Prompt and Circumstance

Mike Richardson, Mark Redgrave, Ryan Neimann & Tom Adams
  • 5.0 (3)
  • Technology
  • Updated Biweekly

It’s human-friendly banter about code, culture, and CEO reality checks—served up by Mike Richardson, Ryan Niemann, Mark Redgrave, and Tom Adams. No jargon. No hype. Just real talk from four guys who’ve seen it all, and aren’t afraid to say what everyone’s thinking.

  1. 1d ago ·  Video

    AI Demands Leadership, Not Just Tools

    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 RedgraveChapters 00:00 — Why Great Leadership Still Wins in the AI Era04:20 — From Seminary to Menswear to a Coaching Practice09:49 — Replace AI Pressure With Deliberate Play15:31 — Find the Bottleneck Before You Buy the Tool17:20 — What's Really Behind Your Team's AI Resistance22:55 — Leading When Uncertainty Becomes Structural33:59 — Systems Matter More Than Effort40:04 — When an Undisclosed Chatbot Reshapes the System45:58 — Treat AI as a Permanent Leadership Condition49:03 — Consumer Agents Go Mainstream Meet the Crew Mike Richardson – Agility, Peer Power & Collective IntelligenceWebsite: https://mikerichardson.live/LinkedIn: https://www.linkedin.com/in/agilityexpertmikerichardson/ Mark Redgrave – Agility, People and PerformanceWebsite: https://www.shift-transform.com/LinkedIn: https://www.linkedin.com/in/mredgrave/ Tom Adams – Executive Coach, Advisor & Trail BlazerWebsite: https://tomadams.com/LinkedIn: https://www.linkedin.com/in/tomadamscoach/

    AI Demands Leadership, Not Just Tools

  2. Sep 21 ·  Video

    Driverless Cars, Chinese Models and the AI Cost Wall

    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 RichardsonChapters 00:00 — Riding a Driverless Car Twice and Trusting It07:18 — Grok Bot's Three-Day Promise to Make a Bazillion Dollars10:48 — The Cheap Chinese Models Nobody Talks About14:01 — End-of-World Warnings Meet Blocked Data Centers15:47 — Who Actually Benefits From the Doom Narrative24:31 — A Manufacturer's Spec Sheets Finally Match the Floor32:55 — Hedge With DeepSeek or Stay All-In on Anthropic?39:24 — Prompt Balancing: Send Cheap Work to Cheap Models43:10 — Retiring WordPress, Zapier and N8N Subscriptions46:01 — Planning for '27: Inventory Your Friction First Meet the CrewMike Richardson – Agility, Peer Power & Collective IntelligenceWebsite: https://mikerichardson.live/LinkedIn: https://www.linkedin.com/in/agilityexpertmikerichardson/Mark Redgrave – Agility, People and PerformanceWebsite: https://www.shift-transform.com/LinkedIn: https://www.linkedin.com/in/mredgrave/Tom Adams – Executive Coach, Advisor & Trail BlazerWebsite: https://tomadams.com/LinkedIn: https://www.linkedin.com/in/tomadamscoach/

  3. Aug 24 ·  Video

    Four Critical AI Bets Every Leader Is Making Right Now

    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-onesToken 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.htmlTools & Resources Mentioned Sympara AI — Conversational intelligence for lead triage; multi‑agent conversations qualify inbound leads and reduce human chasing. | https://sympara.aiHR 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.aiKimi / K‑3 Model — Chinese model with generous/free access; illustrates downward pressure on token pricing. | https://www.kimi.ai/ai-models/kimi-k3Groq — 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/claudeGemini — 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.comSalesforce (“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.comCalls 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...

  4. Jul 13 ·  Video

    When AI Breaks the Game: Lessons from World Cup Technology

    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  Chapters00:28 — 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...

  5. Jun 28 ·  Video

    From Pilots to Product: Making AI a Strategic Advantage

    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 AdamsChapters00:00 — 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...

  6. Jun 15 ·  Video

    Why AI Value Depends on People, Strategy, and Escaping the Activity Trap

    Most organizations are pouring hundreds of thousands of dollars into AI experimentation with little to show for it. They are trapped in what advisor Mark Redgrave calls the "AI activity trap"—lots of movement, no strategic impact. The problem isn't the technology; the tools will reach parity quickly. The real bottleneck is getting people to adopt, adapt, and change. Without a clear CEO mandate that ties AI directly to business strategy, initiatives remain stuck at the director level where budgets get cut and momentum fizzles. This conversation dismantles the common belief that AI adoption is a technical challenge. Instead, it reframes success around two pivotal concepts: strategy-first AI alignment and cross-functional team design. Leaders learn why functional silos kill innovation—70% of project time is wasted in handoffs between departments—and how small cross-functional "skunkworks" teams can deliver results in weeks instead of months. The episode offers a practical path forward for mid-market CEOs who need to stop frenetic experimentation and start connecting AI investment to the metrics that actually matter. Highlights Tie every AI initiative directly to your company's core strategic priorities.Understand employee "why" before introducing AI-driven change.Stop experimenting without strategic alignment to escape the activity trap.Move AI from director-level pilots to an explicit CEO mandate.Break functional silos with cross-functional teams for faster execution.Recognize that 70% of project time is lost in departmental handoffs.Start with small cross-functional teams instead of restructuring the entire company.Treat AI value creation as a people and change management challenge.Important Concepts and Frameworks AI Activity Trap — The frenzy of experimentation without measurable strategic outcomes. Leaders mistake motion for progress, leading to "pilot purgatory."CEO Mandate for AI — The explicit declaration from the C-suite about what AI is and is not for the business, creating organizational alignment and investment clarity.Theory of Constraints — A management framework for identifying the bottleneck in any process. Applied here to show how departmental handoffs consume 70% of elapsed project time.Cross-Functional Team Design / Skunkworks — Organizing people from different functions around a single mission to eliminate handoff delays and accelerate delivery.Ready, Fire, Aim — A business metaphor describing the common mistake of rushing to action without strategic clarity. The antidote: "ready, aim, fire."Simon Sinek "Start with Why" — Referenced and contrasted as a different kind of "why" than the organizational change motivation discussed in this episode.Tools & Resources Mentioned Claude / Anthropic (Claude Code, Opus 4.8)** — AI coding and reasoning model; noted for verbosity and shifting personality across versions.ChatGPT / OpenAI Codex — AI coding model; noted for concise, action-oriented responses in terminal.Google Gemini — AI assistant; described as sitting between Claude and Codex in communication style.McKinsey & Company — Global consulting firm where Mark serves as a senior advisor on large-scale transformation.Shift — Mark Redgrave's mid-market consulting practice focused on strategy, innovation, and AI adoption. | https://www.shift-transform.comCalls to Action Schedule a leadership team conversation focused on one question: How do our current AI initiatives support our business strategy?Identify the key metrics the CEO actually cares about and audit whether your AI projects connect to those metrics.Choose one high-priority strategic pillar and launch an 8-week cross-functional team to prove AI value, rather than funding multiple scattered pilots.Stop any AI experimentation that cannot be clearly tied to a strategic outcome—redirect that budget toward aligned initiatives.Create explicit CEO-level accountability for AI workstreams, with owners and milestones tied to business results.Key Quotes "AI is a people problem, not a technology problem." — Mark Redgrave"If something is important, make it important." — Mark Redgrave"70% of the elapsed time of any project is in someone's inbox." — Mark Redgrave"We're ready, fire, aiming right now. Stop pulling triggers." — Mark Redgrave"The tools will reach parity quickly. The difference is how you leverage them." — Mark RedgraveChapters00:28 — Why AI Model Personalities Impact Your Daily Work  01:20 — The Frenzy of New AI Releases and IPO Mania  07:01 — AI Is a People Problem, Not a Technology Problem  11:26 — Earning Employee Buy-In Through the Strategic "Why"  14:23 — The AI Activity Trap: Motion Without Results  16:19 — Performance vs. Activity: Strategy Must Lead AI  22:28 — Making AI a CEO Mandate, Not a Director Experiment  30:53 — Operating Model as the Hidden Bottleneck to AI Value  39:10 — Cross-Functional Teams That Deliver in Weeks, Not Months  46:43 — Final Advice: Ready, Aim, Fire Instead of Ready, Fire, Aim Meet the CrewMike Richardson – Agility, Peer Power & Collective IntelligenceWebsite: https://mikerichardson.live/LinkedIn: https://www.linkedin.com/in/agilityexpertmikerichardson/ Ryan Niemann – Software CEO & Board OperatorWebsite: https://bob3.pro/LinkedIn: https://www.linkedin.com/in/ryanniemann/Mark Redgrave – Agility, People and PerformanceWebsite: https://www.shift-transform.com/LinkedIn: https://www.linkedin.com/in/mredgrave/Tom Adams – Executive Coach, Advisor & Trail BlazerWebsite: https://tomadams.com/LinkedIn: https://www.linkedin.com/in/tomadamscoach/

  7. Jun 1 ·  Video

    Why Your Team Is Resisting AI (And How to Lead Through It)

    Is your workforce pushing back against AI, even as you're told you must embrace it or fall behind? You're not alone—and the resistance isn't a problem to solve; it's data to act on. In this episode, the hosts confront the growing tension between AI acceleration and the people who are supposed to adopt it. Students booing AI references at graduation ceremonies. Workers quietly undermining AI rollouts. Communities fighting data center development. And leaders caught between "AI is inevitable" and "we're waiting to see how this plays out." The core argument: this is not a technology challenge—it's a people challenge. All major AI tools are approaching parity. The differentiating factor isn't which model you pick. It's whether your people trust you enough to come along on the journey. Mark introduces the trust triangle—capability, consistency, and selflessness—and asks a hard question: in an era where stock prices rise on layoff announcements, can you credibly claim selflessness? Mike connects the resistance to something deeper: employees and new graduates feel hopeless, and nobody is giving them a compelling vision of a future they can build toward. The conversation surfaces the IKEA call center case study, where AI removed mundane work but inadvertently left employees handling only high-difficulty calls—creating unsustainable cognitive load. The takeaway: removing the easy work doesn't automatically make the hard work easier. The hosts offer a practical framework for leaders: be truthful, create agency (which is the antidote to fear), and ensure shared benefit. And on Monday morning? Start by listening—not by telling. Find three ways to engage your team about their AI fears and actually hear what they say. Highlights Resistance to AI isn't an obstacle—it's feedback. Start listening instead of dismissing.AI tools are reaching "awesomeness parity" quickly; the winner will be the organization that builds trust, not the one that picks the best model.Removing mundane work with AI can backfire if employees are left with only cognitively demanding tasks.Agency is the antidote to fear—let your people build, don't do it to them.The only sustainable competitive advantage left is culture, and it must now be an AI-powered culture.Leaders must go on their own learning journey before they can expect their teams to adopt AI.Super-triage is the most critical leadership skill in an era of exponential change.Important Concepts and Frameworks Trust Triangle (Capability, Consistency, Selflessness) — A leadership framework for rebuilding trust during AI transitions. Capability asks "Can you do this?" Consistency asks "Do you do what you say?" Selflessness asks "Are you doing this for the team or for yourself?"Hype Cycle / Trough of Disillusionment — Gartner's model describing how technologies go from peak inflated expectations to a trough before productive adoption. The hosts argue AI is entering the trough of disillusionment as organizations realize the frenzy created overhead, not value.Dunning-Kruger Effect — The cognitive bias where people overestimate their competence early in a learning curve. Referenced as "Mount Stupid"—the peak many organizations reached before realizing they were "busy fools."Flow (in Agile / Lean) — A state of balanced delivery: not too much/too fast/too scattered, and not too little/too slow/too narrow. The antidote to both disorganized chaos and analysis paralysis.Leader-Led Transformation — The principle that AI transformation cannot be delegated. Leaders must be on the learning journey themselves, not just directing from a distance.IKEA Call Center Case Study — When IKEA deployed AI to handle routine call center work, employees were redeployed to handle only complex problems. The unintended consequence was unsustainable cognitive load from 100% hard problems.Kanban Method — A workflow management method for defining, managing, and improving services that deliver knowledge work."In Search of Excellence" by Tom Peters — Classic business book referenced for the quote "Leaders are dealers in hope."Tools & Resources Mentioned Claude (by Anthropic) — AI assistant that one host describes as having a "semi love affair" with, noting it's replaced ChatGPT as their primary toolChatGPT (by OpenAI) — AI assistant referenced as the initial tool that brought AI into mainstream awareness for most peopleMicrosoft Copilot — Microsoft's AI assistant, referenced in the context of Satya Nadella restricting Claude usage to refocus on Copilot due to cost overrunsClaude CoWork (by Anthropic) — A feature/usage pattern for collaborative AI work that one host introduced to their groups, noting a measurable shift in AI adoption across the bell curveCalls to Action On Monday morning, start a listening campaign. Find three ways to engage your team about their views on AI and their fears—and just listen. Do not pitch, defend, or reassure. Just listen.Go on your own learning journey. Before you ask your team to adopt AI, experiment with it yourself. Get messy. Make mistakes. Share what you learn. Credibility comes from doing, not directing.Audit your trust score. Ask yourself: Are you being truthful? Are you giving people agency over their work? Can they see how they will benefit? If any of these pillars is missing, start there.Prune aggressively. If you or your team have built dozens of AI experiments that aren't producing value, kill them. Overhead from unused AI tools is still overhead.Pick one thing. Don't try to transform everything at once. Choose the single highest-impact, lowest-risk use case, start putting one foot in front of the other, and never stop.Key Quotes "This is not a technology challenge, it's a people challenge." — Mark Redgrave"An antidote to fear is agency." — Mark Redgrave"Leaders are dealers in hope." — Mike Richardson (attributing Tom Peters)"People support the things they build. Do not do this to your people. Let them create." — Mark Redgrave"The only protectable, sustainable competitive advantage you have is your culture." — Mike RichardsonChapters 00:28 — Opening and the "busy fools" problem  02:39 — Why pruning your AI experiments is a survival skill  04:55 — The hype cycle arrives: from frenzy to disillusionment  06:55 — The Dunning-Kruger trap and the peak of Mount Stupid  09:13 — IKEA's AI call center lesson: when removing the easy work backfires  11:55 — Resistance as feedback, not opposition  15:04 — Why graduates are booing and what it tells leaders  19:13 — The same but different: enduring change principles in warp-speed change  23:39 — The trust triangle: capability, consistency, and selflessness  26:23 — Super-triage: how to prioritize when demand massively exceeds supply  30:57 — Three trust-builders for Monday morning: truth, agency, shared benefit  38:00 — The one move every leader should make on Monday morning  43:43 — Why leader-led transformation isn't optional Meet the Crew Mike Richardson – Agility, Peer Power & Collective IntelligenceWebsi...

  8. Apr 20 ·  Video

    From Idea to App in Hours: How Non-Technical Leaders Can Build with AI Today

    Business leaders face a critical dilemma: they see the potential of AI but feel overwhelmed by technical complexity, unsure where to start, and frustrated by projects that never move beyond pilot phase. This episode reveals how modern AI tools have evolved to become accessible to anyone with an idea, eliminating the technical barriers that once prevented non-coders from building functional applications. The hosts demonstrate how platforms like Lovable and Replit have transformed from simple interfaces to powerful development environments that handle complex backend integrations automatically. Mark shares his experience building a consumer app with payment processing in days rather than months, while Tom recounts teaching 100 non-technical people to create working apps in just two hours. Mike's journey from AI laggard to building multiple projects shows that the only real barrier is starting—not technical expertise. The discussion moves beyond basic tools to address the real organizational challenges: "pilot purgatory" where AI initiatives never scale, and the integration gap where cool prototypes fail to connect with existing business systems. The solution lies in securing CEO mandates for AI initiatives and focusing on practical integration rather than perfect solutions. With AI tools now capable of handling everything from database structures to payment gateways, business leaders can finally bridge the gap between vision and execution without waiting for technical teams or massive budgets. Highlights Build functional applications in hours instead of months using intuitive AI-powered platformsOvercome analysis paralysis by starting with simple prompts about your business challengesSecure CEO-level mandates to move AI projects from pilot phase to production scaleCreate custom business tools that integrate payment processing and databases without codingTransform from AI observer to builder by leveraging voice interfaces and natural language promptsAvoid "pilot purgatory" by connecting AI initiatives directly to core business strategyUse AI as a $20/month thought partner that knows everything about your industryBuild competitive advantage by creating custom solutions faster than traditional SaaS adoptionImportant Concepts and Frameworks Vibe Coding — An approach to software development that emphasizes natural language prompts, rapid prototyping, and minimal technical barriers, allowing non-coders to build functional applicationsPilot Purgatory — The common challenge where AI and technology initiatives get stuck in proof-of-concept phase, failing to scale to production due to organizational, integration, or strategic barriersCEO Mandates — Top-down strategic directives that prioritize AI adoption and provide the organizational authority and resources needed to move beyond pilot projectsIntegration Gap — The challenge of connecting AI-built applications with existing business systems, databases, and workflows that prevents practical implementationTools & Resources MentionedLovable — AI-powered platform for building web applications with minimal coding, featuring integrated backend services and payment processing | https://lovable.devReplit — Collaborative development environment that enables rapid prototyping and application building through natural language interfaces | https://replit.comClaude (Anthropic) — AI assistant platform used for coding assistance, collaborative workspaces, and business problem-solving | https://www.anthropic.comBook Magic — AI-powered platform for collaborative book writing and content creation | https://bookmagic.aiNetlify — Web hosting and deployment platform for quickly launching applications built with AI tools | https://www.netlify.comStripe — Payment processing platform with AI-ready integrations for e-commerce and subscription applications | https://stripe.comSupabase — Open-source database platform that provides backend infrastructure for AI-built applications | https://supabase.com Calls to Action Start today by opening any AI platform and typing "I run a [your business type] and want to use AI. Where do I begin?"Choose one business challenge this week and use voice commands to explore solutions with ChatGPT or ClaudeSchedule a 15-minute conversation with your CEO about securing a mandate for AI initiativesBuild your first functional prototype using Lovable or Replit within two hours, focusing on solving one specific problemDocument three integration points between your existing systems and potential AI solutionsShare one AI-built tool with your team within seven days to demonstrate rapid prototyping capabilitiesKey Quotes "If you have an idea for something you want to do... put it into Lovable or Replit, and you are off to the races" — Mark Redgrave"Start at A with nothing" — Mike Richardson"Are you gonna be the one in 10,000 people that actually does something or are you gonna be in the other group that has a good idea and does nothing?" — ChatGPT to Mark Redgrave"Shut it down" — Claude Code to Tom Adams"This is a $20 a month thought partner that knows everything" — Mark RedgraveChapters00:00 — The Accessibility Revolution: AI Tools for Non-Technical Builders02:42 — From Zero to App: Real-World Vibe Coding Success Stories06:08 — Teaching 100 People to Build Apps in Two Hours08:30 — Understanding the Vibe Coding Ecosystem: Lovable vs Replit vs Bolt11:23 — Voice-First Development: Building Apps While Walking Your Dog15:59 — From Laggard to Builder: One Leader's AI Transformation Journey18:34 — Multiple Project Momentum: Books, Assessments, and Business Tools22:25 — The $20/Month Thought Partner That Knows Everything26:00 — When AI Says No: Learning from "Shut It Down" Moments29:53 — Layered Intelligence: Combining Human and AI Capabilities33:25 — Categorizing the AI Tool Landscape for Strategic Adoption37:31 — How Custom-Built Tools Are Disrupting Traditional SaaS41:33 — Breaking Through Pilot Purgatory with CEO Mandates44:59 — The Integration Challenge: Connecting AI Tools to Existing Systems46:19 — AI Hype vs Reality: Lessons from Allbirds' Pivot - - - -Meet the CrewMike Richardson – Agility, Peer Power & Collective IntelligenceWebsite: https://mikerichardson.live/LinkedIn: https://www.linkedin.com/in/agilityexpertmikerichardson/ Ryan Niemann – Software CEO & Board OperatorWebsite: https://bob3.pro/LinkedIn: https://www.linkedin.com/in/ryanniemann/ Mark Redgrave – Agility, People and PerformanceWebsite: https://www.shift-transform.com/LinkedIn: https://www.linkedin.com/in/mredgrave/ Tom Adams – Executive Coach, Advisor & Trail Blaze...

See All (21)

Ratings & Reviews

5
out of 5
3 Ratings

About

It’s human-friendly banter about code, culture, and CEO reality checks—served up by Mike Richardson, Ryan Niemann, Mark Redgrave, and Tom Adams. No jargon. No hype. Just real talk from four guys who’ve seen it all, and aren’t afraid to say what everyone’s thinking.

Information

  • Creator
    Mike Richardson, Mark Redgrave, Ryan Neimann & Tom Adams
  • Years Active
    2025 - 2026
  • Episodes
    21
  • Rating
    Explicit
  • Copyright
    © Flourish Press Inc.
  • Show Website
    Prompt and Circumstance