a crisis. For the leaders who understand it, it is a position.
In this episode, Chad introduces a Three-Dial framework for cutting through AI noise and reading the signals that actually matter. Dial one covers the pace of capability growth and why the trajectory, not any single data point, is the right unit of measurement. Dial two reframes the augmentation-versus-automation debate with data on who actually captures value from these tools — and it is not the organizations that simply bought licenses. Dial three surfaces a labor signal that is being widely misread: the quiet thinning of entry-level roles in AI-exposed fields is not a headcount story, it is a succession question — and middle market leaders are uniquely positioned to get ahead of it.
Chad closes with three actions you can execute this quarter: rescope one analytical workflow as AI drafts and your expert judges, move resources from software seats to enablement, and open the five-year succession question with your leadership team before the answer gets expensive.
If you are running a middle market company and you want a clear-eyed read on where the real opportunity is — and what the hype machine is not telling you — this episode is your starting point.
The AI Signal Brief — June 2026The handful of indicators that actually move — and what each one means for a middle-market leadership team. As of 6 June 2026. Verdict: capability racing, adoption early.
The gap that frames everything. Frontier capability is doubling roughly every 4 months. Meanwhile only about 19.8% of U.S. firms are using AI at all. The space between those two numbers is the whole story: the hype says you're behind, but the data says the field is wide open — and the bottleneck on value is your organization's capacity to absorb AI, not the technology's ability to deliver it.
The executive read. Two clocks are running at very different speeds. The capability clock is sprinting — the length of work an AI agent can carry on its own has been doubling about every four months, and the best systems now reach the ceiling of what researchers can reliably measure. The adoption clock is barely ticking — only about one in five U.S. businesses has started. For a middle-market CEO, the gap between those two clocks, not the raw capability number, is the strategic position.
That gap reframes the job. If capability is racing ahead while deployment lags, the constraint on AI value in your business is almost never the model — it's absorptive capacity: workflow redesign, skills, trust, and integration. The people who get the most from AI are experienced operators who restructure how the work is done, and collaborative "augmentation" use is currently winning over hands-off "automation." The winners this cycle won't be the firms with the best AI; they'll be the ones that built the capacity to absorb it. Read the dials below as decisions, not statistics — and remember one month is never a trend.
— THE FAST CLOCK: capability frontier —
METR autonomous time-horizon — about 16 hours of expert work, at the limit of what we can measure. So what: an agent can now carry a task that takes a skilled person roughly two working days, and the frontier is bumping the ceiling of the measurement itself. Now what: re-scope one multi-day analytical workflow as "agent drafts, human judges" and pilot it this quarter rather than waiting. Source: metr.org
Doubling rate of capability — time-horizon doubling about every 4 months (down from ~7). So what: the pace of capability gain has roughly halved its doubling time since 2023, with no plateau visible. Now what: assume next year's frontier model is materially stronger than today's, and build that into any 12-month roadmap. Source: metr.org
"Novel reasoning" benchmark fall-rate — ARC-AGI-2 frontier in the mid-80s%; meta-systems above 95%; the holdout (HLE) still around 35%. So what: the tests built specifically to stump AI are falling fast, and the few that still hold are the real frontier. Now what: retire "AI can't really reason" as a planning assumption, and treat the remaining holdouts as the clock that matters. Source: arcprize.org
Frontier training compute — growing about 5x per year (doubling ~5 months). So what: the raw fuel behind capability keeps compounding, with no sign of slowing through the decade. Now what: don't bet your strategy on an imminent capability ceiling — there isn't one in view. Source: epoch.ai
Algorithmic efficiency — about 3x per year, same result for one-third the compute. So what: even if compute growth stalled, models keep getting more capable per dollar on a predictable curve. Now what: capability you can't justify today gets affordable on schedule — plan for the curve, not today's price. Source: epoch.ai
Inference cost at fixed quality — halving about every 2 months (9–900x per year by tier). So what: "too expensive to deploy at scale" has a very short shelf life right now. Now what: re-run the business case on any shelved AI project every two quarters — the unit economics flip underneath you. Source: epoch.ai
— THE SLOW CLOCK: diffusion & real-world impact —
U.S. firm adoption (Census) — about 19.8% of businesses using AI, ~37% at firms with 250+ staff. So what: only about one in five firms has even started; the hype says you're late, the data says the field is wide open. Now what: play this as a lead position, not a laggard one — move deliberately and well, not frantically. Source: census.gov
How people actually use AI (Anthropic Index) — collaborative "augmentation" now dominant (~52%); hands-off use eased from a peak. So what: this is not a straight march to automation — as AI spreads, people use it more collaboratively and across more tasks. Now what: frame AI internally as leverage for your people, not replacement of them — it's both accurate and adoption-friendly. Source: anthropic.com
Entry-level labor signal (Stanford / ADP) — ages 22–25 in exposed roles down about 13–16%; senior staff steady or up. So what: the bottom rung of the career ladder is thinning while experienced workers hold their ground. Now what: treat this as a 5-year succession question, not a layoff cue — where do your future senior people come from? Source: digitaleconomy.stanford.edu
Productivity field studies — real but uneven, gains concentrate with experienced operators. So what: the value is genuine, but it accrues to people and teams who restructure how the work is done — not to tool access alone. Now what: invest in enablement (skills and workflow redesign), not just licenses — that's where the return lives. Source: nber.org
— IGNORE THE NOISE: loud signals that carry no information —
Launch demos and viral threads — staged, cherry-picked, and optimized for reaction.
Funding rounds and valuations — capital and commercial traction are not capability.
Saturated benchmarks (e.g. MMLU) — uninformative once scores sit near the ceiling.
Pundit timelines and prediction markets — a thermometer of sentiment, not a measurement.
Any figure quoted without a confidence interval — especially at the frontier, where the error bars are now enormous.
How to read this brief. The trajectory is the unit — one month is noise, so judge direction and rate across a quarter or two before acting. Even the best yardstick is bending: the strongest capability measure (METR) is now hitting its ceiling, so read frontier numbers as "at least," not "exactly."
An AI for the C Suite® intelligence brief. Compiled 6 June 2026; next review July 2026.
AI isn't a trend or a buzzword and it's certainly not something you can afford to ignore. Join the AI for the C Suite community today: https://aiforthecsuite.com/ #chadharvey #aiforthecsuite #aic

Most organizations are trying to skip straight to AI orchestration — and it's costing them. Melissa Reeve, founder of HyperAdaptive Solutions and author of HyperAdaptive: Rewiring the Enterprise to Become AI Native, joins Chad Harvey to break down why the support structures that would actually make AI work are being skipped, what a real AI transformation roadmap looks like, and why giants will fall if they don't rewire now.
Melissa spent 25 years as a marketing exec and agile thought leader — including a run as the first VP of Marketing at Scaled Agile, where she helped scale from 60,000 to over a million people trained in their framework. Now she's applied that operating model lens to AI, building her five-stage HyperAdaptive model from 18 months of research into how organizations like Toyota, FedEx, and JP Morgan are actually becoming AI native.
In this episode: 🎹 Why AI is like a piano — easy to touch, hard to master 🏗️ The support structures most mid-market companies are missing 📡 What the AI Activation Hub is and why your org needs one 🔄 The AI Learning Flywheel — how knowledge flows up and down your org 🎯 Why every leader needs an AI North Star (and what Moderna's looks like) ⚠️ The bifurcation problem: AI power users vs. everybody else 🧱 Why legacy operating models rooted in Taylorism won't survive AI 📊 How AI is breaking the annual budget cycle — and why that's a good thing 🤝 Why middle management isn't obsolete — it's your secret alignment weapon 🏆 The 1% Club: the organizations that will actually win the AI race
Whether you're a CFO at a $300M manufacturer or a COO at an 80M SaaS company, this episode gives you a clear-eyed, stage-by-stage framework for building an AI-native organization without burning through capital or burning out your people.
📖 HyperAdaptive by Melissa Reeve — available on Amazon and major retailers
🌐 hyperadaptive.solutions
🔗 Connect with Melissa on LinkedIn: melissa.m.reeve
 
 
🎙️ AI for the C-Suite is the show for senior leaders who know AI matters and need to figure out what to do about it. Subscribe wherever you get your podcasts and follow us on LinkedIn.
🌐 aiforthecsuite.com

Most AI vendor evaluations collapse the layers for simplicity. This episode gives you a reason not to.
When two products run on the same underlying model but feel completely different in practice, most teams can't explain why. That confusion makes clean purchasing decisions harder, weakens your RFP, and leaves you reacting to demos instead of driving the evaluation. The answer comes down to architecture.
In this episode, Chad introduces a four-layer AI architecture framework borrowed from an unlikely source: virology. The four layers are Core (the large language model itself), Harness (the configuration layer that defines personality, memory, and logic), Envelope (the deployment surface that determines who accesses the AI and how), and Spikes (the tools and integrations that let AI take action inside your business). Each layer does different work. Once you can see them separately, vendor comparisons stop being apples to socket wrenches.
Chad also walks through four specific questions to put to any vendor or internal champion presenting an AI proposal (one question per layer) so you can identify where the real differentiation is and where the marketing language is doing the heavy lifting.
For a mid-market organization making a six or seven figure technology decision, this framework is a practical starting point for structuring any AI evaluation conversation.
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