Prompt and Circumstance

AI adoption rarely fails on technology. It fails on leadership: teams resist changes nobody explained, annual planning cycles can't keep up, and the real bottleneck is often a person or a process, not a model. Long-time coach Tom Adams joins to unpack what two decades of work with founders, owner-operators and CEOs reveals about leading through this shift. The conversation traces a career from seminary and a menswear chain that grew to eight stores before collapsing, through a decade on the speaking circuit, to 22 years of coaching. Tom explains why he mirrors client problems into deliberate AI play rather than ROI-driven pilots, covering Eli Goldratt's Theory of Constraints, the shift from seasonal to structural uncertainty, and systems thinking, including why an undisclosed ChatGPT memo can trigger organizational rejection. The close looks at consumer agents going mainstream: Meta's Muse watching Facebook Marketplace, ChatGPT's agentic front end, and Gemini planning a complex Bali-to-London itinerary. The through-line: AI is a permanent condition of leadership, and momentum comes from small, reversible decisions. Highlights Treat AI as a permanent condition of leadership, not a project you implement and move past. Mirror real client problems into deliberate AI play instead of demanding ROI from every experiment. Use Eli Goldratt's Theory of Constraints to find the bottleneck before buying a tool. Treat uncertainty as structural and trust your ability to adjust, not to know everything. Map your company's system first: undisclosed chatbot use already creates organizational rejection. Watch Meta's Muse and ChatGPT's consumer agents teaching the mainstream how to use agents. Important Concepts and Frameworks Theory of Constraints — Eli Goldratt's idea that finding the business's biggest bottleneck unlocks more capacity and throughput. https://tocinstitute.org/ Structural Uncertainty — The condition where unknowns arrive daily rather than seasonally, so leaders must act without full information. Systems Thinking — Seeing a company as visible and invisible dynamics where a push triggers reinforcing or regulating responses. https://thesystemsthinker.com/ Trusted Advisor — What long-term coaching becomes: a truth-telling thinking partner for lonely leaders. Tools & Resources Mentioned Claude — An AI assistant Tom subscribes to and plays with alongside other models. https://claude.ai/ ChatGPT — Referred to as Chat; Tom's doorway into AI and now a consumer agentic front end. https://chatgpt.com/ Gemini — Planned Mark's Bali-to-London flights and now powers live conversational multi-agent platforms. Grok — The X bot named as the competitor Meta's Muse is responding to. https://grok.com/ Meta's Muse — Meta's agent tool, used to watch Facebook Marketplace for interesting watches. https://www.meta.ai/ Calls to Action Pick one bottleneck in your business this month and run a small AI experiment against it. Block weekly play time and turn problems from client or team conversations into prototypes. Map your organization's system before rolling out a new AI process or memo format. Replace one yearly strategic decision with a monthly review to compress decision cycles. Test a consumer agent like Muse or ChatGPT Dots on a low-stakes task before scaling it. Key Quotes "Chat to me is the doorway drug, but it's not the solution." — Tom Adams "AI is a permanent condition of your role as a leader now." — Tom Adams "A lot of leaders are lonely and a lot of times nobody in their world is telling them the truth." — Tom Adams "You do not need to be a radically different leader today than you were two years ago." — Mark Redgrave "The best antidote is to actually think about the systems." — Mark Redgrave Chapters 00:00 — Why Great Leadership Still Wins in the AI Era 04:20 — From Seminary to Menswear to a Coaching Practice 09:49 — Replace AI Pressure With Deliberate Play 15:31 — Find the Bottleneck Before You Buy the Tool 17:20 — What's Really Behind Your Team's AI Resistance 22:55 — Leading When Uncertainty Becomes Structural 33:59 — Systems Matter More Than Effort 40:04 — When an Undisclosed Chatbot Reshapes the System 45:58 — Treat AI as a Permanent Leadership Condition 49:03 — Consumer Agents Go Mainstream Meet the Crew Mike Richardson – Agility, Peer Power & Collective Intelligence Website: https://mikerichardson.live/ LinkedIn: https://www.linkedin.com/in/agilityexpertmikerichardson/ Mark Redgrave – Agility, People and Performance Website: https://www.shift-transform.com/ LinkedIn: https://www.linkedin.com/in/mredgrave/ Tom Adams – Executive Coach, Advisor & Trail Blazer Website: https://tomadams.com/ LinkedIn: https://www.linkedin.com/in/tomadamscoach/

End-of-world AI headlines have you wondering whether to keep investing? Keep going, but watch the cost. Tom, Mike and Mark cover Tom's Waymo rides, Grok Bot's three-day training, cheap Chinese models. Mark calls labs' doom talk self-serving; Tom cites $1,000/month in subscriptions and API calls for a one-person operation, while DeepSeek and Kimi K3 run on your hardware. Highlights Riding a Waymo twice — including a tight LA street — turned a skeptic into a believer. Chinese models like DeepSeek Flash and Kimi K3 are fast, cheap and already in daily use. End-of-world warnings from AI labs read as self-serving cover for a first-past-the-post race. Route routine prompts to cheaper models; 99.9% of work doesn't need the newest model. One-person AI subscriptions and API calls now top $1,000 a month, and prices keep rising. Home-built tools are retiring WordPress hosting, Zapier and N8N subscriptions one by one. Important Concepts and Frameworks Grok Bot — Grok's bot platform that runs multiple bots inside your own private zone, working together on tasks. DeepSeek Flash — Chinese model Tom uses daily, built to need less memory because chips are scarce. Kimi K3 — Chinese model praised for development work and called unbelievably cheap at token level. Prompt Balancing — Sending certain prompts to cheaper models instead of defaulting everything to the newest, priciest one. Orchestrator Model — A premium model told to do no work, only pick and hand off tasks to cheaper sub-agents. First Past the Post — Mark's frame for the AI race: whoever gets there first could control every system and network. Open-Weight Models — Models you download and run on your own hardware, like Tom's Mac Mini, with no cloud call. Tools & Resources Mentioned Waymo — Driverless ride-hailing service Tom rode twice in Phoenix; it also cleared a tight LA street. Grok Bot — Bot platform pushed through a three-day money-making training, with a $200 commitment ask. DeepSeek Flash — Chinese model that is extremely fast and designed to run with far less memory. Kimi K3 — Chinese model Tom uses for development work; Mark's friend calls it really good. Fable 5.1 — Newest model Tom loads as an orchestrator rather than as a worker. Netlify — Hosting Mike used to deploy his event site until he ran out of credits. Zapier — Automation service Tom is down to two flows from, saving $300 a year. n8n — Workflow automation tool costing roughly $450 to $500 a year that Tom is retiring. Calls to Action Tally your monthly AI subscriptions and API spend before your next planning session. Inventory where work is hard or stalled, then map AI against those specific points instead of chasing tools. Test one cheap or open-weight model on real work before assuming you need the premium tier. Set a premium model as orchestrator and route the heavy lifting to cheaper sub-agents. Put the youngest people in your company in the room when you set AI direction. Key Quotes "It thought about it for a second, and then it totally did it." — Mark Redgrave "DeepSeek's Flash version, which just came out, is extraordinarily good. It's extraordinarily fast." — Tom Adams "To me, this is a completely self-serving false narrative, right?" — Mark Redgrave "I run an open weight model on my Mac Mini right now, and it works." — Tom Adams "The absolute truth is 99.9% of us do not need the latest fable, right?" — Mark Redgrave "There's a long list of things that could end the world. This might be another thing on the list." — Mike Richardson Chapters 00:00 — Riding a Driverless Car Twice and Trusting It 07:18 — Grok Bot's Three-Day Promise to Make a Bazillion Dollars 10:48 — The Cheap Chinese Models Nobody Talks About 14:01 — End-of-World Warnings Meet Blocked Data Centers 15:47 — Who Actually Benefits From the Doom Narrative 24:31 — A Manufacturer's Spec Sheets Finally Match the Floor 32:55 — Hedge With DeepSeek or Stay All-In on Anthropic? 39:24 — Prompt Balancing: Send Cheap Work to Cheap Models 43:10 — Retiring WordPress, Zapier and N8N Subscriptions 46:01 — Planning for '27: Inventory Your Friction First Meet the Crew Mike Richardson – Agility, Peer Power & Collective Intelligence Website: https://mikerichardson.live/ LinkedIn: https://www.linkedin.com/in/agilityexpertmikerichardson/ Mark Redgrave – Agility, People and Performance Website: https://www.shift-transform.com/ LinkedIn: https://www.linkedin.com/in/mredgrave/ Tom Adams – Executive Coach, Advisor & Trail Blazer Website: https://tomadams.com/ LinkedIn: https://www.linkedin.com/in/tomadamscoach/

Most leaders are adopting AI tactically—testing tools, automating tasks, buying licenses—without realizing they’re actually placing big strategic bets about the future. This episode unpacks a simple but powerful framework (from Dan Pupius of The General Partnership) to help you see and shape those bets deliberately instead of accidentally. You’ll hear four key “AI bet axes” that sit underneath every AI decision: Token economics – Are you planning for compute to be scarce and expensive, or abundant and cheap? Model self‑sufficiency – Are you assuming today’s scaffolding, glue code, and workflows will still matter once frontier models get much better? Platform structure – Are you locking into a single AI provider, or designing for multi‑model flexibility? Trust and governance – Are you moving fast and cleaning up governance later, or baking in auditability and control from day one? The conversation connects these bets to real examples: an AI lead‑triage product for insurance (SimparaAI), how companies waste millions on tokens by defaulting to “latest, greatest” models, and the emerging role of routing layers like OpenRouter that sit above all the major LLMs. Layered on top of the four bets is an agility lens: don’t predict “the” future—set up your system to be ready for a range of futures. That means firing “bullets before cannonballs” (to borrow Jim Collins’ language): running small, reversible experiments, watching early signals, and preserving your ability to pivot when you’re wrong. If you’re a CEO, founder, or functional leader, this episode will help you: Expose the implicit AI bets you’re already making. Decide where you intentionally lean (e.g., abundance vs scarcity) and where you hedge. Design pilots, architectures, and governance so you gain AI value now without boxing your organization into fragile, high‑risk choices later. Highlights See every AI initiative as a portfolio of bets, not a prediction about “the” future. Use four axes—tokens, self‑sufficiency, platform, governance—to surface your implicit AI strategy. Avoid overpaying for tokens by routing most work to “good enough” models, not always the latest frontier. Assume models will keep improving; bet on integration, workflows, and change management, not wrappers alone. Architect for multi‑model flexibility so you can swap providers without breaking your business. Bake in audit trails and explainability now to reduce legal, HR, and cybersecurity risk later. Apply agile thinking: small experiments, early signals, and reversible decisions beat big locked‑in bets. Treat “bullets before cannonballs” as a design principle for AI pilots and investments. Important Concepts and Frameworks Four AI Bet Axes (Dan Pupius / The General Partnership)Token economics: scarce vs abundant compute and tokens. Model self‑sufficiency: model‑native capability vs heavy scaffolding. Platform structure: locked‑in provider vs commoditized, multi‑model. Trust and governance: permissive “move fast” vs constraint and oversight. Firm site: https://www.thegp.com/ Thesis: https://every.to/thesis/your-ai-strategy-is-making-bets-do-you-know-which-ones Token Economics / TokenomicsCost dynamics of running LLMs (context window, model size, power requirements). Business implication: load‑balance workloads to cheaper “good enough” models. Model Self‑Sufficiency vs ScaffoldingQuestion: “Would this product or workflow still matter if ChatGPT/Claude/Gemini/Grok got 10x better?” Highlights where integration, process redesign, and domain context create durable value. Platform Structure / Multi‑Model StrategyRisk of deep lock‑in to a single vendor vs benefits of an abstraction layer. Examples:OpenRouter – unified API over many models, with routing flexibility. - https://openrouter.ai/ - Google’s emerging platform approach to plug different models behind a common interface. Trust, Governance, and Auditability Building audit trails of conversations and model reasoning into products from day one. Recognizing AI as a new surface area for HR, legal, and cybersecurity risk. Agile AI / Optionality Thinking Don’t “pour cement” around assumptions that may shift. Design for fast, cheap, reversible changes in models, tooling, and workflows. “Bullets, Then Cannonballs” (Jim Collins, _Great by Choice_) Fire low‑risk experiments (bullets), calibrate, then scale with big investments (cannonballs). https://www.jimcollins.com/books/great-by-choice.html Tools & Resources Mentioned Sympara AI — Conversational intelligence for lead triage; multi‑agent conversations qualify inbound leads and reduce human chasing. | https://sympara.ai HR Voice Notes Tool — Voice‑note based HR tool (https://hrvoicenotes.com). Cadre AI — AI platform several peer‑group members are piloting for business impact. | https://www.cadre.ai/ OpenRouter — Unified API gateway to many AI models; enables model routing and provider flexibility. | https://openrouter.ai Kimi / K‑3 Model — Chinese model with generous/free access; illustrates downward pressure on token pricing. | https://www.kimi.ai/ai-models/kimi-k3 Groq — AI hardware and inference platform; cited as launching a powerful new product. | https://groq.com/ Claude — Anthropic’s model family, including the large “Fable”-era model; key in the discussion on prompts and scaffolding. | https://www.anthropic.com/claude Gemini — Google’s AI family; strong on platform and integration capabilities. | https://gemini.google.com/ Grok — xAI’s conversational model; another major frontier LLM. | https://grok.com Salesforce (“Clicks, Not Code”) — Example of configuration‑over‑coding philosophy applied to AI integration. | https://www.salesforce.com/ap/platform/low-code-development-platform/what-is-low-code/ Ramp — Financial automation platform previously led by Dan Pupius. | https://ramp.com Calls to Action Map one current AI initiative against the four bet axes (tokens, self‑sufficiency, platform, governance) and document your implicit bets. Identify at least one area to hedge: a smaller, r...

If you’ve ever rolled out a “smart” system at work only to find your people hate it, this episode is for you. Using the World Cup as a live case study, the hosts unpack how well‑intentioned AI and data can quietly make an experience worse—on the pitch and inside your business. They start with the new sensor‑equipped match ball and semi‑automated offside decisions. Technically, the system is brilliant: accelerometers in the ball stream data into models that track each player’s joints to millimeter precision, interpolating body position at the exact moment of the pass. In theory, that should make decisions fairer. In practice, 72% of fans in a UK YouGov survey say technology hasn’t improved the game. The problem isn’t the hardware; it’s how the tech now dominates the experience, pushing referees into deferring to “objective” AI even when it undermines the spirit and flow of the match. From there, the conversation shifts into business. The same pattern is showing up in AI‑generated SEO audits, reports, and “strategy” documents: clients and employees copy‑paste uncontextualized AI output—what the hosts call “AI slop”—into critical decisions. The result is frustration on both sides and a sense of loss long before any tangible gain arrives. Throughout the episode, they explore core ideas leaders can apply immediately: define the real purpose of AI before deploying it; keep a strong human “referee” in the loop; manage the interface between data and people; and treat AI as one half of “collective intelligence” rather than a replacement for judgment. They close by highlighting the massive change‑management miss at the World Cup—no real communication to a billion stakeholders about why and how the tech would be used—and draw a direct line to what happens when organizations introduce AI without clear outcomes, explanation, or buy‑in. Highlights Use AI to support decisions, not replace them; keep a visible, empowered human referee in the loop. Define the purpose of any AI system up front: accuracy, experience, speed, or something else. Don’t let hyper‑granular data overrule common sense; a strength overused quickly becomes a weakness. Prevent “AI slop”: never ship raw AI output without context, synthesis, and human editing. Shield your teams from unfiltered dashboards and models; manage the interface between data and people. Treat AI + humans as “collective intelligence”; raise human judgment as AI capability rises. Plan real change management for AI rollouts: clear “why,” transparent “how,” and repeated communication. Measure stakeholder sentiment early; avoid a World Cup‑style backlash where most users feel net loss. Important Concepts and Frameworks Sensor‑Driven Decision Systems - Embedded sensors (like accelerometers in a match ball) that stream data into AI models to influence real‑time decisions. Semi‑Automated Offside and VAR - AI models map player joints and body posture from multiple cameras to support offside and foul decisions, feeding into video assistant referee workflows. Strength Overused Becomes a Weakness - A capability (e.g., precision data) is positive until over‑applied, at which point it degrades the system it was meant to improve. Collective Intelligence - The deliberate combination of artificial intelligence and human intelligence; as AI capability rises, human judgment and context must rise alongside it. AI Slop - Low‑quality, generic, or context‑free AI output that gets forwarded as if it were insight—like unedited SEO audits or three‑page reports pasted straight from a chatbot. Front‑of‑Field vs. Back‑of‑Field Focus - The distinction between where the “real game” is (goals, critical plays, strategic levers) and where AI is often misapplied (low‑impact, out‑of‑play areas). Change Leadership and Stakeholder Management for AI - The need to communicate why AI is used, what will change, and how decisions will work—especially when billions (or just thousands) are affected. Tools & Resources Mentioned Goal‑Line Technology & VAR (Video Assistant Referee) — Systems that use cameras, sensors, and replay to assist referees with key decisions in football (soccer). Football AI Pro — A football‑specific language model reportedly trained on 300 million data points to let coaches query tactics and patterns mid‑game (e.g., how teams break low blocks). Large Language Models (Claude, ChatGPT) — General‑purpose AI tools people use for SEO audits, website critiques, and generating story arcs and narratives for go‑to‑market materials. Calls to Action Before adding any AI tool, write a one‑sentence purpose: what exact outcome it is meant to improve. Design and communicate a clear “referee in the loop” role—who makes the final call when AI and humans disagree. Stop forwarding raw AI output; insist on a human pass that adds context, edits, and specific recommendations. Audit where data and AI are exposed directly to employees or customers; add human buffers where needed. Launch small AI pilots with structured feedback so you can see whether people feel more gain than loss. Build a communication plan for every AI change: why it’s happening, what will look different, and how decisions will work. Regularly review where AI is “over‑officiating” in your workflows and simplify or dial back where it hurts experience. Key Quotes “People feel the loss before they feel the gain.” — Mark Redgrave “A strength overused becomes a weakness.” — Mike Richardson “We’re at the mercy of the AI instead of it serving a better game.” — Tom Adams “Who’s in the middle between the system and your people?” — Mark Redgrave “As AI rises up, human intelligence needs to rise up alongside it.” — Mike Richardson Chapters 00:28 — World Cup nerves, superstition, and why venue choices feel decisive 05:31 — Sensors in the match ball and semi‑automated offside decisions explained 08:12 — When ‘more accurate’ makes the game worse: fan backlash against tech 10:33 — Human in the loop under pressure: referees vs. AI and public replays 16:28 — From missed goals to toenail offsides: over‑precision as a design flaw 19:04 — Data, transparency, and who should stand between AI and your people 23:32 — AI slop in business: uncontextualized audits, reports, and “story arcs” 28:10 — AI‑generated fans and content: the new media layer around sport 31:40 — Tactical AI for coaches and the coming wave of on‑field augmentation 33:12 — Inches, seconds, and dynamic complexity: what AI should really illuminate 36:20 — World Cup as a failed AI change‑management case study 40:06 — Looking ahead: cool tech, confused roles, and why humans aren’t leaving the cockpit Meet the Crew Mike Richardson – Agility, Peer Power & Collective Intelligence Website: https://mikerichardson.live/ LinkedIn: https://www.linkedin.com/in/agilityexpertmikerichardson/ Mark Redgrave – Agility, People and Performance...

Most leaders still feel AI is a technical maze they don’t understand—and that keeps them stuck in pilot purgatory: scattered experiments, nothing in production, and no real business value. This episode tackles that head‑on and reframes AI as a people, data, and strategy problem long before it’s a tech problem. You’ll hear how mid‑market CEOs visibly relax when they realize they don’t need to “get the tech” to lead effectively in AI; they need to orchestrate change, align projects to strategy, and mobilize their people around real business outcomes. The conversation unpacks why data—structured and unstructured—is now the primary constraint, and why your biggest challenge is often just finding, cleaning, and connecting what you already have in CRMs, ERPs, email, call transcripts, and document stores. Tom shares an emerging approach he’s building around “conversational intelligence”: multi‑agent AI systems that simulate advisory boards and multi‑voice conversations, complete with auditors and supervisors to make reasoning auditable and enterprise‑ready. This leads into a broader discussion about internal advisory boards, IP, and how individuals might someday curate their own AI “councils” based on the thinkers and operators who’ve influenced them. You’ll also hear concrete examples from local AI summits and peer forums: how leaders are using AI to avoid linear headcount growth, where smaller firms are finding affordable “AI accelerants,” and why Microsoft‑centric companies may have a structural edge because their data is already inside one secure ecosystem. The episode closes with very practical next steps: how to inventory your data, who to involve, how to test offerings with real customers, and why you must be willing to hear “you’re not ready” if you want to move fast and build something that matters. Highlights Reframe AI as a change‑leadership and data challenge, not a technical mystery only engineers can solve. Escape AI pilot purgatory by tying every experiment directly to strategic business outcomes and value creation. Treat data (structured and unstructured) as your main AI bottleneck; inventory and centralize before you scale. Use AI to avoid linear headcount growth as you scale, not as a blunt instrument for layoffs. Explore conversational intelligence: multi‑agent AI “advisory boards” that debate, audit, and document decisions. Leverage existing ecosystems like Microsoft 365 to unlock emails, documents, and transcripts securely with AI. Expect emotional resistance; leaders must tolerate “you’re not ready” feedback to refine real-world propositions. Build human peer forums as an antidote to AI‑driven isolation for CEOs who suddenly “don’t know the top.” Important Concepts and Frameworks Pilot Purgatory - Multiple unconnected AI pilots that never reach production or meaningful business impact. “No Data, No AI” Principle - The idea that usable, connected data—more than algorithms—is the real constraint. Structured vs. Unstructured Data Structured: rows/columns in CRMs, ERPs, financial systems. Unstructured: documents, emails, call/meeting transcripts, notes, shared drives. Conversational Intelligence - Multi‑agent AI systems that simulate real multi‑voice conversations, with agents that consult each other and an auditor to enforce constraints and maintain an auditable chain of thought. Headcount Non‑Linearity - Using AI to grow revenue 2–3x without equivalent growth in support, sales, and operations headcount. Data Lakes and Plumbing - The architectural need to connect disparate data sources (data lakes, warehouses, APIs) as the foundation of any serious AI effort. AI Peer and Advisory Models - Using AI to mirror advisory boards or peer groups where multiple “voices” debate, refine, and contextualize advice. Embedded Ecosystem Advantage (Microsoft 365 + Copilot) - Organizations with email, documents, and collaboration already inside one secure ecosystem can unlock cross‑system insights faster with embedded AI tools like Microsoft Copilot — if properly governed. Strategic Alignment of AI Portfolios - Ensuring dozens of in‑flight AI projects map directly to macro business objectives, not just “interesting” use cases. Tools & Resources Mentioned Cadre AI — AI company providing applied AI solutions; referenced via insights from a lead practitioner (Riley Strickland). Strategic Coach — Entrepreneurial coaching program (Dan Sullivan) that shapes how leaders think about growth and leverage. Alex Hormozi / Acquisition.com — Example of a modern content‑driven business/marketing playbook and associated IP questions in the AI era. Microsoft Copilot — Embedded AI assistant across Microsoft 365, with deep access to emails, documents, and collaboration data. Airtable — Flexible database/spreadsheet used by some firms to replicate and free up structured data locked in legacy systems. Google Cloud Platform (GCP) — Cloud platform Tom uses to harden and productionize multi‑agent conversational systems. OneDrive — Cloud storage often holding unstructured corporate documents inside Microsoft ecosystems. Dropbox — Document storage frequently containing unstructured assets relevant for AI. Calls to Action Inventory your data: list your main structured (CRM, ERP, finance) and unstructured (docs, emails, transcripts) sources and where they live. Pick one strategic business objective (revenue, margin, support load) and map current AI pilots directly to that outcome. Identify at least one “AI accelerant” (internal or local expert) who understands both your data and your business context. Run a short, time‑boxed AI sprint with clear ownership outside IT to move one use case from pilot toward production. If you’re on Microsoft 365, explore what Copilot can do with your existing security and data before adding new tools. Join or create a peer forum to regularly compare AI experiments, failures, and wins with other leaders. Put your emerging AI ideas in front of real customers or executives early, and be willing to hear, “You’re not ready.” Consider where a conversational, multi‑voice AI advisor could reduce decision friction or triage complexity in your organization. Key Quotes "Your challenge isn't really AI anymore; it's really data." — Mike Richardson "Success is not going to be determined by technology, Mr. and Mrs. CEO." — Mark Redgrave "This is not a tech problem… it's a human problem." — Mike Richardson "You can't delegate your strategy. Your strategy's your strategy." — Mark Redgrave "You miss 100% of the shots you don't take." — Tom Adams Chapters 00:00 — World Cup banter, missing co‑host, and scene‑setting 04:27 — Local AI summit: human problem, not a technical one 06:39 — Pilot purgatory, data bottlenecks, and productionizing AI 10:28 — CEOs’ sigh of relief: AI as change leadership, not tech mastery 11:19 — Forty AI projects, strategy alignment, and value creation 13:05 — Using AI...