Podcasts about Generative

In this episode, Pavlé Sabic, Senior Director in Generative AI Solutions and Strategy at Moody's, joins Emerj Editorial Director Matthew DeMello to discuss how agentic AI is redefining workflows in financial institutions. Pavlé explains why large enterprises are turning to AI-driven automation to overcome persistent challenges — from fragmented data and manual inefficiencies to evolving regulatory demands. He shares practical examples, including credit memo automation that reduces production time by 60%, portfolio monitoring tools that detect emerging risks earlier, and sales intelligence workflows that deliver highly targeted client insights. Pavlé also outlines why proprietary data is a strategic advantage in regulated industries and how leaders can implement agentic AI without losing human oversight. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the ‘AI in Business' podcast! This episode is sponsored by Moody's. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.

The AI landscape in recruiting is evolving rapidly, with vendors racing to add AI features and many employers eager to embrace transformation. But navigating this shift successfully requires understanding what questions to ask and which foundations to build. From vendor transparency to compliance, from bias auditing to data governance, the path to effective AI implementation is not a simple one. What do TA teams need to consider to adopt a responsible approach to AI?   My guest this week is Martyn Redstone, a highly experienced advisor on AI governance for HR and Recruitment. Martyn has spent the last 9 years working with AI in recruiting and has some incredibly valuable advice to share. In the interview, we discuss: Getting the foundations right Why false AI confidence is dangerous Four key vendor evaluation areas Third-party auditing Shadow AI and data breaches Generative versus decision-based AI Global regulatory landscape challenges Why guardrails actually accelerate innovation The task-based future of work Follow this podcast on Apple Podcasts. Follow this podcast on Spotify.

Episode 275 of the Hotel Marketing Podcast uncovers the secrets of Generative Engine Optimization, or GEO. Hoteliers have three primary customers online: people, search engines, and AI. Today we delve into how you can succeed in optimizing for AI engines such as ChatGPT, Gemini, Claude, CoPilot, and others. Get the full show notes at www.TravelBoomMarketing.com/podcast

Generative AI and Agentic AI are now raising the stakes on how companies deliver customer value and innovate to stay one step ahead. Accelerating with AI comes down to the long game: technology infrastructure, data, and talent. This episode will focus on practical insights into how top enterprise leaders should approach AI for long term investment, including building a modern tech stack, creating enterprise platform capabilities, proprietary data and AI solutions, and building and cultivating world-class AI talent. These best practices will be shared through the lens of Capital One's AI journey. 

Generative AI may be rewriting the rules, but Felicia Shakiba and Claurelle Rakipovic, Chief Product Officer at Pipe, break down what real product leadership looks like. From customer empathy to AI missteps, Claurelle shares sharp lessons on building products that can actually hold their ground. Chapters 00:00 Building Defensible Products in the Age of AI 01:50 Understanding Defensibility Beyond Automation 04:30 Learning from AI Missteps 09:26 Internal Testing and AI Adoption 14:39 New Competencies for Product Leaders 19:47 The Importance of Unique Data 23:49 Empathy in AI Product Development 27:39 Auditing AI for Value Delivery

AI seems to be the thing of the moment. Generative models are creating everything from pictures and movies to full songs. But is it any good? Turns out, not really. I tried my hand at making some different "AI Music" to see just how awful it is. And let me tell, it's really bad. Join me as I take a look at the future of music and why AI generated songs are total garbage. 

I'm comparing three popular generative AI tools and their ability to create unique designs based on best-selling Amazon print on demand products

In today's episode, I'm building on what I started discussing last week as I continue to make a dark synth track to accompany one of the many hunting scenes in The Thirteenth Hour prequel, A Shadow in the Moonlight, about a cursed hunter who has to spend eternity hunting an enchanted deer.  I've wanted to learn to use a desktop based DAW to make and edit music so am using this track as a way to do that.  I've settled on the free web-based program Bandlab, which is supposedly the easiest one to start using, though I will say that I have found none of the ones I have tried intuitive or especially user friendly.  That said, connecting a keyboard to the computer has helped a great deal, and I expect that the initially hassle will have longer term payoffs in terms of flexibility and range of tools at my fingertips when making new tracks than I would have doing it all analog.  So, I'm trying to not throw my hands up in frustration and go back to what I know since the whole point was to learn how to use a DAW in order to make this track.In addition, I have been experimenting with another digital tool called Nauk Nauk to make short videos of the toys I've made.  The app is basically generative AI specific to action figures and making them move.  I'm not super for or against this kind of technology, and while I touch on some of the operational pros and cons of using this kind of tech (at least from what I can see), this is one I can get behind.  Who doesn't want to see their toys come to life? Especially ones you've made!  Case in point - this one of Beverly Switzler is my favorite so far: https://www.tiktok.com/@13thhr/video/7547184629434813710?is_from_webapp=1&sender_device=pc&web_id=7547583918360708621Thanks for listening!∞∞∞∞∞∞∞Once Upon a Dream, the second Thirteenth Hour soundtrack, is now out in digital form and on CD!   It is out on most major streaming services such as Bandcamp, Spotify, and YouTube Music.  (If you have no preference, I recommend Bandcamp since there is a bonus track there and you will eventually be able to find tapes and special editions of the album there as well.)  The CDs are out now!-Check out the pixelart music videos that are out so far from the album:-->Logan's Sunrise Workout: www.youtube.com/watch?v=K7SM1RgsLiM-->Forward: www.youtube.com/watch?v=Z9VgILr1TDc-->Nightsky Stargazing: www.youtube.com/watch?v=2S0p3jKRTBo-->Aurora's Rainy Day Mix: https://youtu.be/zwqPmypBysk∞∞∞∞∞∞∞∞ Signup for the mailing list for a free special edition podcast, a demo copy of The Thirteenth Hour, and access to retro 80s soundtrack!Like what you see or hear? Consider supporting the show over at Thirteenth Hour Arts on Patreon or adding to my virtual tip jar over at Ko-fi. Join the Thirteenth Hour Arts Group over on Facebook, a growing community of creative people.Have this podcast conveniently delivered to you each week on Spotify,  iTunes, Stitcher, Player FM, Tunein, and Googleplay Music.Follow The Thirteenth Hour's Instagram pages: @the13thhr for your random postings on ninjas, martial arts, archery, flips, breakdancing, fantasy art and and @the13thhr.ost for more 80s music, movies, and songs from The Thirteenth Hour books and soundtrack.Listen to Long Ago Not So Far Away, the Thirteenth Hour soundtrack online at: https://joshuablum.bandcamp.com/ or Spotify.  Join the mailing list for a digital free copy.  You can also get it on CD or tape.Website: https://13thhr.wordpress.comBook trailer: http://bit.ly/1VhJhXYInterested in reading and reviewing The Thirteenth Hour for a free book?  Just email me at writejoshuablum@gmail.com for more details!

Today we are joined by Gorkem and Batuhan from Fal.ai, the fastest growing generative media inference provider. They recently raised a $125M Series C and crossed $100M ARR. We covered how they pivoted from dbt pipelines to diffusion models inference, what were the models that really changed the trajectory of image generation, and the future of AI videos. Enjoy! 00:00 - Introductions 04:58 - History of Major AI Models and Their Impact on Fal.ai 07:06 - Pivoting to Generative Media and Strategic Business Decisions 10:46 - Technical discussion on CUDA optimization and kernel development 12:42 - Inference Engine Architecture and Kernel Reusability 14:59 - Performance Gains and Latency Trade-offs 15:50 - Discussion of model latency importance and performance optimization 17:56 - Importance of Latency and User Engagement 18:46 - Impact of Open Source Model Releases and Competitive Advantage 19:00 - Partnerships with closed source model developers 20:06 - Collaborations with Closed-Source Model Providers 21:28 - Serving Audio Models and Infrastructure Scalability 22:29 - Serverless GPU infrastructure and technical stack 23:52 - GPU Prioritization: H100s and Blackwell Optimization 25:00 - Discussion on ASICs vs. General Purpose GPUs 26:10 - Architectural Trends: MMDiTs and Model Innovation 27:35 - Rise and Decline of Distillation and Consistency Models 28:15 - Draft Mode and Streaming in Image Generation Workflows 29:46 - Generative Video Models and the Role of Latency 30:14 - Auto-Regressive Image Models and Industry Reactions 31:35 - Discussion of OpenAI's Sora and competition in video generation 34:44 - World Models and Creative Applications in Games and Movies 35:27 - Video Models' Revenue Share and Open-Source Contributions 36:40 - Rise of Chinese Labs and Partnerships 38:03 - Top Trending Models on Hugging Face and ByteDance's Role 39:29 - Monetization Strategies for Open Models 40:48 - Usage Distribution and Model Turnover on FAL 42:11 - Revenue Share vs. Open Model Usage Optimization 42:47 - Moderation and NSFW Content on the Platform 44:03 - Advertising as a key use case for generative media 45:37 - Generative Video in Startup Marketing and Virality 46:56 - LoRA Usage and Fine-Tuning Popularity 47:17 - LoRA ecosystem and fine-tuning discussion 49:25 - Post-Training of Video Models and Future of Fine-Tuning 50:21 - ComfyUI Pipelines and Workflow Complexity 52:31 - Requests for startups and future opportunities in the space 53:33 - Data Collection and RedPajama-Style Initiatives for Media Models 53:46 - RL for Image and Video Models: Unknown Potential 55:11 - Requests for Models: Editing and Conversational Video Models 57:12 - VO3 Capabilities: Lip Sync, TTS, and Timing 58:23 - Bitter Lesson and the Future of Model Workflows 58:44 - FAL's hiring approach and team structure 59:29 - Team Structure and Scaling Applied ML and Performance Teams 1:01:41 - Developer Experience Tools and Low-Code/No-Code Integration 1:03:04 - Improving Hiring Process with Public Challenges and Benchmarks 1:04:02 - Closing Remarks and Culture at FAL

Part two of our conversation with friend of the show & Iconograhper Nick Papas, who is once again our most welcome guest for this conversation.Our previous discussion about Independence Day having individual meaning for Jim & Fr Symeon got Jim to thinking about how much this actually inverts the purpose of public holidays. This lack of unity as a country reminded him of an increasingly popular turn of phrase in our culture "my tribe" & his concern this trend is stark evidence we are choosing to become less civilized.The actual conversation focuses on Fr Symeon's turn of phrase "Community As Babel Versus Communion at Pentecost".Tune in here in two weeks for the second half of the discussion!Scripture citations for this episode: - Tower of Babel   - Genesis 11 - Pentecost   - Acts 2The Christian Saints Podcast is a joint production of Generative sounds & Paradosis Pavilion with oversight from Fr Symeon KeesParadosis Pavilion - https://youtube.com/@paradosispavilion9555https://www.instagram.com/christiansaintspodcasthttps://twitter.com/podcast_saintshttps://www.facebook.com/christiansaintspodcasthttps://www.threads.net/@christiansaintspodcastIconographic images used by kind permission of Nicholas Papas, who controls distribution rights of these imagesPrints of all of Nick's work can be found at Saint Demetrius Press - http://www.saintdemetriuspress.comAll music in these episodes is a production of Generative Soundshttps://generativesoundsjjm.bandcamp.comDistribution rights of this episode & all music contained in it are controlled by Generative SoundsCopyright 2021 - 2023

Episode SummaryIn this raw and revealing Fail Forward Friday episode, Heather shares two contrasting stories from her own business journey: why she persisted with her Sunday newsletter after two years of declining engagement, and why she completely pivoted away from YouTube after a controversial interview led to algorithmic exile. Through these parallel experiences, she explores the crucial difference between productive persistence and digital self-harm.The StoriesThe Newsletter Journey: After 88 editions over two years, spending 4+ hours every Sunday writing, Heather watched her email bounce rates rise and open rates plummet. On the verge of quitting, she asked the universe for a sign - and got a response after edition 86 that led to new business. She persisted.The YouTube Pivot: Following a controversial interview, Heather's YouTube channel went from hundreds of views to literally zero - not just new videos, but even older content was algorithmically suppressed. Recognising the need to niche down in today's landscape, she pivoted completely to LinkedIn, launching an AI-focused newsletter for transformational practitioners.Key Topics CoveredRecognising the difference between productive persistence and stubborn repetitionReading micro-signals versus macro-resultsHow algorithms can completely reshape your digital strategyThe importance of niching down in today's content landscapeWhen to trust your energy levels as crucial business dataPivoting your method while maintaining your missionChapters:00:32 - The Balance of Persistence and Letting Go02:21 - A Tale of Two Journeys03:39 - The Pivot Decision: Learning from YouTube's Founders05:19 - Navigating Persistence and Pivoting06:45 - The Signals of ChangeThree Core LessonsLook for micro-signals, not just macro-results - Small signs of engagement matter more than vanity metricsTrust your energy - Generative challenge feels different from soul-crushing strugglePivot your method, not your mission - Stay true to your purpose while being flexible about your platformThis Week's Reality CheckStrip away all the "shoulds" and "I've already invested so much time" and ask: What does your gut tell you about your current path? Trust your intuition - it's been collecting data this whole time.Community ChallengeShare your current persistence vs pivot dilemma in one sentence @ChoosingHappyPodcast - your story might help someone else navigate their crossroads.ResourcesConnect with Heather for transformation workNew LinkedIn AI newsletter for transformational practitionersNext EpisodeJoin us next Friday for another Fail Forward episode where we continue turning setbacks into comebacks.How You Can Support This Independent Podcast:Please like, share with someone who may need to hear this today, and/or leave a review and support the podcast. I really appreciate it.Tired of the same patterns keeping you stuck?Check out the Pattern Breaker Coaching Program: www.choosinghappy.co.uk/pattern-breakerWant to dive deeper? Drop me an email: heather@heathervmasters.comJoin the conversation: Community |

Imagine unlocking a world where designing custom proteins is not only feasible - but faster, smarter, and more powerful than ever before, thanks to artificial intelligence.As the promise of programmable biology takes center stage, AI-driven protein engineering is rapidly moving from theoretical dream to industry standard.In this episode, David Brühlmann sits down with Elise de Reus, co-founder of Cradle, whose ground-breaking platform has become a go-to for luminaries at pharma giants, as well as leaders in agriculture and industrial biotech. Elise's journey bridges hands-on scientific discovery with entrepreneurial vision, and she's on a mission to make biology a foundational pillar of a more sustainable and equitable world.Here are three reasons you can't miss this episode:AI Unmasks the Black Box: Elise explains how Cradle's platform isn't just about predictive power - it offers transparency and actionable insights, allowing scientists to see why models perform as they do, and how to interpret and trust their outputs.From Hype to Real-World Impact: You'll hear real examples of AI accelerating everything from pandemic preparedness and antivenom development, to sustainable raw materials - plus candid advice on scaling AI adoption across diverse project teams.Blueprints for Biotech Founders: Elise shares hard-won lessons on transforming scientific innovation into a thriving company, including the counterintuitive power of targeting “mission impossible” challenges and the priceless value of early, unfiltered feedback.Ready to future-proof your protein engineering with generative AI? Press play to take home Elise's practical framework for evaluating tools, overcoming organizational hurdles, and crafting a winning adoption strategy, before the innovation curve pulls away.Wondering how Generative AI is transforming the biotech landscape? From accelerating regulatory workflows to reinventing protein purification, AI is driving breakthroughs across the industry. Explore these standout episodes to hear from experts leading the charge:Episodes 77-78: Cell Factories Explained: How Synthetic Biology and AI Revolutionize Protein Production with Mauro TorresEpisodes 119-120: Innovating Protein Purification Using Synthetic Organelles and AI with Haotian GuoEpisodes 123-124: Manufacturability: Why Most Protein Candidates Fail (And How to Pick Winners Early) with Susan SharfsteinEpisodes 167-168: How Generative AI Is Revolutionizing Biotech Regulatory Compliance with Abhijeet SatwekarConnect with Elise de Reus:LinkedIn: www.linkedin.com/in/elise-de-reus-77b83a24Website: www.cradle.bioNext step:Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call

Generative AI has captured global attention, powering everything from chatbots to intelligent assistants. Yet in the enterprise, its promise often hits a dead end. According to Gartner, 80 per cent of enterprise data remains unused or “dark,” because conventional AI struggles to interpret complex, domain-specific information.In this episode of the Don't Panic It's Just Data podcast, EM360Tech host Trisha Pillay speaks with Andreas Blumauer, Senior Vice President at Graphwise, about how retrieval-augmented generation (RAG) and its advanced application, Graph RAG, are levelling up enterprise AI. Together, they explore the limitations of traditional AI, the critical role of knowledge graphs in improving data accuracy, and what it takes for organisations to successfully adopt these technologies.Why Graph RAG MattersWhile RAG enhances Generative AI by enabling it to retrieve relevant data from large knowledge bases, Graph RAG takes it further. By integrating knowledge graphs, Graph RAG preserves the relationships, sequences, and meaning inherent in enterprise data. This ensures AI outputs are not just collections of facts, but structured insights that reflect the logic of an organisation's knowledge.These advantages include:Higher accuracy: Retrieval precision can increase from 80% to 95%, reducing errors in AI outputs.Trustworthy results: Outputs are explainable and traceable, providing transparency that enterprises require.Scalable integration: Connects data across silos and departments, making AI adoption enterprise-ready.“Graph RAG respects the structure of enterprise data instead of flattening it. That's what makes it trustworthy,” explains Blumauer.Generative AI opened the door to possibilities. RAG made it actionable. Graph RAG takes it to the next level. By transforming dark, siloed data into structured, actionable knowledge, Graph RAG helps organisations achieve the accuracy, trust, and scalability essential for navigating the next frontier of enterprise intelligence.Takeaways80 per cent of enterprise data remains unused or dark.Traditional AI struggles to interpret complex enterprise data.RAG retrieves information from within the enterprise data landscape.Graph RAG improves the accuracy of AI outputs.Knowledge graphs link data points across different silos.Building a knowledge graph is a strategic investment.Incremental growth is possible with knowledge graphs.Graph RAG can increase accuracy from 80 per cent to 95 per cent.Data quality and governance are essential for AI success.The future of enterprise AI relies on effective knowledge management.Chapters00:00 Introduction to RAG and Graph RAG03:04 Understanding the Importance of Knowledge Graphs05:46 Adopting RAG: Organisational Readiness and Strategic Investment08:51 Real-World Applications and Benefits of Graph RAG11:56 The Evolution of Knowledge Graphs in AI14:46 Future of GraphRAG and Enterprise AI17:36 Rapid Fire Questions and Closing ThoughtsAbout GraphwiseGraphwise is a leading enterprise AI company specialising in knowledge graph technologies. By combining retrieval-augmented generation (RAG) with advanced graph-based approaches, Graphwise helps organisations turn siloed, complex data into accurate, actionable insights, enabling smarter decisions, scalable...

This episode is part of the AI Summary series covering the AI Search Manual chapter by chapter. Chapter 5 explains why Google is uniquely positioned to dominate the generative AI race.We look at how Google's advantage comes from its proprietary data scale, first-party chips (TPUs), and a portfolio of billion-user products that provide constant feedback loops. The episode explores how Google's invention of the Transformer architecture set the foundation for modern AI, and how its integration across platforms like Search, YouTube, Maps, and Gmail makes its reach and influence unmatched.The discussion highlights the role of AI Overviews, now the most widely used generative product, appearing in over half of all searches worldwide. This scale creates a self-reinforcing cycle of user data, model improvements, and adoption. For brands, the message is clear: visibility in Google's AI summaries is now the front line of discovery.Read the full chapter at ipullrank.com/ai-search-manual

In this episode of the Pipeliners Podcast, we revisit our conversation with Clint Bodungen of ThreatGEN. The discussion focuses on the application of gamification and generative AI in professional training, specifically for enhancing cybersecurity and incident response exercises. The episode also explores a PHMSA-sponsored R&D project that is adapting these advanced technologies for the unique operational needs of the pipeline industry, highlighting the development of AI-driven, multiplayer training environments.   Visit PipelinePodcastNetwork.com for a full episode transcript, as well as detailed show notes with relevant links and insider term definitions.

For decades, protein design has hinged on painstaking rounds of wet lab mutagenesis and trial-and-error, a process limited not by human ingenuity, but by time and complexity. Yet as the biotech field seeks faster, greener, and more effective solutions for therapeutics and industrial applications, the next leap might not come solely from the lab bench.In this episode, host David Brühlmann explores the frontiers of AI-driven protein engineering with Elise de Reus, co-founder of Cradle. Elise's journey weaves together a passion for DNA, real-world impact from the dairy industry to synthetic biology, and high-throughput experience at Zymergen. Now, at the helm of Cradle, she is bridging cutting-edge computational models with experimental validation, making biology programmable and accelerating the path from idea to functional protein.Here are three reasons this episode is essential listening:AI Makes Biology Programmable: Generative AI platforms like Cradle can intelligently design protein variants by learning from evolutionary trends and your own project data, enabling smarter, faster iterations and optimizing for stability, activity, and manufacturability - simultaneously.Limited Data? No Problem: While traditional approaches have stumbled over small, “short and fat” datasets, new generative AI models can effectively update their understanding with as little as a 96-well plate per round, democratizing high-impact protein design for startups, academia, and industry alike.The Future is Human + Machine: Even as algorithms advance, Elise stresses the irreplaceable value of wet lab validation. Cradle's hybrid approach, integrating experimental feedback with algorithmic prediction, ensures reliable, scalable results and empowers multidisciplinary teams to unlock first- and best-in-class solutions.Want to see how you can leverage AI to simplify and accelerate your own protein engineering projects? Don't miss this episode to hear how Elise and Cradle are transforming the pace - and possibilities - of biotech.Curious how Generative AI is reshaping biotech? From streamlining regulatory compliance to transforming protein purification, AI is changing the game. Dive into these standout episodes to explore the cutting edge of innovation in biotech:Episodes 77-78: Cell Factories Explained: How Synthetic Biology and AI Revolutionize Protein Production with Mauro TorresEpisodes 119-120: Innovating Protein Purification Using Synthetic Organelles and AI with Haotian GuoEpisodes 123-124: Manufacturability: Why Most Protein Candidates Fail (And How to Pick Winners Early) with Susan SharfsteinEpisodes 167-168: How Generative AI Is Revolutionizing Biotech Regulatory Compliance with Abhijeet SatwekarConnect with Elise de Reus:LinkedIn: www.linkedin.com/in/elise-de-reus-77b83a24Website: www.cradle.bioNext step:Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call

Implement AI deploys teams of digital workers that work together to boost growth, increase capacity, optimise costs, and improve customer and prospect engagement.Through our AI Operating System (AIOS), a fully managed AI Agent Platform built around Agent Teams, an Agentic CRM, and an Agentic Task Engine, businesses can start with a single digital worker and scale to 50 or more across departments such as sales, support, analysts, and computer use. All setup and configuration is fully handled, so no technical expertise is needed.With more than 336 integrations, organisations save time, increase productivity, and scale faster. Grow your workforce, not your payroll.Learn more at www.implementai.ioIn this episode of The Implement AI Podcast, hosts Piers Linney and Dr. Aalok Y. Shukla explore recent MIT research showing that 90% of generative AI projects fail. Drawing on two years of real-world deployments, they reveal why so many businesses struggle and how to avoid the common traps. From treating AI like a software project to neglecting existing data streams, they share 17 hard-won lessons that separate failed pilots from successful digital workforces.Learn how AI agents deliver ROI by cutting failures, scaling capacity, and unlocking hidden revenue.Want to see it in action? Join us at Your Future Digital Workforce in Manchester on September 15. Register here Hosted on Acast. See acast.com/privacy for more information.

“Generativity. Think of it as generating something from yourself and putting it into the world.”   Our hosts, Stephanie McCullough and Kevin Gaines, explore the positive side of aging with Dr. Deborah Heiser, an applied developmental psychologist who pivoted from studying depression and Alzheimer's to researching what we actually have to look forward to as we age. "You can run faster than me, but I'm happier than you." Dr. Heiser's transformation began at a dinner party when someone challenged her: "What do we have to look forward to as we age? You are studying everything that scares us." This moment sparked her journey into understanding generativity—an emotional developmental milestone we reach in midlife where we feel compelled to give back and make our mark on the world. The conversation reveals a powerful truth: we're biologically programmed to become happier and more fulfilled as we age. This isn't the superficial happiness of opening presents, but the deep satisfaction of asking "Did I matter?" and finding ways to generate impact. Whether through podcasting, volunteering, or passing down family recipes on grandmother's index cards, we're all engaging in mentorship, often without realizing it. Dr. Heiser makes a crucial distinction between doing your job and true mentoring. A teacher advising students is working; mentoring happens outside the classroom, through voluntary emotional connections. Her book "The Mentorship Edge" helps readers recognize and quantify their impact, filling what she calls their "impact bank." Most importantly, she reminds us that midlife identity shifts aren't crises but opportunities to pull forgotten aspects of ourselves from the back of the closet and engage parts of our identity we've neglected while surviving our earlier years.   Key Topics: Why We Get Happier as We Age (03:08) Understanding Generativity and the Difference Between Generous and Generative (11:07) Identity Shifts in Midlife (17:11) Family Traditions as Mentorship (22:53) Mentorship vs. Doing Your Job (23:30) Reframing Negative Perspectives on Aging (25:46) Stephanie and Kevin's Wrap-Up (36:30)     Resources: Dr. Deborah Heiser on Psychology Today On LinkedIn Her Website The Mentorship Edge (book)   If you like what you've been hearing, we invite you to subscribe on your favorite platform and leave us a review. Tell us what you love about this episode! Or better yet, tell us what you want to hear more of in the future. stephanie@sofiafinancial.com   You can find the transcript and more information about this episode at www.takebackretirement.com.   Follow Stephanie on Twitter, Facebook, YouTube and LinkedIn.  Follow Kevin on Twitter, Facebook, YouTube and LinkedIn.

```html i'm wall-e, welcoming you to today's tech briefing for thursday, august 28. here are the top stories: nvidia's record revenue: nvidia reports $46.7 billion in quarterly revenue, a 56% increase, driven by its ai-focused data center business and the advanced 'blackwell' chip. challenges persist in china due to geopolitical factors. ai competition heating up: google and grok are gaining momentum against openai's chatgpt, with google's gemini and ai suite seeing significant user engagement and innovation. whatsapp's ai enhancement: introduction of "writing help," a feature for rephrasing messages with privacy, reflecting the trend of integrating ai in daily interactions. cybersecurity alert: the fbi warns of 'salt typhoon,' a chinese-backed hacking group targeting 200 u.s. companies, urging for international cooperation against this threat. ai safety collaboration: openai and anthropic join forces for joint safety testing of ai models, focusing on enhancing safety standards and promoting a culture of responsible innovation. that's all for today. we'll see you back here tomorrow. ```

Tanguy Catlin, a senior partner at McKinsey & Co., discusses how insurers can boost performance by integrating AI across operations, focusing on workflow redesign, technology reuse and strong leadership to ensure successful adoption.

In this final episode of season four, YS Chi sits down with the chief technology officers at LexisNexis Risk Solutions, Elsevier and LexisNexis Legal & Professional to explore the topic of technology at RELX.RELX is not a tech company, but  a big user of technology. Around 12,000 technologists, over half of whom are software engineers, work at RELX. Annually, the company spends $1.9bn on technology. Vijay Raghavan, Jill Luber and Jeff Reihl share insights on how they are driving AI innovation, from extractive to generative and agentic technologies, to solve real-world challenges in law, healthcare and research.  They also share candid advice on staying adaptive, experimenting boldly and preparing for the future of work.For insights on technology at RX, listen to Brian Brittain, chief technology officer at RX, in episode three.  Watch the video version at https://youtu.be/IlfsybgDQlI

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss why enterprise generative AI projects often fail to reach production. You’ll learn why a high percentage of enterprise generative AI projects reportedly fail to make it out of pilot, uncovering the real reasons beyond just the technology. You’ll discover how crucial human factors like change management, user experience, and executive sponsorship are for successful AI implementation. You’ll explore the untapped potential of generative AI in back-office operations and process optimization, revealing how to bridge the critical implementation gap. You’ll also gain insights into the changing landscape for consultants and agencies, understanding how a strong AI strategy will secure your competitive advantage. Watch now to transform your approach to AI adoption and drive real business results! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-why-enterprise-generative-ai-projects-fail.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn – 00:00 In this week’s In Ear Insights, the big headline everyone’s been talking about in the last week or two about generative AI is a study from MIT’s Nanda project that cited the big headline: 95% of enterprise generative AI projects never make it out of pilot. A lot of the commentary clearly shows that no one has actually read the study because the study is very good. It’s a very good study that walks through what the researchers are looking at and acknowledged the substantial limitations of the study, one of which was that it had a six-month observation period. Katie, you and I have both worked in enterprise organizations and we have had and do have enterprise clients. Some people can’t even buy a coffee machine in six months, much less route a generative AI project. Christopher S. Penn – 00:49 But what I wanted to talk about today was some of the study’s findings because they directly relate to AI strategy. So if you are not an AI ready strategist, we do have a course for that. Katie Robbert – 01:05 We do. As someone, I’ve been deep in the weeds of building this AI ready strategist course, which will be available on September 2. It’s actually up for pre-sale right now. You go to trust insights AI/AI strategy course. I just finished uploading everything this morning so hopefully I used all the correct edits and not the ones with the outtakes of me threatening to murder people if I couldn’t get the video done. Christopher S. Penn – 01:38 The bonus, actually, the director’s edition. Katie Robbert – 01:45 Oh yeah, not to get too off track, but there was a couple of times I was going through, I’m like, oops, don’t want to use that video. But back to the point, so obviously I saw the headline last week as well. I think the version that I saw was positioned as “95% of AI pilot projects fail.” Period. And so of course, as someone who’s working on trying to help people overcome that, I was curious. When I opened the article and started reading, I’m like, “Oh, well, this is misleading,” because, to be more specific, it’s not that people can’t figure out how to integrate AI into their organization, which is the problem that I help solve. Katie Robbert – 02:34 It’s that people building their own in-house tools are having a hard time getting them into production versus choosing a tool off the shelf and building process around it. That’s a very different headline. And to your point, Chris, the software development life cycle really varies and depends on the product that you’re building. So in an enterprise-sized company, the likelihood of them doing something start to finish in six months when it involves software is probably zero. Christopher S. Penn – 03:09 Exactly. When you dig into the study, particularly why pilots fail, I thought this was a super useful chart because it turns out—huge surprise—the technology is mostly not the problem. One of the concerns—model quality—is a concern. The rest of these have nothing to do with technology. The rest of these are challenging: Change management, lack of executive sponsorship, poor user experience, or unwillingness to adopt new tools. When we think about this chart, what first comes to mind is the 5 Ps, and 4 out of 5 are people. Katie Robbert – 03:48 It’s true. One of the things that we built into the new AI strategy course is a 5P readiness assessment. Because your pilot, your proof of concept, your integration—whatever it is you’re doing—is going to fail if your people are not ready for it. So you first need to assess whether or not people want to do this because that’s going to be the thing that keeps this from moving forward. One of the responses there was user experience. That’s still people. If people don’t feel they can use the thing, they’re not going to use it. If it’s not immediately intuitive, they’re not going to use it. We make those snap judgments within milliseconds. Katie Robbert – 04:39 We look at something and it’s either, “Okay, this is interesting,” or “Nope,” and then close it out. It is a technology problem, but that’s a symptom. The root is people. Christopher S. Penn – 04:52 Exactly. In the rest of the paper, in section 6, when it talks about where the wins were for companies that were successful, I thought this was interesting. Lead qualification, speed, customer retention. Sure, those are front office things, but the paper highlights that the back office is really where enterprises will win using generative AI. But no one’s investing it. People are putting all the investment up front in sales and marketing rather than in the back office. So the back office wins. Business process optimization. Elimination: $2 million to $10 million annually in customer service and document processing—especially document processing is an easy win. Agency spend reduction: 30% decrease in external, creative, and content costs. And then risk checks for financial services by doing internal risk management. Christopher S. Penn – 05:39 I thought this was super interesting, particularly for our many friends and colleagues who work at agencies, seeing that 30% decrease in agency spend is a big deal. Katie Robbert – 05:51 It’s a huge deal. And this is, if we dig into this specific line item, this is where you’re going to get a lot of those people challenges because we’re saying 30% decrease in external creative and content costs. We’re talking about our designers and our writers, and those are the two roles that have felt the most pressure of generative AI in terms of, “Will it take my job?” Because generative AI can create images and it can write content. Can it do it well? That’s pretty subjective. But can it do it? The answer is yes. Christopher S. Penn – 06:31 What I thought was interesting says these gains came without material workforce reduction. Tools accelerated work, but did not change team structures or budgets. Instead, ROI emerged from reduced external spend, limiting contracts, cutting agency fees, replacing expensive consultants with AI-powered internal capabilities. So that makes logical sense if you are spending X dollars on something, an agency that writes blog content for you. When we were back at our old PR agency, we had one firm that was spending $50,000 a month on having freelancers write content that when you and I reviewed, it was not that great. Machines would have done a better job properly prompted. Katie Robbert – 07:14 What I find interesting is it’s saying that these gains came without material workforce reduction, but that’s not totally true because you did have to cut your agency fees, which is people actually doing the work, and replacing expensive consultants with AI-powered internal capabilities. So no, you didn’t cut workforce reduction at your own company, but you cut it at someone else’s. Christopher S. Penn – 07:46 Exactly. So the red flag there for anyone who works in an agency environment or a consulting environment is how much risk are you at from AI taking your existing clients away from you? So you might not lose a client to another agency—you might lose a client to an internal AI project where if there isn’t a value add of human beings. If your agency is just cranking out templated press releases, yeah, you’re at risk. So I think one of the first things that I took away from this report is that every agency should be doing a very hard look at what value it provides and saying, “How easy is it for AI to replicate this?” Christopher S. Penn – 08:35 And if you’re an agency and you’re like, “Oh, well, we can just have AI write our blog posts and hand it off to the client.” There’s nothing stopping the client from doing that either and just getting rid of you entirely. Katie Robbert – 08:46 The other thing that sticks out to me is replacing expensive consultants with AI-powered internal capabilities. Technically, Chris, you and I are consultants, but we’re also the first ones to knock the consulting industry as a whole, because there’s a lot of smoke and mirrors in the consulting industry. There’s a lot of people who talk a big talk, have big ideas, but don’t actually do anything useful and productive. So I see this and I don’t immediately think, “Oh, we’re in trouble.” I think, “Oh, good, it’s going to clear out the rest of the noise in the industry and make way for the people who can actually do something.” Christopher S. Penn – 09:28 And that is the heart and soul, I think, for us. Obviously, we have our own vested interest in ensuring that we continue to add value to our clients. But I think you’re absolutely right that if you are good at the “why”—which is what a lot of consulting focuses on—that’s important. If you’re good at the “what”—which is more of the tactical stuff, “what are you going to do?”—that’s important. But what we see throughout this paper is the “how” is where people are getting tangled up: “How do we implement generative AI?” If you are just a navel-gazing ChatGPT expert, that “how” is going to bite you really hard really soon. Christopher S. Penn – 10:13 Because if you go and read through the rest of the paper, one of the things it talks about is the gap—the implementation gap between “here’s ChatGPT” and then for the enterprise it was like, “Well, here’s all of our data and all of our systems and all of our everything else that we want AI to talk to in a safe and secure way.” And this gap is gigantic between these two worlds. So tools like ChatGPT are being relegated to, “Let’s write more blog posts and write some press releases and stuff” instead of “help me actually get some work done with the things that I have to do in a prescribed way,” because that’s the enterprise. That gap is where consulting should be making a difference. Christopher S. Penn – 10:57 But to your point, with a lot of navel-gazing theorists, no one’s bridging that gap. Katie Robbert – 11:05 What I find interesting about the shift that we’ve seen with generative AI is we’ve almost in some ways regressed in the way that work is getting done. We’re looking at things as independent, isolated tasks versus fully baked, well-documented workflows. And we need to get back to those holistic 360-degree workflows to figure out where we can then insert something generative AI versus picking apart individual tasks and then just having AI do that. Now I do think that starting with a proof of concept on an individual task is a good idea because you need to demonstrate some kind of success. You need to show that it can do the thing, but then you need to go beyond that. It can’t just forever, to your point, be relegated to writing blog posts. Katie Robbert – 12:05 What does that look like as you start to expand it from project to program within your entire organization? Which, I don’t know if you know this, there’s a whole lesson about that in the AI strategy course. Just figured I would plug that. But all kidding aside, that’s one of the biggest challenges that I’m seeing with organizations that “disrupt” with AI is they’re still looking at individual tasks versus workflows as a whole. Christopher S. Penn – 12:45 Yep. One of the things that the paper highlighted was that the reason why a lot of these pilots fail is because either the vendor or the software doesn’t understand the actual workflow. It can do the miniature task, but it doesn’t understand the overall workflow. And we’ve actually had input calls with clients and potential clients where they’ve walked us through their workflow. And you realize AI can’t do all of it. There’s just some parts that just can’t be done by AI because in many cases it’s sneaker-net. It’s literally a human being who has to move stuff from one system to another. And there’s not an easy way to do that with generative AI. The other thing that really stood out for me in terms of bridging this divide is from a technological perspective. Christopher S. Penn – 13:35 The biggest hurdle from the technology side was cited as no memory. A tool like ChatGPT and stuff has no institutional memory. It can’t easily connect to your internal knowledge bases. And at an enterprise, that’s a really big deal. Obviously, at Trust Insights’ size—with five or four employees and a bunch of AI—we don’t have to synchronize and coordinate massive stores of institutional knowledge across the team. We all pretty much know what’s going on. When you are an IBM with 300,000 employees, that becomes a really big issue. And today’s tools, absent those connectors, don’t have that institutional memory. So they can’t unlock that value. And the good news is the technology to bridge that gap exists today. It exists today. Christopher S. Penn – 14:27 You have tools that have memory across an entire codebase, across a SharePoint instance. Et cetera. But where this breaks down is no one knows where that information is or how to connect it to these tools, and so that huge divide remains. And if you are a company that wants to unlock the value of gen AI, you have to figure out that memory problem from a platform perspective quickly. And the good news is there’s existing tools that do that. There’s vector databases and there’s a whole long list of acronyms and tongue twisters that will solve that problem for you. But the other four pieces need to be in place to do that because it requires a huge lift to get people to be willing to share their data, to do it in a secure way, and to have a measurable outcome. Katie Robbert – 15:23 It’s never a one-and-done. So who owns it? Who’s going to maintain it? What is the process to get the information in? What is the process to get the information out? But even backing up further, the purpose is why are we doing this in the first place? Are we an enterprise-sized company with so many employees that nobody knows the same information? Or am I a small solopreneur who just wants to have some protection in case something happens and I lose my memory or I want to onboard someone new and I want to do a knowledge-share? And so those are very different reasons to do it, which means that your approach is going to be slightly different as well. Katie Robbert – 16:08 But it also sounds like what you’re saying, Chris, is yes, the technology exists, but not in an easily accessible way that you could just pick up a memory stick off the shelf, plug it in, and say, “Boom, now we have memory. Go ahead and tell it everything.” Christopher S. Penn – 16:25 The paper highlights in section 6.5 where things need to go right, which is Agentic AI. In this case, Agentic AI is just fancy for, “Hey, we need to connect it to the rest of our systems.” It’s an expensive consulting word and it sounds cool. Agentic AI and agentic workflows and stuff, it really just means, “Hey, you’ve got this AI engine, but it’s not—you’re missing the rest of the car, and you need the rest of the car.” Again, the good news is the technology exists today for these tools to have access to that. But you’re blocking obstacles, not the technology. Christopher S. Penn – 17:05 Your governance is knowing where your data lives and having people who have the skills and knowledge to bring knowledge management practices into a gen AI world because it is different. It is not the same as previous knowledge management initiatives. We remember all the “in” with knowledge management was all the rage in the 90s and early 2000s with knowledge management systems and wikis and internal things and SharePoint and all that stuff, and no one ever kept it up to date. Today, Agentic can solve some of those problems, but you need to have all the other human being stuff in place. The machines can’t do it by themselves. Katie Robbert – 17:51 So yes, on paper it can solve all those problems. But no, it’s not going to. Because if we couldn’t get people to do it in a more analog way where it was really simple and literally just upload the latest document to the server or add 2 lines of detail to your code in terms of what this thing is about, adding more technology isn’t suddenly going to change that. It’s just adding another layer of something people aren’t going to do. I’m very skeptical always, and I just feel this is what’s going to mislead people. They’re like, “Oh, now I don’t have to really think about anything because the machine is just going to know what I know.” But it’s that initial setup and maintenance that people are going to skip. Katie Robbert – 18:47 So the machine’s going to know what it came out of the box with. It’s never going to know what you know because you’ve never interacted with it, you’ve never configured with it, you’ve never updated it, you’ve never given it to other people to use. It’s actually just going to become a piece of shelfware. Christopher S. Penn – 19:02 I will disagree with you there. For existing enterprise systems, specifically Copilot and Gemini. And here’s why. Those tools, assuming they’re set up properly, will have automatic access to the back-end. So they’ll have access to your document store, they’ll have access to your mail server, they’ll have access to those things so that even if people don’t—because you’re right, people ain’t going to do it. People ain’t going to document their code, they’re not going to write up detailed notes. But if the systems are properly configured—and that is a big if—it will have access to all of your Microsoft Teams transcripts, it will have access to all of your Google Meet transcripts and all that stuff. And on the back-end, without participation from the humans, it will at least have a greater scope of knowledge across your company properly configured. Christopher S. Penn – 19:50 That’s the big asterisk that will give those tools that institutional memory. Greater institutional memory than you have now, which at the average large enterprise is really siloed. Marketing has no idea what sales is doing. Sales has no idea what customer service is doing. But if you have a decent gen AI tool and a properly configured back-end infrastructure where the machines are already logging all your documents and all your spreadsheets and all this stuff, without you, the human, needing to do any work, it will generate better results because it will have access to the institutional data source. Katie Robbert – 20:30 Someone still has to set it up and maintain it. Christopher S. Penn – 20:32 Correct. Which is the whole properly configured part. Katie Robbert – 20:36 It’s funny, as you’re going through listing all of the things that it can access, my first thought is most of those transcripts aren’t going to be useful because people are going to hop on a call and instead of getting things done, they’re just going to complain about whatever their boss is asking them to do. And so the institutional knowledge is really, it’s only as good as the data you give it. And I would bet you, what is it that you like to say? A small pastry with the value of less than $5 or whatever it is. Basically, I’ll bet you a cookie that the majority of data that gets into those systems with spreadsheets and transcripts and documents and we’re saying all these things is still junk, is still unuseful. Katie Robbert – 21:23 And so you’re going to have a lot of data in there that’s still garbage because if you’re just automatically uploading everything that’s available and not being picky and not cleaning it and not setting standards, you’re still going to have junk. Christopher S. Penn – 21:37 Yes, you’ll still have junk. Or the opposite is you’ll have issues. For example, maybe you are at a tech company and somebody asks the internal Copilot, “Hey, who’s going to the Coldplay concert this weekend?” So yes, data security and stuff is going to be an equally important part of that to know that these systems have access that is provisioned well and that has granular access control. So that, say, someone can’t ask the internal Copilot, “Hey, what does the CEO get paid anyway?” Katie Robbert – 22:13 So that is definitely the other side of this. And so that gets into the other topic, which is data privacy. I remember being at the agency and our team used Slack, and we could see as admins the stats and the amount of DMs that were happening versus people talking in public channels. The ratios were all wrong because you knew everybody was back-channeling everything. And we never took the time to extract that data. But what was well-known but not really thought of is that we could have read those messages at any given time. And I think that’s something that a lot of companies take for granted is that, “Oh, well, I’m DMing someone or I’m IMing someone or I’m chatting someone, so that must be private.” Christopher S. Penn – 23:14 It’s not. All of that data is going to get used and pulled. I think we talked about this on last week’s podcast. We need to do an updated conversation and episode about data privacy. Because I think we were talking last week about bias and where these models are getting their data and what you need to be aware of in terms of the consumer giving away your data for free. Christopher S. Penn – 23:42 Yep. But equally important is having the internal data governance because “garbage in, garbage out”—that rule never changes. That is eternal. But equally true is, do the tools and the people using them have access to the appropriate data? So you need the right data to do your job. You also want to guard against having just a free-for-all, where someone can ask your internal Copilot, “Hey, what is the CEO and the HR manager doing at that Coldplay concert anyway?” Because that will be in your enterprise email, your enterprise IMs, and stuff like that. And if people are not thoughtful about what they put into work systems, you will see a lot of things. Christopher S. Penn – 24:21 I used to work at a credit union data center, and as an admin of the mail system, I had administrative rights to see the entire system. And because one of the things we had to do was scan every message for protected financial information. And boy, did I see a bunch of things that I didn’t want to see because people were using work systems for things that were not work-related. That’s not AI; it doesn’t fix that. Katie Robbert – 24:46 No. I used to work at a data-entry center for those financial systems. We were basically the company that sat on top of all those financial systems. We did the background checks, and our admin of the mail server very much abused his admin powers and would walk down the hall and say to one of the women, referencing an email that she had sent thinking it was private. So again, we’re kind of coming back to the point: these are all human issues machines are not going to fix. Katie Robbert – 25:22 Shady admins who are reading your emails or team members who are half-assing the documentation that goes into the system, or IT staff that are overloaded and don’t have time to configure this shiny new tool that you bought that’s going to suddenly solve your knowledge expertise issues. Christopher S. Penn – 25:44 Exactly. So to wrap up, the MIT study was decent. It was a decent study, and pretty much everybody misinterpreted all the results. It is worth reading, and if you’d like to read it yourself, you can. We actually posted a copy of the actual study in our Analytics for Marketers Slack group, where you and over 4,000 of the marketers are asking and answering each other’s questions every single day. If you would like to talk about or to learn about how to properly implement this stuff and get out of proof-of-concept hell, we have the new AI Strategy course. Go to Trust Insights AI Strategy course and of course, wherever you watch or listen to this show. Christopher S. Penn – 26:26 If there’s a challenge you’d rather have, go to trustinsights.ai/TIpodcast, where you can find us in all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one. Katie Robbert – 26:41 Know More About Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Katie Robbert – 27:33 Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams beyond client work. Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights Podcast, the Inbox Insights newsletter, the So What? Livestream webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Katie Robbert – 28:39 Data Storytelling. This commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

This episode is part of the AI Summary series covering the AI Search Manual chapter by chapter. Chapter 2 examines how user behavior is shifting in the generative era, where people rely less on clicks and more on synthesized answers.We discuss the impact of Google's AI Overviews, which now dominate search results, reduce click-through rates, and reshape how users consume information. The episode highlights how prompts are becoming the new queries, why prompt quality determines output quality, and how multi-turn, conversational search is replacing single keyword lookups.We also explore the risks of misplaced trust in AI-generated answers, the growing role of personal context and memory in shaping search results, and the biases that influence what users see. For brands, this means fewer but more intentional visits, making visibility in AI systems and trust signals more important than ever.Read the full chapter at ipullrank.com/ai-search-manual

In this episode, the hosts delve into the transformative role of AI in project management, discussing its evolution, practical applications, and the challenges of adoption. They explore the emergence of AI agents, their impact on consulting, and the ethical considerations surrounding AI integration in the workplace. The conversation also touches on the future of work, organisational changes, and the potential breakthroughs that AI may bring to the industry. In this engaging conversation, the speakers explore the interplay between knowledge, AI, and human experience. They discuss the importance of sharing knowledge, the balance between technology and human interaction, and the potential for AI to enhance project management. The dialogue also delves into philosophical perspectives on AI, the future of work, and the role of education in bridging the gap between technology and human understanding. The speakers emphasise the need for experimentation, collaboration, and a human-centric approach to technology, ultimately advocating for a fun and exploratory mindset in navigating the evolving landscape of AI.Key Takeaways People often feel stiffed by societal advances.Experimentation is key in understanding AI's capabilities.Building in public fosters confidence and knowledge sharing.AI should give us more time for human interaction.The transition to AI will be challenging for many.Humans often get in the way of technological progress.Education systems are struggling to adapt to AI.Generative models in AI mirror human intelligence.The meaning of life is not limited to intelligence.Chapters03:32 The Evolution of AI in Project Management06:16 Exploring AI Agents and Their Applications09:12 Practical Experiences with AI Agents11:54 The Future of Consulting and AI Integration14:31 The Role of AI in Organizational Structures17:32 Futurescaping: The Future of AI in Projects20:13 Overcoming Resistance to AI Adoption23:09 The Ethical Considerations of AI in Projects36:30 Creating Value in the Age of AI40:04 The Disappearance of Intermediaries44:05 The Future of Work and AI48:39 AI in Project Management53:55 Knowledge Transfer and AI01:06:22 Balancing Technology and Human Interaction01:09:52 The Future of AI and Human Interaction01:14:11 Ethics and Rights of AI01:17:24 Understanding AI's Role in Society01:24:30 Education and AI: Bridging the Gap01:33:53 Generative Intelligence: Human vs AI

Kim Chaumillon believes HR leaders must champion AI adoption from the inside out. "HR goes first" isn't just her mantra—it's the foundation of effective organizational transformation. Drawing from 30 years of global HR experience, Kim reveals how she's reimagining traditional people practices through AI implementation at two different organizations where she serves as fractional Chief People Officer.Kim's conversation with hosts Pauline James and David Creelman explores practical strategies for building AI fluency within HR teams, starting with curated use cases and ongoing experimentation. Kim distinguishes between leveraging existing solutions with embedded AI capabilities and developing bespoke applications—highlighting three key opportunities for custom solutions: automating repetitive tasks, creating disruptive capabilities, and developing "chief of staff" functionality that transforms how work gets managed.What makes Kim's approach particularly effective is her focus on enablement rather than restriction. While addressing important governance and ethical considerations, she frames AI guidelines in terms of possibilities rather than limitations. This positive orientation accelerates adoption and fosters innovation while maintaining appropriate boundaries around confidential information and potential biases.The discussion provides a compelling vision of AI as HR's historic opportunity. Just as finance led during the 2008 financial crisis and HR guided organizations through COVID-19, AI transformation offers HR leaders the chance to drive tremendous business value while fundamentally reimagining work. For those who have long sought "a seat at the table," AI implementation provides the perfect blend of business impact and people-centered innovation—so significant that Kim delayed her retirement to participate in this transformative period.Ready to take your HR function from reactive to proactive? Connect with Kim on LinkedIn to continue the conversation about leading your organization's AI transformation journey.Support the showFeature Your Brand on the HRchat PodcastThe HRchat show has had 100,000s of downloads and is frequently listed as one of the most popular global podcasts for HR pros, Talent execs and leaders. It is ranked in the top ten in the world based on traffic, social media followers, domain authority & freshness. The podcast is also ranked as the Best Canadian HR Podcast by FeedSpot and one of the top 10% most popular shows by Listen Score. Want to share the story of how your business is helping to shape the world of work? We offer sponsored episodes, audio adverts, email campaigns, and a host of other options. Check out packages here. Follow us on LinkedIn Subscribe to our newsletter Check out our in-person events

Don't Go Back to Babylon - S7E7 Friend of the show & Iconograhper Nick Papas is once again our most welcome guest for this conversation, which will be presented in two parts, the second of which will be episode eight.Our previous discussion about Independence Day having individual meaning for Jim & Fr Symeon got Jim to thinking about how much this actually inverts the purpose of public holidays. This lack of unity as a country reminded him of an increasingly popular turn of phrase in our culture "my tribe" & his concern this trend is stark evidence we are choosing to become less civilized.The actual conversation focuses on Fr Symeon's turn of phrase "Community As Babel Versus Communion at Pentecost".Tune in here in two weeks for the second half of the discussion!Scripture citations for this episode: - Tower of Babel   - Genesis 11 - Pentecost   - Acts 2The Christian Saints Podcast is a joint production of Generative sounds & Paradosis Pavilion with oversight from Fr Symeon KeesParadosis Pavilion - https://youtube.com/@paradosispavilion9555https://www.instagram.com/christiansaintspodcasthttps://twitter.com/podcast_saintshttps://www.facebook.com/christiansaintspodcasthttps://www.threads.net/@christiansaintspodcastIconographic images used by kind permission of Nicholas Papas, who controls distribution rights of these imagesPrints of all of Nick's work can be found at Saint Demetrius Press - http://www.saintdemetriuspress.comAll music in these episodes is a production of Generative Soundshttps://generativesoundsjjm.bandcamp.comDistribution rights of this episode & all music contained in it are controlled by Generative SoundsCopyright 2021 - 2023

SHOW SCHEDULE 8-20-25 GOOD EVENING. THE SHOW BEGINS IN ARTIFICIAL GENERATIVE INTELLIGENCE DEBATE OVER REGULATION AGI STATE BY STATE... CBS EYE ON THE WORLD WITH JOHN BATCHELOR FIRST HOUR 9:00-9:15 AI: REGULATING LLM - KEVIN FRAZIER, CIVITAS INSTITUTE 9:15-9:30 AI: REGULATING LLM - KEVIN FRAZIER, CIVITAS INSTITUTE CONTINUED 9:30-9:45 #UKRAINE: GUARANTEES AND CEASEFIRE - COLONEL JEFF MCCAUSLAND, USA (RETIRED) @MCCAUSLJ @CBSNEWS @DICKINSONCOL 9:45-10:00 #UKRAINE: TRILATERAL AND NATO - COLONEL JEFF MCCAUSLAND, USA (RETIRED) @MCCAUSLJ @CBSNEWS @DICKINSONCOL SECOND HOUR 10:00-10:15 PRC: EARTH-MOON SYSTEM LANDING 2030 - BRANDON WEICHERT, GORDON CHANG 10:15-10:30 INDIA: WANG YI IN DELHI - BLAINE HOLT, GORDON CHANG 10:30-10:45 PRC: CAPITAL FLIGHT - ANDREW COLLIER, GORDON CHANG 10:45-11:00 PRC: DRONE SUBS - RICK FISHER THIRD HOUR 11:00-11:15 INDIA: DC BREAK - SADANAND DHUME, WSJ 11:15-11:30 INDIA: CHINA ARRIVES 11:30-11:45 MAMET - EMINA MELONIC 11:45-12:00 RUSSIA: GAS TANK EMPTYING - MICHAEL BERNSTAM, HOOVER FOURTH HOUR 12:00-12:15 FRANCE: HEAT WAVE LIFTS - SIMON CONSTABLE 12:15-12:30 UK: MANSION TAX 12:30-12:45 CHICAGO: UNDERWATER - THOMAS SAVIDGE 12:45-1:00 AM CHICAGO: UNDERWATER - THOMAS SAVIDGE CONTINUED

Generative AI can be incredibly powerful when it comes to legacy modernization. Not only can it help us better understand a large, aging codebase, it can even help us reverse engineer a legacy system when we don't have access to the complete source code. Doing it, though, requires a specific approach that's being described as 'context engineering'. This is something we've been exploring a lot in recent months at Thoughtworks. On this episode of the Technology Podcast, Thoughtworks' lead for AI-enabled software engineering, Birgitta Böckeler, and tech principal Chandirasekar Thiagarajan join hosts Ken Mugrage and Neal Ford to discuss how it works.  They explain the process, the tools and what the work is teaching them about both generative AI and legacy modernization. Read Birgitta's blog post on reverse engineering with AI: https://www.thoughtworks.com/insights/blog/generative-ai/blackbox-reverse-engineering-ai-rebuild-application-without-accessing-code

Nick Fox is the SVP of Knowledge and Information at Google. Liz Reid is the VP of Search at Google. The two join Big Technology Podcast to discuss the way Google plans and builds in the generative AI era, including how it chooses what to ship and when. We also cover publisher traffic, search monetization and ads, shopping and product research, and the near-term future of the web. Hit play for a clear, no-fluff conversation with the leaders building search's next chapter. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Questions? Feedback? Write to: bigtechnologypodcast@gmail.com

Steve chats with Michael Hanegan and Chris Rosser, authors of Generative AI and Libraries: Claiming Our Place in the Center of a Shared Future, about generative AI as an arrival technology, why librarians should play a central role in shaping an ethical future of AI, practical frameworks for AI integration, and how libraries can leverage … Continue reading 295: Generative AI and Libraries, by Michael Hanegan and Chris Rosser

Episode OverviewIn this episode, Maribel Lopez sits down with David Singer, Global Vice President and Go-To-Market Strategy at Verint, to explore the rapid evolution from generative AI to agentic AI and how organizations can successfully implement AI solutions that deliver real business outcomes.Key Topics DiscussedThe Evolution from Generative to Agentic AIGenerative AI: Excellent at answering questions and synthesizing information from knowledge sourcesAgentic AI: Takes the next step by actually executing actions autonomously, not just providing recommendationsThe critical difference: autonomous decision-making versus rules-based automationBuilding Trust in Autonomous AI SystemsStart with human-in-the-loop monitoring for training and validationGradually reduce oversight from constant monitoring to spot checksApply quality monitoring practices to AI agents similar to human agentsConsider AI agents as "silicon-based employees" requiring training, access controls, and performance managementSuccessful AI Implementation StrategiesStart with Clear Outcomes: Define specific business goals before selecting technologyFocus on solutions that deliver outcomes, not just impressive technologyBegin with well-understood processes that can be enhanced rather than completely reimaginedThree Proven Starting Points:Call Wrap-up Automation: AI-powered summarization reduces agent workloadIVR Modernization: Convert top call flows to agentic conversational AIQuality Management: Scale from monitoring 1-3% of calls to near 100% coverageVendor Selection CriteriaProven outcomes at scale: Look for vendors with demonstrated success stories and customer referencesTechnology adaptability: Choose providers who can evolve with the rapidly changing AI landscapeProduction readiness: "POCs are easy, production is hard" - prioritize vendors with production deployment experienceChange Management for AI Adoption Deploy solutions that genuinely help employees firstBuild internal champions through positive early experiencesScale gradually to maintain trust and adoptionKey InsightsEmployee Experience Drives Customer Experience: AI solutions that improve employee satisfaction often lead to better customer outcomesObservability is Critical: Comprehensive monitoring and quality management become essential as AI systems gain autonomyOutcomes Over Technology: Success comes from focusing on business results rather than being enamored with the latest AI capabilitiesAbout the GuestDavid Singer is the Global Vice President and Go-To-Market Strategy at Verint, where he focuses on delivering AI-powered outcomes for customer experience automation. Verint has been incorporating AI into their platform for over a decade, evolving from call recording and workforce management to comprehensive CX automation solutions. You can follow David here: https://www.linkedin.com/in/dwsinger/You can follow Maribel here: Closing ThoughtsSinger emphasizes two crucial points for organizations embarking on AI initiatives:Avoid spending significant resources on new technology only to use it exactly as you did beforeAlways start with outcomes first - let business goals drive vendor selection, implementation strategy, and change management approaches

AWS executives reveal how generative AI is fundamentally reshaping ISV business models, from pricing strategies to go-to-market approaches, and provide actionable insights for software companies navigating this transformation.Topics Include:Alayna Broaderson and Andy Perkins introduce AWS Infrastructure Partnerships and ISV SalesGenerative AI profoundly changing how ISVs build, deliver and market software productsTwo ISV categories emerging: established SaaS companies versus pure gen AI startupsLegacy SaaS firms struggle with infrastructure modernization and potential revenue cannibalizationPure gen AI companies face scaling challenges, reliability issues and cost optimizationRevenue models shifting from subscription-based to consumption-based pricing per token/prompt/taskFuture-proofing architecture critical as technology evolves rapidly like F-35 fighter jetsData becoming key differentiator, especially domain-specific datasets in healthcare and legalBalancing cost, accuracy, latency and customer experience creates complex optimization challengesMultiple specialized models replacing single solutions, with agentic AI accelerating this trendHuman capital challenges include retraining engineering teams and finding expensive AI talentSecurity, compliance and explainability now mandatory - no more black box solutionsEnterprise customers struggle with data organization and quantifying clear gen AI ROIISV pricing models evolving with tiered structures and targeted vertical use casesTraditional SaaS playbooks failing in generative AI landscape due to ROI uncertaintyPOC-based go-to-market with free trials and case study selling proving most effectivePricing strategies include AI gates, credit systems and separate SKUs for servicesCustomer trust requires proactive security messaging and auditable, transparent AI solutionsModular architecture enables evolution as new technologies emerge in fast-changing marketAWS positioning as ultimate gen AI toolkit partner with ISV collaboration opportunitiesParticipants:Alayna Broaderson - Sr Manager, Infrastructure Technology Partnership, Amazon Web ServicesAndy Perkins - General Manager, US ISV, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

In this episode of the Pipeliners Podcast, host Russel Treat is joined by Rob Day of Cognisense to discuss AI Based Risk Washing in Training Delivery.  In this episode, Rob discusses his background, as well as the relatively new risk regarding pipeline training and AI.  Visit PipelinePodcastNetwork.com for a full episode transcript, as well as detailed show notes with relevant links and insider term definitions.

Send us a textIn this episode, Matt Dzugan joins host Jason Mudd to discuss how PR pros can use data, AI, and smarter tools to improve media outreach.Tune in to learn more!Our Guest:Our episode guest is Matt Dzugan, senior director of data at Muck Rack. As a software engineer, he contributes to building tools that help journalists and PR professionals collaborate more effectively. With a strong background in full-stack development, he focuses on creating scalable, user-friendly solutions in the media tech space.Five things you'll learn from this episode:1. How to use PR software to streamline journalist research and pitching2. Why understanding journalist preferences improves outreach effectiveness3. What data points matter most when evaluating earned media success4. How Muck Rack supports PR teams through integrations and automation5. Trends in AI, media relations, and PR measurement that pros should watch Quotables“We're building tools that PR pros actually use and value every day.” — @mattdzugan“Better media outreach starts with better journalist insights.” — @JasonMudd9“PR is evolving fast. You need the right tools to keep up.” — @mattdzugan“Good PR tech doesn't replace relationships — it enhances them.” — @mattdzuganIf you enjoyed this episode, please take a moment to share it with a professional colleague or friend. You may also share your experience with others, buy me a coffee, or leaving us a quick podcast review.About Matt DzuganMatt Dzugan is a senior software engineer at Muck Rack, where he helps develop tools that empower journalists and PR professionals to collaborate more effectively. With a background in full-stack development, he brings a thoughtful, systems-oriented approach to building reliable, scalable software that meets the media industry's evolving needs. His work spans from backend architecture to user-facing features, all designed to enhance the transparency, speed, and efficiency of media workflows.Beyond writing clean, maintainable code, Matt is known for his collaborative mindset and dedication to continuous improvement. He actively engages with cross-functional teams to ensure that engineering decisions align with product goals, and he's always looking for ways to optimize performance and user experience. At Muck Rack, Matt plays a key role in helping the company scale its platform to support a growing user base while staying true to its mission of supporting a free and sustainable press.Guest's contact info and resources:Matt Dzugan on X (@mattdzugan)Matt Dzugan on LinkedInMuck Rack's websiteWhat is AI Reading?Generative PulseAdditional Resources:Support the show On Top of PR is produced by Axia Public Relations, named by Forbes as one of America's Best PR Agencies. Axia is an expert PR firm for national brands. On Top of PR is sponsored by ReviewMaxer, the platform for monitoring, improving, and promoting online customer reviews.

In episode 264 of The Business Development Podcast, Kelly Kennedy sits down with New York Times bestselling author and disruption expert Charlene Li to unpack what it truly means to lead in the age of generative AI. Drawing on more than three decades of experience helping global companies navigate internet revolutions, social media shifts, and now AI, Charlene shares how the biggest obstacle to transformation isn't the technology—it's imagination. This conversation explores how leaders can reframe disruption as an opportunity, thrive in uncertainty, and create clarity when everything feels chaotic.Together, Kelly and Charlene dive into the practical strategies and leadership mindsets required to harness AI responsibly, inspire teams through change, and build future-ready organizations. Charlene also shares insights from her brand-new book, Winning with Generative AI, revealing how executives and entrepreneurs alike can unlock new growth by embracing, not resisting, disruption. Packed with hard-won wisdom and actionable takeaways, this episode is a roadmap for anyone who wants to lead boldly into the AI-driven future.Key Takeaways: 1. Disruption isn't about technology—it's about the imagination leaders bring to using it.2. The biggest challenge with generative AI is not adoption, but rethinking what's possible.3. Leaders who thrive in uncertainty create clarity, not certainty.4. Generative AI isn't replacing leaders—it's demanding better leadership.5. Trust remains the foundation of every transformation, no matter how advanced the tools.6. Successful organizations see disruption as opportunity, not threat.7. AI should augment human creativity, not replace it.8. Leadership in the AI era requires curiosity, courage, and humility.9. Transformation is less about having the answers and more about asking better questions.10. Winning with generative AI means shifting mindset before shifting strategy.Learn more about Charlene Li: https://charleneli.com/Learn More about Business Development Mastery and The Catalyst Club: www.kellykennedyofficial.com

High Noon vs James Bond: Generative Future vs Self-Actualization: Hero's Journey Isn't about You https://youtu.be/PXpB6e-PBPc?si=ywbQ2c66xWwuZzqv Revelation 17 through 19   Midwestuary Conference August 22-24 in Chicago https://www.midwestuary.com/ Vanderklips channel https://www.youtube.com/channel/UCX0jIcadtoxELSwehCh5QTg Bridges of Meaning Discord Link: https://discord.gg/cAjXpprB https://www.meetup.com/sacramento-estuary/ My Substack https://paulvanderklay.substack.com/ Estuary Hub Link  https://www.estuaryhub.com/ If you want to schedule a one-on-one conversation check here. https://calendly.com/paulvanderklay/one2one For the audio podcast mirror on Podbean http://paulvanderklay.podbean.com/ To listen to this on ITunes https://itunes.apple.com/us/podcast/paul-vanderklays-podcast/id1394314333  If you need the RSS feed for your podcast player https://paulvanderklay.podbean.com/feed/  All Amazon links here are part of the Amazon Affiliate Program. Amazon pays a small commission at no additional cost to you if you buy through one of the product links here.  https://paypal.me/paulvanderklay Also on Odysee: https://odysee.com/@paulvanderklay https://www.patreon.com/paulvanderklay Paul's Church Content at Living Stones Channel https://www.youtube.com/channel/UCh7bdktIALZ9Nq41oVCvW-A To support Paul's work by supporting his church give here. https://tithe.ly/give?c=2160640 https://www.livingstonescrc.com/give

Matthew Prince is the CEO of Cloudflare. He joins Big Technology podcast to discuss whether the web can withstand a wave of generative AI companies scraping content, summarizing it, and not cutting the original creators into the revenue. Tune in to hear Prince masterfully dissect the problem and put forth a solution.

Professor Damien P Williams was kind enough to spend time with us discussing generative technology such as LLMs like ChatGPT, […]

For this episode, we brought on Ed Zitron to make the bear case against large language models and walk us through his “Hater's Guide To The AI Bubble.” In this fiery debate with Eric Newcomer, Tom Dotan, and Madeline Renbarger, we dig into whether generative AI is the next platform shift or a $500B mirage. From the viral TaskRabbit CAPTCHA myth to SoftBank's high-stakes bets, we debate the hype, shaky economics, and media spin driving the AI boom.

We explore the evolving landscape of interactive storytelling with Elliot Wolf, executive producer of Prime Video's On Call and co-founder of Wolf Games. With a strong background in television and a deep appreciation for narrative craft, Elliot shares how he's bringing storytelling into new territory with Public Eye, an upcoming AI-assisted daily murder mystery game. Designed to offer players a fresh case every day, Public Eye blends traditional storytelling with new technology, allowing users to step into the role of detective and work through immersive investigations.Elliot walks us through the creative and technical process behind the game's development, including how AI tools help generate dynamic content while preserving story structure and character consistency. He explains how Public Eye maintains a balance between player choice and narrative coherence, and how personalization can make interactive experiences more engaging.The conversation also highlights how generative AI is influencing creative workflows, especially in areas like writing, design, and game development. Rather than replacing human input, Elliot emphasizes how AI can support creators by enhancing productivity and enabling new formats. He also shares his thoughts on how these developments could complement more traditional media, potentially offering new ways for audiences to engage with stories and characters between major content releases.Whether you're interested in gaming, storytelling, or the future of entertainment, this is an episode you wouldn't want to miss.About WrapbookWrapbook is a smart, intuitive platform that makes production payroll and accounting easier, faster, and more secure. We provide a unified payroll platform that seamlessly connects your entire team—production, accounting, cast, and crew—all in one place.Wrapbook empowers production teams to manage projects, pay cast and crew, track expenses, and generate data-driven insights, while enabling workers to manage timecards, track pay, and onboard to new projects from any device. Wrapbook brings clarity and dependability to production payroll, while increasing the productivity of your whole team.For crew: The Wrapbook app eliminates the headaches of production payroll by providing a fast, transparent, and secure solution for workers to complete startwork, submit timecards, and track pay.Trusted by companies of all sizes, Wrapbook powers payroll for some of the industry's top production companies, including SMUGGLER, Tuff, and GhostRobot. Our growing team of 250+ people includes entertainment and technology experts from SAG-AFTRA, DGA, IATSE, Teamsters, Amazon, Microsoft, Facebook, and more.Wrapbook is backed by top-tier investors, including Jeffrey Katzenberg's WndrCo, Andreessen Horowitz, and A* Capital.Get started at https://www.wrapbook.com/

Patty and Brian discuss some benefits and pitfalls of generative AI for critical thinking skills.

Derek Ferguson from The Fitch Group returns to share how his team of 600+ developers leverages generative AI tools like Amazon's CodeWhisperer and implements DORA metrics to boost productivity and team health. In this second part of the conversation, he delves into the transformative impact of these tools and the innovative strategies driving adoption and success at scale. Listen to Derek's experiences in introducing cutting-edge tools to a large organization, his lessons in fostering experimentation, and the surprising parallels between today's AI adoption and the internet boom. From the role of community practices versus centers of excellence to pragmatic advice on technology adoption, this episode is packed with actionable insights for leaders and developers alike. Stick around for Derek's perspective on the evolving role of technologists in an AI-driven world and how music creation intersects with his tech expertise. Inside the episode… • Exploring generative AI for software development and its transformative potential. • Implementing DORA metrics to boost productivity and enhance team alignment. • Lessons learned from scaling technology practices across large organizations. • The balance between prescriptive guidance and fostering creativity in teams. • Insights into creating impactful developer communities of practice. Mentioned in this episode • Generative AI tools (e.g., Amazon's CodeWhisperer) • DORA metrics (DevOps Research and Assessment) • Tools for music and tech crossover (e.g., RipX, Replicate) Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge. Subscribe to the Convergence podcast wherever you get podcasts including video episodes to get updated on the other crucial conversations that we'll post on YouTube at youtube.com/@convergencefmpodcast Learn something? Give us a 5 star review and like the podcast on YouTube. It's how we grow.   Follow the Pod Linkedin: https://www.linkedin.com/company/convergence-podcast/ X: https://twitter.com/podconvergence Instagram: @podconvergence Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge. Subscribe to the Convergence podcast wherever you get podcasts including video episodes to get updated on the other crucial conversations that we'll post on YouTube at youtube.com/@convergencefmpodcast Learn something? Give us a 5 star review and like the podcast on YouTube. It's how we grow.   Follow the Pod Linkedin: https://www.linkedin.com/company/convergence-podcast/ X: https://twitter.com/podconvergence Instagram: @podconvergence

Full article: Multimodal Generative Artificial Intelligence Model for Creating Radiology Reports for Chest Radiographs in Patients Undergoing Tuberculosis Screening Widespread radiographic screening for tuberculosis can be challenged in certain regions by limited radiologist availability. Dora Chen, MD, discusses a recent AJR article by Hong et al. that evaluates the potential use of generative AI for chest radiography interpretation in this setting.

Tune in to The Business of Government Hour with host Michael J. Keegan as he dives into the transformative power of generative AI in the government sector! How is this cutting-edge technology already reshaping public services? What steps can agencies take to foster a culture of safe experimentation while managing risks? And how can they scale AI initiatives from pilot projects to enterprise-wide adoption? Join us for an insightful conversation with Professor Alex Richter from Victoria University of Wellington, New Zealand, author of the IBM Center report, Navigating Generative AI in Government. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Welcome to episode #996 of Six Pixels of Separation - The ThinkersOne Podcast. Christie Smith is a former senior executive at Apple, Deloitte and Accenture with over three decades of leadership experience across industries including life sciences, consumer goods and finance. She holds a doctorate in Social Work and Organizational Psychology and now leads The Humanity Studio, a leadership advisory firm focused on redefining the future of work. In her new book, Essential - How Distributed Teams, Generative AI, and Global Shifts Are Creating a New Human-Powered Leadership (along with her co-author Kelly Monahan), Christie outlines a bold new framework for leaders navigating a post-pandemic, AI-driven, decentralized world. This episode explores the urgent need for management transformation - from command-and-control to people-centered leadership - and how today's leaders must adapt to rising expectations around purpose, trust and equity. Topics include the power shift from corporations to individuals, the cultural cost of distributed work, and why organizations must stop measuring productivity and start cultivating human energy. The discussion also unpacks the psychological strain of "always-on" work cultures, the promise and peril of generative AI, and how leaders can build communities, not just companies. At its core, this conversation is about what comes after burnout… what it means to lead with humanity, design systems that elevate people, and use power responsibly in a time of profound disruption. For anyone rethinking what it means to lead, build and belong in the modern workplace, this episode offers a timely and hopeful reframing of what's possible. Enjoy the conversation... Running time: 56:23. Hello from beautiful Montreal. Listen and subscribe over at Apple Podcasts. Listen and subscribe over at Spotify. Please visit and leave comments on the blog - Six Pixels of Separation. Feel free to connect to me directly on Facebook here: Mitch Joel on Facebook. Check out ThinkersOne. or you can connect on LinkedIn. ...or on X. Here is my conversation with Christie Smith. Essential - How Distributed Teams, Generative AI, and Global Shifts Are Creating a New Human-Powered Leadership. The Humanity Studio. Follow Christie on Instagram. Follow Christie on LinkedIn. Chapters: (00:00) - The Evolving Role of Leadership. (03:06) - Emotional Maturity in Leadership. (05:51) - The Impact of the Pandemic on Leadership. (08:55) - Employee Expectations and Organizational Change. (11:54) - The Shift Towards Purpose-Driven Leadership. (15:05) - Navigating Challenges in Large Organizations. (18:11) - The Rise of Entrepreneurship and New Work Models. (21:03) - Community and Connection in the Digital Age. (33:24) - The Human Element in AI and Workplaces. (39:10) - Agency and Connection in Leadership. (45:51) - Legacy and Leadership in a Changing World. (52:10) - Building a New Organization: Culture and Purpose. (58:28) - Curiosity and Hope in the Face of Challenges.

Successful generative AI transition requires leaders to overcome cognitive biases through transparency, empathy, and hands-on engagement—empowering teams to see AI not as a threat, but as a tool for growth and innovation. That's the key take-away message of this episode of the Wise Decision Maker Show, which talks about how to mitigate cognitive biases during the generative AI transition.This article forms the basis for this episode: https://disasteravoidanceexperts.com/leading-the-generative-ai-transition-beyond-cognitive-biases/

Most companies are implementing AI backwards: they're starting with the technology and hoping their people will figure out how to use it. For the season opener of Unserious recorded live at the Meltwater Summit, J.B. and Molly sit down with Jennifer Kattula, CMO of Microsoft Advertising who revealed why the organizations actually succeeding with generative AI are doing the exact opposite. They're starting with their people. Follow Jennifer on LinkedIn and check out Microsoft Advertising to revolutionize your ad creation experience with Copilot.Follow Unserious in your podcast app, at unserious.com, and on Instagram and Threads at @unserious.fun.

Some writers are tempted to use generative AI to write a book (in part or in whole) because it takes time and effort, perseverance and fortitude. And for the author who wants their book out there RIGHT NOW, they don't want to bother going through the learning curve or putting in the days/weeks/months to write the book.This episode isn't for those types of authors.This episode is for the Christian writer who wants to write a book faster the ethical way by taking advantage of 5 tried-and-true, time-saving strategies. Listen in to learn what they are!Schedule a 1:1 coaching session with me.Time Magazine article about MIT's AI study.What additional topics would you like to learn about?Ready to become a better, more confident writer and make a kingdom impact? Join the FREE Christian Authors in Action Facebook group!

In this episode, Portfolio Manager Denny Fish discusses how the continued evolution and implementation of artificial intelligence (AI) is impacting companies, tech investors, and the global economy.

Vercel CEO Guillermo Rauch has spent years obsessing over reducing the friction between having an idea and getting it online. Now with AI, he's achieving something even more ambitious: making software creation accessible to anyone with a keyboard. Guillermo explains how v0 has grown to 3 million users by focusing on reliability and quality, why ChatGPT has become their fastest-growing customer acquisition channel, and how AI is enabling “virtual coworkers” across design, development, and marketing. He shares his contrarian view that the future belongs to ephemeral, generated-on-demand applications rather than traditional installed software, and why he believes we're on the cusp of the biggest transformation to the web in its history. Hosted by Sonya Huang and Pat Grady, Sequoia Capital