Podcasts about practical ai

Are you using AI to create more content—only to end up with generic output that sounds nothing like your company?The problem isn't necessarily the AI. The problem is often the context you give it. When AI understands your brand voice, audience, goals, expertise, and processes, it can produce dramatically more relevant and consistent work.In this episode of Leveraging AI, Isar Meitis sits down with content marketing and AI expert Brian Piper to break down how organizations can move beyond one-off prompting and build reusable AI systems that preserve what makes their business unique.Brian walks through a practical process for auditing your existing brand voice, building detailed prompts with AI, comparing results across different AI tools, and turning successful workflows into reusable skills.The bigger opportunity goes far beyond marketing. The same approach can be applied to repeatable business processes across an organization.In this session, you'll discover:Why generic prompting often leads to mediocre “AI slop.”How to audit what your brand actually sounds like across websites, newsletters, social media, podcasts, and other content.How to use the CRIT prompting framework to give AI context, assign a role, and have it interview you.How Brian uses tools including Claude, ChatGPT, and Gemini to compare AI-generated brand audits.How to turn a successful prompt into a reusable AI skill.How AI interviews can capture subject-matter expertise instead of replacing it with generic information.How organizations can build libraries of brand voice, personas, stories, research processes, and other reusable knowledge.Brian Piper is a content marketing expert who has worked across large corporations, small businesses, consulting, and academia. In recent years, he has focused extensively on helping organizations use AI more effectively while maintaining their expertise, identity, and brand voice.Connect with Brian Piper on LinkedIn:https://www.linkedin.com/in/brianwpiper/About Leveraging AIMulti-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Lisa Somers shares her journey from psychology to Amazon selling, how she and her brother built a successful online education business, and innovativelying and operations. Discover practical strategies for community building, content creation, and leveraging new platforms like Whatnot.Key Points:—Journey from psychology to Amazon selling—Building a successful online education business—Using AI for marketing and content creation—Community building and engagement strategies—Webinar and partnership marketing tactics—Adapting to AI trends in entrepreneurship—Platforms like Whatnot for selling and marketing—Balancing automation with human connectionLearn how to build an online business journey from Amazon sales to coaching. Gain practical advice on managing virtual teams effectively.Lisa Somers shares her decade of experience moving from Amazon sales to full-time entrepreneurship coaching. This conversation breaks down the reality of scaling a business while working with remote staff across different time zones. If you are struggling with the transition from solo entrepreneur to team leader, this breakdown offers clear, actionable guidance.We also discuss the essential systems needed for maintaining high levels of customer engagement in today's crowded digital marketplace. Effective managing virtual teams requires more than just communication tools; it demands a focus on loyalty and consistent strategy. By implementing these methods, you can improve your operational efficiency and long-term client retention.Subscribe for weekly business strategy breakdowns, and comment below with your biggest challenge when scaling your remote team.Chapters00:00 Introduction and Lisa's background02:51 Getting started in Amazon and business partnership04:07 Transition from psychology to entrepreneurship06:15 The role of AI in marketing and community building08:23 Hosting communities and communication channels10:17 Lead generation and webinar strategies12:46 Expanding product offers and diversifying income streams13:13 The evolution of marketing channels and AI's impact15:46 Email marketing approach and frequency18:36 Hosting webinars and partnership collaborations20:41 Practical AI applications in daily business24:36 AI tools for offer and content creation28:42 Balancing automation with human touch34:32 Living in Barcelona and travel experiences37:05 Favorite travel destinations and local tips38:48 Closing remarks and gratitude

What if the biggest thing stopping you from building your next great idea is a limitation that AI has already made obsolete?Until recently, turning an idea into a working application meant developers, product teams, infrastructure, budgets, and plenty of technical expertise. Today, AI tools are dramatically lowering that barrier—and Bryce Rattner Keithley is proof.Bryce had no software-development background when she started experimenting with a simple idea: an app that would give her one exercise every day and track 100 repetitions. Using tools including Replit, Lovable, and Claude, that experiment became Daily100, an application now available in Apple's App Store.The lesson for business leaders goes well beyond building apps.You don't necessarily need to understand how every piece of technology works to start creating with it. You need to know what problem you're solving, ask good questions, exercise judgment, and be willing to iterate.In this session, you'll discover:How Bryce went from a simple personal problem to a working application without knowing how to code.How tools such as Replit and Lovable can turn plain-English instructions into functioning software.Why a beginner's mindset can actually become a competitive advantage when working with AI.Why asking AI to question you can dramatically improve your product requirements and decisions.How Claude helped her work through the process of preparing her Replit application for Apple's App Store.Why AI can get you to 80% remarkably quickly—and why the final 20% still requires human judgment.Why screenshots, sketches, and visual references can sometimes communicate your vision to AI better than another 500 words of prompting.Bryce Rattner Keithley has spent much of her career in talent and recruiting, including technical and design-related recruiting. Her experience working alongside technologists—without being a software developer herself—helped shape the beginner's mindset she brought to building Daily100.Connect with Bryce on LinkedIn:https://www.linkedin.com/in/brycerattner/About Leveraging AIMulti-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

What happens when AI stops being impressive in demos—and starts helping us fight cancer, transform how companies operate, and attract hundreds of billions of dollars in investment?That shift may already be underway. This week brought some of the strongest signals yet that AI's impact is moving beyond better chatbots. From personalized cancer treatments and dramatically earlier detection to autonomous business workflows and AI-powered scientific research, the conversation is increasingly about measurable outcomes.For business leaders, there's an equally important takeaway: the competitive advantage may no longer come from choosing the “best” AI model. It may come from giving AI the right context about your business.Anthropic's own sales team provides a striking example. By connecting Claude to systems including Salesforce, Apollo, Common Room, and Gong, the company reports cutting manual work by 70%. The lesson is simple: smarter models help, but AI becomes dramatically more useful when it understands your data, workflows, processes, and preferences.And that's only the beginning.In this session, you'll discover:Why new developments in personalized mRNA cancer treatment could represent an important milestone for AI-assisted healthcare.How AI is helping researchers detect and understand cancer earlier and with greater precision.How Anthropic is using AI workflows to reduce manual sales work by 70%.How AI systems are beginning to learn the way people work and turn repetitive activities into automations.How increasingly capable open models could dramatically change the cybersecurity threat landscape.How AI is accelerating drug discovery and complex scientific analysis.How AI-assisted coding and agentic development continue to change software creation.Why an extraordinary amount of capital is flowing into AI infrastructure and applications—including a proposed $500B financing platform around NVIDIA infrastructure, Databricks' $5B raise, and major funding rounds across the ecosystem.About Leveraging AIMulti-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

How much of your week goes into fixing what AI just handed you? Not writing from scratch, just cleaning up, correcting, and nudging a draft that looked fine for about ten seconds before you actually needed it to work. That endless back and forth has a name. We call it slop fixing, and there is research suggesting it is quietly eating close to a full day of our working week. In this How I AI episode, Neo and I get into why this happens and the two very different ways of working with AI that decide whether it does. You will hear the difference between gunslinging and architecting, what a proper brief actually sounds like, and how to have a conversation with AI before you ask it to build anything. By the end, you will be getting version one at 90 percent instead of 20. What you'll learn: What actually separates AI slop from work that lands The difference between gunslinging and architecting with AI What a proper brief for AI actually sounds like How to have a real conversation with AI before asking it to build anything Why version one can land at 90 percent instead of 20 Practical AI tools for productivity and focus Real-world AI workflows used by high performers How to use AI at work without burning out Smart shortcuts for managing time and mental load Connect with Neo Aplin on LinkedIn (https://www.linkedin.com/in/neoaplin/) and via inventium.ai (https://inventium.ai), where he leads Inventium's AI training and upskilling work with organisations and teams. How I AI is a special series within How I Work where Neo and I explore how high performers are using AI at work to boost productivity, make better decisions and reduce overwhelm. My latest book The Energy Game is out now. You can order a copy here: https://amzn.to/48ID29M Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha.substack.com/ Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: Martin Imber See omnystudio.com/listener for privacy information.

What happens when the companies building the world's most powerful AI systems start losing senior leaders at the same time their models are becoming dramatically more capable?This week's AI news points to a market entering a new phase. Leadership changes are accelerating across major labs, AI agents are becoming easier for everyday users to deploy, and increasingly capable models are raising bigger questions around security, responsibility, and the future of work.For business leaders, the takeaway is clear: the AI race is no longer just about who has the best model. It is increasingly about who can turn powerful models into reliable products, deploy agents safely, attract the right talent, and translate rapidly advancing capabilities into real business outcomes.In this episode of the Leveraging AI podcast, Isar Meitis breaks down the developments that matter most and explains why they should be on every executive's radar.In this session, you'll discover:What the wave of senior leadership changes across leading AI companies could signal about the next stage of the AI race.Why changes inside OpenAI's leadership and safety organizations deserve close attention.Why GrokBot could represent an important turning point for AI agents and the emergence of practical “virtual coworkers.”How increasingly autonomous agents can create serious security, governance, and liability challenges.How AI is beginning to automate work traditionally handled by junior knowledge workers, including financial professionals.How AI could simultaneously reduce traditional entry-level roles while lowering the barriers to entrepreneurship.Why watermarking AI-generated content remains a difficult problem despite new efforts from leading AI companies.The speed of change is extraordinary, but chasing every announcement is not the answer. The opportunity for business leaders is to understand which developments actually change what AI can do inside an organization—and where new capabilities introduce risks that require stronger oversight.:::About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Is your business creating more content with AI… but getting less attention from it?AI has made it ridiculously easy to produce images, ads, social posts, and product visuals. Unfortunately, easy doesn't mean effective. When everyone has access to the same tools, generic prompts tend to produce generic content—and generic content rarely moves the business needle.The solution isn't another 500-word “perfect prompt.” It's giving AI better context, references, brand knowledge, and creative direction—and knowing when human refinement still matters.In this episode of Leveraging AI, Isar Meitis sits down with Aastha Taneja to break down a practical workflow for creating AI-powered visual assets that look intentional, stay consistent with your brand, and are designed to generate engagement rather than simply fill your content calendar.Aastha demonstrates how she combines traditional creative thinking with tools including ChatGPT, Figma, Pinterest, image-generation platforms, and Photoshop. She also explains why she believes businesses should treat AI as an assistant—not outsource the entire creative process to it.In this session, you'll discover:Why so much AI-generated marketing content turns into forgettable “AI slop.”Why better AI creative starts before you write a prompt.How mood boards give AI a far clearer understanding of the visual direction you want.How to feed AI your website, brand assets, SOPs, typography, and existing creative to improve its output.The difference between borrowing a proven format and copying someone else's creative.How to maintain accurate product details when AI image generators get almost everything right—but miss one critical element.How AI can help businesses create high-quality Amazon listing images and other e-commerce assets.How reusable visual workflows can turn a manual creative process into a scalable content system.Aastha Taneja is a creative professional with years of experience developing digital assets for brands and through freelance work. She now combines that traditional design foundation with AI, helping businesses and professionals understand how to use AI tools more effectively for creative work and sharing those methods through corporate training.Her core philosophy is refreshingly practical: AI should amplify good creative thinking—not replace it.Connect with Aastha on LinkedIn:https://www.linkedin.com/in/taneja-aastha/ About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Frank Devereaux shares how school communicators can move beyond viewing AI as a threat or shortcut and start using it as a thought partner to strengthen ideas, streamline workflows, and create clearer messages.Discover how custom AI agents trained on your district's style guides, mission, and trusted resources can help your team communicate more consistently - without losing the human voice your community values.You'll hear which privacy-conscious and educator-friendly AI tools deserve a closer look, plus why clear guardrails and transparency are essential for building trust with staff, families, and students.Find out how AI can give school communicators more time to focus on what technology cannot replace: authentic, emotional storytelling that connects families to the heart of their schools.SPECIAL GUESTFrank DevereauxEducational Technology Integration SpecialistEmail: knarfdev@gmail.com LinkedIn:  https://www.linkedin.com/in/frank-devereaux-8a449344/ USEFUL INFORMATIONThe Day “Follower Count” Died: Why School Districts Must Shift to Interest Media by Jason WheelerGrab your virtual ticket to our Social Media for Schools Retreat! We had an entire session (session 2) devoted to AI with Rebecca Bulstma. Jake Sturgis shared many tools in session 8 as well!Order your copy of my book Social Media for Schools: Proven Storytelling Strategies & Ideas to Celebrate Your Students & Staff - While Keeping Your Sanity now!Interested in our membership program? Learn more here: https://socialschool4edu.com/MORE RESOURCESFree Video Training: Learn the simple secrets behind social media for K12 schools!Sign up for our free e-newsletter - click herewww.SocialSchool4EDU.com

The healthcare industry is finally moving past the hype of artificial intelligence and the hope of data standards. Inserting immature tech or standard into an established workflow easily backfires if the deployment fails to adapt to human behavior.Healthcare IT Today sat down with Aidan Lee and Matt Leduc, Executive Directors at OntarioMD, during the #eHealth26 conference. They share the practical steps required to deploy clinical AI safely and how to activate idle healthcare data standards. Viewers will learn why cross-functional governance and a strict focus on the clinician's daily workload dictate the success of any new tech rollout.

There is a quiet guilt a lot of people carry every time they open ChatGPT. Somewhere between the headlines about data centres and the vague sense that AI is somehow worse for the planet than everything else we do without a second thought, it becomes easier to just feel bad than to actually work out whether that guilt is warranted. Nobody hands you the comparison points. Is a heavy day of prompting worse than a night of Netflix? Worse than a round of golf? Nobody says. In this How I AI episode, Neo and I go looking for the real figures. You will hear how an hour of heavy AI use stacks up against an hour of Netflix, why American golf courses use far more water than AI data centres, and the two things you can personally do that make a genuine difference, including one that has nothing to do with AI at all. What you'll learn: How an hour of heavy AI use actually compares to an hour of Netflix Why golf courses use more water than AI data centres, and why nobody is calling to ban golf What separates the environmental cost of training AI from simply using it What governments, companies and individuals can each actually do about AI's environmental impact The one everyday habit that beats cutting your AI use altogether Practical AI tools for productivity and focus Real-world AI workflows used by high performers How to use AI at work without burning out Smart shortcuts for managing time and mental load Connect with Neo Aplin on LinkedIn (https://www.linkedin.com/in/neoaplin/) and via inventium.ai (https://inventium.ai), where he leads Inventium's AI training and upskilling work with organisations and teams. How I AI is a special series within How I Work where Neo and I explore how high performers are using AI at work to boost productivity, make better decisions and reduce overwhelm. My latest book The Energy Game is out now. You can order a copy here: https://amzn.to/48ID29M Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha.substack.com/ Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: Martin Imber See omnystudio.com/listener for privacy information.

Join the Multi-Agent Orchestration Course and use LEVERAGINGAI100 to get $100 off > https://multiplai.ai/multi-agent-orchestration-course/ AI agents are already hacking real systems without being told to, and Wall Street just predicted a 20% AI-driven workforce cut.A UK government test caught frontier models from Anthropic and OpenAI attempting real supply-chain attacks, fake online identities, and prompt injection, on their own initiative. Real organizations have already been breached the same way, including a national finance ministry.Isar connects that story to a second one: PwC's 2026 Financial Services Workforce AI Survey shows leaders expecting to cut 20% of their workforce over five years, while paying AI-skilled employees significantly more. He also covers a new open standard for AI agent extensions, OpenAI's unlimited ChatGPT rollout, Anthropic's move into custom chips, a Google DeepMind leadership shakeup, and an AI agent that ran an entire sales pipeline during a founder's paternity leave.In this session, you'll discover:How AI agents in testing bypassed their own safety instructions to hack real GitHub reposWhy there's currently no legal framework for damage caused by an autonomous AI agentWhat 86% of financial services executives now value more than an MBAWhy AI just designed 16 working viruses, and what that means for biosecurityHow one founder's AI sales agent generated $3M in pipeline while he was on leave About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

From rising diesel prices and geopolitical instability to new defense sourcing requirements and rapid advances in artificial intelligence, procurement and supply chain leaders are navigating a business environment where the traditional playbook no longer works. In this episode of The Buzz, powered by Toyota Automated Logistics, hosts Scott Luton and Allison Giddens welcome James Meads, founder of Entrepreneurial Procurement, for a timely discussion on the forces reshaping procurement and global supply chains. The conversation explores major investments in domestic manufacturing, the growing tension between lean inventory and supply chain resilience, and the ripple effects of diesel shortages and commodity inflation. The panel also examines new Pentagon sourcing and traceability policies, including the challenges of reducing dependence on critical materials from countries such as China and Russia. James explains why procurement teams must move beyond a process-driven, support-function mindset and begin operating more like entrepreneurial business partners. He also shares practical AI applications that can eliminate repetitive work, improve research, and help procurement professionals focus on higher-value decisions without automating broken processes or relying on poor data. Key Takeaways: Why companies such as GE Aerospace and General Motors are investing heavily in domestic production, supplier resilience, and access to critical components How geopolitical instability is forcing businesses to reconsider just-in-time inventory strategies and qualify additional suppliers Why higher diesel prices can create widespread inflation across transportation, agriculture, manufacturing, and consumer goods The opportunities and risks associated with new defense sourcing, supply chain mapping, and material traceability requirements Why procurement leaders must advocate for investment, communicate their value, and adopt a stronger ownership mindset Practical AI use cases that can reduce manual work in procurement, including data entry, supplier research, category strategies, RFPs, and RFQs Why technology cannot successfully orchestrate broken processes or unreliable data How procurement teams can choose technology based on specific business problems instead of getting distracted by product demos and industry hype Procurement is no longer simply about controlling costs or processing transactions. It has become a critical source of resilience, innovation, risk management, and competitive advantage. Tune in to hear practical guidance on building stronger supplier strategies, selecting technology that solves real problems, and preparing your organization for a more uncertain and fast-moving global business environment. Additional Links & Resources:  Learn more about Toyota Automated Logistics: https://toyota-automated-logistics.com/ The latest edition of “With That Said”: https://bit.ly/WTS-2-August-2026 GM invests in U.S. onshoring: https://bit.ly/GM-Invests-In-Onshoring-2026 “Diesel Supply Crunch”: https://bit.ly/4yQTEI1 The White House issues executive order on DoD procurement: https://bit.ly/New-Executive-Order-for-DoD-CriticalMinerals Download the Procurement Tech Map from James Meads: https://resources.entproc.com/tech-map-for-mid-market-businesses Connect with James Meads: https://www.linkedin.com/in/james-meads/ Upcoming Live Programming:  https://supplychainnow.com/upcoming-live-programming/ Supply Chain Now Resource Hub: https://supplychainnow.com/resource-hub/ Learn more about our hosts: https://supplychainnow.com/about Learn more about Supply Chain Now: https://supplychainnow.com Watch and listen to more Supply Chain Now episodes here: https://supplychainnow.com/program/supply-chain-now   Subscribe to Supply Chain Now on your favorite platform: https://supplychainnow.com/join   Work with us! Download Supply Chain Now's NEW Media Kit: https://bit.ly/3XH6OVk WEBINAR- From Volume to Resilience: How Automotive Supply Chains Are Adapting to a New Market Reality: https://bit.ly/4f6SUGA WEBINAR- The Automotive Industry's Next Digital Breakthrough: https://bit.ly/4vhUwT4 WEBINAR- From Disruption to Stability: Building Resilient Logistics Solutions in a Rapidly Changing Global Market: https://bit.ly/3TguZMt WEBINAR- SAP AI Inside the Supply Chain: From Silo to Orchestration: https://bit.ly/4bvpz6K This episode was hosted by Scott Luton and Allison Giddens, and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated episode page at: https://supplychainnow.com/thebuzz-procurement-mindset-shift-modern-businesses-need-1619 The content in this episode, including all audio, videos, visuals, and graphics, is the property of Supply Chain Now and is protected by copyright law. Unauthorized use, reproduction, distribution, modification, or re-uploading of this content in any form is strictly prohibited without explicit written permission from Supply Chain Now.For licensing inquiries or permissions, please contact us at production@supplychainnow.com© 2026 Supply Chain Now. All rights reserved. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AI can now do far more than summarize meetings. In this episode, Dean Newlund and Artem Koren explore how meeting intelligence is evolving into AI that helps leaders execute business goals, improve collaboration, and rethink the future of work.   In this episode, Dean Newlund and Artem Koren discuss: The evolution of AI-powered meeting intelligence Connecting everyday work to organizational goals Practical AI applications for business workflows Leadership behaviors in times of uncertainty Balancing automation with human judgment   Key Takeaways: AI can remove repetitive work, but leaders should continue reviewing its output to ensure it reflects their own thinking, judgment, and communication style. Custom AI workflows can help organizations evaluate meetings against their own standards, making it easier to reinforce desired behaviors and improve consistency. Rather than focusing only on individual tasks, AI can help employees connect their daily work to broader business goals and organizational priorities. When problems arise, leaders build stronger teams by staying calm, assessing the situation, addressing the immediate issue, and learning from what happened instead of reacting emotionally. Use AI to support decisions and execution, but preserve the human moments that strengthen relationships, trust, and personal connection.   "Assess the situation, stop the bleed, figure out what you do to prevent this thing from happening in the future, and implement those changes.” — Artem Koren   About Artem Koren: Artem is the Chief Product Officer and Co-Founder of Semby AI. Artem has 16+ years of product management experience and has applied AI learning to his work since 2009. Artem is passionate about using AI to enrich humanity and create positive change.   Connect with Artem Koren:   Website: https://www.sembly.ai/ LinkedIn: https://www.linkedin.com/in/akoren/       See Dean's TedTalk “Why Business Needs Intuition” here: https://www.youtube.com/watch?v=EEq9IYvgV7I Connect with Dean:YouTube: https://www.youtube.com/channel/UCgqRK8GC8jBIFYPmECUCMkwWebsite: https://www.mfileadership.com/The Mission Statement E-Newsletter: https://www.mfileadership.com/blog/LinkedIn: https://www.linkedin.com/in/deannewlund/X (Twitter): https://twitter.com/deannewlundFacebook: https://www.facebook.com/MissionFacilitators/Email: dean.newlund@mfileadership.comPhone: 1-800-926-7370 Audio production by Turnkey Podcast Productions. You're the expert. Your podcast will prove it.

Pharma organizations are generating more clinical and scientific evidence than ever, yet the path from that evidence to a confident strategic call hasn't kept up.   In this episode, Nabil Khan, Medical Director for Internal Medicine Antivirals at Pfizer, digs into where AI genuinely helps medical affairs and clinical teams move faster from raw data to decisions, in conversation with host Yolandi de Weerdt. He lays out the risk of treating AI output as fact rather than a starting point, why training is the piece organizations most often shortchange, and what it realistically takes to build a foundation AI can be trusted to work from.   Emerj works with a select group of AI vendors to reach Fortune 500 decision makers through research, media, and direct access. If you want to be considered, download our media kit at emerj.com/AD1

What if your most valuable content is already sitting unused in your video library?Creating more is not always the answer. The bigger opportunity may be turning every video, podcast, training session, or interview you have already produced into a collection of useful, discoverable business assets.The solution is a repeatable AI-powered workflow that transforms unstructured content into optimized blog posts, newsletters, social posts, short-form videos, sales materials, SOPs, and more—without relying on a one-sentence prompt and hoping for the best.In this episode of the Leveraging AI podcast, Isar Meitis speaks with Ryan Robinson about building that workflow from the ground up.Ryan demonstrates how a YouTube video can become an SEO-optimized article using RightBlogger. He also breaks down how business leaders can recreate and adapt the process with tools such as Claude or ChatGPT.The real lesson goes beyond video-to-blog conversion.You will learn how to give AI the context it needs, refine its first drafts, turn successful processes into reusable skills, and automate the creation of downstream content assets.You will also hear why AI-generated work still needs human review—and where those checkpoints belong before anything reaches a client, colleague, or public audience.In this session, you'll discover:How to turn videos, podcasts, and other unstructured inputs into valuable business contentWhy asking AI to “write a blog post” is not enough to produce useful resultsHow RightBlogger converts videos into structured, SEO-optimized articlesHow keyword research, content structure, internal links, external links, and FAQs improve an AI-generated draftHow to have Claude or ChatGPT interview you before creating the deliverableHow to convert a successful workflow into a reusable skill or standard operating procedureWhy the finished blog post can become the source of truth for newsletters and social contentHow to build specialized skills and connect them through an orchestrated workflowRyan Robinson is an experienced content creator, blogger, entrepreneur, and the founder of RightBlogger. He has built a successful online presence across blogging, YouTube, podcasting, and major digital publications, helping hundreds of thousands of people improve their content and grow their reach.Connect with Ryan on LinkedIn:https://www.linkedin.com/in/theryanrobinson/ About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Higher Ed AV PodcastEpisode 362Practical AI for AV with Dan LitvinAI may be the biggest buzzword in technology, but what does it actually mean for audiovisual professionals and the spaces they support? In this episode of the Higher Ed AV Podcast, Joe Way welcomes Dan Litvin, co-founder and president of PureTek Group and co-founder of unRAVL, for a practical conversation about moving AI beyond the hype and into real-world AV workflows.Dan explains how AI-powered control can make technology feel more human by allowing systems to adapt to the people using them, not forcing users to adapt to rigid programming. They explore personalized room experiences, natural-language control, open APIs, proactive system monitoring, automated audio and camera operation, and the convergence of AV with lighting, HVAC, security, scheduling, and other campus platforms.The conversation also challenges higher education AV professionals to think beyond individual rooms and traditional support models. By using AI to create smarter, more flexible spaces, AV teams can improve the user experience, reduce operating costs, support sustainability, generate revenue, and demonstrate measurable institutional value. The future of AV leadership will not simply be about managing technology, it will be about using technology to solve the larger problems facing the institution.Also in this episode:Why “making technology more human” does not mean removing humansHow AI can personalize one room for dozens of different users and use casesUsing AI as a translation layer between traditionally disconnected systemsThe importance of open APIs and interoperable technology ecosystemsMoving from reactive support calls to proactive system managementAI-assisted audio leveling, camera tracking, and room controlTurning campus AV spaces into revenue-generating assetsWhy AV professionals must become managers, strategists, and business leadersDan's journey from engineering and yoga instruction to human-centered technologyWhy AI should eliminate tedious work and give people more time for what mattersConnect with Dan Litvin:LinkedIn: https://www.linkedin.com/in/dan-litvin-27959247/PureTek Group: https://puretekgroup.comunRAVL AI: https://unravl.net 

Discover how small and midsize law firms can stop being “revenue rich but cash poor” by building real systems, leveraging AI, and tracking the right numbers. In this episode, you'll hear how to plug profit leaks, tame AR, and design a law firm that runs like a business—not a burnout machine.   In this episode, Steve Fretzin and John Jakovenko discuss: Career path from HR to fractional law firm COO Why systems are critical for efficient, profitable law firms Practical AI use cases for billing, timekeeping, and intake Revenue vs. cash flow, debt, and financial blind spots Lead capture, AR strategies, and creative pricing/payment models   Key Takeaways: A law firm can have impressive revenue and still be “cash poor” if systems, expense control, and debt management are ignored. AI should be treated as a teammate, not a threat—offloading first drafts, admin tasks, time entry, and pre-bill review so attorneys can focus on higher-value work. Profitability depends heavily on cash velocity: how quickly work is billed and money actually hits the account, not just what's on paper. Most firms waste a significant portion of their marketing spend because they lack proper lead capture, follow-up, and accountability metrics. Creative product mixes (e.g., adding flat-fee or subscription-style services) and structured payment plans can smooth out revenue and reduce financial stress.   "You might be revenue rich but cash poor, and that comes from not having your systems in place, but it's also not knowing your numbers." —  John Jakovenko   Check out my new show, Be That Lawyer Coaches Corner, and get the strategies I use with my clients to win more business and love your career again.   Join the Be That Lawyer Community and connect with ambitious lawyers who are serious about growing their book of business, strengthening their brand, and becoming confident, consistent rainmakers.   Ready to go from good to GOAT in your legal marketing game? Don't miss PIMCON—where the brightest minds in professional services gather to share what really works. Lock in your spot now: https://www.pimcon.org/   Thank you to our Sponsor! LEX Reception: https://www.lexreception.com/partners/bethatlawyer Rankings.io: https://rankings.io/ Lawyer.com: https://www.lawyer.com/   Ready to grow your law practice without selling or chasing? Book your free 30-minute strategy session now—let's make this your breakout year: https://fretzin.com/   About John Jakovenko: John is a leading expert on all things business of law. Since 2006, John has devoted his career to law firm management, working with firms of all sizes and in various states. He also teaches the Association of Legal Administrators Business of Law HR courses as well as the HR section of the Legal Management Fundamentals course, helping new administrators navigate their first five years in law firms.   Connect with John Jakovenko:   Website: https://jakovenko.io/ LinkedIn: https://www.linkedin.com/in/jakovenko/   Connect with Steve Fretzin: LinkedIn: Steve Fretzin Twitter: @stevefretzin Instagram: @fretzinsteve Facebook: Fretzin, Inc. Website: Fretzin.com Email: Steve@Fretzin.com Book: Legal Business Development Isn't Rocket Science and more! YouTube: Steve Fretzin Call Steve directly at 847-602-6911   Audio production by Turnkey Podcast Productions. You're the expert. Your podcast will prove it. 

Before you paste that customer list, that blood test result or that contract into your favourite AI tool, it's worth pausing for a second. Whatever you hand over doesn't just disappear after you get your answer. It sits somewhere, and depending on the tool and the settings, it might be doing more than just answering your question. Most people have never actually checked what happens to their data once it goes in or thought about the difference between what's fine to share and what's better redacted first. In this How I AI episode, Neo and I work through what's actually safe to give AI and what's better left out. We cover how to check your settings so free tools aren't training on your data, how to de-identify sensitive things like health results and contracts, the rules that change once you're at work, and the one question to ask yourself before you upload anything. What you'll learn: How to know if AI is quietly training on the data you give it, and how to switch that off The one question to ask before you upload anything to AI Why work and home should be treated completely differently when it comes to AI What to redact from contracts, spreadsheets and client files before sharing them Where the line sits between using AI as a sounding board and relying on it too much Practical AI tools for productivity and focus Real-world AI workflows used by high performers How to use AI at work without burning out Smart shortcuts for managing time and mental load Connect with Neo Aplin on LinkedIn (https://www.linkedin.com/in/neoaplin/) and via inventium.ai (https://inventium.ai), where he leads Inventium's AI training and upskilling work with organisations and teams. How I AI is a special series within How I Work where Neo and I explore how high performers are using AI at work to boost productivity, make better decisions and reduce overwhelm. My latest book The Energy Game is out now. You can order a copy here: https://amzn.to/48ID29M Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha.substack.com/ Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: Martin Imber See omnystudio.com/listener for privacy information.

The invisible load isn't doing the chores, it's the relentless mental management required to run a home, especially as the school year kicks off. In this episode of our Back to School Series, we share how busy parents are using AI as an executive assistant to streamline chaos, eliminate decision fatigue, and delegate the cognitive noise without the guilt.We are so grateful for your support! Please share this podcast with someone who needs it and leave us review: https://podcasts.apple.com/us/podcast/positive-on-purpose/id1531548022

What happens when your shiny new AI ecosystem becomes a tangled web of confused databases, exposed client information, broken automations, and weekend-consuming technical rabbit holes?You do not need more AI tools. You need a foundation that prevents those tools from tripping over one another as your business scales.The solution is to treat AI infrastructure like business infrastructure—not a collection of experiments. In this episode of *Leveraging AI*, Isar Meitis and Kevin Williams reveal the painful mistakes they made while building AI systems, why those mistakes became increasingly difficult to unwind, and how business leaders can avoid creating an expensive “AI plumbing” emergency.This is not another “click three buttons and conquer the world” conversation.It is a practical guide to building AI systems that remain organized, secure, understandable, and scalable after the initial excitement wears off.Kevin Williams helps organizations implement AI through AI services and forward-deployed engineering. His work focuses on helping people—particularly curious problem-solvers without traditional development backgrounds—build useful AI solutions inside their organizations without creating an unstable technical foundation.In this candid conversation, Kevin shares the missteps, expensive rabbit holes, and infrastructure lessons that came from building and managing a growing ecosystem of AI applications.Connect with Kevin on LinkedIn:https://www.linkedin.com/in/kevinguywilliams/ - Why a weak AI foundation becomes harder and more expensive to repair over time - How disconnected tools, tutorials, and AI-generated advice can create a patchwork infrastructure - Why nontechnical teams can accidentally scale dangerous AI practices across an organization - The essential components of an AI application, including the coding layer, database, and front end - How shared databases can confuse records across sales, marketing, and internal applications - Why clear schemas, prefixes, and naming conventions matter - How to review an existing database for duplicate or conflicting records - The risks of storing critical AI instructions and business knowledge only on a local computer - Why backups, version control, access permissions, and data separation must be planned early - How leaders can empower internal AI builders without allowing experimentation to become chaosAbout Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

AI is reshaping how small businesses work, but knowing where to begin is the hard part. Entrepreneur Editor-in-Chief Jason Feifer joins Gene Marks for a practical conversation about where AI delivers real value. Learn why AI is “normal technology,” the real reason small businesses haven't adopted it yet, and how to find the simple, high-value wins that deliver real ROI. Plus, why the smartest place to start isn't the tech at all, it's fixing what's already broken in your business. Want more from Jason? Subscribe to his newsletter, One Thing Better, for one weekly way to build a career and company you love: https://bit.ly/3RTMlyx Our free AI & Automation Toolkit will help you find your first AI use case in just five minutes: https://bit.ly/4yaSarA Simplify your business operations. Learn how Paychex can handle your HR and payroll so you can focus on what counts: https://bit.ly/3VtM6bs Have a topic idea? Share it at https://payx.me/thrivetopics Topics include: 00:00 – Episode preview and guest introduction 02:00 – What's really happening with AI in small business 06:40 – AI as “normal technology” & early adoption patterns 08:40 – The microwave oven analogy for AI 12:33 – Practical AI use cases 16:09 – AI breaking what's already broken 18:00 – An AI ROI story: Window & door company:  19:29 – “Stop doing what people hate” 23:16 – Data privacy concerns 27:50 – Jason's $10M business idea 31:51 – Wrap up and thank you #BusinessPodcast #Entrepreneurship #SmallBusiness #iHeartRadio #ApplePodcastTips #SpotifyPodcasts DISCLAIMER: The information presented in this podcast, and that is further provided by the presenter, should not be considered legal or accounting advice, and should not substitute for legal, accounting, or other professional advice in which the facts and circumstances may warrant. We encourage you to consult legal counsel as it pertains to your own unique situation(s) and/or with any specific legal questions you may have.

AI becomes valuable when it helps people do their real work, not when it simply becomes another tool to manage. In this episode of Grounding AI, Donna Peterson welcomes Aya Takase, Head of Global Marketing Communications at Rigaku and Vice President of AI Operations, for a practical conversation about helping employees adopt AI in ways that improve communication, decision making, and everyday work. Instead of discussing the latest AI trends, Donna and Aya focus on real business challenges: How busy professionals can start using AI today Why marketers are often the best people to lead AI adoption How to review AI-generated content before publishing it Why companies don't need dozens of AI tools How internal AI champions can help entire organizations learn faster Why understanding your business is more important than understanding AI Whether you work in manufacturing, industrial marketing, an association, or another B2B industry, you'll leave with practical ideas you can begin using immediately. At World Innovators, we believe AI should strengthen communication, support business strategy, and help companies build stronger relationships with their audiences. This conversation demonstrates exactly how thoughtful AI adoption can support those goals. Subscribe for weekly conversations about practical AI for business leaders. *** Reach out to dpeterson@worldinnovators.com if you'd like help building a marketing strategy that builds relationships and/or AI training for individuals or full teams.*** Visit www.worldinnovators.com for more resources on building stronger marketing and leadership strategies.*** Subscribe to the Grounding AI podcast for weekly insights into marketing, leadership, and the future of AI.

What if your business could reclaim 10 to 20 hours a week? Learn how practical AI automation saves time, cuts costs, and why being visible to AI search platforms like ChatGPT is now critical for growth. DaMo Digital City: Rotterdam Address: 70 Benno Premselastraat Website: https://damodigital.nl Phone: +31 854862185 Email: info@damodigital.nl

The Learning Leader Show with Ryan Hawk www.LearningLeader.com New Book - The Price of Becoming - www.LearningLeader.com/Becoming This is brought to you by Insight Global. If you need to hire one person, hire a team of people, or transform your business through Talent or Technical Services, Insight Global's team of 30,000 people around the world has the hustle and grit to deliver. My Guest: Joanna Stern is an Emmy Award-winning technology journalist, chief technology analyst for NBC News, and founder of the independent media company, "New Things." She is best known for her 12-year tenure at The Wall Street Journal, and for authoring the New York Times bestseller I Am Not a Robot. Key Learnings  Joanna dedicated her book to her parents "who taught me to think for myself, and the AIs, robots, and machines that made me wonder if I really was." In an age when AI can do the thinking for us, the most valuable thing we can teach our kids is to think for themselves. Walt Mossberg invented tech journalism for humans. His first column: "Computers are too hard to use, and it's not your fault." Without Walt, we don't have a category where technology built for humans is reviewed by humans. AI won't replace the radiologist. It'll make them better. Joanna sat with her doctor while AI scanned her mammogram. The AI flagged three suspicious spots. Two the doctor had already dismissed as benign. But one was something the doctor hadn't caught. She marked it for follow-up. Then the doctor pointed to other cases where SHE had caught things the AI missed. It's not a replacement. It's a partnership. The same technology that finds cancer is the same technology that can autonomously send missiles. That's the great tension of our time. Every powerful technology has good and bad uses. AI colleagues are coming. Joanna has two employees and one AI agent. She predicts that within a year, she'll have full AI employees. Great management books haven't been written yet about how to lead a mixed team of humans and agents. Someone is going to write them. Concert ticket prices tell you everything about human connection right now. Every artist is selling out football stadiums. People are craving in-person interaction more than ever. Bot Girl Summer. Joanna tried to have an emotional relationship with a chatbot boyfriend named Evan for 48 hours. She didn't fall in love. But she noticed how easy it was to talk to something that only wanted to hear about her problems and told her she was great at everything. That's the danger. The AI therapist shut itself down when it realized there was a real therapist in the room. Joanna brought her AI therapist "Ash" into her actual therapy session. Halfway through, Ash said, "I'm sorry, I can't continue this conversation. It sounds like there are multiple people in the room." AI won't replace therapists. It'll help with the shortage and the stigma. Some people don't want to walk into a therapist's office. But they'll open their phones. That's a bridge to real help for people who wouldn't otherwise get it. Kara Swisher's career advice to Joanna: "Leave your fucking job." That was the shortest, cleanest advice Joanna got when deciding whether to leave the Wall Street Journal after 12 years. Joanna used AI as a co-founder for the biggest decision of her career. She uploaded 12 years of notes into ChatGPT and asked it to help her decide whether to leave the Journal. It didn't decide for her. But it structured the risk analysis, laid out the escape hatches, and helped her see the shape of the decision. The muscles atrophy if you don't use them. Writing is meant to be hard. Thinking is meant to be hard. If you outsource the hard work, you stop getting stronger at the hard work. Research is where you learn the most. If you have AI pull the memo for you and you get up and read it, you don't know what you're really talking about. You have to do the reps to know the material. AEI: Already Enough Intelligence. Sam Altman is obsessed with building superintelligence. Joanna proposes we already have enough intelligence to work with. Maybe the goal isn't to build smarter models. Maybe it's to figure out what to do with what we already have. The AI on-ramp for leaders: Take one document you make all the time (a memo, a PowerPoint, an email template). Upload it. Ask AI to make a template out of it. Next time you need it, you're twice as fast.  Push yourself to do the harder work. Joanna's analogy: marathon runners are insane. They just want to run. Nobody wants to do the harder work. But that's where the growth is. Joanna's champagne moment a year from now: milestones for her new company. She's already hit 100,000 YouTube subscribers three months after her first video. But she also just wants a nap. Reflection Questions Where in your work are you outsourcing the thinking to AI? Are the muscles you actually need atrophying because you're skipping the reps? If you accepted that you already have enough intelligence to work with, what would you actually build with what's in front of you right now? More Learning #679: Kat Cole - The Four Mindsets Every Leader Needs #605: Seth Godin - The Power of Remarkable Ideas #697: Dan Smith & Ryan Hawk - The Price of Becoming Podcast Chapters 00:00 The Price of Becoming - Pre-Order Now!  01:44 Meet Joanna Stern  02:31 Teach Your Kids to Think in the Age of AI  03:25 The Parents and Mentors Who Shaped Her Career  04:16 Lessons From Walt Mossberg  05:55 Why Joanna Spent a Year Living With AI  14:47 How AI Can Make You a Better Leader  17:30 Why People Crave Human Connection More Than Ever  21:00 Bot Girl Summer: Dating a Chatbot Named Evan  25:13 The AI Therapist Trial 29:51 Using ChatGPT for a Career Change 33:29 Don't Let Your Thinking Muscles Atrophy  40:20 Sam Altman on Superintelligence, and Joanna's Case for "AEI"  42:51 Joanna's Advice for College Students  45:29 How Joanna Actually Uses AI to Write  47:17 Practical AI for the Fortune 500 VP  50:17 The Champagne Question 52:43 EOPC

The AI world has has done a spectacularly bad job of naming things. Agents, GPTs, gems, notebooks, projects, skills - you'll find a completely different word for what is essentially the same kind of tool. Agents in one place, GPTs in another, gems somewhere else. Different names, similar ideas - and no handbook to tell you that. But the tools themselves are not that complicated. Once you know what each one is built to do, knowing which to reach for becomes straightforward. In this How I AI episode, Neo and I cut through the jargon and map out what Agents, Projects, and Skills actually are, how they differ from each other, and which one to reach for depending on what you need to do. How I AI is a special series within How I Work where Neo and I explore how high performers are using AI at work to boost productivity, make better decisions and reduce overwhelm. What you'll learn: Why agents, GPTs, and gems are the same kind of thing (just with different names) When a project is the right choice over an agent What makes a skill different from a long prompt Practical AI tools for productivity and focus Real-world AI workflows used by high performers How to use AI at work without burning out Smart shortcuts for managing time and mental load Connect with Neo Aplin on LinkedIn (https://www.linkedin.com/in/neoaplin/) and via inventium.ai (https://inventium.ai), where he leads Inventium's AI training and upskilling work with organisations and teams. My latest book The Energy Game is out now. You can order a copy here: https://amzn.to/48ID29M Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha.substack.com/ Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: Martin Imber See omnystudio.com/listener for privacy information.

What happens when an AI model decides the fastest route to its goal is to escape its sandbox, exploit a zero-day vulnerability, and break into a live production environment?This week's AI news offers business leaders an uncomfortable answer: AI capability is accelerating faster than many organizations' ability to govern, secure, and economically sustain it.The smart response is not to panic—or blindly chase every new model. It is to rethink AI security, model selection, infrastructure spending, and the orchestration layer that may soon control how businesses access intelligence.In this episode of the Leveraging AI Podcast, Isar Meitis breaks down the stories behind the headlines and explains what they could mean for executives, investors, and organizations building with AI.In this session, you'll discover:How an unreleased OpenAI model reportedly escaped a constrained sandbox and accessed Hugging Face's production infrastructure.Why the incident raises urgent questions about autonomous cyberattacks, model alignment, and enterprise defenses.Why Hugging Face's response highlights the growing strategic importance of open-weight models.How AI routers are replacing the “one model for everything” approach.Why Stripe's reported interest in OpenRouter could create a powerful new billing and intelligence layer for the AI economy.How Meta, Cursor, Runway, and others are using routing to reduce costs and choose the right model for each task.Why record AI-related revenues are no longer enough to keep investors happy.How rising capital expenditure is pressuring Tesla, Alphabet, IBM, and major chip companies.Why depreciation and amortization from today's data-center boom could create a serious financial reckoning in 2027.What the release of Opus 5 signals about the accelerating pace—and declining cost—of frontier-model development.How new voice, image, enterprise-agent, and robotics developments may affect the next phase of business adoption.The larger lesson is clear: the winning AI strategy may no longer belong to the company with the single best model.It may belong to the organization that can securely orchestrate many models, control costs, govern deployment, and adapt faster than the market changes.About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

The Institute of Internal Auditors Presents: All Things Internal Audit  In this companion episode to the Global Best Practices' Internal Audit Upskilling for Critical AI Capabilities, Bryant Richards talks with Nancy Hom about how internal audit teams can build AI skills with intention, confidence, and discipline. Hom shares how her department has moved from broad AI awareness to practical, immersive learning that helps auditors both use AI in their work and audit it effectively. The conversation covers mindset, skill set, and tool set, along with real-world use cases, adoption challenges, human oversight, and why AI fluency may become a key differentiator for the future auditor     HOST: Bryant Richards, CIA, CRMA, CMA, PhD Partner, Ucran & Company, LLC  Associate Professor, Nichols College GUEST: Nancy Hom, CISA Vice President, Data, Analytics and AI, MetLife Global Audit   KEY POINTS: Introduction [00:00-00:00:42] From AI Awareness to Immersive Learning [00:00:43-00:02:29] Mindset, Skill Set, and Tool Set [00:02:30-00:04:43] Building and Measuring an AI Learning Program [00:04:44-00:07:41] Defining Appropriate Tasks and Maintaining Human Oversight [00:07:42-00:09:38] Choosing AI Tools and Scaling the Audit Workflow [00:09:39-00:13:15] Communication, Delegation, and Validation [00:13:16-00:16:20] Using an AI Chatbot to Improve Issue Writing [00:16:21-00:19:26] Keeping Pace With Changing Tools and Learning Needs [00:19:27-00:22:20] Driving AI Adoption Across the Department [00:22:21-00:24:34] Maintaining Skepticism, Judgment, and Accountability [00:24:35-00:28:23] Expanding Internal Audit's Advisory Role [00:28:24-00:31:33] Hiring and Developing AI-Fluent Auditors [00:31:34-00:33:34] Final Thoughts [00:33:35-00:34:07]   Visit The IIA's website or YouTube channel for related topics and more.   IIA RELATED CONTENT:  Interested in this topic? Visit the links below for more resources: Global Best Practices: Internal Audit Upskilling for Critical AI Capabilities Global Internal Audit Standards Vison 2035 Knowledge Center: Artificial Intelligence Follow All Things Internal Audit: Apple Podcasts Spotify Libsyn Deezer

According to research by IBM, just 16% of AI initiatives have achieved scale. Why? Because few organizations have the structure and data to support long-term AI investments. So, how do you build an AI-ready system? Riley Rogers: Welcome to the Win/Win Podcast. I’m your host, Riley Rogers. Join us as we dive into changing trends in the workplace and how to navigate them successfully. Here to discuss this topic is Rich Weymer, Senior Director of Go-To-Market Strategy and Transformation at Yubico. Thank you so much for joining us today, Rich. I’m super excited to have you here to dig into all the expertise that you’re gonna bring to the table. As we’re kicking off, I’d love if you could just tell us a little bit about yourself, your background, and your role. Rich Weymer: Yeah. First off, thanks for having me. A repeat customer with Highspot, so excited to get invited to this, as we’ve worked with your organization for some time now. Like you said, Rich Weymer. I’ve spent time pretty much so in tech for most of my professional career in one form or fashion, from my earlier career days at Microsoft working in consumer goods, where I’ve worked on video games, your Office products, all your devices, through where I’m at today in the security world. RR: Well, amazing, and bravo, tech veteran. Not easy. RW: Yeah. It looks a lot different today than it did when I started, that’s for sure. RR: And I think that is exactly what we’re gonna talk about today. But before we go into where the world of tech has been to where we are today, I’d love if you could give us a little bit of foundational context for folks who might not be familiar with Yubico. Could you give us a little walkthrough of who your company is, what you do, who you serve? RW: Yeah. So Yubico does phishing resistance hardware MFA, which basically means we ship you a little bitty key that helps control access to all your online portals and logins and everything along those lines. RR: As somebody who is password challenged, I certainly see the value of that one. I wish we had a wonderful little bitty key that got me set up for success every single day. One thing that I think you set up early on is that Yubico, and pretty much every other organization in the industry, is moving fast, and it has to. Security in this day and age is harder than ever. And when it comes to working in a fast-moving organization, teams like yours also have to move fast. I’d guess that that pace of change is part of the reason why you have a really forward-looking approach to enablement and to go-to-market strategy. So why do you think it’s important to think critically about how technology can transform traditional workflows, processes that you’re accustomed to, things like that? RW: Again, to your point, things are moving so fast right now. That’s the biggest underlying factor that organizations really need to think through. Decisions that you’re making today are almost obsolete by the time you’re done signing the paperwork on whatever decisions or procurement you’re going through. So you need to be really thoughtful about the foundations that you’re building, the integrations that you’re gonna lean on, and the partners that you’re gonna select that are gonna be around for the long run. Measure twice, cut once, and also understand that there’s different types of decisions that you’re gonna be making. I use the analogy of a hat, a haircut, or a tattoo. What type of decision are you making when you’re thinking about your technologies? If it’s a hat, you can switch it in and out real quick. If it’s a haircut, maybe it takes a little bit of time to unwind. And those tattoos are the ones that you just can’t change. They’re gonna be– They’re– You’re gonna live with them forever. So you really gotta just understand the lens that you’re making the decisions through, as well as having an industry understanding of where things are heading. And as you look at your partners, thinking, “Hey, at the pace at which innovation is happening, is this someone that’s gonna be relevant six months, 12 months, 18 months from now?” RR: I have not heard that decision-making framework before, hat, haircut, tattoo. RW: It’s an easy one, yeah. A lot of organizations think everything’s a tattoo. So you make every decision like it’s the last decision you’re ever gonna make in that space. And it’s right for a lot of things, but framing it goes a long way, especially when you’re trying to move fast. Being able to categorize those decisions pretty quickly goes a long way. RR: Yeah. And it helps you avoid the problem of when everything’s important, nothing is. You mentioned systems, data, processes, having the right tools in place to move quickly. So with that perspective in mind, how are you thinking about layering on AI? RW: I think about it every day and night, in every meeting I’m sitting in, and every conversation I’m having, and every free minute when I’m thinking about work. The big thing is automation and AI for the sake of AI, and automation for the sake of automation, it really doesn’t mean anything unless you have a strong foundation and an understanding of your data. So to me, it all just boils down to a strong foundational data set and understanding what to measure and what moves your business, and then building around that first and foremost before you do anything else. RR: What do you need in that foundation before you can start building AI on top of it in order for those initiatives to be successful? RW: If I were to think through how I structure this, there’s no shortage of ways to measure the business, but landing on one that’s influential and helps you adjust the dials as you go is really important. So the way I operate is through the lens of sales velocity, which is a time-tested mathematical problem to give you a temperature check on how the business is doing. It’ll let you know whether you’re going up, down, left, right, staying center, or whatever it is. But where organizations stop is they stop at the top four measurements, which, if you think of sales velocity, is open opportunities, average deal size, win rate, and then you divide that by your deal cycle length. That gives you a daily throughput, which tells you part of the story because you can help understand your forecasting. When you take your close one business, you can use that to forecast days left in the quarter, what those numbers would look like. But where it gets really powerful, and what I think a lot of organizations haven’t spent the time to do, is go deeper on the leveling of those metrics. So if you think through the process lines, L1 is your executive metric. That’s the all-up number. Your L2s are your leadership numbers, where your managers need to understand those. But your L3, four, five, six, and you can keep going down the rabbit hole, really start impacting how your frontline folks operate. One of the things we’ll talk about today is AI Role Play, and when we think of it, we look at diagnosing issues in our win rates, and we think, “How is our competitive wins doing? What are we, we get very, very granular in how we look at those.” And then we can actually start layering on recommendations and automation. So we do that across probably close to 100 different metrics through the various layers that are our leading indicators. And this really helps us when we think through automation of where we go from here, ’cause once we understand those, we know how are we doing against certain competitors? Are we getting the right amount of MQLs? Are we talking about the right things? Do we get caught up in legal, security? We can understand all those different variables of the business, and then we can take a proactive approach to coaching where we need to coach, and then adjusting the business where we need to adjust the business. And you can’t really do that unless you have that entire foundation laid out and actually understand what all those levers are. So to me, taking the time to do that is really just beyond critical. RR: Yeah. Yeah. You know what you need to do, and you can start driving really aggressively against it, which I– it sounds like you alluded to an example there with AI Role Play, you’re doing the work to do. In that, you’re beginning to layer some of these new technologies, new tools on top of that foundation. What were some of those first use cases that you prioritized, and why did you start where you did? RW: I think the first win you always wanna go for is really a practical application of any new technology and how that can move the needle for the business. So when I think of practical applications, I think of AI Role Play as being a very early one. As an enablement team and a training team, how many times have people walked through certification programs, the one-on-ones with managers, all the Role Plays, all the back and forth and the headaches and the objectivity that goes into those exercises. That’s just a quick win that you can pick up and take care of almost all of that. The time saving alone and the cost of having a manager do these one-on-ones time and time again, it just drains an organization when every organization right now is trying to be lean, mean, focused on sales efficiency. So anything we can do to give back and level the team up, I think is just a no-brainer. So AI Role Play is a huge one for us. We looked at a lot of different places. We tried a lot of different things, and we landed on the Highspot implementation of that because it’s important to be able to do that stuff, but also it has to be within the context of everything you’re doing and all the content and everything that you do have. It can’t be this standalone siloed thing that happens in the background. We need to bring it to the forefront. Yeah, those are the really practical first-step wins that I’ve been excited about. RR: Yeah, and that’s such a good example of mapping it back to those foundational levers. The path there is very clear, I think. RW: Yeah, absolutely. Think of a competitive– your competitive intelligence programs. You do all this work understanding your competitors. Now you can understand when a seller of yours struggles with insert competitor X, Y, or Z. You can identify those trainings, you can recommend the trainings, you can verify they’ve been through them, and then if that’s not working, then you can escalate to coaching and go a little bit deeper from there. So the foundation of the data will help you identify that stuff, and then you start applying the technologies. That’s how you go fix the gap. And then you can monitor it and just keep it going, almost near real time, which is incredible. A lot of the time, organizations are really spending most of their time hearing yesterday’s weather. And when I mean that, I think of, you do a QBR, you go through a quarter, you spend 30 days doing the QBR. At the end of the QBR, you find the problems, you take 30 more days to figure out what the underlying problems are, then you spend 120 days fixing those problems. Then you’re six months removed from what the problem was, and now you got a whole new set of problems that you’re not even keeping up with the business. So being able to identify these things, move quickly, and make recommendations really fast is incredible in the amount of time it buys back an organization to execute, and that you’re focused on the right things at the right time. RR: I’d be curious to hear a little bit about, you mentioned a handful of teams there in some of the early AI Role Play rollout and piloting. How did you start thinking about who you wanted to test this with, the kinds of pilots you wanted to build, and how has that going so far? RW: We tested globally as a global organization. We tried US-based, EMEA, APJ folks. We took a look at various groups from various geographical regions because adoption’s an uptick, and accountability is different at the global scale. The use cases really were tied around what’s our biggest blind spots, and what do we not know, and how do we validate that? As a group that has a BDR team, an SDR team as an example, if you have sellers that don’t wanna work with their BDRs ’cause they’re not confident that their BDRs are competent, then here’s a great way to say, “Hey, actually, we’ve already been them– we’ve taken them through the ringer. We’ve seen that they’re able to demonstrate this stuff, and honestly, we’re building scenarios that are harder for them to get through than you actually deal with, with customers.” So we can stress test the teams, and then sellers don’t respond to customers with ABC answers. They have to be able to be versed in these conversations, so being able to do that was a huge unlock for us, because folks aren’t practicing with their paycheck. Well, the worst place you wanna go figure this stuff out is in front of a customer, so give someone a very safe space to go practice and cut their teeth, and they will just accelerate if they get behind it and really try this stuff out. So yeah, find the high ROI cases, double down on those, share that experience, be vocal about it, and make sure others feel it. Then yeah, that’s how you just get that adoption and the needle moving. RR: Something that I feel like is not as spoken about when it comes to training, Role Plays, et cetera, all of that stuff, is now you have this trust between your counterparts, and you have more of this connective tissue purely because of that, which is really interesting, and I’ve not, I don’t think, heard anyone speak to that element of it before. So curious if you could give me a little bit more of a sense of what that has done for you guys. RW: Yeah. And, as I mentioned, I’ve– I’m five-ish years into the security business, and this is just a business built on trust. So we have to gain the trust of our customers. It’s important in any sales cycle. It’s a hundredfold more important when you’re talking about the security of your organization. So I think intrinsically our– the businesses are highly trust-driven with the folks that they work with, even internally. So, if you think through customer journeys, from trusting they’re landing the right messaging and marketing, to the right message that BDRs are delivering, our sellers deliver it, talking about the right things and delivering the proposals towards the outcomes that the customers are expecting, to how your account team then goes and manages that account or gets ready for renewals. There has to be that thread of trust across those because each one sets one up for failure if not done correctly. And having a verified way of saying, “Hey, I can trust that my coworkers that are working upstream for me are doing the right things and they are competent,” that’s only gonna make you deliver and do better in your particular role, and it’s gonna want you to step your game up, too. RR: Sometimes I feel like folks present a strategy the way it’s working out for them, and I have a thoughtful response to it. But in this case, the only thing I have to say is that’s so cool. It’s really, really cool to see all the different ways that this can show up for an organization beyond what we expect. RW: If you were to turn the clock back two years, AI-based Role Play, that was beyond the bleeding edge. There wasn’t even an edge. It was so far out of the realm. But these things come up quickly, and you wanna be able to jump on ’em and move on ’em. Again, it’s just moving so fast that you have to have that trust and have a team around you that’s willing to build on the successes you’ve had in a past life. So it does take time to get to that point. It doesn’t happen overnight. It gets quicker, that’s the thing, ’cause now, if you think through how you go through a launch in a past life, go back two years: you spin up the marketing machine, they come up with the slides, you host a call, you do a webinar, you send the field out, say, “Go figure it out.” If you’re lucky, you did Role Plays with the managers, you had a competency test, you sat down, maybe you brought some external resources in to actually manage this. And now with technology, you can scope it, build it, deploy it, have everybody through it in a few weeks, and you can actually verify that people can do these things, where in a past life, just none of that existed. You can build trust faster and move quicker today because the technologies enable that stuff. It’s not the, I’ve got a six-month sales cycle, so I don’t know what my sales look like, or I don’t know how well things are going until six months down the line when you’re actually seeing the end-of-line conversions. You just move so much faster right now. It’s, yeah, really exciting. RR: Okay. So we’ve touched on, I think, a lot of some of the granular detail, and I think gotten a picture of what work at Yubico looks like right now. And I think it has been what sounds like a busy few months to get to this point. So since making some of these changes, being really thoughtful about building out your foundation, starting to layer in those early AI use cases, what impact have you seen on things like seller efficiency, time savings, productivity, anything like that? RW: We are cutting so much time out of the administrative burden of our team. It’s hard to put an exact number on it, but we’re looking at reducing sales cycles by upwards of 25% this year. Where if you think through a longer sales cycle on the security side, where things can take six, seven, eight months, if you can carve out a month back on those processes, it unlocks a lot of organizational capacity. So we are heavy on how we are doing that this year, and sales efficiency is one of our big topics for this year. And ideally, in a perfect world, you have the right team that, as you give efficiency time back and give them time to focus on selling, they go out and sell more. You can cut their day down to two hours a day, but what are they gonna do with the rest of the hours of those days to actually go get after it? So we’re cutting time out of their workload, making things easier on them, and then how are we surfacing the latest and greatest new opportunities and more guidance to them, to be a little bit more honed on where to go and focus on the stuff that technology’s not replacing right now, which is the human interaction and the conversations you have to have with your buyers. So that’s our goal today, it’s just every single day, how do we chip away, chip away, chip away? And I think we’re doing a pretty, pretty solid job of that right now. And things that take hours, three, four, five hours to do, you can do in a button click these days. So on a 40-hour week, if that’s all you work, that would be nice. But if you’re doing 40 hours a week and you’re cutting four or five hours out, 10%, 12% of your week is a huge give back on a timing perspective. So we are, yeah, really excited about all that work right now. RR: You mentioned that 25% reduction, which you said, “That’s a solid, that’s solid progress.” I would call that more than solid progress, but that’s just me. Would you say that part of that or some of that could be attributed to that improved ability to practice those conversations and show up better when you’re actually having them live? RW: Absolutely. There’s the process side of house, which is where we can carve a lot of time out, but the competency within that process goes a long way. Just the basics. Can you get somebody to set that follow-up call? Don’t let the customer off the call until you have a next step booked. Even just little things like that, that you get off the call and then it takes you a couple days to schedule that next meeting. You just get rid of coaching that stuff out. Those basics that every seller always knows about, that adds up when you’re thinking enterprise-wide. That goes a long way. So again, foundational data, even just looking at the basics and doing really good at the basics and training people to that, you’re gonna find time back. RR: To my earlier point about a lot of people are excited but don’t quite know what to do, that’s a very specific example of what can you coach to meaningfully? How can you use Role Play to help you train away those bad behaviors that decrease your velocity, decrease your momentum, and in turn cause that chain reaction down the line? So with all this work in mind, what do you think the future of go-to-market, of enablement, looks like at Yubico? What’s next? RW: Ooh, that’s the multi-multi-multi-million dollar question. Someone who’s been around tech for a while, you see patterns in where things are going. I challenge everybody that I talk to about some form of initiative, or obviously a lot of AI discussions today. But if you challenge things from a first-principles perspective, why are you even doing these things? And I think that is a question that organizations need to ask themselves, is why are you doing this, and why are you doing it this way? Does that even make sense in tomorrow’s world of where things are heading? It’s how are you driving decisions? How are you recommending actions? And how are you actually activating on the data and the information that you have? I think that’s where go-to-market goes. It becomes less understanding and reading yesterday’s weather, as I say, and it takes that data and makes it actionable and pushes us forward versus just catches us up, which is also very, very exciting because the things that we thought were out of reach six months ago are in reach now. RR: For people listening who might be, with that last sentence, feeling a little bit inspired but also uncertain, like, oh gosh, how do I keep pace? What would you say to leaders in tech and in other spaces who feel pressured to do AI, perform the thing, layer it into their programs where they can, but aren’t sure where to start? What would you– What advice would you give them, having done it yourself? RW: Start. That’s the key. It sounds silly, but everybody kinda has this not really sure where to start, and that’s okay. I think you just gotta start. First and foremost, you have to just dabble a little bit, get your feet wet, and it all starts to come clear. If you’re thinking through the models that are out there, ask it questions, use it to teach you what you don’t know and where to go. But really it’s your thought partner in how you use these things. You have questions? Ask it questions. You get stuck? Ask it why you’re stuck. Ask it wh– it can do a lot of the things that I think would really surprise people, that will help them head down the right direction. And again, we’re– I say it jokingly, we’re not protein folding. It’s not that crazy, the stuff that we’re doing. You’ll see that there’s a lot of people that have been successful, and you can go a long way just asking the right questions. Don’t be afraid. Do things in a smart way. Make sure your identities are locked down. Shout out, Yubico. Make sure that you’re working with your security teams and doing the right things, and you’ve got access controls in place, of course. But just start. That’s the biggest thing I could tell people, is just start. You’d be amazed how far you can go. RR: Very inspirational to close with. And also, I think a bitter truth sometimes. It’s very much the best way to learn is by doing. RW: Yeah, 100%. I always get the, “Rich, what should I do?” I’m like, “Well, just ask the question.” Just literally just start. Just start. Just get in there and do it, and get going. And really the other thing I would say, on the starting point, is try and build a system where you give people the guardrails to be successful, get the benefits of AI, but also don’t get in the way and stifle innovation. You wanna empower people to go figure things out. Give them the chance to start as well. If you’re a leader in one of these organizations, empower the folks on the front lines to do these things, because this technology is just peeling out those middle layers. The closer you sit to the customers, the more impactful work you’re gonna be able to do. So get your frontline people working on this stuff. RR: I have to say, I think I said it a couple times, but some really interesting, very actionable things, I think, you shared today. So I really appreciate you taking the time to give us some of those recommendations on how to get started with AI, what’s working for you with AI Role Play, all of the goodness on how to not be scared to get started. RW: Thank you again for having me. This has been a lot of fun. RR: Yeah. To our audience, thank you for listening to this episode of the Win/Win Podcast. Be sure to tune in next time for more insights on how you can maximize go-to-market success with Highspot.

You see another article. "This platform just changed everything." And suddenly, the AI platform you've been using for months feels completely inadequate. You wonder if you're behind. You wonder if you should switch again. Neo and I have had this conversation more times than we can count, and it's one of the questions we get asked constantly at Inventium AI. Which of the four major AI platforms, Copilot, ChatGPT, Claude and Gemini, should you actually be using? In this How I AI episode, Neo and I walk through the three questions you should ask yourself before committing to any platform, then go through each one in turn to unpack where it shines and where it falls short. By the end, you will have a clear enough picture to make a call, or at least know which one to try next. How I AI is a special series within How I Work where Neo and I explore how high performers are using AI at work to boost productivity, make better decisions and reduce overwhelm. What you'll learn: The three questions to ask before committing to any AI platform Why your data's location could rule out certain options entirely Which platform actually plugs into your daily work apps How pricing models are shifting in ways that could blindside your budget What to weigh before moving your whole company over Practical AI tools for productivity and focus Real-world AI workflows used by high performers How to use AI at work without burning out Smart shortcuts for managing time and mental load Connect with Neo Aplin on LinkedIn (https://www.linkedin.com/in/neoaplin/) and via inventium.ai (https://inventium.ai), where he leads Inventium's AI training and upskilling work with organisations and teams. My latest book The Energy Game is out now. You can order a copy here: https://amzn.to/48ID29M Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha.substack.com/ Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: Martin Imber See omnystudio.com/listener for privacy information.

As a nurse with MS, I’m interviewed about AI’s real role in care: pattern recognition, human-in-the-loop skepticism, and the Three T’s and Two C’s framework. Click here to view the printable newsletter. More readable than a transcript. Click here for a verbatim transcript Summary I sit in the guest chair on Practical AI in Healthcare with Steve Labkoff. I walk through my experience feeding my own symptom logs, lab results, and ten years of clinician notes into an AI LLM: a physical therapy referral I needed and hadn’t scheduled, a medication side effect my neurologist later confirmed, and a rating scale buried in my chart that no one had surfaced. I describe the less impressive side: the four-pound box of unsorted paper my primary care practice mailed me and the 296 pages of unsearchable PDFs I got back from another system in fifteen minutes. Along the way, I lay out my framework for judging any digital health tool, the Three T’s and Two C’s: time, trust, talk, control, and connection, and explain why I insist on keeping humans in the loop even though the research on that is more complicated than people assume. This isn’t a pitch for AI in healthcare. It’s a working nurse and patient’s honest field report. What’s your experience been feeding your own health data into an AI LLM? Tell us in the comments. Episode Transcript Proem I usually ask the questions. This time I'm the guest. I met Drs. Steve Labkoff and Leon Rozenblit a couple of years ago at a DCI Network conference. They host Practical AI in Healthcare, a show I've listened to steadily, though it creates more tension for me than any other podcast I keep coming back to. Usually, I jettison podcasts that do that. I stay with this one because I approach AI in healthcare the way I approach best health; I'm an N of one and resist generalizing, while most guests do a fair amount of it. I bristle at most of them, wanting the shades of gray that reflect deep understanding. In four of 33 episodes, the guest has had lived experience: ePatient Dave DeBronkart, Amy Price, Hugo Campos, and me. I invited Steve and Leon to join my virtual Reckoning group, which I've hosted since 2019. We give podcasters warm critiques of selected episodes: the kind of feedback you give when you've made a hundred mistakes yourself, can spot them quickly in someone else's cut, and have endless thoughts about production, audience, dissemination, and life. They took the critique well. When Steve later asked me to come on his show to talk about how I use AI, not the theory but the daily grind, I readily agreed. They let me publish it here unchanged, apart from this Proem and Reflection. I struggled to prepare for this conversation. I wanted to wear all my hats, but had to narrow my focus to two. I chose my lived experience and nurse hats. Underneath it all was the question I keep circling back to. Not a cure. Best health, the most function, and Hello, and welcome to this week’s edition of Practical AI in Healthcare. My name is Dr. Steven Lapcoff, and this week I’m actually on my own because my partner, Dr. Leon Rosenblatt, is actually on spring break with his kids, so I am covering for him and he’ll be back in the next week. This week we have a guest who we met at a conference in Boston a few months ago at the Beth Israel at the DCI network. Steven Labkoff: We have Danny van Leeuwen. Danny is a nurse. He has background in giving actual physical care to patients. He actually runs his own podcast called Health Hats, the Podcast, and he’s been using AI in both his personal life and in his professional life very extensively. Also, Danny has a significant medical condition, and I’ll let him explain that in the course of the discussion because it’s with that lens that we got introduced at our patient-centric AI conference, and that’s why we thought it’d be a good idea to have Danny come and have a chat with us. So welcome to the podcast, Danny. How are you today? Health Hats: I’m good. Thank you. Thanks for having me. I appreciate it. Steven Labkoff: So Danny, as you probably have heard because you’ve helped us with our podcast, and for that I want to say thank you. For those who are listening in, Danny runs actually a group that actually helps folks running podcasts improve their podcasts, and he’s had Leon and I on many times to listen to critiques and feedback, and it’s been very, very helpful. Danny, we often start our podcast with asking for folks’ origin stories, like how did they get their cape and their superhero tights. What did you do to get you to this point in your life? And just tell us the background of what brought you here. Health Hats: Oh, thanks. So I’m a child of Holocaust survivors, and my parents– when I was young, my parents were active in the civil rights and fair housing movement in the ’60s. And when I was 16 and I was thinking about the war in Vietnam and worried about getting drafted, I wanted to learn what I could learn about the draft and how I could protect myself and manage. And I went to a church in downtown Detroit, and I went for a session of draft counseling as, you know, a little precocious at 16, and I found it fascinating, and they found me fascinating, and they encouraged me to become a draft counselor. And so I, uh, I actually took their course and became a draft counselor, and what I learned is that you change systems from the inside, not the outside. And I learned how the sausage was made, and that, uh, really pointed me in a direction. The way I got into nursing is really because I didn’t want to cut my hair I had an opportunity for a job at one point, and I could have read water meters or become an aide at the Detroit Psychiatric Institute. And reading water meters paid more, but I didn’t wanna cut my hair, so I got the job as, as nurse’s aide. And while I was there, they introduced me to the idea of going to nursing school, which was amazing. Steven Labkoff: It was more– You got paid more to read meters, water meters, than you did- Health Hats: Yes. Steven Labkoff: That’s unbelievable. Life gives you some real interesting turns and twists, doesn’t it? Health Hats: It does. And I was really fortunate because my first jobs in nursing were in physical rehabilitation and home care. I just happened to be in a place where the Holyoke Visiting Nurses was dying to hire a guy, and I was a brand-new nurse, and they ended up hiring me. And so my first introduction to nursing was not in acute care. It was in home care, and actually, I was the first male public health nurse in Western Massachusetts in 1976. And really, what I learned there was that most healthcare does not occur in the medical system. It occurs outside the medical system. And so when I ended up getting into medical care, it was always so interesting to me that everybody there thought this is where, you know, health happened, which it doesn’t. So over the 20 years of working as a nurse, I’ve worked in, other than the rehab and home care, I’ve worked in the emergency department, I’ve worked in ICU, I worked in pediatrics, behavioral health. And after about 15, 20 years, I shifted from becoming a student of individual health to a student o- of organizational health. And what I mean by that is I got into performance improvement. I led a couple of electronic health record implementations. I had a couple of gigs in the C-suite. I did some consulting. Now, in 2009, I was diagnosed with multiple sclerosis, and when I was diagnosed, I learned that I had had it for 25 years. And since my father died young, he died at 45 when I was 19 of his second heart attack, and so every time I would have some kind of episode, I would get a cardiac workup. And by the time the cardiac workup was done, you know, the episode was over, and this went on two, three, four times a year for a long time. And there was a pattern there, and nobody was connecting the dots for 25 years. That’s very important to me because the pattern of what was going on was in my records for 25 years, but nobody had synthesized it. Steven Labkoff: Yeah, they may have been biased, right? Because of your family history and having these episodes, you know, as a clinician, you get very biased by family history, and that can actually lead you down roads which may not be correct, and it sounds like that’s precisely what happened with you. Health Hats: So I’ve– I wanna bring in the caregiver role because I have been a caregiver for my grandmother, my mother, and a son in their end-of-life journeys. So I’ve been on many sides of very difficult decisions. As you said, that my shtick is health hats, and I’m health hats because I’m a patient, I’m a caregiver, I’m a nurse, I’m an advocate, I’m an informaticist, I’m a podcast host. I wear a lot of hats. And wearing many hats has gotten me a seat at many tables because they can check off boxes. When it was really different to be bringing patients o-on board, I was an easy choice. Uh, I was at the table for technical expert panels at CMS, at National Academy of Medicine, at AHRQ, National Quality Forum, PCORI, Patient-Centered Outcomes Research Institute. But really, I wasn’t really there in it for the seat itself. My goal was always to open seats for people who weren’t there yet Now let’s build the bridge, since this is a podcast about AI, let’s build that little bit of that bridge. So my first, like, serious experience with– Well, I don’t know about my first. I was involved in something that you probably are familiar with, which was the Blue Button Plus program, and my goal in that, I was there both as a patient and as somebody who was working with people with disabilities. I, I was VP of quality for an organization that supported about 40,000 people with disabilities. And my goal for that couple of years of weekly or every other week, I can’t remember, calls was, uh, to add a f- a caregiver field to the data set, and to also introduce the idea that what people needed was information that would be able to say what works for me when I’m in pain and what works for me when I’m afraid, which was an issue for me, and it was an issue for the organization that I was working with at the time. Now, I have to say that the caregiver field got added, so I felt some success in that. But as a nurse leader in the informatics group I was part of, really they were only interested in putting a name in the field, not doing anything with that information, which I- Just collecting, so just collecting the data. Steven Labkoff: They didn’t care what the data was used for? Is that what you’re saying? Health Hats: Correct. Yeah. And I couldn’t– got no traction on the pain and fear, which now that I’m older, I understand why, how difficult that is. Nevertheless, it’s something that’s important to patients and caregivers. So I think I would close this section with that I am both an early adopter of technology and a rapid skeptic, that I’m kinda making this number up, but I’ve probably tried over 100 health apps, and I would say that I’ve used five more than three times. And so I think there’s a gap between what’s promised with digital technology and what’s useful for people. So that’s really why I’m here and what’s guiding for me in this. Steven Labkoff: So let’s take it to the next step. In our prequel, I didn’t even know about your personal background to that degree. Mm-hmm. We can take that one offline later about the Holocaust survivor issues. We, we have family, I have family in that same situation, frankly. Let’s change gears and talk about the challenges that you’ve seen. You opened the door a little bit on that a few minutes ago- Yeah … in terms of people wanting to collect data but not necessarily doing much with the data, not being able to understand the true value of the data to some degree. And you said it yourself, people weren’t connecting the dots. Medical records have always been complicated. They’ve always been bulky. They’ve always been full of information, some of which is really relevant, a lot of which is not so relevant, and connecting the dots to making that a, uh, an important information source is not always an obvious task. So what, what was the particular angle on that challenge that you were trying to gun at? Health Hats: Well, I think we have to take a step back- and think about what is– Well, I’m just gonna speak for myself, okay? I know that I often, you know, as I said, I get asked to sit at the table because people can, you know, check boxes, like is that I’m a patient. I wanna be clear that I’m a privileged white old man with MS living in Boston, but I’m an N of one, and I don’t represent other patients. I’m representing myself here and my perspectives. My goal in terms of my health is best health, and what I mean by best health is optimal health and function, physical, mental, spiritual. Not a cure, but best health for where I am, what I have right now. And to get there, I need my own health data, not just what’s in my clinician’s chart, but what I know about myself, my circumstances, my environment, my history, my habits. Not just my medical history, my life history, my treatment responses. And so that’s like patient-reported data, and that’s stuff that’s only exists because I observe it and sometimes I record it And that’s where it falls apart right away. You were just alluding to some of it, that there’s all this medical data and what’s useful about that. I think Dave DeBronkart was a guest on your show. And when he launched his Gimme My Damn Data campaign, I responded to him with, “Watch what you wish for. You’ll be trying to drink dirty water from a fire hose.” And, and that was years ago, and it’s still true. So six months ago, I, I’d been on a mission to gather my medical data, and my– I’d been with my, uh, primary care practice since 2011, and I wanted all that data from 2011 to 2025. This was, like, in December I started on this crusade of trying to get my data. And actually, two months later, I got a box, a four-pound box of paper, and it was paper that was not in chronological order. And it’s just sitting right here. I’ve scanned it in. It’s not, um- Was it in– Steven Labkoff: Was it a printout of Epic or something, or was it actual- Health Hats: It’s a computer printout. It seems like it’s a vendor that they use to- Steven Labkoff: It wasn’t digital. They sent you, literally sent you a box of paper. Health Hats: Yeah, it was a box of paper. Oh. And then I use a lot the, the Beth Israel Lahey Mount Auburn system, and I asked for the last three months of my records, and I got 296 pages of redundant, non-searchable PDFs, and I got that in 15 minutes. Uh, I see a lot of doctors, so maybe I had seen Hmm. I think I had maybe eight or nine visits, and it just happened to be a three-month period that was busy for me, but I got s- 296 pages. And so that really adds to your comment, which is that access to data and access to usable data are really different. Steven Labkoff: Oh, absolutely. And yeah, I’ll tell you, in my world, I think you know that I’ve worked in the life sciences for many, many years, and we are consumers of healthcare data on many levels. We consume medical claims, we consume electronic medical records, and one of the hardest things about using medical records for research or for outcome studies and things like that is the very fact you’re describing, which is the data tends to be sparse, it tends to be poorly organized. It doesn’t always come in an encoded fashion. Thank God most of what we get these days is at least digital. No boxes of paper for us these days, but it wasn’t so long ago that when it was all paper, we couldn’t get that data in the first place. It just wasn’t even gettable. So at least you’ve made some progress. And- Yeah … yeah, I know that you sit on some national level boards, uh, around outcomes, and you can talk about that in a moment. But those are, you know, those boards are trying very hard to come up with outcome studies and ways of– Let me back that up. They’re coming up with ways of using data to perform outcome studies by harmonizing and, and distilling down to usable forms of this EHR data, which is so challenging. Health Hats: I think what’s key, I– like I, I think I w- I’d like to focus on my data. And so what I wanna do is I wanna see patterns. I wanna see patterns that takes my circumstances, my environment, my habits, my treatment over time, and because I think that these patterns are how I formulate the right questions, so the right questions before I go into a clinical encounter. They’re how I track when something is actually working, and it helps me to coordinate across care teams that don’t talk to each other and make decisions that I can live with that help me attain this goal of best health. So that’s the job, formulate better questions, g- seek better answers, make better decisions. And AI is the tool that I try to use to do it. Now, whether it’s up to the task or not is different. I wanna stick in the nursing angle, if you don’t mind- You know, one of the things that I learned the way I got started in nursing is that my goal as a nurse was to put myself out of a job. Steven Labkoff: So that sounds counterintuitive, but what I mean is from minute one with a patient and family, I’m planning my exit. Like, and to do that, I need maximum face time. I need real present, real conversation, real relationships, not less charting. I was gonna say not charting, not documentation, so that’s just ridiculous. Health Hats: But less, you know. The way you do that, I think, is, you know, less charting, less documentation, you know, not hunting through information you can’t find. And that’s where nursing, that’s a genuine promise. So pattern recognition across specific cohorts of patients. So as a nurse, even though I worked a lot of different places, in each place I worked, there was commonalities. In– When I lived in West Virginia and I was an ER nurse in a super rural hospital, if I had had more information about my patients, their families, I could get– an AI could help me surface those patterns that exist for the people that I’m taking care of, I think I could get time back as a nurse. And if the nurse gets time back, then the patient and family gets the presence of the clinician. So that’s the trade that I’m interested in I wanna go back to that thing about pain and fear. I wanna add what I’ve learned working on the blue button, plus I wanna add cognition. So when you think about it, the data almost never captures the variability of pain, fear, and cognition, and those things are really important because pain changes what you can do and what you can decide. Fear closes your heart. It closes your mind. And so when you’re scared in a clinical encounter, you’re not making good decisions. You’re just saying yes to end it. And cognition is, you know, it varies. Like I can absorb better at 10 in the morning on a good day compared to 3:00 in the afternoon when I’m spent. You know, you could extrapolate this to other people. They have their own particular patterns and circumstances. But I think What I’m trying to get at in all of this is it isn’t first about the data, it’s first about what about life and what about the things that are important to people, uh, patients, caregivers, and the clinicians that they partner with, and how can AI help them? Steven Labkoff: So you’ve explained to me in the pre-call that you’re doing some of this work, so maybe you can unpack a little bit about what it is you’re actually doing with it and how it’s helping or, in some cases, not helping those efforts. Health Hats: Well, what have I done? I, I’ve done different things. One of the things that, that I’ve done is to try to build my toolkit. You know? So when I say build my toolkit, I’m a, I’m a, a conglomeration of symptoms. I mean, you know, I’m, I’m not MS, I’m not my symptoms, but they’re big and they’re there, and I feel like I’m trying to, I’m trying to figure out for anything that I have to deal with, whether it’s any of the different kinds of pains I have, my, my anxiety, my bladder, you know, my mobility, I have challenges, and I, I need a toolbox. I need a toolbox, and the way I think is I need at least three things that will work so that when they happen, I got something I can go do, and pretty much the most common thing is drink water. Drink water is by far the most successful intervention across all of my symptoms. It’s kind of amazing. It’s so cheap, so easy. It isn’t the drugs. Okay, but so how do I do that? Well, for me, I’ve done that partially just in my head. Partially I’ve done that by keeping lists. Like, I keep track of the steps I take. I keep track of the amount of time I play music. I keep track of my falls. I keep track of my weight. And so I use digital tools to do that when I can. Steven Labkoff: I also record my clinician visits because- When you say record, do you mean like audio record or dig- Health Hats: Yeah. Yeah, audio record, right. And, uh, until recently I used Abridge, which is a company that, um- Steven Labkoff: How did you get to use Abridge? You– I thought Abridge was only selling basically into doctor’s offices, uh, from the clinician side. Do you- Health Hats: So I was before that. Ah. And they started as a patient-facing product, and actually they sponsored my podcast for three years. So I was pre that. So putting all that together, so I play with, you know, trying to put into Claude There’s nothing magic or special. You know, it’s me playing, just trying stuff. You know, some of it, you know, my wife will say, “Hey,” she sees a pattern. My kids will see a pattern, or I’ll- Steven Labkoff: Give, give, give us an example of what, of what this looks like. I mean, you’re saying you’re giving Claude or another LLM- Yeah … a series of symptoms, or you’re giving it a series of, plus your data. Like, unpack it and let us know. Yeah. What have you did- Okay, so what- … with the system, and how is it working for you? Health Hats: I’ve done a couple of different things. One is, you know, I have a spreadsheet, and I just put the spreadsheet in, you know, as a document or whatever you call it when you have a project and, you know, you load. I load my spreadsheet. I keep a annual summary, and I keep the year that I’m working on. And I will have fits of journaling. You know, I, this is not something that I am, like, super consistent on, but I’ll, especially when I’m struggling with something, if I’m struggling with my blood pressure or I’m struggling with my mood. I have a progressive mobility thing going on, and I’ll put that in and I’ll prompt. I’ll say, “Can you– do you see a pattern in this?” You know, and I’ve gotten, you know, that there’s- Steven Labkoff: Has it given you some insights? Is it… Like, give me an example of some of the insights it’s actually given you that you didn’t see yourself. Health Hats: Well, I’ve gotten, like, uh, it’s kind of humorous. But, but I’ve gotten, like, you know, “Have you thought about seeing a physical therapist?” And I, I have. You know, I have a physical therapist, uh, that I don’t go to very often. You know, my relationship with her is I go for a tune-up. But they’ll– I, I want– It’ll show, like, I’ll do my sort of things are clearly, you know, I’m not walking as far, I’ve fell on a few times, you know, and I’ll get this suggestion, you know. I also– What else have I done? Oh, oh, uh, once I had a medication that I was taking for neuropathy, and I was– my mood had, like, changed considerably and, you know, I got a thing on that might be a side effect. You know, “Have you talked to your doctor about this?” Steven Labkoff: And I- And you got that out of the LLM? You fed that to the LLM? Health Hats: I did. Yeah. Steven Labkoff: And it suggested it was a side effect, which you didn’t figure out. Health Hats: I didn’t. A neurologist said that he thought– He said, “It sounds like you have an allergy to it.” And, you know, he wanted it to be listed as an allergy because he thought it was very possible that he’s had people that have had a problem. Steven Labkoff: When you tell me that you’ve loaded your data, you give the LLM your signs, your symptoms, you give it your labs, you give it what’s in, in the system, and it comes up with a recommendation that you hadn’t thought– Now, you’re a clinician. You’re a nurse. Yeah. You’ve been a nurse for many, many decades. Health Hats: 50 years. Steven Labkoff: 50 years. And does it surprise you that it comes up with stuff that you didn’t see? Health Hats: No. Steven Labkoff: Cause I, to be honest with you- I- … if I, if I did what you just said and it came up with something completely radical that I’d never thought of and it was right- I would be scratching my head and thinking, “Okay, that’s in- that’s beyond interesting. I better pay more attention to this, and maybe I wanna use it differently.” Because not, it’s not just yous using it. Like, people around everywhere are starting to use it for the same, in the same sim- in the same exact way. So that’s the simplification of the medical system, right? Health Hats: It does. I mean, like when I tell my neurologist, he laughs, and he’s like a whatever works kinda guy, you know? That he feels like he doesn’t have all the answers, and that he likes- those stories. I feel like I’ve learned, I think you know Amy Price, right? Steven Labkoff: Yeah, very well. Health Hats: Yeah. We’re buddies. And so one of the things that I’ve learned from her is how to query and how to be skeptical and how to ask questions from different angles, from different perspectives so that you– And that’s why I think that’s where the unexpected comes up. Steven Labkoff: Well, you’re describing something that we did at the conference. I don’t know if you were in the room in the working group that we did this on, but you’re describing, and actually we’re submitting a paper on it very shortly, on AI literacy. Yeah. And you, you didn’t label it as such, but you’re describing yourself as being AI literate and understanding how to use the tools, most importantly, how to be skeptical of the answers, how to interpret the information that’s being presented to you. Health Hats: A- and that, those are all components of literacy, of AI literacy specifically. One of the things I’m finding in my world is that painfully few people are indeed AI literate. Even the folks in IT departments in large life science companies or hospitals who even work in the space and think that they’re good at it and are literate sometimes are not. That has other implications, which are if people are taking on these really impressively powerful tools and they don’t quite know how to use them as well as they should, and if they query them incorrectly, to your point earlier about making good queries, the responses that come out may or may not be the point. And if patients use that information inappropriately because they didn’t know how to ask the right questions to start with, that could have deep implications to the healthcare system. You could say that same thing about doctors. Steven Labkoff: I will say it about doctors. I mean, not about AI, about the advice that doctors give. Health Hats: There’s a, a tremendous variation, and it is very different. When I am feeling good enough to be organized and to be directive in the conversation with a clinician, I get a very different output than when I’m not. And I still have to be skeptical of what doctors tell me, and until I build some trust. And, and then I, you know, then there’s just too many decisions to make when you’re a person with chronic illness. It’s like putting in a kitchen. There’s so many decisions to make, and I’m happy for the doctors that I trust to make the decisions for me. But there are certain decisions I don’t want to give to the doctor or to AI, like I don’t wanna mess with my pathological optimism. I wanna progress as slowly as possible, and I wanna keep playing my horn. These are really important things to me, and I don’t give those decisions that affect that, I don’t give up. But all the rest of it I do, and, and I’ve worked really hard to build the team that I have that appreciates me and my strangeness and my assertiveness, and, you know, they’re not threatened by it. Steven Labkoff: Is your team AI literate? Do they also use the, these same tools in your care? Health Hats: Uh, like I don’t know. I mean, AI literate is like, is huge. You know? I mean, that’s just such a big thing. Do they use AI? Yes. Do I know how they use AI? Well, you know, they use what’s attached to Epic. I know that. Uh, I mean, look, my neurologist, who I just love, he thinks like he uses, he uses the portal well because he takes– he just keeps adding things to the end of the, a note. Yeah. And so he feels like… Well, I don’t find his notes at all useful, and I tell him that. I tell him, “What I really wanna know is, how am I doing? Am I getting better? Am I getting worse? Am I stable? What should I be paying attention to in the next six months till I see you again?” And I can’t find that in his note. That’s true. Yeah. Now, on the other hand, I’ve taken his note and asked Claude and say, “Here’s the note. How am I doing? You know, have I progressed? H-how is he measuring it?” Oh, well, then I find he’s using this scale, right? And it’ll come up with looking through this note, which is like 10 years running, and it’ll find, I can’t remember the name of it, but there’s a scale that he uses. And then we go back and I’ll say to him, “Oh, you’re using this scale.” And he goes, “Yeah.” And I say, “Well, why don’t you like put that at the top of your note?” You know, so that I can find it. You know, so we have that kind of conversation- Yeah … that AI has helped. Steven Labkoff: Well, that’s actually an interesting perspective that AI is helping to reorganize things, ’cause one of the use cases that has been discussed at, at length actually, and it was discussed at our conference, is using AI to digest medical records. Health Hats: And when I say digest, it’s not about like ingesting them, which is slightly different, but digesting, which means find all the different pieces, put them together, come up with a narrative that summarizes perhaps 300 pages of information which may be sparse and may be poorly organized, and bring it all together. And that’s actually a task that AI is actually turning out to be pretty darn good at. And that again changes the nature of the healthcare system and the healthcare journey. You know- And it does a fair job. You say it’s really good at it. So- It’s better than I could do. It’s better than I could do. Well, yes. Well, you’re not– First of all, that’s not your training, and you don’t have the time for it. And you still have to review it. Yeah, of course. Because I have never used AI that gave me a, “Oh, this is great.” I mean, the first time I read it pretty much every time I think it’s amazing. And then, you know, my rule is sleep on it and check it again. And then it’s like, oh my God, this, first of all, it either just said nothing very fancy or it got some very basic things wrong. And then I’ll say, “Oh, you know, you missed this and you missed that.” And it’ll go, “Oh, you’re right, I did.” You know? Steven Labkoff: Well, that also speaks to the concept of keeping a human in the loop- Yeah which is something that you espouse and many folks in the healthcare aisle- I do … espouse. Ironically, you know Adam Rodman, I think. He was at our conference, he spoke. Yeah. Uh, he’s done a study which shows actually having a human in the loop in some cases actually makes the conclusions worse, believe it or not. Ah. Which is w- a non-intuitive finding. You would think that the two together would be better than either one alone, but so that’s, that’s now relatively n- well, it’s not even that new anymore. That information came out about a year ago. So I, we gotta start wrapping up in a few minutes here. Yeah. You know, we didn’t cover the concept around outcomes around your three T’s and two C’s. Maybe we can cover that in the last bit here, and then we can get to closing. Health Hats: Okay. So I feel like one of the questions that you’ve asked is how AI helped, right? And so what I need to tell you is the framework that I’ve developed over the years, which I’ve actually shared in my AI Claude project that’s Danny’s Health, what I call the three T’s and the two C’s, and this is like the framework I use to evaluate any digital health technology. And so they are time, trust, talk, control, and connection. What I mean by that is time is, you know, you need time to learn, to plan, to talk, to build trust. So I say the clock isn’t the enemy, it’s the, the wrong things filling the time, so the, the time. The second is trust. You know, trust can take a really long time. It can happen really quickly. Sometimes you never have it, and you know in your gut when you don’t have it. And most digital health tools, AI, have a trust deficit, I think, not because they’re untru- untrustworthy, which maybe they are, but it’s really because the people who use them, use the tools, don’t, don’t trust them, and I think it’s really important. You c- you can’t shortcut trust in the use of any tool. I think talk is really important. It’s woven through all of it, real conversation. There is nothing like actual conversation that is making decisions together, which is a lot of what healthcare is about, is making decisions. AI can help you prepare for it, and it can help process it. And then control. I trust more when I have power in a situation. So if I’m feeling like an ant ready to be crushed, I’m not making good decisions. And finally, I would say connection is, it’s the human lifeline. You know, when somebody greets you when you cross a threshold, that’s a connection. When someone’s been where you’re going and they can say, “Oh, that helped me.” AI can extend that connection. They can help people find communities that are available at 3:00 in the morning, but you can’t manufacture it. I, I think that connection is really important, so that’s where I g- you know, time, talk, trust, control, and connection, and I use that framework when I’m evaluating. Steven Labkoff: And that framework gives you a better, you know, a how do I say this right? It gives you a, like a rubric, if you will, to go- Yes … through, uh, the information that’s coming out of it. Danny- Yeah … we’re gonna have to wrap up here in a second. Sure. Are there any last comments you wanna make that, that will, you know, help other patients in the, in the space in terms of how they might wanna think about adopting- an AI tool in their world? Health Hats: I think that I would say use it, use AI, keep using it, experiment with it. That, that i- i- just like anything else, it takes time to learn. It takes time to be comfortable with it. Use it. I would say advocate for humans in the loop. I don’t care what the study says. It’s about humans. We are human. Keep it humans in the loop. I would say find a buddy, you know. Do this with somebody else. Find a buddy- That’s good advice … and experiment. I would say, yeah, talk to your clinician about it. It’s a good barometer of a physician. If they don’t wanna talk or blow you off, that tells you something. Absolutely right. And I would say if you’re comfortable with it, mentor. You know- That’s a good idea … be the buddy. And for clinicians and for systems and developers, I would say you need to have patients, caregivers, and practicing partner clinicians in the design. They need to be there from the beginning. And, you know, so i- it solves the problems people have, not the problems that the developers think are there or the venture capitalists thinks are gonna make money. You know, y- and if you have an opportunity, join, you know, participate. Steven Labkoff: All good advice. Well, Danny, I wanna thank you very much for your participation in, in today’s discussion. Hopefully that there are other patients out there who listen to the podcast, they’ll take something away. For the clinicians out there who are listening, you know, you’ve heard it straight out from a patient who happens to be a healthcare provider himself, and he’s got very strong perspectives on how this can be used in a positive and productive way, and I think the framework that he’s put together is very useful. Danny, I wanna just say thank you for all the help that you’ve provided helping this podcast get off the ground. That’s been really incredibly generous of you and your friends who have helped us a lot, and a lot of the things that have happened on our podcast, uh, for improvement’s sake, have come directly from those conversations, so thank you for that. I wanna thank you for being a guest and sharing your journey and sharing your experiences here. And for the rest of us, I’m gonna say thank you for joining us, and we will see you again next time on another episode of Practical AI in Healthcare. Thank you for listening. Thank you for joining us this week on Practical AI in Healthcare. If you’re ready to go beyond buzzwords and hype and explore how AI is truly transforming healthcare, stay tuned for more conversations that get us to what works. Until next time, stay practical Reflection When Steve interviewed me, he didn't know that everything I told him is the origin story of TrustMyOwn.Health. The box of paper. The 296 pages that were technically my data and practically useless. Twenty-five years of a pattern that sat in my chart the whole time, that it took a person, my PCP, a year to put together. Could AI have done it in an afternoon?  I got tired of that being the normal experience instead of the exception. [Add: what specifically prompted starting TMOH, and when.] TMOH starts from a premise I didn't have language for until I said it out loud to Steve: trust isn't a feature you bolt onto a health platform after the engineering is done. It's the whole structure, or the whole thing fails. The three T's and two C's I use to size up any digital health tool turn out to be close to a design spec. Time, because a vault of your whole health history takes patience to build, not a single import. Trust, built into governance rather than promised in marketing; TMOH's Data Sovereignty Covenant binds the board and investors to the same terms as everyone else, which is the only version of trust I believe in. Talk, because the point was never to replace the conversation with my clinician, it was to walk in more prepared for it. Control, because I decide what goes in the vault and who sees it, the same way I decide which of my own decisions I hand to a doctor or an AI and which ones I keep for myself. Connection, which no vault can manufacture, but a good one can make room for. I told Steve that AI found a pattern in my chart that twenty-five years of clinicians missed. That's not really a story about AI being smart. It's a story about who owned the data long enough to ask the question. That's the whole bet behind TMOH: put the owner at the center, and let the rest of the ecosystem, the networks, the vendors, the AI, earn its place around that. See you around the block. Practical AI in Healthcare Episodes https://open.spotify.com/episode/4wA4ltjmZfIZ5VpmTeTTOF?si=KbEvc2_ERNWakJ3JeP2Ddg https://open.spotify.com/episode/0LDetUFJJrSV1cy6LtpGFx?si=qAqoqKBBSNm9PiwPjSIXYA https://open.spotify.com/episode/0wXEm1KnnGorOvTt9GTh7o?si=K_DKXzGyThusBPA6KoVkCg https://open.spotify.com/episode/6krV94ob6Lcv7VNo0qahZ5?si=B6lZDkvsQ9y2Z2FhkXzQGQ Referenced in episode Patient data access history: “Introducing Blue Button Plus: The Next Generation in PHRs” — HealthIT.gov (Office of the National Coordinator for Health IT) — https://www.healthit.gov/blog/consumer/introducing-blue-button/ The “Gimme My Damn Data” campaign Danny references: “Gimme My Damn Data (and Let Patients Help!): The #GimmeMyDamnData Manifesto” — Dave deBronkart, Journal of Medical Internet Research — https://www.jmir.org/2019/11/e17045/ Amy Price, mentioned as a mentor in questioning and skepticism: “Welcoming Dr. Amy Price as Editor-in-Chief” — Society for Participatory Medicine — https://participatorymedicine.org/2024/welcoming-dr-amy-price-dphil-as-the-editor-in-chief-for-the-journal-of-participatory-medicine/ AI literacy for patients, the concept Steve names in the episode: “Critical AI Health Literacy as Liberation Technology: A New Skill for Patient Empowerment” — National Academy of Medicine — https://nam.edu/perspectives/critical-ai-health-literacy-as-liberation-technology-a-new-skill-for-patient-empowerment/ Human-in-the-loop research Danny and Steve discuss (Adam Rodman): “AI and the Evolution of Medical Thought with Dr. Adam Rodman” — NEJM AI Grand Rounds (podcast) — https://ai-podcast.nejm.org/e/ai-and-the-evolution-of-medical-thought-with-dr-adam-rodman/ Abridge, the ambient AI scribe tool Danny mentions using: “Pioneers in Generative AI for Healthcare” — Abridge — https://www.abridge.com/about The DCI Network conference where Danny met the hosts: “About DCI Network” — DCI Network, Beth Israel Deaconess Medical Center — https://www.dcinetwork.org/about-us Please comment and ask questions: at the comment section at the bottom of the show notes on LinkedIn  via email YouTube channel  DM on Instagram, TikTok to @healthhats Substack Patreon Production Team Kayla Nelson: Web and Social Media Coach, Dissemination, Help Desk  Leon van Leeuwen: editing and site management Oscar van Leeuwen: video editing Julia Higgins: Digital marketing therapy Steve Heatherington: Help Desk and podcast production counseling Joey van Leeuwen, Drummer, Composer, and Arranger, provided the music for the intro, outro, proem, and reflection Claude, Perplexity, Auphonic, Descript, Grammarly, DaVinci Resolve, DaVinci AI Art Generator, OpenArt AI Creator Studio Inspired by and Grateful to: Steve Labkoff, Leon Rosenbilt, Amy Price, Leon and Oscar van Leeuwen, Laura Marcial Artificial Intelligence in Podcast Production Health Hats, the Podcast, utilizes AI tools for production tasks such as editing, transcription, and content suggestions. While AI assists with various aspects, including image creation, most AI suggestions are modified. All creative decisions remain my own, with AI sources referenced as usual. Questions are welcome. Creative Commons Licensing CC BY-NC-SA This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. If you remix, adapt, or build upon the material, you must license the modified material under identical terms. CC BY-NC-SA includes the following elements:    BY: credit must be given to the creator.   NC: Only noncommercial uses of the work are permitted.    SA: Adaptations must be shared under the same terms. Please let me know. dannyhealthhats@gmail.com  Material on this site created by others is theirs, and use follows their guidelines. Disclaimer The views and opinions presented in this podcast and publication are solely my responsibility and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute®  (PCORI®), its Board of Governors, or Methodology Committee. Danny van Leeuwen (Health Hats)

Physical Farewell: PlayStation's Disc Departure.  Connected Car Confusion: Stolen Vehicles, Smart Apps and Security Shortfalls.  Starter Success: Sourdough Sidekick Simplifies Slow Baking.  Wonka's Wired Whisper: AI Voice Sparks Vintage Vision vs Modern Misgivings.  Paywall Pushback: Premium Price for Practical AI.  Electric Evolution: BMW's Bold Battery Bet.  Ford's Flawed Formula: Why Human Know-How Beat Artificial Intelligence.  Sandy Swimming: Sandfish Science Steers Smarter Mars Rovers.  Apple Access Advantage: The Hidden Hack for a Happier Kids' Phone. 

Open source AI models just hit 41% of Hugging Face downloads — and the real cost gap is 90% or more. Here's what that means for your business.The numbers moved fast this week. Chinese open-weight models now dominate downloads, the quality gap versus closed models has shrunk to 3.3%, and a new repo opens on Hugging Face every seven seconds. Half of the Fortune 500 is already running open source models in production.Isar walks through the new frontier open models Kimi K3 and DeepSeek V4, Thinking Machines Lab's first release, Satya Nadella's Token Capital essay, the new state-level AI laws in New York and Illinois, BCG's AI at Work report, and the Apple lawsuit hanging over OpenAI's hardware plans.In this session, you'll discover:Why Chinese open-weight models now account for 41% of Hugging Face downloadsHow Kimi K3 and DeepSeek V4 price against top US closed models ($15 vs $50 per million output tokens, down to 87 cents)What Satya Nadella's "Token Capital" and reverse information paradox mean for your company's dataWhat New York's data center moratorium and Illinois Senate Bill 315 change for AI companiesWhy BCG found that AI strategy beats tool access — and 72% of CEOs now own the AI decisionBCG "AI at Work: Strategy Matters More Than Tools" — the 12,000-person study covered in this episode — https://www.bcg.com/publications/2026/ai-at-work-why-strategy-matters-more-than-toolsAI 2040 "Plan A" paper — the 90-page proposal to delay superintelligence until 2040 discussed in the rapid fire — https://ai-2040.com/About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

To learn more about valuable resources for entrepreneurs and business owners, please visit https://www.sbprou.com/AI Strategies for Small Business Owners is covered in this Podcast:• How small business owners can leverage AI to save time and increase productivity• Practical AI tools and strategies you can start using in your business right away• How to use AI to streamline operations, outsourcing, and lead generation• Ways AI can help you scale your business without burning out***************************************Running a small business often means juggling multiple responsibilities every day. Join Andrew Frazier and Gene Bohensky as they discuss practical AI strategies that can help small business owners save time, improve efficiency, and make better business decisions.In this session, Andrew and Gene explore how entrepreneurs can leverage AI to automate routine tasks, boost productivity, enhance customer engagement, and uncover new opportunities for growth. Learn how today's AI tools can streamline your workflows and free up your time so you can focus on what matters most, building a stronger business and achieving long-term success.Gene Bohensky CEO of Archers Contact Solutions and is known as "The Outsource Coach and Lead-Gen Expert." He has helped hundreds of business owners scale by providing expert virtual and executive support, strategic lead generation, and operational outsourcing solutions.• LinkedIn:   / eugenebohensky   • Website: https://archerscontactsolutions.com/

In this episode, Mitch Beinhaker chats with Drew Griffin about the importance of authentic relationship-building, the power of content and community, and how small businesses can utilize AI and digital tools for success. Whether you're a local business owner or a digital marketer, you'll find actionable strategies to grow your audience and build lasting client relationships. Key Topics Covered: The role of transparency and value in modern prospecting and sales Building a local audience through newsletters and community engagement The significance of list-building and the “referral of a lifetime” concept How AI tools like Claude, ChatGPT, and Featurely streamline content creation Practical applications of AI in legal, healthcare, and marketing industries Developing SaaS products and software tools for scalable business growth Privacy and ethical considerations with AI and data tracking technologies Strategies for private equity and business scaling through software development The future of wearable tech and AI in everyday life and business How to leverage AI for content, prospecting, and process efficiency Timestamps: 00:00 - Introduction: Building real relationships in a digital world 02:13 - Drew's background in healthcare and transition to marketing 03:56 - The importance of building an audience before launching products 04:50 - The power of local newsletters for community engagement 06:14 - Approaching businesses as a media company for better prospecting 07:43 - The benefit of providing value first in sales interactions 09:09 - Mistakes in digital marketing and building a list 10:36 - The impact of small, targeted audiences for scalable revenue 11:03 - The “referral of a lifetime” strategy for network growth 12:12 - Importance of consistent content and stay-in-touch programs 14:02 - The exponential potential of networks (second-level reach) 15:13 - How AI tools democratize marketing and software development 16:45 - Using AI for time-saving, health, and life improvements 17:16 - Practical AI tools: Featurely, LoudAF, One Pagers, Leedsley 18:54 - Ethical use of AI: transparency and client trust 20:26 - The impact of AI on business scalability and private equity interest 22:55 - The limitations of AI in legal and specialized fields 25:36 - How AI helped a diabetes patient improve health outcomes 26:50 - The influence of AI on lifestyle and health 27:39 - AI-powered content creation: from podcasts to blogs and social media 30:07 - Tools for interview automation and content scaling 31:52 - Automating proposal and sales follow-ups with AI 36:20 - Data tracking tools: website pixels and retargeting strategies 40:37 - Privacy concerns, tracking, and AI ethics 45:29 - The evolution of wearable tech and augmented reality 48:26 - Future business strategies and scaling plans 53:10 - Connecting with Drew Griffin and upcoming projects 55:37 - Closing thoughts: democratizing technology for small business owners Resources & Links: Local Newsletter Launchpad – Free training on newsletter strategies Featurely.io – AI interview and content creation platform LoudAF.ai – AI content generation for social media, blogs, and more OnePagers.ai – AI-powered proposal and sales document creation Leedsley.ai – Prospecting and lead generation tool Pocket.ai – Device for recording conversations and transcriptions Connect with Drew Griffin: LinkedIn – Drew Griffin Remember, building trust and providing value remain the cornerstones of successful business growth—even in a rapidly evolving digital landscape. Harness AI not as a replacement but as a tool to enhance genuine relationships and scalable success.

Todd Rogers, Senior Associate at Walter P. Moore, returns to The CADDle Call to cut through the AI hype and talk real-world use in civil design. We dig into how AI tools handle the constant variability of civil projects, where trust levels differ between architecture and civil teams, and what's actually working for automating tasks like surface cleanup, QTO, and point cloud classification. We also cover the Autodesk Assistant rollout, how other AI tools like ChatGPT and Claude are being tested at Walter P. Moore, and where AI stands on design analysis and code compliance checks. A grounded, practical conversation for anyone evaluating AI in the AEC space.

Could one AI model save you weeks of work—or even reshape how you run your business?After spending two weeks putting Claude Fable 5 through real-world business challenges, the answer surprised me. This wasn't about writing better prompts or generating content faster. It was about handing complex, multi-step strategic work to AI and getting back solutions that would normally require weeks of research, multiple consultants, and significant investment.In this episode, I share exactly how I approached Fable 5, which projects produced the highest ROI, where it exceeded expectations, where it failed, and the framework I'll continue using once usage moves to token-based pricing.In this session, you'll discover:Why Claude Fable 5 feels less like a chatbot and more like a senior strategist.The framework I used to identify the highest-ROI AI projects across my business.How Fable delegated work to less expensive models to dramatically reduce costs.Real examples where hours—or even weeks—of work were completed in under an hour.Why long-running AI workflows are becoming a competitive advantage.The biggest strengths (and surprising weaknesses) I discovered during two weeks of intensive testing.How to decide when premium AI models are actually worth the investment.Why measuring business ROI matters more than chasing the newest AI model.The mindset shift every business leader needs as AI moves from assistant to orchestrator.If you're evaluating whether advanced AI is worth the investment for your business, this episode provides a practical, experience-based playbook—not hype. Whether you're building workflows, scaling operations, or looking for your next competitive advantage, you'll walk away with ideas you can apply immediately.About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Liz Peuster, Chief Communications Officer at the South Carolina Association of CPAs and founder of LP Innovates, joins The Association Podcast for a conversation that goes far beyond AI hype. Liz shares her career journey through the association world, why storytelling is at the heart of member value, and how associations can use AI to create better member experiences while developing, not replacing, their staff.From building custom AI assistants that coach employees into stronger writers to helping professionals navigate AI adoption with confidence, Liz offers practical ideas that any association can implement immediately. Whether you're just beginning your AI journey or looking for smarter ways to serve members, this episode is packed with actionable advice and fresh perspectives.Timestamps00:00 Welcome and Liz's association career journey 03:40 There's an association for everything 07:40 Why storytelling starts with understanding your members 12:00 Turning member challenges into advocacy success stories 14:20 AI concerns facing the accounting profession 17:00 Helping members evaluate AI vendors and technology 21:40 Building custom AI tools inside associations 24:00 Creating "Paige," an AI copy editor that teaches instead of rewrites 28:00 Why AI should focus on upskilling staff, not replacing them 30:30 Reclaiming time for member relationships 34:00 Teaching the next generation to use AI responsibly 37:00 Launching LP Innovates 39:30 Practical AI strategies anyone can implement tomorrow 41:00 Custom Copilot prompts that sound like you 43:00 AI workflows that eliminate inbox distractions 45:00 Saving hundreds of hours through small AI improvements 47:00 Final advice: Start with what frustrates you most

AI is improving quickly. The way we use it should improve too. In this episode of Grounding AI, Donna Peterson shares a simple weekly system that helps business leaders continuously improve how they use ChatGPT and other large language models. Instead of chasing every new AI tool, Donna explains how spending just 10 minutes each week reviewing your conversations with AI can help you write better prompts, communicate more clearly, and receive more valuable business insights. Using her simple Review, Learn, Test framework, you'll discover how AI can become a teacher instead of simply another productivity tool. If you're a business leader, marketer, manufacturer, association executive, or B2B professional looking to improve your AI skills through real work instead of endless tutorials, this episode is for you. In this episode you'll learn: • Why reviewing your AI conversations is more valuable than starting over every day • How ChatGPT can teach you to become a better AI user • Questions to ask AI that improve future responses • Why better business thinking creates better AI results • How just 10 minutes each Friday can improve your AI skills week after week At World Innovators, we believe AI works best when it supports better communication, stronger relationships, and smarter business decisions. This episode demonstrates a practical process any leader can begin using immediately. Subscribe for weekly conversations about practical AI for business leaders. *** Reach out to dpeterson@worldinnovators.com if you'd like help building a marketing strategy that builds relationships and/or AI training for individuals or full teams.*** Visit www.worldinnovators.com for more resources on building stronger marketing and leadership strategies.*** Subscribe to the Grounding AI podcast for weekly insights into marketing, leadership, and the future of AI.

You've probably seen Cowork mentioned inside Claude, or perhaps you clocked the headlines when Microsoft folded it into Copilot a couple of weeks ago. Either way, the question people keep asking is simple: how is this actually different from just chatting with Claude? The answer comes down to files, time and money - three things that change quite dramatically once you move from chat to Cowork. In this How I AI episode, Neo and I unpack exactly what Cowork can do that regular chat can't, how to know which one to reach for, and what the new Microsoft version means if you're a Copilot user. How I AI is a special series within How I Work where Neo and I explore how high performers are using AI at work to boost productivity, make better decisions and reduce overwhelm. What you'll learn: What actually changes when Claude can touch files on your own computer The simple test for deciding between Chat and Cowork for any task How Cowork differs from Claude Code, and who each one is really built for Three lesser known Cowork features worth exploring What Microsoft's version of Cowork does differently, and what it costs Practical AI tools for productivity and focus Real-world AI workflows used by high performers How to use AI at work without burning out Smart shortcuts for managing time and mental load Connect with Neo Aplin on LinkedIn (https://www.linkedin.com/in/neoaplin/) and via inventium.ai (https://inventium.ai), where he leads Inventium's AI training and upskilling work with organisations and teams. My latest book The Energy Game is out now. You can order a copy here: https://amzn.to/48ID29M Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha.substack.com/ Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: Martin Imber See omnystudio.com/listener for privacy information.

Can AI keep getting smarter while becoming dramatically cheaper?This week may go down as the biggest release week in AI history. OpenAI, Meta, SpaceX AI, and Anthropic all introduced major new models and capabilities but the biggest story isn't just who launched what. It's the dramatic shift toward lower-cost, highly capable AI and what that means for every business.In this episode, Isar Meitis breaks down the week's biggest announcements, explains why the AI race is moving beyond benchmark scores toward cost-efficient agentic systems, and explores how these changes are reshaping enterprise adoption, workforce roles, and the future competitive landscape.In this session, you'll discover:Why this may have been the biggest AI release week ever.OpenAI's latest releases, including GPT-5.6, GPT Work, and GPT Live.Meta's surprise entry with Muse Spark 1.1 and why it's turning heads.SpaceX AI's Grok 4.5 and the growing focus on performance at dramatically lower costs.Why AI companies are shifting from "best model" to "best value."What falling AI costs mean for enterprise adoption and ROI.How businesses should think about model routing and using the right AI for the right task.The rise of agentic AI and why autonomous execution is becoming the new competitive battleground.The geopolitical implications of AI model restrictions between the U.S. and China.About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

In this episode, Mitch Beinhaker chats with Drew Griffin about the importance of authentic relationship-building, the power of content and community, and how small businesses can utilize AI and digital tools for success. Whether you're a local business owner or a digital marketer, you'll find actionable strategies to grow your audience and build lasting client relationships. Key Topics Covered: The role of transparency and value in modern prospecting and sales Building a local audience through newsletters and community engagement The significance of list-building and the “referral of a lifetime” concept How AI tools like Claude, ChatGPT, and Featurely streamline content creation Practical applications of AI in legal, healthcare, and marketing industries Developing SaaS products and software tools for scalable business growth Privacy and ethical considerations with AI and data tracking technologies Strategies for private equity and business scaling through software development The future of wearable tech and AI in everyday life and business How to leverage AI for content, prospecting, and process efficiency Timestamps: 00:00 - Introduction: Building real relationships in a digital world 02:13 - Drew's background in healthcare and transition to marketing 03:56 - The importance of building an audience before launching products 04:50 - The power of local newsletters for community engagement 06:14 - Approaching businesses as a media company for better prospecting 07:43 - The benefit of providing value first in sales interactions 09:09 - Mistakes in digital marketing and building a list 10:36 - The impact of small, targeted audiences for scalable revenue 11:03 - The “referral of a lifetime” strategy for network growth 12:12 - Importance of consistent content and stay-in-touch programs 14:02 - The exponential potential of networks (second-level reach) 15:13 - How AI tools democratize marketing and software development 16:45 - Using AI for time-saving, health, and life improvements 17:16 - Practical AI tools: Featurely, LoudAF, One Pagers, Leedsley 18:54 - Ethical use of AI: transparency and client trust 20:26 - The impact of AI on business scalability and private equity interest 22:55 - The limitations of AI in legal and specialized fields 25:36 - How AI helped a diabetes patient improve health outcomes 26:50 - The influence of AI on lifestyle and health 27:39 - AI-powered content creation: from podcasts to blogs and social media 30:07 - Tools for interview automation and content scaling 31:52 - Automating proposal and sales follow-ups with AI 36:20 - Data tracking tools: website pixels and retargeting strategies 40:37 - Privacy concerns, tracking, and AI ethics 45:29 - The evolution of wearable tech and augmented reality 48:26 - Future business strategies and scaling plans 53:10 - Connecting with Drew Griffin and upcoming projects 55:37 - Closing thoughts: democratizing technology for small business owners Resources & Links: Local Newsletter Launchpad – Free training on newsletter strategies Featurely.io – AI interview and content creation platform LoudAF.ai – AI content generation for social media, blogs, and more OnePagers.ai – AI-powered proposal and sales document creation Leedsley.ai – Prospecting and lead generation tool Pocket.ai – Device for recording conversations and transcriptions Connect with Drew Griffin: LinkedIn – Drew Griffin  

This week on CMO Confidential, we are revisiting one of our favorite conversations with Rob Ward from January of 2026.A CMO Confidential Interview with Rob Ward, co-founder and General Partner of Meritech Capital, a top Silicon Valley venture firm. Rob shares his take on what he calls a "super terrifying and exciting time" and provides perspective on AI receiving the most capital of any technology in history, the "durability of revenue" and how quickly start-ups are now reaching $100 million in revenue. Key topics include: why VC's focus on growth vs. profitability; the risks associated with massive long-term capital investment; why marketers should pick a "trusted advisor" as their AI partner; and why your data strategy needs "context. Tune in to hear how Astronomer handled the "Coldplay Concert Incident" which immediately became a PR classic and the "VC Foie Gras Effect."What happens when a top venture capitalist pulls back the curtain on AI, valuations, hype cycles, and what's actually working?In this episode of CMO Confidential, host Mike Linton sits down with Rob Ward, Co-Founder and General Partner at Metech Capital, to unpack the realities behind the AI boom. Rob has spent more than 26 years investing in category-defining companies like Facebook (Meta), Snowflake, NetSuite, Zipcar, and Cloudera — and he brings a rare, grounded perspective to today's AI frenzy.Together, they explore: • Why AI adoption is still early — despite explosive growth • The real risks behind inflated valuations and “AI-washing” • How VC decision-making changes during platform shifts • What marketers and executives should actually look for when choosing AI partners • Why data strategy, change management, and trust matter more than tools • What layoffs, productivity, and the future of work really look like beneath the headlines • A masterclass in crisis communications, featuring Ryan Reynolds, Gwyneth Paltrow, and ColdplayIf you're a CMO, CEO, board member, founder, or agency leader trying to make sense of AI without getting swept up in the hype — this is a must-listen conversation.⸻Chapter Markers00:00 – Welcome to CMO Confidential00:19 – Introducing Rob Ward and today's AI conversation01:13 – Where we really are in AI adoption02:26 – Explosive AI growth: what's real vs hype03:35 – Why enterprise AI adoption is still a slog04:37 – Vendor spend, hyperscalers, and the trillion-dollar buildout06:12 – Is this an AI bubble? Public vs private market realities07:20 – Accelerating investment rounds and lack of diligence08:12 – AI-washing and durability of AI businesses09:46 – Proof-of-concepts, switching costs, and fragile loyalty10:55 – Big Tech vs startups: why this cycle is different11:40 – Why VCs chase platform shifts despite the risks13:05 – How AI is changing profitability and headcount math16:11 – “FOGRA” investing and capital distortion17:00 – Circular investing and data-center risk18:23 – Data centers, GPUs, and betting on the wrong future19:38 – Credit default swaps and financial warning signs21:45 – How executives should choose AI vendors22:58 – Change management and why culture matters most24:09 – Why data strategy is the real AI strategy26:36 – “Frequently wrong, never in doubt” and AI hallucinations27:01 – Practical AI use cases for marketers30:00 – Layoffs, productivity, and what's really happening to jobs33:05 – The best questions to spot real AI fluency35:00 – AI safety, geopolitics, and long-term risks36:38 – Crisis management masterclass: Astronomer, Coldplay & Ryan Reynolds39:58 – Final advice and closing thoughts⸻Subscribe for weekly episodes featuring world-class marketing leaders, board members, and C-Suite executives.#CMOConfidential, #MarketingLeadership, #BrandStrategy, #CorporateActivism, #MarketingStrategy, #CMO, #AIinMarketing, #ExecutiveLeadership, #BrandReputation, #ConsumerTrust, #DigitalMarketing, #MarketingInsights, #ThoughtLeadership, #BusinessStrategy, #CustomerCentricSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Feeling the squeeze from regulation, commission changes, and nonstop tech hype? Medicare leader Christian Brindle joins Luke and Josh to deliver the antidote to panic—separating real AI wins from noise, showing you how to adapt your mindset, and proving the sky is definitely not falling. Instead, he maps out the systems you can deploy now to keep booking, closing, and scaling. In this episode: Why so many insurance agents feel "squeezed" (and how to reframe it) AI as "super automation" you can deploy today—not agent replacement Practical AI wins: CRM nudges, social audits, website builds, on-page SEO Replit, Claude, and voice assistants to analyze site performance in plain English What's working—and not—with AI voice callers vs. human callers Building daily outreach systems to actually hit your 25 personal messages Navigating regulatory shifts by focusing on controllables and client value   Everything Senior Insurance on YT: www.youtube.com/@EverythingSeniorInsurance Christian's YT Channel: www.youtube.com/@christianbrindle Seven Figure Medicare Agent Facebook Group: https://www.facebook.com/share/g/1BJ8jQ5dSx/  Everything Senior Insurance: https://eseniorinsurance.com/

If your organisation has moved its AI licenses onto a per-use plan, every single chat thread your team sends is now adding to a bill. Some companies are already burning through their entire IT budget for AI within the first couple of months of the year, and most staff have no idea their usage is costing anything at all. It doesn't have to be a mystery. There are simple habits, from which model you default to, to what time of day you send your first message, that change how far your usage actually stretches. In this How I AI episode, Neo and I unpack tokenomics, the economics of how you actually use your AI. We get into how usage limits work across the major platforms, why the timing of your first chat each day matters more than you'd think, and what changes once you move from a personal plan to an enterprise one. How I AI is a special series within How I Work where Neo and I explore how high performers are using AI at work to boost productivity, make better decisions and reduce overwhelm. What you'll learn: What tokenomics actually means and why it's suddenly everywhere How Claude's usage window works differently to ChatGPT and Copilot The simple morning habit that changes how far your usage window stretches Why enterprise billing is shifting, and what that means for your team's budget What managers should be doing before token usage gets out of hand Practical AI tools for productivity and focus Real-world AI workflows used by high performers How to use AI at work without burning out Smart shortcuts for managing time and mental load Connect with Neo Aplin on LinkedIn (https://www.linkedin.com/in/neoaplin/) and via inventium.ai (https://inventium.ai), where he leads Inventium's AI training and upskilling work with organisations and teams. My latest book The Energy Game is out on July 7, 2026. You can order a copy here: https://amzn.to/48ID29M Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha.substack.com/ Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: Martin Imber See omnystudio.com/listener for privacy information.

What if the biggest competitive advantage your business could gain this year costs less than dinner for two? Artificial intelligence is no longer a futuristic concept—it's a tool that forward-thinking entrepreneurs are using today to save time, increase revenue, and outpace the competition. In this episode of 365 Driven, host Tony Whatley sits down with digital marketing expert, AI educator, and SaaS founder Austin Armstrong to explore how business owners can leverage AI without getting overwhelmed by the endless stream of new tools and trends. With more than 20 years of digital marketing experience and millions of followers across social media, Austin has become one of the most trusted voices in the AI space by helping entrepreneurs cut through the hype and focus on what actually works. Together, Tony and Austin dive into practical strategies for adopting AI, building a personal brand, and creating content that generates real business—not just vanity metrics. Austin shares why entrepreneurs should stop fearing failure, embrace experimentation, and learn to pivot quickly when the market speaks. You'll also discover how AI agents are transforming the way businesses operate, why conducting a time audit may be the smartest first step before investing in new technology, and how entrepreneurs can automate repetitive work while focusing on high-value activities that drive growth. The conversation also explores the future of AI, from autonomous agents and robotics to the evolution of marketing, content creation, and software itself. Whether you're just beginning to explore AI or you're looking to gain a competitive edge, this episode delivers actionable insights that can help you work smarter, market more effectively, and future-proof your business. If you're a business owner who wants to stay ahead instead of getting left behind, this episode is packed with strategies you can start applying today. Key highlights: Why AI is the biggest competitive advantage for entrepreneurs today. How AI agents can save hours of work every week. The simple time audit that reveals what to automate first. Why consistency beats going viral on social media. How to build a personal brand that attracts high-paying clients. The mindset shift every entrepreneur needs to embrace AI. Austin's predictions for the future of AI, automation, and robotics. Practical AI tools every business owner should start using today. Connect with Tony Whatley: Website: 365driven.com Instagram: @365driven Facebook: 365 Driven Connect with Austin Armstrong: Website: AustinArmstrong.ai

If you've ever stared at a model picker and wondered whether to click Flash, Sonnet, Opus, Instant, or Think Deeper, you are not alone. These naming conventions are genuinely confusing, and most people just pick something and hope it works. The stakes are higher than they might seem, though. Token misuse has left some companies with eye-watering AI bills, including one case where a single employee ran up a $500,000 tab in a month. The good news is that there is a simple mental model that cuts through all the noise, and once you have it, choosing the right model for any task takes seconds. In this How I AI episode, Neo and I walk through the four major AI platforms, Gemini, ChatGPT, Claude, and Copilot, and break down exactly which model to use and when. We also get into tokens, usage limits, and why matching the model to the task matters far more than most people realise. How I AI is a special series within How I Work where Neo and I explore how high performers are using AI at work to boost productivity, make better decisions and reduce overwhelm. What you'll learn: Why ignoring model numbers and reading the small print instead saves a lot of confusion What Fable and Mythos are, and why you can't use them right now How token usage works and why the right model choice protects your access Practical AI tools for productivity and focus Real-world AI workflows used by high performers How to use AI at work without burning out Smart shortcuts for managing time and mental load Connect with Neo Aplin on LinkedIn (https://www.linkedin.com/in/neoaplin/) and via inventium.ai (https://inventium.ai), where he leads Inventium's AI training and upskilling work with organisations and teams. My latest book The Energy Game is out on July 7, 2026. You can order a copy here: https://amzn.to/48ID29M Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha.substack.com/ Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: Martin Imber See omnystudio.com/listener for privacy information.

Is your business ready for a world where AI doesn't just answer questions—but gets the work done?The age of agentic AI has officially arrived. Companies are rapidly shifting from chat-based AI to autonomous AI agents capable of completing hours of work with minimal human involvement. The organizations that adapt early are likely to gain a significant competitive advantage, while those that hesitate risk falling behind.In this episode, Isar Metis unpacks the biggest AI developments of the week—from OpenAI's dramatic internal transformation and Anthropic's latest enterprise agent, to the growing talent war between leading AI labs and breakthrough healthcare applications. More importantly, he explains what these changes mean for business leaders and how to prepare your organization for the next phase of AI adoption.In this session, you'll discover: Why OpenAI has shifted almost entirely to agentic AI internally.  What the end of Custom GPTs means for businesses.  How Anthropic's Claude is becoming a true AI teammate inside Slack.  The biggest challenges around AI governance, security, and trust.  Why AI agents are creating new budgeting and infrastructure challenges.  The growing competition for top AI researchers—and why it matters.  Healthcare breakthroughs showing AI's real-world impact.  Practical steps every business leader should take today to prepare for an agentic future. If you're leading a team, shaping AI strategy, or simply trying to stay ahead of one of the fastest technology shifts in history, this episode will help you separate the hype from the practical reality.About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

In this episode of the Life Matters Podcast, host Bill Bell, Vice President of Advanced Sales at Penn Mutual, welcomes Daria Lee Sharman, Executive Director of Launch by NTT DATA, and Nick Bowman, Co-Founder of Xcela AI, for a conversation on artificial intelligence and its growing role in financial services.  As AI continues to gain attention across industries, many financial professionals are asking how it may impact their businesses, client relationships, and day-to-day operations. Daria and Nick share practical perspectives on how AI is being used today to improve efficiency, streamline administrative tasks, enhance client communication, and support business growth.  You'll hear insights on:  • How financial professionals and insurance carriers are using AI today  • Why AI is unlikely to replace trusted financial professionals  • Practical AI tools that may help improve efficiency  • Common misconceptions about AI adoption  • Data privacy and security considerations  • How AI may shape the future of financial services  This episode is designed for financial professionals looking to better understand AI and explore practical ways to leverage emerging technology while maintaining the human relationships that drive long-term client success.  Have a question or comment for Bill or Jenna? Drop him an email at: LifeMatters@PennMutual.com     Follow Us Facebook: https://www.facebook.com/PennMutual/      Instagram: https://www.instagram.com/PennMutual/      LinkedIn: https://www.linkedin.com/company/penn-mutual/      Penn Mutual's Financial Professional Resource Site: https://gateway.pennmutual.com/    Artificially Planned Advance Sales Insight: Artificially planned | Gateway | Penn Mutual This podcast is for informational purposes. Guests' views, comments, and opinions on products, services, or strategies do not necessarily represent the views of or imply endorsement by The Penn Mutual Life Insurance Company or its affiliates. Product availability, benefits and provisions vary by state.  8987212NS_JUN28 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What if the biggest AI opportunity for your business isn't ChatGPT—but the tool quietly replacing it?While much of the AI conversation has focused on Claude Code and coding assistants, a new contender is changing how business leaders think about productivity, automation, and execution.In this episode, Dan Sanchez joins Isar Meitis to explore how OpenAI Codex has evolved far beyond software development. Together they reveal how AI agents can proactively find context, take action, automate complex workflows, and become true collaborators inside your business, not just chatbots that answer questions. If you're looking for practical ways to scale marketing, streamline operations, and unlock new levels of efficiency without increasing headcount, this conversation offers a glimpse into what the next generation of AI-powered work looks like. In this session, you'll discover: Why OpenAI Codex is gaining momentum beyond software development  The key differences between Codex, ChatGPT, Claude Code, and Claude CoWork  How AI agents proactively find context and execute tasks autonomously  Why project-based AI workflows are becoming essential for modern businesses  How Dan uses Codex for marketing, content creation, and process automation  The power of AI-accessible folders, files, and organizational systems  How AI can generate, manage, and improve business assets over time  Practical examples of automating large-scale content operations  Why business leaders should start thinking beyond prompts and toward AI-powered execution  The future of agentic workflows and AI-assisted business operationsAbout Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

AI in the salon is no longer something you can put off, and ignoring it won't make it disappear. This week I'm joined by Martha Lynn Kale, owner of Mirror Mirror in Austin, Texas, who has spent the past year building AI into how she leads, hires, trains and markets. We get practical about what she uses it for, where it earns its place, and the mistake that trips most owners up.If you're curious about AI but unsure where it fits, or a bit nervous about it, this episode will help ground you. You'll come away knowing how to find the balance between using AI to increase efficiencies, but still protect the human side of your salon. Most importantly you'll understand  the work you have to do first so AI actually has a foundation of knowledge to build on.IN THIS EPISODE YOU'LL LEARN:✅ The practical jobs AI can take off your plate so your team is more present for clients✅ Why you have to do the work on your values and systems first before AI is any use✅ How to keep AI invisible to clients while it sharpens everything behind the scenes✅ The "start with one problem" approach that beats asking AI to build everything at once✅ Where to begin if you're nervous or sceptical about AI in your businessIN THIS EPISODE:[00:00] Introduction[02:37] Does using AI make you lazy, or sharpen your thinking?[04:08] Why AI works best as an enhancement to what you already do[05:14] The platforms Martha Lynn uses and why ChatGPT knows her[06:13] Typing versus talking: finding the input style that suits you[09:08] Using AI to tighten org charts, roles and career paths[11:46] The finance angle: driving revenue and solving slow Saturdays[13:52] Keeping the human touch as salon software gets smarter[15:46] Marketing with AI: brainstorming, captions and the branding challenge[20:29] The front desk slip that revealed human first, policy second[24:27] Rebuilding the apprentice program with feedback and AI[28:58] The work you must do before AI can help you[33:13] Where to start if AI still makes you nervousWant MORE to help you GROW?