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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.
Access just crushed ownership. Google's putting Gemini behind corporate firewalls. Citi's wealth advisors are getting AI augmentation. Salesforce doubled its paying agent customers, and Dell can't build servers fast enough to meet demand. But here's the thing, one dependency failure earlier this year took down half the Internet.
Ash:Today's about building a control plane before your AI strategy becomes someone else's outage.
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 brought to you by Cohen Partners Executive Search, the boardroom pulse, and execsuccession.com. Today is Friday, 09/12/2025. Let's get busy.
Freeman:Google drops a bombshell. Gemini goes behind the firewall.
Ash:Google just changed the game for regulated enterprises. Gemini, their flagship model is now available for on premises deployment. Not hybrid, not private cloud, on prem. In your data center with your keys, your identity management, your audit logs. This isn't incremental, it's architectural.
Ash:Every bank that said we can't use cloud AI because of data residency just lost that excuse. Every health care system that claimed regulatory compliance prevents adoption needs a new objection. The reality is AI just graduated from PowerPoint promises to production possibilities in environments where a single data leak triggers congressional hearings. Your move. Demand an on prem pilot that proves three things, Identity stays internal, logging remains under your control, and latency under load doesn't crater performance.
Ash:Get those answers in writing with SLAs attached.
Freeman:Citi weaponizes AI for wealth management rolling out in phases.
Ash:Citi's Ask Wealth Platform launched globally for advisors with advisor insights piloting client communications, augmenting wealth management with AI for analysis and recommendations, rolling out in phases over the coming quarters. Make no mistake. This is artificial intelligence entering production in one of the most regulated verticals on Earth. The governance architecture they're building should be your template. Every prompt gets red teamed before deployment, sensitive topics trigger automatic refusals, and there's a provenance trail so detailed you could hand it to regulators before breakfast and sleep soundly.
Ash:If you're in financial services or any regulated industry, here's the mandate. No customer sees an AI generated word until you have a written audit policy that would survive a deposition, period.
Freeman:Salesforce's agent revolution hits escape velocity.
Ash:Salesforce revealed 6,000 paying customers for AgentForce doubling quarter over quarter with 60% more moving from pilots to production. These aren't tire kickers. They're companies routing real work through autonomous agents that handle everything from lead qualification to service ticket resolution. While you're debating whether agents are ready, your competitors are already harvesting gains in lead qualification and service. If you haven't identified three golden paths where agents could reclaim five hours per week per team, you're not behind.
Ash:You're donating market share.
Freeman:Dell and HPE can't build AI servers fast enough. Our boardroom number today,
Ash:greater than 100%. That's Dell's year over year growth in AI server shipments for the 2025. HPE is singing the same tune, order books overflowing, lead times stretching, prices firming. Here's the plain English explanation. Compute scarcity is real, it's getting worse, and the companies treating GPU access like spot purchases are about to discover what out of stock means at enterprise scale.
Ash:Your capacity strategy needs to look like commodity hedging, reserve your baseline, negotiate burst privileges, secure rollover rights, and lock in pricing before the rest of the market realizes there's a shortage.
Freeman:This year's dependency failure delivered a master class in fragility.
Ash:Remember that cascading outage from June when a single upstream dependency failed and took down platforms you thought were bulletproof? That wasn't a black swan, it was a preview. Multi cloud doesn't equal resilient if your providers share hidden dependencies three layers deep. Your homework, run a forty eight hour drill titled what breaks when x breaks. Map every critical dependency, identify convergence points, and build contracts with objective triggers that activate failover without requiring a conference call.
Ash:If your resilience plan needs human intervention to activate, it's not a plan, it's a prayer. After an ad, we'll architect the control plane that lets you run multiple models across multiple venues without losing your mind or your governance.
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Freeman:Today's deep dive, the AI control plane playbook because chaos at scale is still chaos.
Ash:A Fortune 100 CTO shared this yesterday. We're buying the ability to orchestrate AI, to route work to the right model in the right place with the right governance at the right price. The control plane is our new competitive moat. That's today's strategic imperative, Building the layer that sits above your models and makes them dance together. Not a single brain, but a conductor that chooses which brain handles which task, enforces your policies, logs everything that matters, and fails over gracefully when something breaks.
Ash:Let's start with what a control plane actually is, and I'll keep it simple. Your control plane needs four engines running in parallel. The routing engine decides which model and venue handles each request. Gemini for complex reasoning, Claude for nuanced writing, LAMA for cost sensitive bulk work, on prem for sensitive data, cloud for burst capacity, edge for real time inference. The policy engine enforces your rules before anything else happens.
Ash:Data residency requirements, masking protocols, retention policies, these aren't suggestions. They're gates that won't open without compliance. The provenance engine stamps every interaction with forensic detail. Who asked, what ran, which version, what data touched it, what came out. Not for nostalgia, for the audit that's definitely coming.
Ash:The resilience engine handles the inevitable failures. Provider goes down, root around it, latency spikes, shift to backup, quality degrades, pin the last known good version, all automatic, all policy driven, no heroes required. Miss any of these four and you don't have a control plane. You have a suggestion box with API access. Next, you need to design for data gravity and that means bringing compute to your data, not the other way around.
Ash:Here's the architectural truth Oracle figured out first. Data has gravity. The bigger it gets, the harder it is to move. Stop trying to fight physics. Keep your data, identity, and logging exactly where they are.
Ash:Then bring the compute to them through high bandwidth interconnects or on prem deployments. Demand architectural diagrams that show actual measured latency under production loads. Not theoretical, not it should be fast. Real numbers from real tests with your real data volumes. If the diagram has more hand waving than hard numbers, the implementation will too.
Ash:Now let's talk governance. And I mean governance so clear that even compliance will understand it. Your AI governance policy should fit on an index card. First, customer prompts and outputs never train vendor models without explicit opt in. Second, data residency zones are immutable.
Ash:Data and inference happen only where approved. Third, every output carries full provenance for ninety days minimum. That's it. If your governance document is longer than your employment agreement, it's not governance. It's procrastination disguised as prudence.
Ash:When it comes to evaluation, stop running beauty contests and start building a harness. Stop asking which model is best. Start asking which model wins for our specific workflows. Pick three critical business processes. Write 10 representative prompts for each with known good outputs.
Ash:Score every model on accuracy, latency, cost, refusal rate. The winner isn't the one with the best benchmark scores or the slickest demo. It's the one that requires the least human editing at acceptable economics for your actual work. Run this evaluation quarterly. When vendors ship improvements, let the harness decide if you adopt or pin prior versions.
Ash:Your cost reporting needs to speak the language of business, not tokens. Kill cost per token reporting immediately. Your CFO doesn't care about tokens, they care about outcomes. Start measuring cost per customer issue resolved, cost per document processed, cost per transaction approved, and cost per insight generated. These are the metrics that matter.
Ash:Set team level monthly caps with automatic alerts at fifty, seventy five, and 90%. Cache common prompts. Why pay to answer the same question twice? Root heavy lifting to cheaper models when quality permits. Publish payback periods for every use case, and terminate anything that can't justify its existence in quarters, not years.
Ash:Resilience can't wait for committees, you need automatic triggers, define objective conditions that activate failover automatically. When your primary provider status page shows degradation, reroute batch work. When latency exceeds baseline by 50% for five minutes, shift to secondary. When error rate crosses 1%, pin last stable version and alert operators. Then, and this is big, run a twenty four hour fire drill, not a tabletop exercise.
Ash:A real drill where you fail over production traffic. If your resilience plan has never been tested under fire, you don't have resilience. You have hopes and prayers. The control plane needs owners, not committees. And here's where 40% of companies fail.
Ash:Gartner's latest data shows 40% of AI projects fail on integration, not on model quality, not on data, on integration. The difference between success and failure, having the right architects who've actually built multi model systems before. You need five named owners, and finding them requires specialized recruiting expertise. The capacity owner negotiates reservations like a commodity trader, someone who's managed cloud spend above 50,000,000 and understands burst pricing, reserved instances, and rollover economics. This isn't your traditional IT buyer.
Ash:The platform architect, and this is the hardest hire, needs proven experience building multi model orchestration, not someone who's deployed a chatbot. Someone who's integrated three or more AI providers handled failover between them and kept latency under fifty milliseconds. Companies like Cohen Partners specialize in finding these unicorns who've actually shipped heterogeneous systems. The evaluation lead runs model bake offs and guards version control. Look for someone who's built testing harnesses for ML systems, not just someone who knows Python.
Ash:They need to understand drift detection, AB testing at scale, and how to measure business outcomes, not just accuracy scores. The privacy lead who can navigate between legal engineering and vendor contracts. This person has redlined AI agreements before, understands data residency across jurisdictions, and can translate GDPR into architectural requirements. Traditional privacy officers won't cut it. You need someone who speaks both compliance and API.
Ash:The FinOps owner who's managed AI spend above 10,000,000 annually and can build cost models that predict runway, not just report consumption. They've implemented chargeback systems, built team level quotas, and know how to cash inference results to cut costs by 30%. Here's the strategic reality. Retail companies thinking they just need a chatbot are discovering that even simple point solutions require orchestration when you scale beyond pilot. That customer service bot needs to fail over when providers hiccup, route sensitive queries to compliant models, and integrate with your existing ticketing system.
Ash:Suddenly, need that platform architect whether you planned for it or not. The talent premium for these roles is real. Expect to pay 20 to 40% above traditional enterprise architect salaries. But the cost of hiring wrong, that's your 40% failure rate staring back at you. Partner with specialized recruiters who can differentiate between someone who talks about multi model architecture and someone who's actually built it under production load.
Ash:Finally, your governance needs a cadence that matches how fast this market moves. Monday means capacity and spend review. Fifteen minutes, no slides, just numbers. Wednesday brings quality and drift assessment. What's working?
Ash:What's breaking? What needs attention? Friday delivers your customer readiness check. What can we responsibly promise next week without triggering a rollback? When incidents hit, they will at some point.
Ash:Your forty eight hour response team activates with pre approved options and clear escalation paths. No emergency meetings to decide who decides. The plan executes itself. Your two week challenge with concrete deliverables. Identify one strategic AI initiative for 2026 where competitive advantage is measurable.
Ash:Schedule vendor briefings specifically about capacity near your data, not generic cloud credits. Within fourteen days, three documents land on your desk. Document one, a capacity reservation quote with time phased ramps starting at twenty five percent scaling to 100%, rollover provisions for unused hours, and price protection that survives inflation in writing with SLAs. Document two, a proximity architecture showing exactly how compute connects to your data, identity integration diagrams, unified logging architecture measured latency under your actual workloads. If it's not specific enough to implement, it's not real enough to buy.
Ash:Document three, a reversibility clause. Two sentences that let you pin versions and reroute workloads if accuracy drops 15%, latency doubles, or costs exceed budgeted bands. Your escape hatch, pre negotiated. If these documents materialize, you're building competitive advantage. If they don't, you're buying marketing.
Ash:The thread is unmistakable. Companies winning with AI aren't those with the biggest models or the most pilots. They're the ones with control planes that make multi model, multi venue deployments feel like a single, governed, resilient system. Access beats ownership, but control beats everything. Stop thinking about AI as a tech decision.
Ash:Start thinking about it as an orchestration challenge where the conductor, your control plane, determines whether you create symphony or noise. To make this actionable, I've updated the AI capacity strategy toolkit at boardroomdailybrief.com with control plane templates, evaluation harnesses, governance frameworks, and those finance friendly calculators that turn tokens into business cases. For a second opinion on your control plane architecture, email me at ash@boardroomdailybrief.com. 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.