Leadership in Change with AI - Podcast

Cory Blumenfeld: The Founder Bottleneck, Delegation, and the Human Premium

Most of us start a business to get our time back, and somehow end up the one person it can’t run without. In this Substack Live I sat down with Cory Blumenfeld [https://substack.com/profile/423652963-cory-blumenfeld], a five-time founder who built a virtual assistant company called BlueMoso [https://bluemoso.com/], on how to stop being the bottleneck in your own company. We get into why the system usually only exists in the founder’s head, how to hire for the match instead of just the skill, and the line I keep coming back to: AI is for tasks, people are for outcomes. Watch the full conversation above. Also, click below to subscribe on Apple Podcasts! 👇 Outline (00:00) – Intro: who Cory is and how he got to Substack (01:20) – Why human content is becoming a premium tier (01:56) – Five businesses, zero experience, and learning by building (08:42) – The founder bottleneck: leading from the backseat (10:06) – If you got sick tomorrow, would the business survive? (14:28) – Type-A founders, trust, and hiring for the match (16:46) – The hiring framework: skill, work style, communication, personality (20:03) – VAs are for outcomes, AI is for tasks (22:10) – Humans with AI, not AI versus humans (23:25) – Everyone is starting at zero with AI (33:49) – Why he named it Taking My Time Back My Takeaways The founder is the bottleneck. So the whole conversation really starts here. The minute a company can’t survive without the founder in every decision, the founder has stopped leading and started blocking. Cory said it about as bluntly as it gets: “Any founder, any CEO trying to lead from the backseat drives the car off the road.” I’ve watched this one up close, and the test I keep coming back to is simpler than people want it to be. If it’s all hinging on you, you’re not leading well, you’re just busy. The system only exists in your head. Here’s the part founders hate to hear. When delegation fails, we blame the hire. Cory’s take is that the real problem is upstream, because the playbook never left your head. He fixed it by writing his whole content process down so completely that his assistant couldn’t fail. “I documented my strategy, made it super clear so they couldn’t fail. I set them up for success, and it started working.” That reframed it for me. The bottleneck usually isn’t your team’s ability, it’s the stuff you’ve never bothered to get out of your own head. Hire for the match, not just the skill. When I asked Cory how he actually trusts people enough to step back, he didn’t start with skill. He started with fit, work style, communication style, personality, and then skill. “Hiring someone is not just skill alone. What’s their work style? What’s their communication style? The match is everything.” And he was honest about why most of us struggle with it. A lot of founders are type-A with trust issues, me included on some days. If you hired someone for a skill you don’t have, the worst thing you can do is hover. Give them the room to be good at the thing you brought them in for. AI is for tasks, people are for outcomes. This is the line I’ll be repeating for a while. Cory draws a clean line between what you hand a person and what you hand a machine. “You don’t bring in a virtual assistant to work on a task. You bring them on to deal with an outcome. You bring on AI to deal with individual tasks.” I told him on the spot that’s a quote CEOs could put on their walls, and I meant it. It also points at where this is going. When everyone has AI doing the repetitive work, the human part, the judgment, the relationship, the actual experience, stops being the cheap part and becomes the premium one. Figuring out where AI fits and where your people have to stay is most of what I work on with the leaders I coach. If you want to talk it through, my calendar is here [https://jsalinas.org/call]. One Question to Sit With If you got sick tomorrow and unplugged for two weeks, would your business still be standing when you got back? Sit with the honest answer. Watch the full conversation above, and go subscribe to Taking My Time Back [https://takingmytimeback.substack.com/], Cory’s Substack on helping founders win their time back. About Cory Blumenfeld Cory Blumenfeld [https://substack.com/profile/423652963-cory-blumenfeld] is the Founder and CEO of BlueMoso, a managed virtual assistant service for founders, executives, and agencies, with a vetted talent pool of more than 70 assistants and specialists. He’s a five-time founder with two exits across health tech, fintech, and outsourcing, and he runs his companies remotely from Playa del Carmen. He writes Taking My Time Back [https://takingmytimeback.substack.com/] on Substack. About me Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here [https://jsalinas.org/]. Written by a human, for humans. Thank you Ankita Chatrath [https://substack.com/profile/13594119-ankita-chatrath], Janet Macaluso [https://substack.com/profile/49449603-janet-macaluso], and many others for tuning into my live video with Cory Blumenfeld [https://substack.com/profile/423652963-cory-blumenfeld]! Join me for my next live video in the app. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit leadershipinchange.com/subscribe [https://leadershipinchange.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2]

Mallory Erickson: AI, Fundraising, and the Human Work

Much of the AI conversation in the business world, for those who have not used AI much, is some version of: kick your feet up, drink the piña colada, watch your revenue shoot up. I’ve seen that promise up close on the fundraising side of the humanitarian work I do, and on a lot of teams AI is the enemy in the room before it’s anything else. Mallory Erickson [https://substack.com/profile/331047755-mallory-erickson] started exactly where most of her sector started, nervous, protective, sure AI was going to hollow out the most human work there is, and then she built something that does the opposite. She’s spent her whole career on connection, she’s trained over 100,000 fundraisers, and instead of letting AI replace the fundraiser, she flipped the entire thing and made the donor the robot so the human gets to practice. Watch the full conversation above. Also, click below to subscribe on Apple Podcasts! 👇 Outline (0:31) – Bringing AI into deeply human work without hollowing it out (1:03) – From accidental fundraiser to founder of Practivated (2:30) – Why she avoided tech, and the fear that started it (4:16) – Personalization at scale, but for fundraisers (9:26) – The rise of “robot fundraisers” and why it broke her (10:28) – The flip: what if the donor is the robot? (15:50) – Start with the problem, not the AI (21:55) – Rolling AI out to a hesitant team: validate the fear (25:26) – Give your team a stipend to play (31:42) – The results: 280% more human donor touchpoints (32:39) – The LeBron test: the best still put in the reps (38:11) – A charge to practitioners: build what you need My Takeaways The flip. Most AI products aimed at fundraisers are built to replace the fundraiser, the autonomous “robot fundraiser” that writes the appeals and works the donors so you don’t have to. Mallory watched that trend and reacted the way I wish more people would, by refusing it: “If our answer to fundraiser burnout is to replace human fundraisers with robots, we can do so much better than that.” So she flipped it. “What if the fundraiser isn’t the robot? What if the donor is?” Inside Practivated the AI plays the donor, the human role plays the hard conversation, gets scored, and builds the muscle memory before the real ask. I keep coming back to that flip, because it’s the same move every leader should be making, which is to point AI at the practice and the reps instead of at the people doing the work. It’s the version of AI I argued for in Can AI Make You a More Human Leader? [https://leadershipinchange.com/p/can-ai-make-you-a-more-human-leader], and she actually built it. Start with the problem, not the AI. This is the part of the conversation I keep coming back to, because it’s exactly what I tell the executives I coach. Mallory has closed deals lost on purpose, walked away from organizations that came to her wanting “more AI” with no real problem behind it. Her test is simple: what have you been trying to solve that you’ve thrown everything at and still can’t crack, and is there an emerging tool that could finally move it? As she put it, “AI is not the point. The goal is to solve your challenges.” Validate the fear before you roll anything out. I asked her how she’d introduce AI to a team that’s all over the map on it, and her first move surprised me, because it had nothing to do with the tool. She said there shouldn’t be AI adoption without first being ready to acknowledge and validate people’s fear, not to agree that AI is scary, just to give the fear a place to exist before it hardens into resistance. “It’s really important that we’re acknowledging and validating how people feel.” Then she does something I love, she gives her whole team a monthly stipend to just play, with two rules: have a hypothesis going in, and come back and tell the team what you learned. The reps don’t stop because you’re good. The skeptic line Mallory hears most is “I’ve been fundraising for twenty years, I don’t need to practice.” Her answer is the one I’m going to start stealing in my own coaching: “Can you imagine if we said LeBron James is such a good basketball player, he doesn’t need to practice anymore? Simone Biles? Absolutely not. The people at the top of their game are putting in the reps.” And the numbers are backing her up, a 280% increase in human-driven donor touchpoints, onboarding cut from eight weeks to two, ask effectiveness up 33% in the first 30 days. AI didn’t replace the human in any of that, it gave the human a place to practice so they could show up more present when it actually counted. One Question to Sit With Where in your own work are you reaching for AI before you’ve named the problem it’s actually supposed to solve? Mallory’s whole practice is built on refusing to skip that question, and I haven’t stopped chewing on how often the rest of us do. Watch the full conversation above, and then go follow Mallory’s [https://www.linkedin.com/in/mallory-erickson-bressler/] work at Practivated [https://practivated.com/] and her What the Fundraising [https://malloryerickson.com/podcast/] podcast. About Mallory Erickson Mallory Erickson is an executive coach, fundraising consultant, and the host of the What the Fundraising podcast. She has trained over 100,000 fundraisers, co-founded the Fundraising AI initiative, and is the founder of Practivated, an AI practice platform where fundraisers role play donor conversations and get coached on what to do differently next time. Her North Star, in her own words, is improving the lives of fundraisers. You can find her work at practivated.com [https://practivated.com/]. About me Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here [https://jsalinas.org/]. Written by a human, for humans. Sidenote… do you write on Substack? Build smarter, not just harder, with NewsletterCompass.com [https://www.newslettercompass.com/]. As a co-founder, I’m giving you 50% off for life… 👇 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit leadershipinchange.com/subscribe [https://leadershipinchange.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2]

The Map of AI, Redrawn: Gennaro Cuofano on the Semiconductor Moment

If you’ve spent any time trying to make sense of where AI is actually going, not the headlines but the structure underneath all of it, you’ve probably run into Gennaro Cuofano [https://substack.com/profile/19791492-gennaro-cuofano]’s work without knowing it was his. He built The Business Engineer [https://businessengineer.ai/?lli=1&utm_source=mention&utm_content=writes](spun off from FourWeekMBA) into a research hub more than 90,000 people follow, and he just launched something new called The AI Supercycle. [https://thesupercycle.ai/] I wanted to sit down with him because he maps this stuff for a living, and his read on it is different from almost everyone else’s. His core claim is that we keep comparing AI to the internet when the better comparison is the 1950s, the moment we built the computer for the first time. That one reframe changes where you look for advantage, who you watch, and how far ahead you’re allowed to plan. Watch the full conversation above. Also, click below to subscribe on Apple Podcasts! 👇 Outline (1:10) – From FourWeekMBA to The Business Engineer (4:34) – Why launch The AI Supercycle now (8:18) – The semiconductor moment, not the internet (14:43) – The nine-layer map and why governance sits on top (17:41) – Spinning hundreds of agents to run a business (19:45) – Governance, government, and the Palantir layer (24:01) – Why human conversations beat the AI content flood (26:35) – The Builder PM and the role that split in two (31:25) – Orchestrating agents in real disaster response (38:24) – From SaaS to Agent-as-a-Service (40:02) – Audience question: what happens to SaaS? My Takeaways The semiconductor moment. Gennaro’s whole thesis hangs on a historical analogy, and it’s the thing I keep coming back to. Everyone wants this to be the internet, a 20 or 25 year cycle. He thinks that’s the wrong map. As he put it: “We’re building the computer. It’s not the web. It’s the 50s, 60s, 70s, when we built the computer for the first time. This is a 30 to 50 year cycle.” What got me is that most analysts I talk to are afraid to forecast past three years right now, and here’s someone calmly drawing a map for the next three decades. Governance on top. I told him I loved his nine-layer map of AI, because he does something nobody else does, he puts governance at the very top, above the models, above the chips, above everything. His reason is blunt: the frontier now answers to government, and there’s no getting around it. “That’s why the governance layer is going to become one of the most important for the top of the frontier. There’s no way out.” He pointed to Palantir already routing different AI models through everything the government does. If you only read the tech headlines, you miss the layer that actually decides what ships. The Builder PM. Gennaro’s been a filter between clients and technical teams for years, so this one is lived, not theoretical. Building got easy. Everything around the build, the integration, the deployment, the org politics, got harder. So the product role split. The new version turns a client’s specs into something testable in six months instead of three years, which makes the PM a hybrid of builder and orchestrator. I see the same thing in my World Relief work, where we now run a team of agents for disaster response and a human has to sit at the center coordinating the writers, the researchers, the photographers. You stop being the doer and become the architect. From SaaS to Agent-as-a-Service. His prediction for the next 18 months is that per-seat, per-human pricing breaks, because your biggest customer stops being a person and becomes a fleet of agents consuming your software millions of times over. “There will be many SaaS companies worth 100x more, because now with the agents you get a level of consumption you didn’t have before.” The way I said it back to him: the pie went from being this big to unlimited. Sidenote… do you write on Substack? Build smarter, not just harder, with NewsletterCompass.com [https://www.newslettercompass.com/]. As a co-founder, I’m giving you 50% off for life… 👇 One Question to Sit With When everyone has the same frontier models for twenty dollars a month, where does your advantage actually move to? Gennaro’s answer is “down the stack, toward governance and infrastructure,” and I haven’t stopped chewing on it since. Watch the full conversation above, and then go subscribe to The AI Supercycle [https://www.supercycle.ai/]. It’s the clearest map of this moment I’ve found. About Gennaro Cuofano Gennaro Cuofano is the creator of The Business Engineer, a deep-tech research hub spun off from FourWeekMBA, the business-model-strategy blog he started in 2015 and grew past 90,000 subscribers. A tech executive by day, he writes about the structural shifts reshaping business in the AI era and just launched The AI Supercycle, a newsletter and podcast decoding where the next 30 years are heading. Subscribe at supercycle.ai [https://www.supercycle.ai/]. About me Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here [https://jsalinas.org/]. Written by a human, for humans. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit leadershipinchange.com/subscribe [https://leadershipinchange.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2]

Bryan Cassady: Why AI-First Strategy Fails and Objectives Win

AI keeps getting better, so why aren’t your results getting better? Bryan Cassady [https://www.linkedin.com/in/bryancassady/]’s answer: Most people are getting more output, not better work, because they skip the thinking and go straight to the tool. In this Substack Live we get into his Type 3 error (doing the wrong thing, fast), why objectives have to come before any tool, the real $11,000-a-year cost of AI that most leaders never count, and what a generative organization actually is. If your team has AI and still isn’t getting results, this is the conversation to watch. Also, click below to subscribe on Apple Podcasts! 👇 Outline (00:00) – Bryan’s mission: a million people getting results from AI by 2027 (01:02) – Better models, worse results: the human half of the equation (03:16) – How Bryan started: “six months ahead of everyone else” (05:16) – Objectives first, tools second (07:33) – The Type 3 error: doing the wrong thing, but doing it right (09:57) – The Dan Ariely story: six months on a hallucinated theorem (11:28) – Think first: Einstein’s 55 minutes on the problem (15:26) – From an answer economy to a question economy (16:20) – Strategic clarity and the $11,000 cost of AI (18:48) – What a generative organization actually is (24:00) – The 3% AI can’t do, and why it sets you apart (31:00) – Bryan’s giveaways: free books, tools, and a beta course My Takeaways The Type 3 error. So here’s the frame I keep coming back to from this whole conversation. Bryan put it like this: a Type 3 error is when “you’re doing the wrong thing, but you do it right.” And the problem with AI is you can now do the wrong thing really fast and feel great about it the whole time. I’ve had people hand me a polished 25-page deliverable that nailed every step of a question nobody actually needed answered. Objectives first, tools second. Bryan’s line on this is the one I’d put on the wall: “AI first is silly. It’s like being a carpenter who wants to be a hammer-first carpenter. A hammer can do cool things, but it doesn’t do everything.” Most leaders are out shopping the 75,000 listings on a site like There’s an AI for That (a directory of AI tools) when the real first move is naming the objective and then picking the tool that serves it. Get that order wrong and you just bought a faster way to drift. The $11,000 nobody budgets for. Here’s the part that should stop every leader who thinks AI is a $20-a-month decision. Bryan cited a Gartner study putting the time people spend controlling, revising, and re-editing AI output at around five hours a week, which works out to roughly $11,000 a year for a typical US knowledge worker. “Do you get $11,000 more benefit from using AI? Of course you can, but only if you do it smart.” Most don’t. And that’s before you count what poorly rolled-out AI does to a team’s trust in their own leadership. The 3% AI can’t touch. This is where it got good. Bryan's argument, borrowed from an innovation method called TRIZ [https://www.triz.co.uk/what-is-triz], is that about 97% of what we call innovation is really recombining things that already exist, and AI does that better than almost any human. But the last 3%, the genuinely new, the thing that doesn't exist yet, that's still you. “Humans have an ability to look forward. AI has an ability to look backward.” That 3% is the creativity and judgment no model hands you, which is exactly what the AI Leadership Triad [https://leadershipinchange.com/p/the-ai-leadership-triad-3-skills] is built around. Pair it with his bigger shift, that we’ve moved “from an answer economy to a question economy,” and the job gets clearer: stop racing the machine on answers and get very good at the question. Two things Bryan is giving Leadership in Change readers: * Both of his books, free, plus his PDF innovation tools — books.genorg.ai [https://www.books.genorg.ai/] * A free, private check on how you actually use AI: paste his assessment into ChatGPT or Claude, and it scores you 0 to 10 from your own chat history — get the assessment [https://shares.showellapp.com/yNgk9qeArvSKm9Wm8nYxmVGY] * A free spot as a beta tester for his new AI course (10 lessons, 10 minutes a day, with a before-and-after read on how you actually use AI) — grab a beta seat here [https://forms.gle/faPr6L9GXmpYxn7UA] One Question to Sit With When’s the last time you spent five whole minutes on the problem before you opened the AI? Bryan says when he asks a room that, almost every hand goes down. Watch the full conversation above, and then go grab Bryan’s free books and tools at books.genorg.ai [https://www.books.genorg.ai/]. About Bryan Cassady Bryan Cassady [https://www.linkedin.com/in/bryancassady/] is the founder of GenOrg [https://genorg.ai/] and the author of The Generative Organization. He’s an AI keynote speaker [https://bryancassady.com/] who has trained more than 40,000 people, and says he’s now helped around 90,000 toward his goal of a million people getting real results from AI by 2027. He also sits on the board of the US association for TRIZ, the structured innovation method he teaches. Get both of his books and his innovation tools free at books.genorg.ai [https://www.books.genorg.ai/]. About me Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here [https://jsalinas.org/]. Written by a human, for humans. Thank you Farida Khalaf [https://substack.com/profile/47192869-farida-khalaf], Claire Machado [https://substack.com/profile/168845660-claire-machado], Duncan The Sage [https://substack.com/profile/254449706-duncan-the-sage], and many others for tuning into my live video with Bryan Cassady [https://substack.com/profile/123257765-bryan-cassady]! Join me for my next live video in the app. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit leadershipinchange.com/subscribe [https://leadershipinchange.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2]

Scaling AI Without Breaking Your Culture

This one got into the part of AI adoption nobody really wants to talk about. Ryan Deeds runs AI at ALKEME [https://www.alkemeins.com/], an insurance company that bought its way from seven agencies to ninety-three in about four years, and his whole argument is that the technology was never the hard part. The hard part is getting fifteen hundred people to actually trust a tool and use it. We talked about the golden path, why every rollout fails for the same reason, the eighteen months it really takes to hit adoption, and the bot pipeline that lets anyone at the company build their own software. Watch the full conversation above. 📩 Subscribe to Leadership in Change Outline (00:11) – Scaling without breaking culture (00:49) – Seven agencies to ninety-three in four years (02:07) – From data guy to Head of AI (03:41) – The golden path across 150 workflows (05:32) – “AI is a bunch of hype” and the cultural work underneath (08:17) – Why every rollout fails for the same reason (12:54) – The 85% that fail, and what they all skipped (14:21) – Fear, mistakes, and fifteen dead initiatives (15:05) – Eighteen months to 70% adoption (19:43) – Why the model wars are wasted noise (21:14) – There’s no easy button (24:05) – The bot pipeline that lets anyone build (27:19) – Scaling AI without it blowing up in your face (29:23) – The most overrated AI advice My Takeaways The failure is always culture. Ryan has built nine CRMs and implemented seven more, and he says the result never changes. “Got nothing to do with technology. It’s culture.” I brought up the stat that 85% of AI implementations fail, and his read matched what I see all the time: people get handed a tool, get told to use it, and decide not to, either because they think it’s there to replace them or they don’t trust the people who bought it. There’s no easy button. Ryan’s least patient moment was about the model wars. “You cannot judge current state AI in current state,” he said, and the people arguing over which model beat which this week are usually the ones not actually building anything. What matters to him is boring on purpose: how many people used it, how many minutes did it save. “If I was judging what I created, I’d be freaking Elon.” That’s the line I keep coming back to, because most of us are measuring the wrong thing. Eighteen months to seventy percent. I appreciated how honest he was about the timeline. He told his CEO up front it would take about eighteen months to reach full adoption, and that full adoption, in his world, is 70%. The pushback he plans for doesn’t come from the technology. It comes from moving people out of a culture where nobody was accountable and into one where everybody can see your numbers. Closest to the work is clean. This was the part that genuinely surprised me. ALKEME has fifteen hundred employees and a build team of nine, so Ryan built a pipeline instead. An employee emails an idea, a bot pins down the narrowest version of success, another bot builds the MVP on the company’s own infrastructure and sends back a working link to iterate on. “I would way rather empower you, employee E, to bring your concept to fruition.” The person closest to the problem builds the fix, inside the guardrails. Don’t pour a bucket of AI water on everything. For an insurance company, a broken tool isn’t a small thing, so I asked how he moves this fast without it blowing up. His answer was discipline about scope. “It’s not just pour a bucket of AI water on everything and see where it hits.” They ran fifty thousand historical submissions through one tool before they trusted it, locked permissions to each user, and pushed everything through a pipeline that gets checked. “We’re just very careful about where it touches.” It’s the same discipline ALKEME brings to the coverage side, where a single missed risk like a cyber attack is exactly what their clients are paying to be protected against. If your business carries that exposure, their team handles cyber insurance [https://www.alkemeins.com/] directly. If you’re the person inside your company trying to get AI actually adopted and not just installed, that’s most of what I work through with teams. You can start at jsalinas.org [https://jsalinas.org/] and book a free call with me. If you rolled out an AI tool tomorrow and nobody used it, would you call that a technology problem or a culture problem? Sit with which one you’d actually fix first. Watch the full conversation above, and go connect with Ryan Deeds on LinkedIn [https://www.linkedin.com/in/ryancdeeds/] and see what ALKEME [https://www.alkemeins.com/] is building. About Ryan Deeds Ryan Deeds is Head of AI & Enablement at ALKEME [https://www.alkemeins.com/], an insurance company that has grown to around 93 agencies and 1,500 employees in roughly four years. He spent 30 years in the insurance agency space, hosted The Digital Broker podcast from 2017 to 2019, and now builds the internal tools and adoption systems that let ALKEME scale AI without losing its culture. Connect with him on LinkedIn [https://www.linkedin.com/in/ryancdeeds/]. About ALKEME ALKEME is a national insurance company built from more than 90 independent agencies, serving clients across commercial, personal, and specialty lines. One of those lines is cyber insurance [https://www.alkemeins.com/], coverage that protects businesses against cyber attacks, data breaches, and the operational fallout that follows. If your organization needs to think seriously about cyber exposure, talk to ALKEME [https://www.alkemeins.com/]. About me Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here [https://jsalinas.org/]. Written by a human, for humans. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit leadershipinchange.com/subscribe [https://leadershipinchange.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2]