AI Literacy for Entrepreneurs

"AI Literacy for Entrepreneurs", with host Susan Diaz, helps you integrate artificial intelligence into your business operations. We'll help you understand and apply AI generative in a way that is accessible and actionable for entrepreneurs at all levels. With each episode, you'll gain practical insights into effective AI strategies and tools, hear from leading practitioners with deep expertise and diverse use cases, and learn from the successes and challenges of fellow business owners in their AI adoption journey. Join us for the simplified knowledge and inspiration you need to leverage AI effectively to level up your business.

AI Literacy for Entrepreneurs is a business podcast hosted by Unknown Host, with 278 episodes on record and a Required Pod Score of 80.

Unknown Host hosts AI Literacy for Entrepreneurs, a business show with 278 episodes published.

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  AI Literacy for Entrepreneurs

  EP279 How higher ed moves from AI interest to governed use with James Hutson

  Sep 23, 202648 min

  A previous president from Lindenwood University (a former IBM executive) stands up in town halls and tells people he uses Claude - that it helped him organize the financial report. HR follows and says they used Gemini for an employee questionnaire. Then James Hutson PhD. goes and asks employees whether they understood that as permission. The answer? 'No. The policy says I'll get fired if I use it.' That gap is the subject of this episode. James is Senior Professor and Director of AI-Enabled Academic Transformation at Lindenwood University, with two PhDs - the first in art history, the second in AI, which he went back for in 2023 once it became clear the technology was going to touch everything. He's since published over 200 studies on AI use across disciplines and industries. His argument is that policy and governance have been swapped for each other. Policy is what you can't do - mostly, don't get us sued. Governance is how things get done, and far fewer organizations have that. Most think they have a strategy when what they have is a liability shield. Also in this one: why he thinks a policy should be deliberately vague, what his AI readiness assessments consistently find, why employees hide their own efficiencies, and the reason agentic AI projects keep failing at organizations that seemed ready for them. About Dr. James Hutson James Hutson is Senior Professor and Director of AI-Enabled Academic Transformation at Lindenwood University in St. Charles, Missouri. He holds two PhDs - the first in art history, the second in artificial intelligence, completed in 2023. He has published over 200 studies on AI adoption spanning education, finance, and industry, has designed more than 25 online degree programs, and established Lindenwood's XR and Gaming Lab. His recent book is Art History in the Age of Artificial Intelligence. What we get into (00:47) Two PhDs - art history first, then AI in 2023, once it was clear this touched everything (02:25) What keeps him up at night, and the three sleepless nights Ethan Mollick warned about (04:52) The earliest adopters were administrators. They just never told anyone. (08:44) Four institutional responses: lock down, FERPA-only, all-in, and the ungoverned middle (10:00) Why blanket bans rest on a legal assumption that hasn't been tested (12:08) Susan on dragging the next generation into the future (12:52) Policy versus governance, and why most "strategies" are liability shields (15:00) "People will do what they're allowed to do" (16:50) When governance becomes a compliance checkbox (17:31) The entrepreneurial mindset - curiosity plus a growth mindset (21:05) The three literacies: functional, ethical, disciplinary (26:33) If knowledge is cheap, what makes people indispensable? (27:33) Power skills, habits of mind, and what should actually be assessed (29:43) Stop testing recall. Test reasoning. (31:15) Adaptive governance - why a policy takes a year to write and is obsolete on arrival (34:14) The programmer who automated his own job, and whether firing him was the right call (36:31) Secret cyborgs - why people hide how easy the work has become (37:26) Susan on trust, and why efficiencies have always been hidden (38:32) What AI readiness assessments actually turn up (40:00) The three things employees say in every single organization (42:41) Why agentic AI fails: the invisible work nobody documented (45:28) Resources, and an open offer of his data Quotes "People will do what they're allowed to do." - the head of faculty development at Lindenwood, quoted by James Hutson "Governance is how you get things done. It's not telling people what they can and cannot do." - Dr. James Hutson "People say yeah, of course we have a strategy - but what they mean is they have a policy, which is 'don't get us sued'." - Dr. James Hutson "You can't accuse students of cheating with AI if you didn't show them how to use it to begin with." - Dr. James Hutson "Don't penalize them. Reward that type of discovery of change, so that you're not caught off guard." - Dr. James Hutson "People do not know how they get work done. And this is why agentic AI is failing." - Dr. James Hutson "We know what needs to be done. It's human nature and psychology that's slowing us down." - Dr. James Hutson The Holy Trinity James's term for the three voices that have to say the same thing before anyone believes any of them: Leadership - this is the policy, we are an AI-forward institution, I want you to do this HR - you can use the tools, you are not going to be fired for it IT - we are not monitoring you, and we are not going to report you for using an unapproved tool His observation is that these three are rarely in sync in any organization, and that most leaders listening will immediately think of a time they collided. The three literacies Functional - can you actually use it? How does it work? Ethical - what does responsible use mean in your specific field? Journalism, chemistry, and GIS have genuinely different standards. Disciplinary - what does your discipline require, including disciplines that are actively choosing to resist it? Developed with his colleague Dan Plate, who chairs Lindenwood's AI governance committee. Resources Lindenwood's employee use case policy for generative AI is here - https://www.lindenwood.edu/policies/list/use-of-generative-ai-in-employment-policy/ And its AI and emerging technology policy: https://www.lindenwood.edu/policies/list/ai-and-emerging-technology-literacy/ James suggests pasting one alongside your own and asking a model what would need to change James on LinkedIn: linkedin.com/in/jameshutsonphd His site: jameslhutsonphd.com His book: Art History in the Age of Artificial Intelligence Get host Susan Diaz's book, Swan Dive Backwards: wearenorthlightai.com/swan-dive-backwards Enjoyed this? This podcast isn't sponsored or funded. Ratings and shares are how people find it.

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  AI Literacy for Entrepreneurs

  EP278 AI Governance in Higher Ed with Dr. Eugene Chan

  Sep 9, 202643 min

  Most organizations govern AI the same way cities govern speeding. They set a rule, then punish people who break it. Dr. Eugene Chan thinks that's the least interesting tool available. He's a behavioural scientist - founder of the consultancy Behavieural and Professor of Business at Tyndale University - and his argument is that punishment is a lagging response to a problem you could have designed out. His analogy is a speed camera versus a speed bump. Both reduce speeding. The camera does it by fining you after the fact, and it needs maintenance forever. The speed bump does it by making the behaviour physically inconvenient, and you install it once. One punishes. The other just makes the wrong thing harder to do. Apply that to shadow AI, or to the human-in-the-loop review that everyone claims to have and nobody actually does, and the conversation changes shape entirely. Host Susan Diaz and Eugene also get into why universities are harder to govern than corporations, why a single institution-wide AI rule can't work when the philosophy faculty and the accounting faculty need opposite things, why the white-font trick some professors are using is a symptom rather than a solution, and what a provost should map before writing a single line of policy. Susan and Eugene are building a governance mapping engagement for higher education institutions together. If that's you, get in touch. About Dr. Eugene Chan Dr. Eugene Chan wears two hats. He's the founder of Behavieural, a consultancy that uses behavioural science to help organizations solve trust challenges - AI adoption, customer loyalty, brand and PR. He's also Professor of Business and Marketing at Tyndale University, and has taught at TMU as well as in Australia and the United States. His PhD in management is from the Rotman School. What we get into (00:37) Two hats: behavioural science consultancy and the business school (01:34) Policy versus governance, and why even the consultants can't define it clearly (02:43) Susan's working definition: policy is the best practice, governance is the daily behaviour (03:00) Confusing access with literacy - why handing out licences isn't adoption (04:14) AI as the internet 25 years ago, and why there's no chief internet officer (04:55) Who actually owns AI governance? "Talk to IT" isn't an answer (06:07) The case for the CTO, the case for legal, the case for HR (07:38) Why universities are structurally harder than corporations (08:14) Three groups, three different toolsets, one institution (08:55) Why "use AI and you fail" is a more rational position than it sounds (10:47) But banning it is not enforceable. It's like banning the calculator. (11:38) The better question: under what circumstances should it be used? (12:21) Why this can't be one rule for a whole university (12:41) Susan's classroom approach - use it, then defend it (14:53) Why that teaches a skill most working professionals don't have (16:34) Susan on delegation, and what "senior human in the loop" means (18:24) The white-font trick professors are embedding in assignment briefs (19:34) Turnitin, AI detectors, and what a 30% match actually tells you (21:35) Governing by consequence, and where that runs out (22:42) Speed cameras, speed bumps, and a Toronto argument (24:22) Friction instead of punishment (25:23) Applying it directly to shadow AI (26:19) Cameras need maintenance. Speed bumps don't. (27:31) Susan's reframe: leading indicators and lagging indicators (28:53) Why human-in-the-loop fails when everything's on one screen (30:12) Five screens instead of one (31:28) The student problem - personal devices, personal subscriptions (34:13) What higher ed leaders should be thinking about beyond cheating (35:22) Map it, survey it, treat it as a situation analysis (37:38) Silos, and why this has to come from the provost level (38:50) Why an honest map is cathartic rather than damning (39:16) The questions to answer before you write any governance (40:14) If AI frees up staff time, where does that time go? (41:29) The flywheel, and why the first turn is the hardest Quotes "You cannot ban the use of AI. At least you cannot enforce it. It's like avoiding the use of a calculator." - Dr. Eugene Chan "The challenge is not banning the use of AI. It's finding the right use cases." - Dr. Eugene Chan "On paper, there is human in the loop. But in practice it fails." - Dr. Eugene Chan "Speed cameras do work, but they require maintenance. Speed bumps? You install it once, and it's pretty much lifetime." - Dr. Eugene Chan "Can we somehow change the environment, or change the structure, so that it still produces the desired result - without fear of fines being the primary motivator?" - Dr. Eugene Chan "It's basically like a SWOT analysis. Understanding where you are at." - Dr. Eugene Chan, on mapping AI readiness "People are confusing access with literacy." - Susan Diaz "There's something deeply cathartic about mapping it out. It's neither good nor bad. It has no sentiment. It's just a picture of things as they are." - Susan Diaz Resources Dr Eugene Chan's consultancy: behavieural.com Get host Susan Diaz's book, Swan Dive Backwards: wearenorthlightai.com/swan-dive-backwards Working in higher education? Susan Diaz and Dr Eugene Chan are offering a governance readiness mapping engagement for institutions - a picture of how AI is actually being used across faculties, where the gaps are, and what governance should address first. Reach either of them on LinkedIn - Susan Diaz , Dr Eugene Chan Enjoyed this? The podcast isn't sponsored or funded. Ratings are how people find it. Please leave us a rating

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  AI Literacy for Entrepreneurs

  EP277 Is AI Going to Take the Gift of Failure Away? asks Julie Cole

  Aug 26, 202638 min

  Episode description Julie Cole and her co-founders started Mabel's Labels in a Hamilton basement 23 years ago, back when customers were still nervous about typing a credit card number into a computer. They figured out early that they weren't really running a label company - they were running a tech company that happened to make labels. Nothing off the shelf did what they needed, so they built it themselves. Which is why her read on this moment carries some weight. She's watched every technology wave since. She's not a skeptic and she's not a cheerleader. She uses AI, has a clear personal rule for when she will and won't, and is clear about where companies are getting it wrong with customers. But she circles one worry: young entrepreneurs are now building in a day what took her team six months in that basement, and she isn't certain the lessons survive the shortcut. She's worried AI is going to take the gift of failure away. Also in this one: the LinkedIn post that made her furious, why she has two full-time people whose entire job is email, what her funeral-director son has to do with AI-proof careers, the Air Canada chatbot case, and why turning up in person has become a strong marketing strategy. About Julie Cole Julie Cole is co-founder and Senior Director of Mabel's Labels, the Hamilton-born company she and three co-founders launched from a basement in 2003 and grew into a household name. She's a recovered lawyer, a mom of six, an award-winning entrepreneur, and the best-selling author of Like a Mother: Birthing Businesses, Babies, and a Life Beyond Labels. She's a regular on Canadian television and a fixture at women's entrepreneurship events across the country - which, as this episode argues, is not incidental to how she's built the brand. What we get into (00:33) Why a label company is actually a tech company (01:03) Hamilton, 2003, and the fact that there wasn't a nerd among them (02:24) Susan's shoe labels, and a core memory involving a three-year-old choosing fonts (03:08) Watching kids grow up through the icons they pick (04:02) Twenty-three years of technology waves - what's different this time (05:26) Six months in a basement versus one day now. What gets lost? (06:45) You can't put your head in the sand about this (08:10) "I didn't know you could write like that" - the LinkedIn post that stung (08:54) What fresh hell is this (09:49) Where companies are getting AI badly wrong with customers (11:13) Julie's litmus test for when she'll use AI and when she won't (11:55) Six kids, generational skepticism, and hiding the tab when they walk in (13:25) Susan's podcast platform story: how a 30-minute task became 24 hours (14:47) The Air Canada chatbot case, and who's responsible for what your bot says (15:58) Founder-forward PR, and the CEO as the next influencer (17:14) The fake expert problem, and how people will start telling the difference (18:17) Lower your production quality. Let the dog in. (19:08) Turning up in person is having a moment (19:55) "Don't tell me you're authentic. Show me." (20:29) LinkedIn's report-AI-slop button, and the problem LinkedIn built for itself (21:13) "If I rest, I rust" (22:34) Fifty employees, and the two whose full-time job is email (23:18) On companies that won't hire until you prove AI can't do the job (24:10) "I want them all to be plumbers" (26:15) The funeral director son, and jobs AI can't touch (27:26) Where AI actually shows up in Julie's week (28:25) Taking pain away from her team, not work away from them (29:26) Kids' budgets, biochem formulas, and a grade 10 history practice test (31:13) The gift of failure (31:59) Calculators, plagiarism, and what professors used to catch by eye (33:18) Susan's answer: make them defend it like a thesis (34:48) Neurodivergence, executive function, and where AI is an unambiguous win Quotes "I'm worried that AI is going to take the gift of failure away from them." - Julie Cole "I like them to struggle. I like them to sit in their boredom. I like them to sit in frustration. Good stuff comes from that." - Julie Cole "Back when we created something like that, it took us six months. They did it in a day." - Julie Cole "You can save time all you want, but if you end up losing customers along the way, is it worth it?" - Julie Cole "People are really going to start noticing who are the experts because they went to AI, and who are the experts because they've lived it and learned it." - Julie Cole "Don't tell me you're authentic. Show me." - Julie Cole "It's not taking work away from them, it's taking pain away from them." - Julie Cole, on using AI instead of queuing work with her design team "If I rest, I rust." - Julie Cole Resources Mabel's Labels: www.mabelslabels.com Julie's page, including her book: www.mabelslabels.com/juliecole Like a Mother: Birthing Businesses, Babies, and a Life Beyond Labels Julie on LinkedIn: linkedin.com/in/julie-cole-llb-ma-3794671 Julie on Instagram: @juliecoleinc If this episode landed, read this next in Swan Dive Backwards: Chapter 12, Does AI Make You More or Less Creative? - the paintbrush-versus-photocopier question, and why AI-produced work reads as forgettable. Susan's book, Swan Dive Backwards is available on Amazon Amazon Canada https://lnkd.in/dixxKJgV Amazon USA https://lnkd.in/dFH5khHw Amazon UK https://amzn.eu/d/07y3Gkvl Enjoyed this? This podcast isn't sponsored or funded. Ratings and shares are genuinely how people find it. Drop us a 5-start rating please.

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  AI Literacy for Entrepreneurs

  EP276 Your AI Strategy Is a Hammer. Where's the Nail? - with David Cohen

  Aug 12, 202633 min

  "We need AI" is the most common brief in business right now. David Cohen's read on what it means: usually nothing in particular. David is the founder of Superposition, a consultancy for data and AI consultancies - a meta consultancy, as he calls it. He's spent his career inside both big and boutique consulting shops, which makes him refreshingly blunt about what's actually happening in the services world right now. His diagnosis is that we're still in the solution-in-search-of-a-problem stage. Everybody's curious. Everybody's excited. There's enormous movement, enormous hype, and very few real outcomes. And most organizations hiring AI help have not identified the problem they're hiring it to solve. Susan pushes on the bigger question underneath: if we're in the knowledge era and knowledge just got cheap, what happens to everyone who sells expertise for a living? David's answer is that knowledge didn't lose value - access did. What's scarce now is contextualized knowledge. The kind that accounts for internal politics, history, and the things no system can pick up from the outside. Also in this one: why consulting content is so relentlessly boring, what surprised Susan when she asked clients why they hired her, and the two words that should precede any AI purchase. About David Cohen David Cohen is the founder of Superposition, a consultancy built for data and AI consultancies. He's a lifelong consultant with both big-firm and boutique experience, and over a decade of delivering data and AI transformation work at F500 scale. He now advises boutique founders on the existential problems of running a firm - go-to-market, positioning, pricing models, and hiring. He builds his workshops and content to be collaborative and genuinely fun, on the theory that the consulting world has more than enough beige. He's based in Dallas. What we get into (00:58) Schrödinger's data consultants - where the name Superposition comes from (02:39) Why he serves other consultants, and the personal story behind it (04:31) What clients actually mean when they say "we need AI" (spoiler: FOMO) (06:03) The scale check - the average person does not care about AI tools (07:38) Automation has existed for decades. Generative AI is not the origin story (09:39) The two things every consultant is really selling - expertise and risk mitigation (10:49) Why clients now assume they shouldn't have to pay for intelligence (11:42) The consulting growth model, explained at a fifth-grade level - and why it's breaking (12:26) Is the knowledge era ending? David's answer: knowledge didn't change, access did (13:45) In the age of infinite information, the right information is the value (14:18) Contextualized knowledge - internal politics, feelings, history, and what AI can't pick up (15:22) The hype cycle, honestly assessed. Blockchain, Metaverse, VR, social (16:47) What AI becomes after the hype dies - the Google test (18:14) Asking rather than deploying first (19:58) Why consulting content is bland, boring, and forgettable (23:06) Susan's client research, and the two answers she never expected (24:51) The clarity problem: consultancies that don't know what they're selling (27:06) Ann Handley's joke about world hunger and 500-word LinkedIn posts (27:39) "A hammer in search of nails" - why real outcomes are still rare (28:59) The problem-first test, and when you should not be solving anything (30:04) Where the water's coming in the boat - assessing before building (30:54) Touch grass Quotes "I don't think they typically mean anything in particular. They usually say that out of a place of FOMO." - David Cohen, on "we need AI" "In the age of infinite information, having access to the right information is the actual value. " - David Cohen "It's knowledge that makes sense based on factors that an AI system could never understand or pick up. Internal politics, feelings, context of the greater situation." - David Cohen "Any consultant worth their salt is going to be focusing on the business outcome and not the tool." - David Cohen "We are still in the solution in search of a problem stage. The hammer in search of nails stage." - David Cohen "If you have not identified a real problem, there is nothing to solve there and you should not be trying to solve it." - David Cohen "I'm not going to add one more drop to the bucket of infinite bad content." - David Cohen Resources Superposition: superpositionstrat.com David on LinkedIn (he posts daily): linkedin.com/in/davcohen06 The Superposition newsletter: superpositionstrat.beehiiv.com YouTube, including his game show format: youtube.com/@Superpositionstrat If this episode landed, read this next in Swan Dive Backwards: Chapter 5, The Audit - the practical version of David's problem-first test, including the 60-minute starter. And Chapter 13, ROI Beyond Time Saved, for what to do once you've stopped counting hours. Susan's book, Swan Dive Backwards is available on Amazon Amazon Canada https://lnkd.in/dixxKJgV Amazon USA https://lnkd.in/dFH5khHw Amazon UK https://amzn.eu/d/07y3Gkvl Enjoyed this? Drop a five-star review on Apple Podcasts. It's a free resource, and reviews are how new listeners find it.

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  AI Literacy for Entrepreneurs

  EP 275 "Do I Trust Myself?" Grace Gravestock on the Real Reason AI Adoption Stalls

  Jul 29, 202634 min

  Episode description Everyone's arguing about whether AI can be trusted. Almost nobody's asking the harder question underneath it. Grace Gravestock has spent more than twenty years leading change management on some of the highest-profile technology projects around - including the team that set the stage for the $67B Dell EMC integration. Her verdict on why AI pilots stall is unsentimental: it's the same reason ERP stalled, and CRM before that. People aren't using the technology the way it was intended. You can have the best tools in the world, and if people don't use them, it doesn't matter. But the conversation turns when Susan brings up the trust deficit - the LinkedIn feeds full of complaints about AI slop, the comment sections litigating whether a video is real. Grace flips the question entirely: the crisis isn't whether we trust the machine. It's whether we still trust our own judgment. Along the way: the Mac Mini that sat unused for months, why the longest-tenured experts are your real bottleneck, why young people are hitting the brakes hardest, and the one change that rescued a technology rollout that had already failed. About Grace Gravestock Grace Gravestock is a change management leader with more than twenty years on large-scale technology projects. She was part of the team that set the stage for the $67B Dell EMC integration, one of the largest tech mergers in history. Her work spans Deloitte, Dell, and Warner Brothers, plus years consulting inside the utility sector. She got her start in the UK on a government services rollout that touched every citizen in the country, at the front edge of the eGov boom. She's now turning that expertise toward AI adoption, and is writing Three Secrets to Making Change Fun and Easy. What we get into (01:31) From a UK-wide government rollout to AI adoption - Grace's twenty-year arc (03:13) Why AI pilots are failing, and why the answer is boringly familiar (04:56) Why training isn't the root - and what has to happen before it (06:02) The surprise: young people are putting the brakes on hardest and fastest (07:10) Secret one - we get to choose. Why agency is the whole ballgame (08:23) Why your longest-tenured experts are the real bottleneck (and why you need them anyway) (10:35) The Mac Mini that sat there for months (11:47) Susan on being a ChatGPT person who barely opened Claude (12:29) "Am I training my own replacement?" - the fear leadership keeps answering wrong (13:22) Choosing to become more human rather than less (14:30) Monday morning heart attacks, and the work people don't want to go back to (16:50) The people who keep their jobs will be the ones who know how to orchestrate (18:48) How leaders should communicate when they don't have all the answers - WIIFM radio (20:06) The single change that rescued a failed rollout: executive bonuses (21:28) Two utilities, same state, wildly different outcomes (23:36) "We are not planning on cutting jobs. We're planning to 10x our results." (25:06) The trust deficit, LinkedIn, and the inauthenticity problem (28:35) The question that reframes everything: do I trust myself? (30:09) What leaders can actually do this week Quotes "You can have the best technology, and if people don't use it, it really doesn't matter." - Grace Gravestock "The biggest reason people hate change and fear change is because it potentially means instability." - Grace Gravestock "We get to choose how, not if, we're going to engage. AI is not going away." - Grace Gravestock "There's no such thing as living in your glass house and throwing stones. You've got to be in the trenches." - Grace Gravestock "Here's the biggest thing that we don't talk about, Susan. Do I trust myself? Do I trust myself to make my own decisions and use my own brain?" - Grace Gravestock "The people that have jobs are going to be the ones that know how to orchestrate." - Grace Gravestock Grace's three secrets Consciously choose. Change begins as an individual decision, not an org chart mandate. Align. Does this choice fit who you are and where you're going? Be open to new ways. New tools, new working patterns, new definitions of the job. Applied to AI: You do get to choose how, when, and where. Resources Grace's book waitlist and a short video on the three secrets: funandeasychange.com Connect with Grace on LinkedIn: linkedin.com/in/ggravestock Susan's book, Swan Dive Backwards: wearenorthlightai.com/swan-dive-backwards If this episode landed, read this next in Swan Dive Backwards: Chapter 8, Culture as the OS - the trust recession, psychological safety, and why adoption questions are almost never technical ones. And Chapter 3, The AI Literacy Divide, for the fear Grace names from the change management side. Enjoyed this? Leave us a five-star rating. This podcast is a free resource, and ratings are how new listeners find it.

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