AI Literacy for Entrepreneurs

In the final episode of the Podcast-to-Book series, host Susan Diaz sits down with change leader and AI education lead Melissa Penton (Sun Life) for a human-first conversation about what actually makes AI adoption work. They talk productivity vs room-for-life, why one-prompt culture is snake oil, the shift from prompt engineering to context engineering, and the simplest enterprise question that changes everything: "What would make Monday easier for employees?" Episode summary Susan closes out the Podcast-to-Book sprint with a conversation that feels like the point of the whole series: AI isn't a tool problem. It's a people problem disguised as a tool problem. Melissa Penton shares her lens as a long-time change manager working in AI readiness and education inside a large organisation. Her focus isn't faster work. It's making room for what matters - and designing adoption in a way that's safe, honest, and grounded in real human tension points. Together, Susan and Melissa unpack why generic prompting courses aren't enough, why people get hives when they hear words like "workflow" and "agentic," and how leaders can create real change by starting with everyday pain. They also go deep on psychological safety, the fear of "training your robot replacement," and what it looks like to lead with humility in the biggest transformation most of us will live through. Key takeaways Productivity is the doorway. Room-for-life is the goal. Saving time is nice. The real win is using that time to live in your "zone of genius" and have space for the things you care about. One-prompt culture is snake oil. Useful AI work is iterative, messy, and conversational. The magic isn't the prompt. It's the human steering, correcting, and refining. Prompt engineering is evolving into context engineering. The skill isn't "write a clever prompt." It's learning to give the right context, ask better questions, and build on responses. Enterprise adoption should start with one simple question: "What would make Monday easier for my employees?" That question forces leaders to solve real friction instead of buying shiny tools. The biggest people problem masquerading as an AI problem is readiness. AI is being thrown at people who don't know where to start, how it fits their real lives, or how it changes their work without threatening them. Training should be experiential, not theoretical. Courses can help. But capability sticks when people learn by doing, inside real workflows, with real tasks, and real feedback loops. Psychological safety is non-negotiable. People won't share pain points if they fear automation will erase their job. Leaders shouldn't make promises they can't keep. They should make learning safe and transferable. Workflows don't have to be scary. A workflow is just the steps you already take. "Ask a question → make notes → read notes → act." That's a workflow. Low-risk experiments lead to higher-risk breakthroughs. The "AI coffee warmer" might feel silly. But it's part of the lab. Small experiments teach the muscles needed for bigger transformations. Leadership in the AI era requires humility. Admit you're learning. Model curiosity. Use AI to explore recurring organisational stuck points, mediate perspectives, and surface patterns in conversations. Timestamps 00:03 — Susan sets the scene: the final stretch of the 30-day podcast-to-book sprint 01:12 — Meet Melissa: change management, training, and leading AI education/readiness 02:31 — Productivity vs "making room for what matters" (crochet, hikes, real life) 03:29 — Time saved is table stakes… what are we doing with the time? 03:58 — Zone of Genius living and why AI should move you toward it 06:44 — Snake-oil prompts, "one prompt fixes your life," and why it makes Susan grumpy 08:02 — "I am the prompt": AI as an iterative, human conversation 09:21 — Prompting → context engineering (asking better questions is the skill) 09:50 — The enterprise question: "What will make Monday easier for employees?" 11:02 — Voice mode and why it changes tone, cadence, and output quality 14:27 — The biggest "AI problem" is actually a people/readiness problem 16:20 — Start with real tension points, not an abstract AI adoption plan 18:23 — Why "prompting courses" can repel people (language matters) 20:39 — Courses aren't bad… they're just not sufficient 21:49 — Cleaning workflows as the gateway drug to agentic thinking 22:44 — Agentic AI explained simply: consecutive steps without you in the middle 23:20 — "Workflow" definition for normal humans (no hives required) 24:12 — First 3 moves in 30 days: Monday, conversations, embedded learning 26:00 — Psychological safety: fear of replacement and why honesty matters 31:03 — Skill recognition: you're learning transferable capability, not training your replacement 33:35 — Whole-human value: you are not your job title 34:22 — The spiritual lens: AI should expand what humans can become 35:34 — Why "small silly tools" still matter (science lab thinking) 37:29 — Low-risk testing as the path to bigger breakthroughs 38:00 — Leadership advice: be humble, be curious, use AI to explore stuck patterns 40:58 — Where to find Melissa: LinkedIn + Substack 41:30 — "Purple person": bridging tech and business communication Connect with Melissa Penton on LinkedIn Substack: Confessions of an AI User If you're leading AI adoption, steal this question and use it today: "What would make Monday easier for our people?" Then pick one friction point. Make it safer. Make it simpler. Let the learning compound. Connect with Susan Diaz on LinkedIn to get a conversation started. Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here.

Host Susan Diaz sits down with sales strategist Gazzy Amin, founder of Sales Beyond Scripts, to talk about the real ways AI is changing revenue, planning, and scale. They cover AI as a thinking partner, how to use it across departments in a small business, why audits matter more than hype, and how mindset quietly determines whether you treat AI as a threat or an advantage. Episode summary This episode is part of Susan's 30-episodes-in-30-days "podcast to book" sprint for Swan Dive Backwards. Susan and Gazzy zoom in on the selling process first. Then they zoom out to the whole business. They talk about three camps of AI users (anti, curious, invested), and why the curious group has a huge edge right now. Gazzy shares how she uses AI as a co-pilot across marketing, sales, and operations. Not just for captions. For thinking, planning, campaign creation, and building repeatable systems. They also go into mindset. Gazzy's approach is clear: protect your mental real estate. Don't let recession talk, doom narratives, or fear-based chatter shape your decisions. Use AI to help you widen perspective, challenge limiting beliefs, and plan like a CEO. Key takeaways AI is more than a copywriting tool. It's a strategic brainstorming partner that reduces burnout and speeds up decision-making. If you want to scale, map your departments and ask AI how it can support each one. Marketing, sales, finance, operations, hiring, delivery, and client experience. Do a year-end audit before you set new goals. Feed AI your revenue data, launches, offers, and calendar patterns. Then let it ask you smart questions you wouldn't think to ask yourself. AI doesn't erase experts. It raises your baseline. You show up to expert conversations more informed, so you can go deeper faster. Documentation and playbooks become an unfair advantage. When knowledge lives only in your head, your business is fragile. AI helps you turn what's in your brain into systems other people can run. Scale is doing more with less. AI can increase output without needing to triple headcount, if you're intentional about workflows and training your team. Women have a big opportunity here. AI can reduce the invisible workload, expand access to expert-level thinking, and help women-led businesses grow faster - if women stay in the conversation and keep learning. Timestamps 00:00 — Susan introduces the 30-day podcast-to-book sprint and today's guest, Gazzy Amin 01:10 — The three types of entrepreneurs using AI (anti / curious / invested) 02:10 — AI as a thinking partner vs a task-doer 03:50 — Why most people don't yet grasp AI's full capability (and why curiosity matters) 05:00 — Using AI for personal life tasks as a low-pressure entry point 06:46 — Recession narratives, standing out, and using AI to challenge limiting beliefs 07:40 — "Create an AI per department" and train it for specific use cases 09:10 — The opportunity window: access to expertise that used to cost tens of thousands 10:10 — The 2025 audit: asking AI to interview you and pull out patterns 12:55 — Will AI devalue experts? Why the human layer still matters 16:45 — How AI changes your conversations with experts (you go deeper, faster) 18:20 — Mindset tools: music, movement, and protecting your "mental real estate" 21:00 — Documentation, playbooks, and why small teams need systems 24:00 — Training AI on your real sales process to improve onboarding + client experience 27:20 — Do AI-enabled businesses become more valuable? Scale, output, and leverage 30:00 — Why people default to content (and how to make AI content actually sound like you) 34:15 — Women, AI, the wage gap, and why this moment is non-negotiable 40:00 — Gazzy shares her CEO Growth Plan Intensives and how she uses AI in sessions 42:55 — Where to connect with Gazzy (Instagram + LinkedIn) Guest info Gazzy AminFounder, Sales Beyond Scripts Best place to connect: Instagram (behind-the-scenes and real-time business building) - https://www.instagram.com/authenticgazzy/ If you want to use AI to scale in 2026, start here: Run a 2025 audit with AI. Pick one department and ask AI how to improve that workflow. Document one process that currently lives only in your head. Connect with Susan Diaz on LinkedIn to get a conversation started. Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here.

Host Susan Diaz sits down with Jennifer Hufnagel (Hufnagel Consulting), an AI educator and AI readiness consultant who's trained 4K+ people. They break down what "AI readiness" actually means (spoiler: it's not buying Copilot), why AI doesn't fix broken processes or dirty data, and how leaders can build real capability through training programs, communities of practice, and properly resourced AI champions. Episode summary Susan Diaz and Jennifer Hufnagel met in "the most elite way possible": both were quoted in The Globe and Mail about women and AI. Jennifer shares her background as a business analyst and digital adoption / L&D consultant, and how she pivoted when clients began asking for AI workshops right after ChatGPT's release. Together, they map a simple but powerful framework: AI awareness (practice + play, foundational learning, early change management) AI readiness (software stack, data quality, workflows, current state, and - quietly - the "people audit") AI adoption (implementation, strategy, and ongoing integration) Jennifer explains why "audit" language scares people, but the work is essential - especially talking to humans about what's frustrating, what takes time, and where fear is showing up. She shares what she's seeing after training thousands: AI fluency is still low, people obsess over tools, and many assume AI will solve problems that are actually process or data issues. The second half gets practical: what "workflows" really mean (step-by-step checklists), how AI now makes documenting processes easier than ever (voice → SOPs), why prompt engineering isn't dead but "100 prompts for your bookkeeping business" is mostly snake oil, and why one-off training sessions don't create real fluency. They close with how to build sustainable AI capability: proper training programs, leadership-led culture, communities of practice, and protecting champions from becoming unpaid help desks. Key takeaways AI readiness is the middle of the journey. Jennifer frames AI maturity as: awareness → readiness → adoption. Most organisations skip readiness and wonder why adoption stalls. Readiness includes software, data, process… and people. You can call it a software/data/process audit, but you still have to talk to humans about their day-to-day work, pain points, and fears. That's where the truth lives. AI fluency is still lower than the headlines suggest. Jennifer questions rosy "90% adoption" stats because many rooms she's in still show low real-world usage beyond basic experimentation. Stop obsessing over tools. Companies are writing AI policy around tools and forcing everyone into a single platform. Jennifer argues the real goal is discernment, critical thinking, and clarity - not "pick one tool and pray". AI doesn't fix broken processes or dirty data. If your workflows aren't documented, AI will scale the chaos. If your data is messy, the analysis will be messy too. Readiness comes first. A workflow is just a checklist. Jennifer demystifies "workflow" as step-by-step instructions and ownership: who does what, when. Sticky notes on a wall is a valid start. Process documentation is easier than ever. You can dictate steps into a model (without passwords) and ask it to produce an SOP/checklist - getting knowledge out of people's heads and into a shareable format. Prompting isn't dead, but promise-all prompt packs are mostly hype. Prompting differs by model, and the best move is often to ask the model how to prompt it - and how to troubleshoot when output is wrong. One-off AI workshops don't create fluency. AI changes too fast. Real capability requires programs, practice, communities of practice, office hours, and change management - plus leadership modelling and culture. Don't burn out your AI champions. Champions need dedicated time, resources, and leadership sponsorship. Otherwise they become unpaid AI help desks and the entire initiative becomes fragile. Community of practice is the unlock. Jennifer shares her in-person "AI Chats & Bites" group and encourages finding online + in-person + internal communities to keep learning alive. Episode highlights 00:01 — The 30-day podcast-to-book sprint and why people are saying yes in December 00:40 — Susan + Jennifer meet via The Globe and Mail "women and AI" feature 01:21 — Jennifer's origin story: business analyst → digital adoption/L&D → AI readiness 04:09 — The three-part framework: awareness → readiness → adoption 05:03 — Readiness: software stack, data quality ("dirty data"), and mapping current state 06:13 — "People audit" without calling it that: interview humans about pain + fear 08:02 — What Jennifer sees after ~4,000 trainees: fluency still low + stats don't match reality 09:38 — AI doesn't fix broken processes; it scales whatever is there 10:55 — Workflows explained as checklists; "won the lottery" handoff test 12:18 — Dictate your process into AI → generate SOPs/checklists 14:24 — Prompting isn't dead; ask the model to help you prompt + troubleshoot 17:50 — Why one-off training doesn't work; AI fluency requires a program + practice 22:15 — Burning out champions and why AI culture must be top-down 27:49 — Communities of practice: online + local + internal 31:00 — Common mistakes: vending-machine mindset, believing output, not defining the problem 35:31 — Women and AI: opportunity, fear, resilience, and "be in the grey" 39:51 — Where to find Jennifer: hufnagelconsulting.ca + LinkedIn Guest info Jennifer Hufnagel Website: hufnagelconsulting.ca Email: hello@hufnagelconsulting.ca Best place to connect: LinkedIn - Jennifer Hufnagel If AI adoption feels stuck in your organization, don't buy another tool first. Start with readiness: Map one workflow end-to-end. Talk to the humans doing it daily. Clean up the process and data enough that AI can actually help. Then build fluency through a program - not a one-off workshop - and protect your champions with real time and resources. Connect with Susan Diaz on LinkedIn to get a conversation started. Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here.

Host Susan Diaz is joined by Chris McMartin, National Lead for the Scotiabank Women Initiative (Business Banking), for a real-world conversation about how women are approaching AI. They talk about time poverty, fear of asking "dumb" questions, the shame myth of "AI is cheating", and why the most powerful move right now is women holding the door open for each other - learning in community and sharing what works. Episode summary This episode is a candid, energetic conversation with Chris McMartin - aka "Hype Boss" online and a long-time hype woman for women entrepreneurs. They explore what's different about women's AI adoption, and it's not lack of interest. They discuss the reality of learning AI at 10pm on the couch after the "82-item checklist" of the day is finally done. And the catch-22 at play - AI can save time, but learning it takes time first - like baking cookies with kids turning a 20-minute task into a two-hour event. From there, they unpack a deeper barrier: many women hesitate to ask questions because they don't want to look silly. Chris argues women often use AI "just a little bit" because it doesn't require admitting what they don't know - meaning AI becomes a copywriting helper instead of a real growth lever. They also confront the "AI is cheating" narrative. Chris shares her no-apologies stance: if AI improved your grammar overnight, that's not shameful - it's smart. And if you're worried about being judged for questions, ask AI itself - because it won't judge you. The conversation closes with practical advice for women-led teams (especially 5-50 people): start by identifying the task everyone hates and use AI there first, and schedule learning time during business hours instead of relegating growth to late-night exhaustion. Along the way, Susan brings in a powerful metaphor: "hold the door" leadership - women who are already in the room have a responsibility to bring others in with them. (Metaphor inspired by Game of Thrones and Bozoma Saint John. Key takeaways Women aren't unwilling. They're time-starved. Many women try to learn AI at the end of the day, when they're exhausted - because that's the only time left. AI has a "cookie problem". It has huge benefits later, but it costs time upfront to learn - just like baking with kids. That learning curve is real, and it's a major adoption barrier. Fear of questions limits adoption. Chris observes women often hesitate to ask "how do I use this better?" which keeps AI usage stuck at surface-level tasks like captions and posts. "AI is cheating" is a myth that needs to die. Chris's take: using AI to communicate more clearly isn't unethical. It's an upgrade. She also notes men rarely apologize for finding ways to do things better. Ask AI how to use AI. If you feel silly asking humans, ask your LLM: "What questions should I answer so you can help me solve this?" That's the difference between generic output and useful work. Community is a women's superpower. Women often collaborate with "competitors" with zero weirdness. That community-of-practice energy is exactly what AI learning needs. For women-led teams: start with pain. Chris's first practical move: ask your team what task they hate most, then use AI to reduce or remove that pain point to build buy-in. Schedule learning like leadership. Don't push AI learning to 10pm. Put it on the calendar during work hours. Your development is part of the job. Grants can fund AI training and tech upgrades. Chris reminds listeners that many grants support technology advancement and hiring expertise - even for non-tech businesses - and AI can reduce the pain of grant writing. Episode highlights [00:03] Meet Chris McMartin + the Scotiabank Women Initiative. [02:00] "10pm on the couch" and why time poverty shapes women's learning. [02:44] The cookie analogy: AI saves time later, but learning costs time now. [05:00] Women using AI 1%: safe tasks without asking questions. [06:46] Why this matters: many "at risk" roles are held by women. [09:35] "AI is cheating" + the grammar glow-up story. [11:42] "Ask AI questions - AI doesn't judge you." [13:00] Relationship mindset: don't be transactional with AI; ask better questions. [16:21] "Hold the door" leadership and building rooms where women feel welcome. [21:43] Two tactical tips: solve a pain point first + schedule learning time. [33:48] Grants as a funding path for training and tech improvements. [38:57] Say yes to conversations even if you "don't know enough." [41:18] Where to find Chris + her podcast I Am Unbreakable. If you're a woman entrepreneur (or you lead women in your organization), take one action from this episode this week: Ask your team what task they hate most. Pick one painful workflow and test AI there first. Put one hour of AI learning on the calendar during business hours. And if you're already "in the room" with AI? Hold the door. Invite someone in. Connect with Susan Diaz on LinkedIn to get a conversation started. Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here. Connect with Chris McMartin on LinkedIn.

Host Susan Diaz is joined by Shona Boyd, a product manager at Mitratech, a SaaS company, and a proudly AI-curious early adopter, for a grounded conversation about what AI literacy actually means now. They talk about representation, critical thinking, everyday meet-you-where-you-are workflows, shadow AI, enterprise guardrails, and why leaders must stop chasing AI features that don't solve real user problems. Episode summary Susan introduces Shona Boyd - AI-curious early adopter and SaaS product manager—whose mission is to make AI feel less scary and more accessible. Shona shares how her approachable AI philosophy started in product work: she used AI to build audience insights and feedback loops when job seekers weren't willing to do interviews, and quickly realized two things: (1) AI wasn't going away, and (2) there weren't many visible women or people who looked like her leading the conversation. So she raised her hand as an approachable reference point others could learn from. From there, the conversation expands into what AI literacy has evolved into. It's no longer just "which tool should I use?" or "how do I write prompts?" Shona argues that today literacy is about critical thinking, learning how to talk to an LLM like a conversation, and choosing workflows that benefit from AI rather than chasing hype. They also get practical: Shona gives everyday examples (Medicare PDFs, credit card points, life admin) to show how AI can meet you where you are, without requiring you to build agents or become super technical. Finally, Susan and Shona go deep on organizational adoption: why handing out logins without policies is risky, how shadow AI shows up (hello, rogue meeting note-takers), why leadership sponsorship matters, and what companies should stop doing immediately: AI for the sake of AI. Key takeaways Representation changes adoption. When people don't see anyone who looks like them using AI confidently, they're less likely to lean in. Shona chose to be a visible, approachable point of reference for others. AI literacy has shifted. It's no longer mainly about which model or prompt frameworks. It's about: learning the language (LLM, GPT, etc.) staying curious building critical media muscles to evaluate what's true, what's AI, and what needs sources. Workflows aren't just corporate. A workflow is simply: tasks + the path to get them done. Shona's examples show AI can help with day-to-day life admin (PDFs, policies, benefits, points programs), which makes AI feel more approachable fast. The first output is not the final. "I can spot AI content" usually means people are publishing raw first drafts. High-quality AI use looks like: draft → critique → refine → human judgement. What good organizational training is NOT: handing out tool logins with no policy, no guidance on acceptable use, and no understanding of enterprise vs personal security. Shadow AI is already here. People are adding unapproved AI note-takers to meetings and uploading sensitive info into personal accounts. Blanket bans don't work - they push experimentation underground. Adoption needs product thinking. Shona suggests leaders treat internal AI like a product launch: run simple feedback loops (NPS-style checks) analyse usage patterns to find sticking points apply AI where it solves real pain, not where competitors are hyping features. Leadership ownership matters for equity. When AI is run department-by-department, you create "haves and have-nots" (tools, training, access). Top-down support plus safe guardrails reduces inequity and increases psychological safety. Spicy take: stop doing AI for the sake of AI. If you can't explain how an AI feature improves real user life in a non-marketing way, it probably shouldn't ship. Episode highlights [00:01] The 30-day podcast-to-book sprint and why leaders are still showing up in December. [01:14] Shona's origin story: using AI to build audiences and feedback loops in a job board context. [02:17] The visibility gap: not many women / people who looked like her in early AI spaces. [05:55] What AI literacy means now: critical thinking + conversation with an LLM + workflow selection. [07:16] "Workflows" made real: Medicare PDFs and credit card points examples. [10:13] Three essentials: foundational language, curiosity, and critical media literacy. [12:23] What training is NOT: handing out logins with no policy or guardrails. [15:49] Handling fear and resistance with empathy and a human-in-the-loop mindset. [23:27] Product lens on adoption: NPS feedback loops + usage analytics to find real needs. [28:14] Shadow AI: rogue note-takers, personal accounts, and why bans backfire. [31:17] Policies at multiple levels, including interviewing and candidate use of AI. [36:49] "Stop AI for the sake of AI" and the race to ship meaningless features. [39:13] Where to find Shona: LinkedIn (under Lashona). Connect with Susan Diaz on LinkedIn to get a conversation started. Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here. Connect with Lashona Boyd on LinkedIn

Host Susan Diaz sits down with her business buddy and go-to-market consultant Suzanne Huber to talk about what AI has actually changed in marketing. Together they explore AI as "robot arms" (an extension of expertise), why first-draft AI content gets a bad rap, how modern marketers use AI for research, planning, editing, and proposals, and why thought leadership and personal brand matter more than ever. Episode summary Susan and Suzanne have been talking about AI since 2022. In this episode, they make it official. Suzanne introduces a metaphor that sticks: AI as "robot arms". You're still the driver. AI can extend your reach, speed up the grunt work, and help you close expertise gaps—but it still needs human judgment, critical thinking, and craft. They compare marketing before vs after AI: headlines, research, applying feedback, simplifying complex plans into executive-friendly formats, cross-checking sources (especially Canadian vs US), and building repeatable workflows with custom GPTs. They also tackle the bigger questions: Does expertise still matter? Is personal brand becoming more important in the age of AI? What should writers do if they feel threatened? Spoiler: AI can speed up output. But insight, values, differentiation, and taste are still the human edge. Key takeaways AI is "robot arms" not a replacement brain. It's an extension of expertise. You still need to steer, evaluate quality, and avoid publishing raw first drafts that can damage trust. First-draft AI is the content factory problem. AI-assisted content gets a bad reputation when junior-level or high-volume systems publish credible-sounding fluff with no real subject matter expertise behind it. Craftsmanship still matters. Marketing got faster because the grunt work collapsed. Headlines, rewrites, reformatting, applying feedback, outlining, and turning long documents into charts/tables can happen in minutes - not hours. You still refine, but you're starting from a better baseline. Research and fact-checking changed dramatically. Instead of trawling search results for hours (and getting US-default sources), AI tools can surface targeted sources fast - then humans choose what's credible and relevant. Custom GPTs shine for repeatable processes. Susan shares how she uses custom GPTs (including MyShowrunner.com) for guest research, interview questions, emails, and packaged deep research briefs - turning recurring work into reusable systems. Expertise always matters - especially for positioning and thought leadership. Differentiation, values, hot takes, and human intuition are what attract the right people (and repel the wrong ones). AI can assist, but it can't replace lived POV. Personal brand matters more in the age of AI. As audiences get more suspicious of generic content and AI avatars, trust increasingly attaches to real humans with visible ideas, proof, and consistency. For writers who feel threatened: use it or get outpaced. AI can accelerate production for factual formats (press releases, timely content). Writers who combine craft + AI + fast learning become the force multipliers. But journaling/introspective writing still belongs to the human-only zone. Episode highlights [01:29] Suzanne's "robot arms" metaphor: AI as an extension of expertise. [02:47] Why first-draft AI should never leave your desk. [03:56] The telltale signs of lazy AI writing (and why it gets a bad rap). [05:00] Before vs after AI: the research + writing process changes. [07:24] Simplifying complex work: plans → tables → charts for execs. [09:10] Deep research for Canadian sources without wasting hours. [10:25] Custom GPT workflows (MyShowrunner + research briefs). [12:29] Where expertise still matters in an AI-saturated world. [16:56] Personal brand: attracting the right people + repelling the wrong ones. [20:00] AI for proposals and even pricing guidance. [22:00] Advice for writers who feel threatened by AI. If you've been resisting AI because you're worried it will erase your craft, try this reframing: Use AI for the grunt work. Keep the human parts for the parts that build trust: taste, judgement, voice, and values. And if you want a simple starting point, ask yourself: What could use "robot arms" in your marketing workflow this week - headlines, research, rewrites, proposals, or planning? Connect with Susan Diaz on LinkedIn to get a conversation started. Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here. Connect with Suzanne Huber on LinkedIn.

Why are women adopting AI at lower rates than men - and what's really going on underneath the stats? In this episode, host Susan Diaz and Heather Cannings, Women Entrepreneurship Program Lead at InVenture and producer of StrikeUP Canada, dig into time poverty, "cheating" fears, late-night upskilling, and what real support for women entrepreneurs needs to look like in an AI-forward world. Episode summary Susan is joined by Heather Cannings, who leads women's entrepreneurship programs at InVenture and runs StrikeUP, a national digital conference that reaches thousands of women entrepreneurs across Canada and beyond. Heather shares what she's seeing on the ground: huge curiosity about AI, mixed with pressure, fatigue, and a sense that it's "one more thing" women are expected to learn on their own time. Many of the women she serves are juggling multiple roles - business owner, employee, caregiver - then experimenting with AI at 10-11 pm after the workday and bedtime routines are done. They unpack the emotional layer too: why AI still feels like "cheating" or being an imposter for many women the question of whether you have to disclose using AI how to reconcile charging premium prices while using AI behind the scenes Susan and Heather link lower AI adoption rates among women to a wider pattern: another version of the wage gap and systemic inequities in who has time, safety, and support to skill up. The conversation then turns to: Practical, real-world use cases women are already using AI for (grant writing, content, summarizing long docs). What good support systems actually look like for time-strapped entrepreneurs (designing for constraints, not fantasy calendars). How small, scrappy businesses and large organizations can learn from each other on speed, governance, and risk. The uncomfortable reality that many roles most at risk of AI automation - admin, entry-level comms, research - are heavily female. They close with a hopeful lens: how women can use AI to increase their value and control over time and income, why this moment is a genuine opportunity for democratization, and how Heather's StrikeUP event is trying to meet women exactly where they are. Key takeaways AI doesn't feel neutral for women - it feels like another test. Many women entrepreneurs are curious about AI but also feel judged, worried about "doing it wrong," or like they're cheating if they use it. Imposter syndrome shows up as: "Is this really my work if AI helped?" Time poverty is the real barrier, not lack of interest. Heather sees women using AI at 10-11 pm after full workdays and caregiving, trying to finish newsletters, social posts, or grant drafts. They are upskilling - just in stolen moments, not spacious strategy sessions. Support systems must be designed for real constraints. Don't assume people have: unlimited time teams strong internet quiet workspaces Many women join digital events from cars, back rooms, or storage areas between tasks. Training and support must be consumable, flexible, and realistic. One-off AI webinars aren't enough. A single 60-minute "intro to AI" often just generates an overwhelming to-do list. What works better: smaller, workshop-style sessions hands-on guidance on a specific task or tool practical, "do it in the room" support so women leave with something done, not just inspired. Women are already using AI for practical, high-impact tasks. Common use cases include: writing and improving copy content planning and social media summarizing long documents drafting grants and pitches The focus is on time savings, staying within tight budgets, and safely getting more done—not chasing cutting-edge AI for its own sake. Enterprise and small business can - and should - learn from each other. Big firms: bring resources, governance, and policy thinking. Small businesses: bring speed, scrappiness, and the ability to implement immediately. Ecosystem players (non-profits, funders, educators) can translate between the two and help find a healthy middle ground. There's a gendered risk in AI-driven job change. Roles often flagged as "at risk" - admin, entry-level comms, research - are heavily staffed by women. Without intentional upskilling and redeployment, AI could quietly deepen existing inequities. There's also real opportunity. AI can be a "quiet force in the background" that removes 5-10 hours of repetitive work a week - enough to change a woman's lifestyle, income, and capacity. It can help women move up the ladder, redesign roles, or reshape their businesses around higher-value work. Designing AI with women's realities in mind matters. Women shouldn't just be users; they should help shape how tools are designed, so AI reflects real constraints like caregiving, part-time work, and patchy access - rather than assuming a mythical founder with unlimited time and support. Episode highlights [00:01] Susan sets the scene: 30 episodes in 30 days and how Heather fits into the series. [00:57] Heather introduces InVenture and her role as Women Entrepreneurship Program Lead, plus the StrikeUP conference. [01:55] Why AI remains a hot topic for StrikeUP's audience of women entrepreneurs. [02:57] AI as a catch-22: it can save time, but learning it feels like "one more thing." [03:56] "Is this cheating?" – women's fears about using AI and being judged. [05:09] AI, transparency, pricing, and the complexity of "should I tell clients I used AI?" [05:39] How this ties to stats showing women adopting AI 25% less than men—and why Susan sees it as another version of the wage gap. [07:07] Draft vs final: why treating AI output as a first draft, not finished work, is crucial. [08:33] The problem with generic, AI-generated content about "women in AI" that sounds impressive but says very little. [09:20] Real-world use cases Heather sees among small business owners. [10:22] The 11 pm pattern: women learning AI in stolen, exhausted moments. [12:06] Why women are resilient and experimenting—but lack daytime access to deep learning and setup time. [13:27] Designing support systems that don't assume unlimited time, teams, or bandwidth. [14:24] Making training consumable, recorded, and accessible from phones, cars, and storage rooms. [15:34] Why one-off webinars don't work—and the case for small, workshop-style sessions. [18:09] What big firms can learn from scrappy entrepreneurs (and vice versa). [20:10] The myth that corporates "have it all figured out" on AI. [22:19] AI and job loss: the gendered impact on admin, entry-level comms, and research roles. [23:20] Reframing: how women can use AI to increase their value and move up. [25:16] Adaptation over doom: calculators, the internet, and why we'll adjust again. [27:04] Heather's vision: AI as a quiet force helping women gain more control over time and income. [28:41] StrikeUP 2025 details: date, format, giveaways, and on-demand access. If you support or are a woman entrepreneur, use this episode as a prompt to ask: Where are women in your world already using AI - in stolen moments - and how could you meet them there with better support? How can you design AI training and tools that assume real constraints, not fantasy calendars? What's one concrete way you can help a woman in your ecosystem use AI to increase her value and control, instead of feeling like she's at risk of being automated away? Connect with Susan Diaz on LinkedIn to get a conversation started. Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here. You can learn more about StrikeUP and register for the free digital conference at strikeup.ca and connect with Heather Cannings on LinkedIn.

What does it really mean to build an AI-forward company that is still deeply human-first? In this episode, host Susan Diaz and senior HR leader and mentor culture advocate Helen Patterson talk about jobs, guardrails, copyright, environmental impact, and why mentorship and connection matter more than ever in the age of AI. Episode summary Susan is joined by Helen Patterson, founder of Life Works Well, senior HR leader, and author of the upcoming book Create a Mentor Culture. They start with a Y2K flashback and draw a straight line from past tech panics to today's AI headlines. Helen shares why she sees AI as the latest evolution of technology as an enabler in HR - another way to clear the admin and grunt work so humans can focus on growth, development, and real conversations. From there, they dig into: The tension between "AI will kill jobs" and tens of thousands of new AI policy and governance roles already posted. How shadow AI shows up when organizations put in blanket "no AI" rules and people just reach for their phones anyway. The very real issues around privacy, copyright, and intellectual property when staff feed proprietary material into public models. The less-talked-about environmental impact of AI and why leaders should demand better facts and more intentional choices from tech providers. In the second half, Helen brings the conversation back to humanity: mentorship as a counterweight to disconnection, her One Million Mentor Moments initiative, and how everyday "micro-mentoring" at work can help people adapt to rapid change instead of being left behind. They close with practical examples of using AI for good in real life - from travel planning and research to late-night dog-health triage - without letting it replace judgement. Key takeaways This isn't our first tech panic. From Y2K to applicant tracking systems, HR has always framed tech as an enabler. GenAI is the newest layer, not an alien invasion. Looking back at history helps calm "sky is falling" narratives. Jobs are changing, not simply disappearing. Even as people worry about AI-driven job loss, platforms like Indeed list tens of thousands of AI policy and governance roles. The work is shifting toward AI-forward skills in every function. Blanket "no AI" rules don't work. When organizations ban external tools or insist on only one locked-down platform, people quietly use their own devices and personal stacks anyway - creating shadow AI with real privacy and IP risk. Guardrails and education beat prohibition. Copyright and confidentiality need more than vibes. Without clear guidance, staff will copy proprietary frameworks or documents into public models and re-badge them. Leaders need simple, well-communicated philosophies about what must not go into AI tools. Environmental impact is part of human-first. Training and running large models consumes energy. The real solution will be systemic (how tech is built and powered), but individuals and organizations can still use AI more efficiently, just like learning not to leave all the lights on. Mentorship is the ultimate human technology. Helen's work on Create a Mentor Culture and One Million Mentor Moments reframes mentoring as everyday, one-conversation acts that share wisdom, reduce fear, and help people reskill for an AI-forward world. Tech should support that, not replace it. Upskilling beats layoffs. When roles change because of AI, the most human-first response isn't to cut people loose, it's to invest in learning, mentoring, and redeployment so existing talent can grow into new, AI-augmented roles. Use AI to simplify life, not complicate it. From planning multi-country trips to triaging whether the dog really needs an emergency vet visit, smart everyday use of AI can save time, money, and anxiety - freeing up more space for the work and relationships that actually matter. Episode highlights [00:01] Susan sets the scene: 30 episodes in 30 days to build Swan Dive Backwards in public. [00:39] Helen's intro: Life Works Well, heart-centred high-performance cultures, and her focus on mentorship. [03:43] What an AI-forward and human-centred organisation looks like in practice. [04:00] Y2K memories and why today's AI panic feels familiar. [06:11] 25–35K AI policy jobs on Indeed and what that says about the future of work. [07:49] Jobs lost vs jobs created—and why continuous learning is non-negotiable. [15:19] The danger of "everyone is using AI" with no strategy or safeguards. [19:25] Shadow AI, personal stacks, and why hard bans don't stop experimentation. [21:13] A real-world IP scare: proprietary material pasted into GPT and re-labelled. [23:06] GPT refusing to summarise a book for copyright reasons—and why that's a good sign. [24:03] The case for a simple AI philosophy doc: purpose, principles, and communication. [25:24] Environmental concerns, fact-checking, and the server-room-to-laptop analogy. [30:17] New social media laws for kids and what they signal about tech accountability. [30:41] One Million Mentor Moments: why one conversation can change a career. [31:22] From elite programmes to everyday mentor cultures inside organisations. [35:01] AI for mentoring and coaching: bots, big-name gurus, and internal use cases. [36:30] Using AI for travel planning, research, and everyday life admin. [37:35] Susan's story: using AI to triage a dog-health scare instead of doom-scrolling vet sites. [38:37] Life Works Well's roots in work–life harmony and simplifying with tech. [39:35] Where to find Helen online and what's next for her book. If you're leading a team (or a whole organization), use this episode as a prompt to ask: Where are we treating AI as a tool in service of humanity - and where are we forgetting the human first? Do our people actually know what's OK and not OK to put into AI tools? How could we use mentorship - formal or informal - to help our people navigate this shift instead of fearing it? Connect with Susan Diaz on LinkedIn to get a conversation started. Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here. You can connect with Helen Patterson on LinkedIn and follow her work on Create a Mentor Culture and One Million Mentor Moments via lifeworkswell.ca

What does modern sales leadership look like when AI is in the mix? In this episode, host Susan Diaz and sales leadership coach Kirsten Schmidtke unpack how AI and humanity can peacefully coexist in sales, why scale starts with clarity and process (not tools), and how leaders can shift from output-obsessed hustle to outcome-focused, identity-level leadership in an AI-forward world. Episode summary Susan sits down with sales leadership consultant Kirsten Schmidtke to talk about AI, scale, and the "identity-level shifts" leaders need to make in modern sales. They start at the intersection of mindset and skillset - why AI is now part of the sales skill stack, but can't replace the human mindset, judgment, and presence required to sell well. Kirsten shares how sales organizations have moved from using AI as a basic copy/research tool to embedding LLMs in meetings, CRMs, and internal platforms, and even building their own AI features once they deeply understand their market and product. From there, they zoom out to the trust recession, spammy AI outreach, and the difference between being AI first and AI forward. They discuss AI as a way to free people into their zone of genius (hello, The Big Leap), the historical pattern of tech disruption and new job creation, and why AI should be seen as a massive upgrade to human potential - not a replacement. In the second half, they dig into scale and operations: why AI will only scale chaos if you don't have clear goals, processes, and SOPs. Why many sales orgs still lack documented sales and go-to-market processes. And how documenting before automating is the hidden unlock for using AI well. Kirsten closes with her identity-based leadership model (be → do → have), her outcome-over-output philosophy, and practical invitations for leaders who want to use AI to reduce burnout instead of fuelling hustle culture. Key takeaways Modern sales lives at the intersection of AI and humanity. AI is becoming part of the sales skillset, but the mindset - who you are being as a leader or seller - still drives how effectively those tools get used. Sales orgs have evolved past AI as copy tool. Early use was mostly email drafting and light research. Now teams are: choosing an LLM of choice (ChatGPT, Copilot, Perplexity, etc.) and tailoring it to their sales strategy embedding AI in meeting tools to surface questions and summaries in real time building AI into internal platforms based on deep knowledge of market, product, and GTM. We're in a trust recession - and lazy AI is making it worse. Spray-and-pray LinkedIn DMs and generic AI pitches erode trust and make buyers more sceptical and confused. Being AI forward means intentional, human-centred use of AI, not pushing AI for its own sake. AI should move you toward your zone of genius, not further into busywork. Borrowing from Gay Hendricks' The Big Leap, Kirsten and Susan talk about AI as a way to strip away tasks in your zones of incompetence/competence so you can spend more time in your zone of genius - and potentially unlock higher human experiences and contribution. Scale requires clarity and process before tools. AI isn't a magic scale button. Without a clear what and why, it can't help with the how. Leaders must: define the outcome and purpose of what they're scaling decide what not to do document the current process (SOPs) before asking AI to automate or optimise it. Otherwise AI just scales the chaos. Most salespeople are executors, not system builders. They're brilliant at doing the thing - calls, meetings, negotiation - but often not trained to design processes and ops. Pairing them with ops-minded people (and AI) to document and structure their best practices is where real scale lives. Identity-level leadership: be → do → have. Instead of "when I have the title, I'll be a leader", Kirsten coaches leaders to start with identity: "I am the leader of an AI-forward sales organization." That identity shapes thinking, then actions, then results. Shift from output to outcomes to avoid AI-fuelled burnout. If you treat AI as a way to cram more tasks into the same day, you just recreate hustle culture. Focusing on outcomes (what actually changes for customers, teams, and the business) allows you to use AI to create space - for thinking, rest, and higher-value work - instead of filling every spare minute. Episode highlights [00:01] Meeting Kirsten and why you can't talk about modern sales without talking about AI. [01:07] Mindset + skillset at the intersection of AI and humanity in sales. [02:35] How sales orgs first used AI as a copy / research tool—and what's changed. [04:45] Embedding AI in meetings and tools vs building AI features in-house. [06:11] The "spray and pray" LinkedIn problem and AI's role in the trust recession. [08:53] Being "AI forward" instead of "AI first." [10:39] Why humans remain safe: discernment, judgment, spidey senses, and taste. [11:39] Arianna Huffington, Thrive, and using AI to free time for human development. [13:19] The Big Leap and using AI to move into your zone of genius. [17:01] Tech history, job loss, and why we're in the messy middle of another big shift. [19:34] What scale really means: more impact with less time and effort. [20:33] Why AI can't fix a lack of clarity—and how it can accidentally add work. [23:32] "AI will scale the chaos" if you skip documentation and SOPs. [25:08] Salespeople as executors, not ops designers, and the power of pairing them with systems people. [27:47] Branding, buyer clarity, and why AI can't replace the hard work of positioning. [31:00] Identity-level shifts for leaders: adopting "I am…" statements. [35:21] AI and burnout: from productivity for productivity's sake to outcome-focused leadership. [37:25] Newtonian vs Einstein time and rethinking how we use the time AI frees. [39:59] "Outcome over output" as a leadership mantra in the age of AI. [40:38] Kirsten's invitation: a Sales Leader Power Hour to work on your mindset and identity. If you're leading a sales team - or are the sales team - and you're feeling the tension between AI, scale, and leadership start here: Pick one sales process and document it end-to-end. Identify one step where AI could genuinely reduce effort or time. Ask, "Who do I need to be as a leader of an AI-forward sales org?" and let that identity shape your next move. Connect with Susan Diaz on LinkedIn to get a conversation started. Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here. To go deeper on mindset and identity shifts, connect with Kirsten Schmidtke on LinkedIn and book a Sales Leader Power Hour here: https://www.kirstenschmidtke.com/sales-leader-power-hour

In every organization there's at least one person quietly doing wild things with AI - working faster, thinking bigger, and building their own personal AI stack. In this episode, host Susan Diaz explains how to find those secret power users, support them properly, and turn their experiments into an organizational advantage (without burning them out or making them your unpaid AI help desk). Episode summary This solo episode is a field guide to the people already living in your organization's AI future. Susan starts by painting the familiar picture of the "suspiciously fast" teammate whose work is cleaner, more strategic, and clearly powered by AI - even if no one has formally asked how. She names them for what they are: AI power users who have built quiet personal stacks and are effectively acting as your R&D lab for AI. From there, she walks through: How to spot these people through audits, language, manager input, and usage data. Why most organizations ignore or accidentally exploit them. A practical three-part framework - Recognition, Resourcing, Routing - to turn power users into supported AI champions instead of secret heroes headed for burnout. The equity implications of who gets seen as a "champion" and how to ensure your AI leaders reflect the diversity of your workforce. She closes with a simple champion blueprint and a piece of homework that any founder, leader, or manager can act on this week. Key takeaways You already have AI power users. The question isn't "Do they exist?" It's "Where are they, and what are we doing with them?" Power users are your unofficial R&D lab. They're not theorising about AI. They're testing it inside real workflows, finding what breaks, and figuring out how to prompt effectively in your specific context. They are rarely the most technical people. Your best champions are often people closest to the work - sales, customer-facing roles, operations - who are simply determined to figure it out. Not just IT. If you ignore them, three things happen: They get tired of doing extra work with no support. Their workflows stay trapped in their heads and personal accounts. Your organization misses the chance to scale what's working. Use the 3 Rs to turn power users into champions: Recognition - Name the role (AI Champions/Guides), make their contribution visible, and invite them into strategy and training conversations. Resourcing - Give them real time (10-20% of their week), adjust workload and goals, and reward them properly - ideally with money, training, and access. Routing - Turn personal hacks into shared assets: playbooks, Looms, internal training, and workflows embedded in L&D or ops. Connect - don't overload - your champions. Give them a direct line to IT, security, legal, and leadership so they can sanity-check ideas and inform strategy, without becoming the AI police. Equity matters here. If you only see loud voices and people closest to power, you'll miss quiet experimenters, women, and people of colour who may be building brilliant systems under the radar. Use multiple ways (surveys, nominations, self-identification) to surface a diverse group of champions. Champions must be guides, not gatekeepers. Their role is to make it easier and safer for others to experiment - not to punish or shut people down. A simple champion blueprint: identify → invite → define → resource → amplify. Done well, your champions become the bridge between today's experimentation and tomorrow's AI strategy. Episode highlights [00:02] The "suspiciously fast" colleague and what their behaviour is telling you. [02:00] Personal AI stacks and why Divers "swan dive backwards" into AI without waiting for permission. [03:37] The risk of ignoring power users: burnout, trapped knowledge, and missed scaling opportunities. [05:03] Why power users are effectively your AI research and development lab. [06:33] How to surface power users through better audit questions, open-ended prompts, and usage data. [07:25] Listening for phrases like "I built a system for that" and "I just play with this stuff because I'm a geek." [08:25] Using managers and platform data to spot a small cluster of heavy AI users. [09:37] The danger of quietly turning champions into unpaid AI help desks. [10:33] The 3 Rs: Recognition, Resourcing, and Rooting. [11:18] What real recognition looks like—naming, invitations to strategy, and public acknowledgement. [12:05] Resourcing: giving champions time, adjusting workloads, and updating job descriptions. [13:14] Rooting: creating playbooks, Looms, and embedding workflows into L&D and ops. [14:29] Connecting champions with IT, security, legal, and leadership. [15:45] The equity lens: who gets seen as a champion and who's missing. [17:16] The risk that women and marginalised groups get left behind and automated first. [18:30] Using surveys, nominations, and explicit invitations to diversify your champion group. [19:07] Why champions should be guides, not AI police or gatekeepers. [19:47] The 5-step "champion blueprint": identify, invite, define, resource, amplify. [22:15] Your homework: talk to one secret power user this week and ask how you can make space for their experimentation. Think of one person in your organization who's already that secret AI power user. This week, have a conversation that goes beyond "cool, can you do that for everyone?" and into "This is important. How can we make space for you to keep experimenting like this and help others learn from you?" That's the first step in building your AI champion program - whether or not you call it that yet. Connect with Susan Diaz on LinkedIn to get a conversation started. Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here.

Should you build custom GPTs, agents, digital interns, Gems, and artefacts… or just learn to prompt better? In this roundtable, Susan, social media + AI power user Andrew Jenkins, and GTM + custom GPT builder Dr. Jim Kanichirayil unpack when you actually need a custom build, when a strong prompt is enough, and how to stop treating AI output like a finished product. In this episode, Susan brings back two favourite guests who sit on different ends of the AI usage spectrum: Andrew Jenkins - multi-tool explorer, author, and agency owner who "puts the chat in ChatGPT" and loves talking with his data. Dr. Jim Kanichirayil - founder of Cascading Leadership, builder of thought leadership custom GPTs for go-to-market, content, and analysis. Together they break down: How Andrew uses conversation, prompt optimizers, projects, and tools like NotebookLM and Dojo AI to "talk to" his book, podcast, and data. How Dr. Jim uses a simple Role-Task-Output framework to design custom GPTs, train them on his voice (and the voices of his clients), and keep them on track with root-cause analysis when they drift. The messy reality of limits, context windows, and why AI is still terrible at telling you what it can't do. Why using AI on autopilot (especially for outreach and content) is a brand risk, and how to use it as a drafting and analysis system instead. Key takeaways You don't have to choose only prompts or only custom GPTs. Strong prompting is the starting point. Custom GPTs make sense when you see the same task, drift, or "bleed out" happening over and over again. Start every workflow with three things: Role, Task, Output. Who is the AI supposed to be? What exact job is it doing? What should the output include and exclude? Then ask the model: "What else do you need to execute this well and in my voice?" Knowledge bases are just your best examples and instructions in one place. Transcripts, scripts, PDFs, posts, style packs, platform-specific examples - they're all training material. AI does best when you feed it gold standard samples, not vibes. Projects and talking to your data are the future of reading and research. Andrew uses his entire book in Markdown as a project, then has conversations like "find me five governance examples" instead of scrolling a PDF. NotebookLM turns bullet points into decks, mind maps, and videos, then lets you interrogate them. AI is a 60-70% draft, not a finished product. If you post straight from the model, it will sound generic, over-written, and slightly robotic. The job is to take that draft and ask: "Does this sound like me? Would I actually say this?" Automation is good. Autopilot is dangerous. Using AI to analyze content performance, structure research, or standardise parts of a workflow = smart. Letting AI write content and outreach you never review = reputation risk and audience fatigue. More content is not the goal. Better feedback loops are. Dr. Jim chains GPTs: one for drafting with his voice, one for performance analysis, one for insights. That loop makes the next round of content sharper instead of just… louder. Episode highlights [00:13] The core question: build digital interns (agents/custom GPTs) or just prompt better? [01:09] Andrew's origin story and why he "puts the chat in ChatGPT." [03:39] How Andrew uses prompt optimizers, multiple models, and Dojo AI as an agentic interface. [07:24] Dr. Jim's world: sticking to GPT, building tightly scoped custom GPTs for repetitive work. [08:37] When "bleed out" in prompts tells you it's time to build a custom GPT. [09:26] Using root-cause analysis inside the GPT configuration when outputs go off the rails. [10:25] Projects, books in Markdown, and "talking to your own material" via AI. [13:05] Case study: using AI to surface case examples from a 3.5-year-old book instead of scrolling PDFs. [14:27] NotebookLM for founders and students: one email of bullet points → infographic, map, slide deck, video. [19:03] The Role–Task–Output framework and the importance of explicitly designing for your voice. [22:02] Platform-specific style packs and use cases (spicy vs informational vs editorial). [26:29] The frustrating reality of token limits and why models rarely warn you before they hit a wall. [36:54] What's happening "in the wild": early-stage founders treating AI output as final product. [39:01] Why "more" isn't better, "better" is better: drafts, polish, and content analysis GPTs. [42:03] Automation vs autopilot in B2B social, and why Andrew refuses to buy from a bot. [43:29] Emerging tools: Google's Pommely, Nano Banana for image creation, and AI browsers like Atlas, Comet, and Neo. If you've been stuck wondering whether to spend time on custom GPTs or just prompt better, this episode gives you the mental models to decide. Share it with: The teammate who keeps saying "we should build a GPT" but hasn't defined the workflow. The founder treating AI drafts as finished copy. The ops brain in your org who secretly wants to be a bridge builder. Then ask as a team: "Where do we actually need great prompts, and where do we need a repeatable GPT or project with a real knowledge base?" Connect with Susan Diaz on LinkedIn to get a conversation started. Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here.

Many teams have a Notion page full of prompts. Very few have real, repeatable AI workflows. In this episode, host Susan Diaz and product/go-to-market leader Jason Dea dig into how to move from playing with prompts to designing workflows, building tiny specialist agents, and avoiding a new wave of shadow AI inside organizations. Susan is joined by venture studio and SaaS veteran Jason Dea from Coru Ventures in Toronto. They unpack why AI is not a magic wand or a single feature, but an enabling technology that only delivers value when it's wired into actual workflows. Jason shares his "swarm of bumblebees" metaphor for AI, how he builds small specialist agents to clone his own work style, and why enterprises are about to repeat the mistakes of shadow IT if they don't get serious about orchestration and governance. They close by talking about leaders using AI in their own day-to-day work, and Jason's personal experiments with family apps, coding, and even a butterfly-catching game for his daughter. Key takeaways Prompts ≠ workflows. Collecting prompts in a shared doc feels productive. But until you map the 8–10 steps of a job and decide where AI fits, you're just doing experiments, not transformation. AI is not a magic one-shot. It's an enabling technology. The real gains come when you see your work as a chain of small tasks and let AI take over the repetitive, boring, or "toil" links in that chain. Think "swarm of bumblebees." You are the queen bee. AI is a swarm of tiny worker bees, each doing one specific task very well (emails, slides, requirements, research), not one mega-agent doing everything. Documenting workflows doesn't have to be fancy. A workflow is just "tell me the 10 steps." Start with the human sequence. Tools come second. Once it's visible, the friction points where AI can help become obvious. Shadow IT is turning into shadow AI. Cheap, bolt-on AI features and swipe-a-card tools make it easy for every team to spin up their own stack. Without orchestration, you recreate silos, risk, and tool sprawl at AI speed. IT should govern, not own everything. Governance, security, and guardrails matter. But AI also democratises small bits of "coding" and automation, letting non-technical teams build more, faster—if they have guidance. Leaders need hands-on literacy. The fastest way out of the hype is to use AI yourself for your own toil. Drafting emails. Planning. Decomposing big tasks. You get more realistic about what it can and cannot do. AI is an "unstuck" tool in work and life. From relearning to code, to building tiny family apps, to cataloguing knick-knacks and designing games for kids, AI opens up projects that were unrealistic even five years ago. Episode highlights [00:01] Jason's background in startups, SaaS, product, and go-to-market, and his role at Coru Ventures. [02:00] Where we are on the Gartner hype cycle and why the trough of disillusionment is inevitable and useful. [04:40] Why some people can't imagine life before ChatGPT—and why that's not true for everyone inside organisations. [05:50] Mapping work as a sequence of steps instead of hunting for a single "magic" AI prompt. [08:01] The "swarm of bumblebees" metaphor: you as the queen, AI as many small worker-bee agents. [09:59] How to define a workflow in plain language: "tell me the 10 steps," tools aside. [11:00] Paperwork and OCR as a classic example of where generative AI finally unlocks messy, grey-area tasks. [13:50] Using AI first to remove the tasks you hate and identify the links you should outsource to machines. [15:20] Jason's "digital clone" AIs trained on his own content and patterns. [19:00] Building multiple mini-AIs: one for social posts, one for slide decks, one for product requirements. [21:10] Bolt-on AI features everywhere + messy workflows = amplified confusion and risk. [22:10] From shadow IT to shadow AI: why orchestration and shared understanding of workflows is critical. [24:40] Startups' speed vs enterprises' risk aversion, and what each can learn from the other. [27:10] Why IT should set guardrails while letting departments experiment and build more on their own. [30:10] Jason's advice to leaders: use AI yourself to see where it really helps and what it really takes. [36:00] Personal-life AI: relearning to code, family apps, cataloguing home items, and a butterfly game for his daughter. [38:00] Susan's idea: vibe-coding a family recipe app as a way to preserve memories and workflows. If your organization has a folder full of prompts but no clear AI workflows, this episode is your sign to pause and rethink. Share it with: The person who keeps buying new AI tools. The leader who thinks "IT will figure it out". The teammate who's already acting like the queen bee and quietly building their own swarm. Then ask as a team: "Where are our 10-step workflows, and which links should really be done by AI?" Connect with Susan Diaz on LinkedIn to get a conversation started. Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here.

Lots of teams are playing with AI. Few are documenting, sharing, or governing what actually happens. In this episode, Susan unpacks the hidden cost of experiment-only AI literacy inside enterprises, from duplicate spend to shadow AI, and offers a path from Wild West to structured innovation. Episode summary In this solo episode, Susan looks at what really happens when AI experimentation is encouraged, but never captured or guided. She explains why leadership often only sees one of two AI universes running inside the same company. Then she breaks down how to keep curiosity alive and add just enough structure to protect brand, budgets, and people. Key takeaways AI is already in your organisation, whether it's "approved" or not. Even with blanket bans, people de-identify data and reach for personal tools like ChatGPT or Claude on their phones. You're probably running two parallel AI universes. One official, "enterprise safe" tool stack that leadership can see. One unofficial, personal stack that actually solves problems. Experimentation is good culture. "Experiment-only" is expensive. Without reporting, shared learning, or guardrails, you get duplicate tools, compliance risk, brand drift, and fake efficiency. People are treating AI the way they once treated Google. If they can't get answers inside the firewall, they go around it. That behaviour is normal… but now the stakes are much higher. Stop chasing a single super-agent. AI can replace steps, not entire, multi-step, values-based processes that require judgement, politics, and context. The real leverage is in literacy, not licences. Tools without shared language, playbooks, and training will never compound into competitive advantage. Episode highlights [00:02] The conference metaphor: high inspiration, zero notes, nothing sticks. [01:30] The uncomfortable truth: people are using AI, even if policy says they shouldn't. [03:20] Why internal "safe" chatbots often feel generic and miss political and market nuance. [05:22] How smart staff quietly step outside approved tools and into personal LLMs. [10:05] The rise of two AI universes: official vs shadow, and where leadership can actually see. [14:22] Experimentation as a sign of healthy, curious culture. Where it tips into risk. [16:35] Hidden costs: duplicate spend, overlapping capabilities, and tool sprawl. [17:28] Shadow AI, compliance risk, and what happens when sensitive data hits public models. [18:05] Brand voice drift and micro-messaging shifts that compound over time. [20:21] What leaders can do next: audits, simple guardrails, sandboxes, and shared findings. [21:19] What a real AI playbook is (hint: documented workflows, not a buzzword PDF). [22:24] The core question: do you actually know how your people are using AI today? If you suspect there's an invisible AI Wild West running inside your organization, start here. Listen to the full episode and then ask your leadership team one question: "Do we really know how our people are using AI today?" If the honest answer is "not really", that's your starting point for an AI audit and a literacy plan. Connect with Susan Diaz on LinkedInfor to get a conversation started. Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here.