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The Boardroom Daily Brief is a daily business podcast for executives, board members, and leadership-minded professionals who want fast, strategic insights. Hosted by Ash Wendt, each episode delivers breaking business news, leadership strategy, governance insights, and talent development advice—without the fluff. Whether you're a CEO, investor, or rising leader, you'll get clear, actionable intelligence to navigate boardroom decisions, stay ahead of market trends, and lead with confidence.
The next CEO doesn't need prompt tricks. They need judgment. Because when AI hallucinates a promise to your biggest customer at two in the morning, the model made it up isn't a defense. It's a confession. Today we're defining what AI fluency actually means for successors.
Ash:So boards can stop guessing about who's ready and start promoting based on proof, not PowerPoints.
Freeman:The boardroom daily brief delivers strategic intelligence for executives who need clarity fast. Cut through the noise, get to the decisions that matter, and understand the implications before your competitors.
Ash:Welcome to the boardroom daily brief. I'm Ash Wendt, delivering daily intel for executive minds. Thanks to our sponsors, Cowen Partners Executive Search, The Boardroom Pulse, and execsuccession.com. Let's kill two dangerous myths before they kill your succession planning. Myth one, AI fluency means being an AI engineer who speaks in vectors and tensors.
Ash:Wrong. The CEO's job isn't to tune models, it's to make trade offs that survive auditors, customers, and regulators while still moving the business forward. Myth two, your AI strategy is that gorgeous slide deck your consultants built. Also wrong. Real AI strategy is the evaluation harness that catches problems before customers catch them, the vendor contracts that protect you when things break, the data rights that keep you out of court, and the rollback plan that saves you when everything goes sideways.
Ash:Here's what AI fluency actually means for the person running your company. Seven decisions they must own in plain English, measurable in cash and risk. Decision one, data boundaries. The question that keeps lawyers awake. What can we use?
Ash:Where does it live? Who approved training on it? If your successor can't point to a data processing inventory with actual approvals and expiration dates, they're gambling with other people's information, and that gamble ends in either lawsuits or headlines, usually both. Decision two, model procurement and the second source salvation. When do we build?
Ash:When do we buy? When do we broker? And here's the critical part. Where's the backup plan when your primary vendor either fails spectacularly or triples their prices overnight? Single vendor romance feels efficient until it becomes a crisis.
Ash:Your successor must be able to say four words that matter. We can switch vendors. Decision three, the evaluation harness that catches disasters before launch. How do we test quality before a model touches a single customer? Accuracy, bias, safety, latency, and cost, all measured against a real baseline, not hopes and prayers.
Ash:No harness means no launch, period. Look at Google's Gemini disaster in February 2024. The model generated historically inaccurate images because their evaluation harness was tuned for diversity without a common sense baseline. A fluent leader catches this during testing, not on Twitter after the world is laughing out loud. Decision four, security and privacy, where the real monsters hide.
Ash:Where can prompt injection happen? Where will data leak? When do we run on private infrastructure versus shared? How do we redact, watermark, and log everything so when a regulator shows up? And they will.
Ash:We can prove what happened. Here's your wake up call. In February 2024, a tribunal ruled Air Canada was liable for their chatbots invented refund policy. The airline's defense that the AI was a separate legal entity got laughed out of court. If your AI makes promises, you're keeping them.
Ash:The model hallucinated is now legally equivalent to we screwed up. Decision five, unit economics. Because AI isn't magic, it's math. What does this workload cost per thousand tokens? Per inference?
Ash:Per user hour? And critically, what happens to that cost when volume doubles? Leaders who can't price AI workloads can't steer the business. Here's the thing. AI fluency isn't just about stopping disasters.
Ash:It's about moving fast when the math works. Spending $10,000 on inference to save a 100,000 in manual labor is a great trade, even if the model occasionally stumbles. That's judgment, not perfectionism. Decision six, vendor contracts that actually protect you. Version pinning, data residency, training opt out defaults, log access, audit cooperation, and a reopener clause when regulations change.
Ash:These aren't legal trivia. They're operating leverage. You're not just looking for confidentiality. You're looking for zero retention APIs. OpenAI Enterprise and Azure OpenAI now default to not training on your data, but watch the exceptions.
Ash:Human review for safety and third party plugins. If your successor can't find the opt out of human review clause in Adobe's or Zoom's terms from late twenty twenty three, they're accidentally donating your intellectual property to the public domain. Decision seven, the rollback plan, your emergency parachute. The only thing worse than a bad model is a model you can't turn off. Where's the kill switch?
Ash:Who owns it? What happens to customers when you pull it? Google didn't try to fix Gemini's bias issues in real time. They executed a hard stop on image generation. That's fluency.
Ash:Knowing when you can't patch, you must pull. Six questions boards should ask every successor and expect actual answers, not word salad. First, what's our AI bill of materials? Models, data sources, evaluation suite, logging, human in the loop processes. Who owns each piece?
Ash:Second, how do we measure quality and catch failure? Where does bias show up? How fast can we detect drift? What's our false positive cost in actual dollars? Third, what are our data boundaries?
Ash:Who approved training? Where's retention controlled? What triggers legal review? Fourth, what's our evaluation and rollback plan? Where do we test?
Ash:When do we ship? How do we retreat without destroying customer trust? Fifth, where are we vendor fragile? What's single source today? What's the timeline to a second option?
Ash:Sixth, what are the unit economics at scale? How does this change our p and l next quarter, not next decade? And here's the bonus question nobody asks but should. Do we have a shadow AI inventory? Every unsanctioned tool someone bought on a corporate card running wild in your organization, who's shutting those down or bringing them into compliance?
Ash:Evidence gates for AI fluency, the artifacts that prove someone can actually do this job. Gate one, a data processing inventory that lists sources, owners, approvals, expirations, and training permissions. Signed, sealed, and refreshed monthly. Gate two, a vendor comparison where two models were evaluated with the same harness and a backup option was made ready for deployment. Gate three, an evaluation report that led to a real go or no go decision complete with test sets thresholds and cost per inference calculations.
Ash:Gate four, a red team log showing they caught a real issue before it escaped, including the fix and customer communication plan. Gate five, a rollback drill executed either in anger or practice with time to safe state measured in minutes, not meetings. Let me show you the difference between AI theater and AI fluency in thirty seconds. A mid market logistics company deployed a routing bot. Tuesday morning, the bot starts hallucinating, promising two hour delivery windows that don't exist.
Ash:The AI enthusiast leader wastes three days begging engineers to prompt engineer the lies away. The AI fluent leader looks at the bleeding, $4,000 in penalties per hour, and pulls the rollback trigger immediately reverting to the boring but honest rules based engine. One approach costs 6 figures and customer trust. The other cost fifteen minutes of downtime. That's the difference between knowing AI and knowing business.
Ash:Here's your boardroom test that separates pretenders from practitioners. The scenario, a vendor promises 40% cost reduction, legal flags training data risk. The CISO warns about prompt injection vulnerabilities. Give your candidate twenty minutes to frame two options, price the risk, cost the reversibility, and make the call. No buzzwords allowed, just trade offs they can defend when things go wrong.
Ash:Launch a shadow mission that converts potential into proof in ninety days. Three concrete deliverables, migrate one AI workload to a second vendor, prove you can switch, ship an evaluation harness with weekly runs and publish three reports, prove you can measure, retire one AI theater initiative that burns cash without creating value, prove you can kill sacred cows. Present to the board at day 45 and day 90. Reality beats slides every time. Your fourteen day installation starts now.
Ash:Week one, publish the AI decision rubric on one page everyone can read. Name owners for data, models, evaluation, logs, and rollback. No committee ownership allowed. Stand up a lightweight evaluation harness starting with one high visibility workflow. Write the vendor calendar showing when you'll rebid and what protections you'll require.
Ash:Baseline the unit economics of one workload in actual dollars. Week two, run the boardroom scenario for your top successor and score it with your rubric. Launch the shadow mission for candidate number two. Put the AI decision review on the executive calendar. Thirty minutes every other week.
Ash:Ship or kill decisions only, no philosophy discussions. Add two numbers to your wall next to revenue and cash. Two metrics that matter more than any AI hype. Governance velocity, hours between a model change request and a go or no go decision. If approving a prompt update takes three weeks, governance isn't protecting you, it's strangling you.
Ash:Target, under forty eight hours for minor changes. Golden set stability, weekly run of 100 non negotiable safety and brand prompts. If the new model answers 98 correctly, you're stable. If it drops to 92, you have regression. Pass or fail, no artistic interpretation, the objections you'll face and the responses that hold the line.
Ash:We don't have time for evaluation, then you don't have time for customers. Evaluation isn't overhead, it's insurance. We can't afford a second source. You can't afford single vendor fragility, price the outage, price the lock in, then tell me again about affordability. The CEO shouldn't be in model details.
Ash:Correct. The CEO should be in decision details, data rights, unit economics, and reversibility. That's not technical, that's strategic. What you tell the board when they ask about AI readiness, we've defined AI fluency in plain English. We have the bill of materials, the decision rubric, the evaluation harness, the vendor calendar, and the rollback drill.
Ash:Here are the artifacts. Here's the data. Governance velocity under forty eight hours. Golden set stability at 98%. If we're wrong, we'll know fast enough to fix it.
Ash:What you tell the successor when they ask what you expect, you don't need to be an engineer. You need to make three kinds of AI decisions under pressure. What data we can use, which models we can trust, and when we need to stop. Use the rubric. Move one workload to prove you can switch.
Ash:Ship a harness to prove you can measure. Kill one vanity project to prove you can focus. That's the job. Here's the truth about AI and leadership that nobody wants to admit. Fluency isn't about writing clever prompts or name dropping the latest models.
Ash:It's about making defensible decisions when the technology is powerful, the risks are real, and the regulations are still being written. Your next CEO doesn't need to understand transformers and attention mechanisms. They need to understand trade offs and reversibility. They need to know when to ship fast because the math works, and when to stop cold because the risk is existential. Define the decisions, install the harness, migrate the vendors, shorten the latency, raise the stability.
Ash:That's how you make your next leader AI fluent without turning them into an engineer. Because when your chatbot starts making promises you can't keep, or your model starts leaking data you shouldn't have used, or your vendor quadruples their price because you're locked in, that's when AI fluency matters. Not in the boardroom with slides, in the real world with consequences. That's it for the boardroom daily brief. I'm Ash Wendt, delivering daily intel for executive minds.
Ash:Get in, get briefed, get results.
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