Podcasts about cios

Today, we are dropping another episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.In this episode, Craig Thomas, Sr. Solutions Engineer at Wallarm, examines what rogue AI actually means in practice, where the risk materializes, and what it takes to move from detection to control.QuestionsWhen we say "rogue AI," what do we actually mean? Is it only malicious AI, or can legitimate systems become risky too?What are the most common ways AI systems drift outside intended boundaries? Once an organization understands what rogue AI looks like, where does that loss of control typically begin, and who is responsible for preventing it?How do shadow LLMs, unsanctioned agents, and unmanaged AI workflows create risk even when no attacker is involved? If AI drift often starts with normal business activity, where do shadow AI systems fit into that picture?Why can an AI action look legitimate in isolation but still create serious business, security, or compliance risk when viewed as part of a larger sequence of actions? As these shadow systems become more embedded in everyday workflows, why is it so difficult to recognize risk in real time?How do APIs, integrations, and connected systems amplify the impact of those seemingly legitimate actions? What changes once those actions begin flowing across APIs, business applications, and interconnected systems?What kinds of unexpected outcomes worry CIOs and CISOs most today when AI systems are operating across those interconnected environments? As that connectivity expands, what are security and business leaders most concerned about?And given those concerns, what does meaningful oversight actually look like when AI systems can act at machine speed? How should organizations distinguish between the experimentation they want to encourage and the unmanaged AI behavior they need to control? One challenge is balancing governance with innovation. How do organizations avoid slowing down AI adoption while still maintaining control?We know that many organizations can detect risky AI behavior after the fact. But if they can't stop it in real time, what critical gap still remains? Even with governance programs in place, many organizations are still operating reactively. In closing, what's the key difference between detecting AI risk and actually controlling it?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/cu-craigthomas/Full AbstractIn this episode, Craig Thomas, Sr. Solutions Engineer at Wallarm, examines what rogue AI actually means in practice, where the risk materializes, and what it takes to move from detection to control.Not every AI threat starts with an attacker. Some of the most consequential AI risks organizations face today come from systems that are working exactly as designed, just not quite as intended. An agent that calls an API it was never supposed to reach. A workflow that exposes PII because nobody mapped the data path before deployment. A shadow LLM standing up in an AWS account because a developer needed to move fast and approval processes were slow. None of these require malicious intent to create serious business, security, or compliance exposure.Rogue AI is a broader category than most governance frameworks account for. It includes the unsanctioned, the unmonitored, and the unpredictable: AI systems that drift outside intended boundaries, take actions that look legitimate in isolation but create risk in sequence, and operate at machine speed in ways that make after-the-fact detection feel like a consolation prize. The gap most organizations have is not in detecting that something went wrong. It's closing the loop fast enough to matter.Meaningful AI governance requires more than policy and discovery. It requires the ability to observe AI behavior at runtime, understand what triggered each action and what it touched, and enforce boundaries before consequences compound. That closed AI control loop, from knowing what is running to seeing what it does to stopping what it should not, is the operational standard AI transformation demands. Most organizations are not there yet.Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://click.cash.app/ui6m/mt82fpxl #CashAppPod. Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. See terms and conditions at https://cash.app/legal/us/en-us/card-agreement. Cash App Green, overdraft coverage, borrow, cash back offers and promotions provided by Cash App, a Block, Inc. brand. Visit http://cash.app/legal/podcast for full disclosures.* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

What We CoveredWhat if your wearable could do more than track steps — and actually help detect cardiovascular risk before symptoms appear?In this episode, Joe talks with University of Pittsburgh's Pengfei Zhou & Matt de Lima Barbosa, along with Dell Technologies' Adrienne Garber, about how AI, edge computing, and wearable devices are shaping the future of heart monitoring.01 Why wearables are the next frontier for heart health: how real-time sensor data from everyday devices could detect cardiovascular risk before symptoms ever appear.02 What AIoT actually means in practice: how Pengfei's research combines AI and connected sensors to build deep learning models that go far beyond step counting.03The role of embedded IT in research speed: how Matt's team connects faculty to secure infrastructure and technical support so researchers can move faster and focus on the science.04 How Dell is partnering with higher ed researchers: why Adrienne's team invests in university innovation programs — and what that looks like when it reaches researchers working on real health problems.05 Why localized AI wins on speed, privacy, and personalization: the case for keeping AI processing at the edge instead of sending sensitive health data to the cloud.06 What the future of higher ed innovation actually requires: why the collaboration between researchers, IT, and technology partners like Dell is the ingredient most people overlook. FeaturingPengfei Zhou, Assistant Professor, University of Pittsburgh School of Computing and InformationMatt de Lima Barbosa, Director of Information Technology, University of Pittsburgh School of Computing and InformationAdrienne Garber, Chief Technology & Innovation Strategist, Higher Ed, Dell Technologies Timestamps(01:00) Inside Pitt's School of Computing and Information(02:45) Pengfei Zhou's teaching and research focus(03:53) AIoT, wearables, and heart monitoring(07:04) How Dell's higher ed innovation pilot reached Pitt(10:41) Why localized AI matters for health data(12:18) How embedded IT helps researchers move faster(13:41) Dell's role as connective tissue between researchers and IT(18:18) Combining PPG and ECG signals for better blood pressure monitoring(21:00) The “Who Not How” Moment: Helping researchers move faster(25:12) AI, deep learning, and solving real problemsListen now: YouTube x Apple x SpotifyWhenever you're ready, there are 3 ways you can connect with TechTables:1.

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

In a relationship-driven niche like healthcare IT, the deal happens in the room, not the inbox. In this episode of Content Amplified, Larry Kaiser, Chief Marketing Officer at Optimum Healthcare IT, explains why a little over 60% of his budget goes to events and how he stretches each one across his whole content engine. Larry walks through his year at the two biggest healthcare IT shows, Vive and HIMSS, where he runs booths, stage presentations, and sponsored white-labeled receptions that put his team in front of CIOs as the only vendor in the room. He shares how he takes a single filmed event presentation and repurposes it into a blog post, a white paper, and a podcast episode to pull maximum value out of a $25,000 sponsorship. He also makes the case for LinkedIn-first distribution, with 205,000 followers and close to 60,000 newsletter subscribers, because email is dead with healthcare IT CIOs who told him it will never reach them. Plus why he keeps his content roughly 90% brand and 10% individuals. If you spend real money on events, this conversation shows you how to make it work harder.About LarryLarry Kaiser is the Chief Marketing Officer at Optimum Healthcare IT, a staffing and digital transformation consulting firm that works with provider organizations on Epic EHR, ServiceNow, Workday, and AWS. He has spent 22 years in healthcare IT, an industry he never expected to stay in this long. Larry got his start as an RFP manager and became CMO about six years ago, hitting nearly every aspect of marketing in healthcare IT along the way.Show NotesConnect with Larry on LinkedIn: https://www.linkedin.com/in/lawrencekaiser/Text us what you think about this episode!

Trump chama pessoas que criticam acordo com o Irã de invejosas, más e estúpidas. E Brasil entra em campo hoje contra o Haiti na 2ª rodada da fase de grupos da Copa.See omnystudio.com/listener for privacy information.

Ben Silver and David Tykocinski, co-CIOs of Maverick Capital, say their investment strategy has, in some respects, been the same for three decades: Rather than trying to time the market, the firm aims to drive performance by taking a long-term view, partnering with management teams, and doing deep diligence on its investments. In an interview with Goldman Sachs' Tony Pasquariello on the Great Investors podcast, they discuss the investment opportunities in AI and healthcare as well as their complementary skills in a shared role. This episode was recorded on June 4th, 2026. The opinions and views expressed herein are as of the date of publication, subject to change without notice, and may not necessarily reflect the institutional views of Goldman Sachs or its affiliates. The material provided is intended for informational purposes only, and does not constitute investment advice, a recommendation from any Goldman Sachs entity to take any particular action, or an offer or solicitation to purchase or sell any securities or financial products. This material may contain forward-looking statements. Past performance is not indicative of future results. Neither Goldman Sachs nor any of its affiliates make any representations or warranties, express or implied, as to the accuracy or completeness of the statements or information contained herein and disclaim any liability whatsoever for reliance on such information for any purpose. Each name of a third-party organization mentioned is the property of the company to which it relates, is used here strictly for informational and identification purposes only and is not used to imply any ownership or license rights between any such company and Goldman Sachs. A transcript is provided for convenience and may differ from the original video or audio content. Goldman Sachs is not responsible for any errors in the transcript. This material should not be copied, distributed, published, or reproduced in whole or in part or disclosed by any recipient to any other person without the express written consent of Goldman Sachs. Disclosures applicable to research with respect to issuers, if any, mentioned herein are available through your Goldman Sachs representative or at ⁠http://www.gs.com/research/hedge.html⁠ Goldman Sachs does not endorse any candidate or any political party. Copyright 2026. All rights reserved. Learn more about your ad choices. Visit megaphone.fm/adchoices

Faça sua inscrição para o Legado (16 produtos pelo preço de 1)Conheça a Conta SimplesO que é preciso para desafiar os maiores bancos do país e construir uma fintech bilionária praticamente do zero?Essa é a história dos fundadores da Conta Simples, empresa que nasceu dentro de uma biblioteca universitária, enfrentou dezenas de recusas de investidores, atravessou a pandemia e encontrou uma oportunidade que os grandes players do mercado financeiro ignoravam: ajudar empresas a terem mais controle e eficiência na gestão de seus gastos.Mas o que fez a Conta Simples crescer tão rápido? Como seus fundadores identificaram uma dor que passava despercebida pelos bancos tradicionais? Como conseguiram ser aprovados pelo Y Combinator, uma das aceleradoras mais prestigiadas do mundo? O que aprenderam ao levantar milhões de reais em investimento e construir uma empresa que já movimentou mais de R$ 80 bilhões em transações?Para responder essas e muitas outras perguntas, convidamos Rodrigo Tognini, cofundador e CEO da Conta Simples, e Ricardo Gottschalk, cofundador e vice-presidente da empresa, para o episódio 303 do podcast Os Sócios. Vamos conhecer os bastidores da construção de uma das fintechs que mais crescem no Brasil, sua trajetória empreendedora, os desafios de escalar um negócio em um dos mercados mais competitivos do país, além de lições valiosas sobre inovação, gestão, crescimento e o futuro dos serviços financeiros.Hosts: Bruno Perini @bruno_perini e Malu Perini @maluperiniConvidado: Rodrigo Tognini e Ricardo Gottschalk

In this episode of Gartner ThinkCast, we dive into what strong leadership really looks like when the path ahead is unclear. From economic disruption to AI-fueled transformation, uncertainty is now a defining feature of the business landscape. Gartner expert Mary Mesaglio returns to offer a succinct and timely playbook. Drawing from conversations with CEOs, CIOs and senior leadership around the globe, Mary shares three practical strategies for leading through volatility, ambiguity and change. Learn why clear communication, hyper-focused priorities and transparent guiding principles are the essential tools every executive and new leader needs today.   You'll learn: Why corporate-speak fails in times of change and what to say instead The 10x10x10 rule for reinforcing priorities with your team How guiding principles offer stability and direction   Dig deeper: See why Gartner is the world authority on AI Try out AskGartner for more AI-powered insights Learn how to create a strategic workforce plan  

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

Melhores oportunidades se deram tanto para produto disponível, quanto para safra nova e produtores executaram boas estratégias. Curva futura do dólar indicou patamares acima dos R$ 5,50. Chicago fecha no vermelho realizando lucros.

Today, we are dropping another episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.We all know that you can't secure what you can't see, which is why AI discovery is a first principle for AI security, but what's really required for AI discovery? It's more than just LLMs and agents. Today's episode is entitled AI Discovery isn't just AI, and joining us is Tim Ebbers, Field CTO at Wallarm. Tim and I discuss the real requirements for AI discovery, and why the connections between assets and infrastructure are part of the puzzle.QuestionsSecurity teams often say, “You can't secure what you can't see.” In the context of AI, what exactly do they need to see? What supporting infrastructure matters most when mapping AI risk, such as APIs, cloud services, Kubernetes workloads, data stores, identities, and external integrations?Where does shadow AI typically appear first inside an enterprise environment? How can it be prevented?How do relationships between assets change the risk picture? For example, why does it matter which API an agent can call or which data source a workflow can reach?What makes AI discovery harder than traditional application or cloud asset discovery? What are the similarities and differences?How should organizations prioritize what they find? Is every AI asset equally risky?What does “continuous discovery” mean in a world where AI services can be deployed, connected, or changed in minutes?Once an organization has visibility into its AI footprint, what's next? What are the biggest gaps in today's AI security programs?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/tebbers/Full AbstractMost security teams know that you can't secure what you can't see. In the context of AI, that rule turns out to be a lot harder to satisfy than it sounds.AI discovery isn't just a matter of cataloging your LLMs and agents. The real picture includes the APIs those agents call, the data sources they reach, the infrastructure they run on, and all the AI that got deployed without anyone telling security. Building that picture requires understanding relationships, not just inventories, because risk doesn't live in assets in isolation. It lives in what those assets can do together.In this episode, Tim Ebbers, Field CTO at Wallarm, examines what a complete AI control loop actually requires at the discovery stage: what needs to be visible, why the connections between assets change the risk calculation, where shadow AI tends to appear first and how it becomes unmanaged risk, and what makes AI discovery structurally different from traditional cloud or application discovery. It also looks at what organizations should do once discovery is in place, and where the biggest gaps remain in AI security programs today.If your team is building toward continuous AI governance, this is where that work starts.Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://click.cash.app/ui6m/mt82fpxl #CashAppPod. Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. See terms and conditions at https://cash.app/legal/us/en-us/card-agreement. Cash App Green, overdraft coverage, borrow, cash back offers and promotions provided by Cash App, a Block, Inc. brand. Visit http://cash.app/legal/podcast for full disclosures.* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

SUMMARY: As tools like Mythos create new AI-cybersecurity concerns, CIOs and CISOs need to be prepared for two challenges: Security Remediation and Patch to Production. SHOW: 1037SHOW TRANSCRIPT: The Enterprise AI Show #1037 TranscriptSHOW VIDEO: https://youtu.be/H5KxoiEIfUoSHOW SPONSORS:Nasuni - Activate your data for AI and request a demoOutShift by Cisco - “Scaling Out Superintelligence”  The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:Project Lightwell (Red Hat and IBM)Athena (Chainguard)Anthropic Project GlasswingOpenAI GPT 5.5-CyberTHESIS: Major initiatives are forming to help enterprise organizations combat security vulnerability threats found or created using new AI-cyber tools such as Anthropic Mythos. What are the key considerations, and what additional steps do organizations need to take to be advantaged by these capabilities? Part 1The Breaking Point and the Mythos MomentThe scope of open source security and supportPatches, disclosures and upstream open sourceClearinghouses, EOs, Laws and CommunitiesRemediation - Build vs. BuyPart 2How fast can you get from Patch to Production?Mitigation before patchingFast path and stable patch pipelines?Automation in patching vs. automation in deploymentFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

Baixe o Business Model Canva - Trilha Do Zero ao Topo: https://acrobat.adobe.com/id/urn:aaid:sc:us:0084f67e-479f-4825-9b5e-75ee8e9ee9a3?viewer%21megaVerb=group-discoverVocê começaria uma trilha difícil sem mapa? No empreendedorismo, fazer isso pode custar caro.Neste episódio da Trilha Do Zero ao Topo, mostramos por que o plano de negócios não é burocracia: ele é o mapa que ajuda a transformar uma ideia em empresa. E mais: explicamos como usar o Business Model Canvas para organizar seu negócio de forma simples, visual e prática.Neste vídeo, você vai aprender:o que é um plano de negócioscomo analisar a concorrência e o mercadoa matriz SWOT/FOFA e outras ferramentas importantes para montar seu projetocomo funciona e como montar o seu plano de negócios com o Business Model CanvaAinda não tem uma ideia de negócio? Assista ao primeiro episódio da Trilha e veja por onde começar: https://www.youtube.com/watch?v=s4P0sTes8ww

O Grupo SmartFit se tornou um negócio multibilionário, com mais de 2 mil unidades em 16 países e um modelo de negócios que está transformando o mercado de fitness e bem-estar.Neste episódio do Economia & Negócios, Patricia Chaccur e José Inácio Pilar conversam com Cristina Duclos, CMO Global do Grupo SmartFit, sobre a estratégia por trás da expansão acelerada da empresa, o posicionamento das suas diferentes marcas, o papel da tecnologia e da inteligência artificial na experiência dos alunos e a evolução do fitness para um ecossistema completo de wellness.A executiva também explica por que as canetas emagrecedoras não estão reduzindo a demanda por academias, como a geração Z está mudando a relação com atividade física e por que a SmartFit quer se tornar uma das marcas mais admiradas do Brasil.Entre os temas da entrevista:Como o Grupo SmartFit ultrapassou 2 mil academias em 16 paísesA estratégia de expansão internacional do grupo - O posicionamento das marcas SmartFit, Bioritmo, Nation CT, Velocity e Vidya StdudioInteligência artificial aplicada ao fitness e ao atendimentoO impacto de Ozempic e Mounjaro no mercadoWellness, longevidade e saúde socialO futuro das academias nos próximos anosApoie o jornalismo independente. Assine O Antagonista e Crusoé com 10% via Pix ou Google Pay:  https://assine.oantagonista.com.br/  Siga O Antagonista no X:  https://x.com/o_antagonista   Acompanhe O Antagonista no canal do WhatsApp. Boletins diários, conteúdos exclusivos em vídeo e muito mais.  https://whatsapp.com/channel/0029Va2SurQHLHQbI5yJN344  Leia mais em www.oantagonista.com.br | www.crusoe.com.br #SmartFit #EconomiaENegocios #SucessoEmpresarial #ModeloDeNegocios #Ozempic #GeracaoZ #TecnologiaEHealth #AcademiaSmartfit #MercadoFitness #Empreendedorismo

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

In this episode of The Broadband Bunch, host Pete Pizzutillo sits down with Rob Lawrence, Technology Strategist at Microsoft, to separate the reality of agentic AI from the growing hype surrounding autonomous systems. As organizations race to experiment with AI agents, Rob argues that the biggest challenges aren't the models themselves—they're the operating environments, governance frameworks, data quality, accountability structures, and organizational readiness required to deploy them successfully. Pete and Rob discuss why many AI pilots succeed while production deployments struggle, the return of disciplines like project portfolio management and process engineering, and why data governance may be the most important prerequisite for successful AI adoption. Rob also talks about the role of identity and permissions, the risks of poorly governed agents acting on flawed data, and why organizations need better observability into AI-driven workflows. Along the way, he shares advice for CIOs, CTOs, and broadband operators looking to move beyond experimentation and build a responsible foundation for agentic AI.

Agentic AI has taken off in software engineering, but most CIOs still cannot make agents work in everyday knowledge work in the enterprise. Aaron Levie, co-founder and CEO of Box, explains why that gap exists and what enterprises must change to close it. Drawing on what Box sees across its enterprise customer base, including 68% of the Fortune 500, Levie covers data access, verification, budgets, architecture, and the new roles required to realize real value from enterprise AI agents.======This episode is brought to you by Gartner IT Symposium/Xpo™. Ready to scale agentic AI from pilot to production? Join top CIOs and IT executives at Gartner IT Symposium/Xpo, taking place this October 19th through the 22nd in Orlando, Florida. Over 300 Gartner analyst-led sessions will cover top priorities shaping IT—from AI value, governance, and cybersecurity to cost optimization, IT operating models, and beyond. Get practical, actionable insights—and connect with peers tackling the same challenges you are.Secure your spot today at gartner.com/us/symposium.======YOU'LL DISCOVER✅ Why agentic coding raced ahead while knowledge work agents lag, across three properties: text based work, verifiability, and data access✅ The "AI psychosis" pattern Levie says makes CEOs overestimate agents, and why distance from the last mile of work distorts executive judgment✅ Why you should retry a failed AI project roughly every six months as frontier models keep improving✅ The forward-deployed engineer role, internal and external, and why it becomes essential to enterprise AI adoption✅ Why your IT and data architecture, not the model you pick, often determines what you actually get from agents✅ The end of venture-subsidized tokens, and why the line of business, not just IT, now has to own the AI budget✅ Why Levie says you should not vibe-code core systems of record like ERP or CRM, and where agent value actually accrues✅ Value maxing versus token maxing: how to judge AI ROI and avoid a surprise overnight token bill⏱️ TIMESTAMPS0:00 The promise of agentic coding5:11 Why knowledge work resists agents8:52 The AI psychosis trap for CEOs14:57 Be ambitious, then retry in six months17:25 The rise of the forward-deployed engineer21:09 Frontier models need your data architecture27:14 The end of subsidized tokens31:18 How knowledge workers should prepare36:37 Where software value shifts39:03 Reimagining workflows around abundance43:03 Value maxing versus token maxing49:46 Advice for CIOs

We are delighted to bring you Series 3 of the 2026 Fiftyfaces Podcast. This set of 10 compelling conversations takes a slightly different tack to usual with a particular focus on some of the building blocks behind firms - such as strategy (as described by Matthew Kentridge), motivation (Kirstie Sneyd) and planning (Brett Hickey). We look at what happens when institutions such as public pension funds are leanly resourced and the creativity that can flow from this in terms of staffing, outsourcing and collaboration (Nadia Oumata). Staying with motivation we look at how our teachers and mentors can have play such a formative role (Kristina Hooper), at the enrichment that can come from a portfolio career (James Mitchell), at the inertia in financial markets (Crawford Spence) and the evolution of ESG (Michael Viehs). Sally Bridgeland returns to the podcast to speak about her "soon to be published" book that looks at two schools of thought and approach to the workplace - what she describes as the chess club v. the drama club.Our guests are strategists, founders, coaches, CIOs, academics, authors, cyclists . . the list goes on. Tune in from later this week for an educational journey . . Thanks to Alvine Capital Management and Franklin Templeton for sponsoring Series 3 of 2026. 

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

Recorded live at Mercer's Global Investment Forum in Orlando, in this special edition of the One theme, Two CIOs, Three questions podcast Anne Marie Schultz, US Investments Commercial Leader is joined by US CIO Andrew McDougall and Global Alternatives CIO Garvan McCarthy on the key issues shaping investor decisions today. From geopolitical and energy risk to AI-driven growth, private markets liquidity, and portfolio resilience, they discuss what institutional investors may be underestimating — and how to think about positioning for the second half of the year. This content is for institutional investors and for information purposes only. It does not contain investment, financial, legal, tax or any other advice and should not be relied upon for this purpose. The materials are not tailored to your particular personal and/or financial situation. If you require advice based on your specific circumstances, you should contact a professional adviser. Opinions expressed are those of the speakers as of the date of the recording, are subject to change without notice and do not necessarily reflect Mercer's opinions.This does not constitute an offer or a solicitation of an offer to buy or sell securities, commodities and/or any other financial instruments or products or constitute a solicitation on behalf of any of the investment managers, their affiliates. For the avoidance of doubt, this is not formal investment advice to allow any party to transact. Additional advice will be required in advance of entering into any contract.Read our full important notices - click here

Segundo imprensa chinesa, existem mil acampamentos para emagrecer no país; BBC ouviu relato de mulher que passou 28 dias em um deles.

SUMMARY: If the cost of public AI continues to rise, because of various market shortages, should CIOs start looking at backup plans to better own their AI journeys and futures?SHOW: 1036SHOW TRANSCRIPT: The Enterprise AI Show #1036 TranscriptSHOW VIDEO: https://youtu.be/ZgkMF7G3YfoSHOW SPONSORS:OutShift by Cisco - “Scaling Out Superintelligence”  The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:Andy Weir (The Martian) on Eps. 193Systems of Record Won the SaaS Era - Clearinghouses Will Win the Agents EraHarness Engineering is where Enterprise AI becomes realTHESIS: It comes up as different control points, but CIOs are ultimately trying to figure out how to get the value from Enterprise AI while delivering a set of consistency across different teams and use-cases. Let's explore what this “Enterprise Harness” is starting to look like. Enterprise Clearinghouse Enterprise Intelligence (a.k.a. Middleware)Enterprise Catalog - Models as a Service, Agents as a ServiceEnterprise Skills or Shareable Prompt HarnessesSymantec Routing to ModelsAI Gateway ControlsFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

Introduction Most distribution management tools were built in the nineties and haven't changed much since. Carriers and MGAs are onboarding agents manually, tracking licenses in spreadsheets, and managing compliance through email threads. Ido Deutsch spent nine years solving that problem from the inside - before realizing the solution was worth selling to the rest of the market. Deutsch is the co-founder of ProducerFlow, a distribution management platform that started as an internal tool at Agentero, the digital insurance network he helped build from a single client and barely a product in 2016 to a scaled distribution business. When carriers kept asking how Agentero was handling agent onboarding so efficiently, Deutsch knew the tool had a market of its own. ProducerFlow launched as a standalone product in March 2024. In this conversation, Josh Hollander and Deutsch dig into what carriers and MGAs consistently get wrong about distribution infrastructure, why the market no longer accepts SaaS-only tools, and why fixing your data before layering in AI is the only move that matters. Guest Bio Ido Deutsch is the Co-Founder of ProducerFlow and Head of Go-to-Market at Agentero, a digital insurance network connecting carriers, MGAs, and independent agencies. A serial entrepreneur who grew up in Israel and built three companies before moving to the US in 2014, he joined Agentero's founder Luis Pino while still at Berkeley's MBA program and spent the next nine years building the company's distribution network and technology from scratch. ProducerFlow, launched in 2024, automates agent onboarding, licensing, compliance, and distribution management for carriers and MGAs. Key Topics • Built from necessity, not theory - ProducerFlow wasn't designed in a whiteboard session. It was built because Agentero was onboarding hundreds of agencies a month and couldn't keep up manually. The existing market solutions were either too old, too rigid, or too expensive. So they built their own, and carriers started asking to use it. • The best clients are switching from something - Deutsch's most successful clients aren't building from scratch. They've already tried one of the legacy tools, overpaid, been underdelivered, and are ready for something that actually integrates with their existing stack. That frustration is the clearest buying signal. • SaaS-only is no longer enough - The market has shifted. Carriers don't want a tool; they want an outcome. ProducerFlow offers a full managed service for clients who want to outsource compliance entirely, or infrastructure-only for those who want to run it themselves. The key insight: whoever wins in distribution tech has to be willing to do the work, not just sell the platform. • Fix the data before you touch the AI - Deutsch's consistent message across the conversation: AI is only as good as the data it runs on. He's seen top-five carriers and major brokers with years of data that's disorganized, siloed, and hard to query. Layering AI on top of bad infrastructure gives confident wrong answers. Fix the foundation first. • Speed to onboard is the core metric - The time from meeting a new agency to the moment they can quote and bind is ProducerFlow's north star. Faster onboarding means better agent experience, higher retention, and more written premium. Everything else is secondary. • The CIO is gaining ground - Deutsch has watched the power dynamics inside carrier organizations shift. Head of AI titles are proliferating, but the real influence is moving toward CIOs and information security leaders as data privacy, AI governance, and "where does my data go" questions dominate every sales cycle. Notable Quotes "We couldn't find anything that worked for us. So we built our own. And then carriers started asking, how do you do that?" "Our best clients typically tried the solution already. They overpaid, were underdelivered, and then they see how ours works. We try not to over promise, but we definitely over deliver." "The market doesn't really accept SaaS-only tools anymore. They want you to solve an outcome, replace a whole function, and do the work." "Fix your data and fix how you look at things. Everything is going to be based on that. AI is only as good as your infrastructure. If the data isn't right, it will just give you very confident answers that are wrong." Resources Guest: • ProducerFlow: https://www.producerflow.com • Ido Deutsch on LinkedIn: https://www.linkedin.com/in/ido-deutsch/ Host & Organization: • Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/ • Horton International (USA): https://www.horton-usa.com/ • Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show Subscribe & Review If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Apple Podcasts, and Spotify.

FAÇA SUA PRÉ-MATRÍCULA PARA O LEGADO (16 produtos pelo preço de 1)Abra sua conta na Coinbase, uma das exchanges de cripto mais seguras do mundo, e ganhe R$50 em BTC na sua primeira compra de R$1.000Descomplique seu IR com a MyprofitVocê conhece os ETFs e fundos do Grupo Primo?Nos últimos anos, começamos um processo de transformação na forma como o investidor brasileiro investe — indo de fundos de previdência a estratégias mais modernas de alocação, incluindo o nosso ETF de renda fixa, o GPCA11.Nosso ecossistema de ativos vem se desenvolvendo com o objetivo de oferecer melhores opções para o investidor brasileiro, combinando eficiência, diversificação, custos competitivos e maior inteligência na construção de carteira.Em um cenário de inflação e juros ainda relevantes, entender como proteger e construir patrimônio ao longo do tempo se torna essencial — e é nesse contexto que soluções mais eficientes, como ETFs de renda fixa de baixo custo, ganham cada vez mais importância.Mas afinal: como esses produtos funcionam na prática?Qual é o mais indicado para cada perfil de investidor?E como eles se organizam dentro de uma estratégia de investimento mais ampla?Apesar de serem instrumentos cada vez mais presentes no mercado, ainda existe muita dúvida sobre estrutura, funcionamento e custos desses produtos — e entender isso pode mudar completamente a forma de investir.Para responder essas e outras perguntas, recebemos Guilherme Cadonhotto, Head de Alocação da Grão.Ele será transmitido nesta quinta-feira, dia 11/06, ao meio-dia, no canal Os Sócios Podcast.Hosts: Bruno Perini @bruno_perini e Malu Perini @maluperiniConvidado: Guilherme Cadonhotto @gui.cadonhotto

Fred Laluyaux has spent 25 years on the same problem: enterprises are drowning in decisions no human should be making. With 50 million digitized decisions across companies like Unilever, Exxon, and Hershey, he now has the data to prove it. When operators override the machine, performance goes down. Not sometimes — in aggregate, every time. In this episode, Fred breaks down the agentic vs. deterministic tradeoff most CIOs are getting wrong, why the software stack most companies rely on today is heading for collapse, and what a company whose entire stack is just SAP and Aera tells you about where enterprise software is going. Hit play. 3 Takeaways: After 50 million digitized decisions, the data is clear: when operators override the machine, performance drops. One Aera customer runs their entire operation on SAP and Aera. Nothing in between. That's where the stack is going. Fred calls them "born in digital" decisions — they can't be made by humans because the value is gone before the meeting starts. Chapters: [03:08] Fred's Career Journey and Lessons Learned [05:17] Why Aera Was Created [05:45] The Vision for a Self-Driving Enterprise [08:28] The Decision Memory Problem in AI [10:28] The Reality of AI ROI [11:58] From Analytics to Decision Intelligence [12:56] Humans vs Fully Autonomous Systems [15:28] What It Means to Digitize Decisions [18:42] How Aera Actually Works [22:42] Trust, Governance, and the Waymo Analogy [27:51] Deterministic vs Agentic AI [29:13] The Cloud Capacity Wake-Up Call [30:15] Where Aera Fits in the Enterprise Stack [31:54] Fast ROI and the “4-4-4” Framework [32:55] Why the Software Stack Is Collapsing [36:21] Delayering Organizations and New AI Roles [39:02] Born-Digital Companies and Micro-Decisions [43:57] Explainability, Governance, and Feedback Loops About Fred:  Fred Laluyaux is Co-Founder, President, and CEO of Aera Technology, the leader in decision intelligence and creator of Aera, the first decision intelligence agent. An entrepreneur and Silicon Valley veteran, Fred brings an impressive track record building successful startups and driving technology innovation. Prior to launching Aera, Fred was the CEO of Anaplan, which he grew to a $1 billion valuation. He has held several executive positions at SAP, Business Objects, and ALG Software. As a thought leader on the future of work and host of the Decision Intelligence podcast, Fred frequently shares his vision with influencers through media interviews and speaking engagements at industry conferences. His views have been published in business and trade publications. A technology and startup advisor, Fred is an investor and active board member of several startups in the U.S. and Europe. Guest Highlights: "We're in 2026, and the reality is that our models have not changed for 100 years. We're still relying on people to decide how to forecast, how to allocate inventory, how to change a plan." "We've got enough data, I mentioned the 50 million decisions, to demonstrate that whenever the humans are touching the system and are messing with the recommendation, they actually degrade the performance." "The autonomy is not another version or better version of my planning tool or my replenishment tool. It replaces the need to have a human touch with that software, and therefore I don't need that software anymore." Get Connected: Ian Faison: https://www.linkedin.com/in/ianfaison Fred Laluyaux: https://www.linkedin.com/in/flaluyaux/ Our Sponsor:  This episode is brought to you by Aera Technology. Enterprise AI has hit its stride. Across industries, companies are moving beyond pilots and proofs of concept, and into real, enterprise-wide results: better decisions, faster execution, and meaningful bottom-line impact.  Aera's agentic decision intelligence is built to help you seize the opportunity. Aera dynamically composes decision flows using unified decision data and multi-engine orchestration to drive action at scale. It continuously senses what's happening across your enterprise, recommends and executes the best course of action within your transaction systems, and learns from every outcome to keep improving. Leading global companies are already using Aera across supply chain, inventory, logistics, and finance, delivering rapid ROI through reduced costs, lower working capital, and better customer outcomes. This is the self-driving enterprise. And it's here now. Visit AeraTechnology.com to book a demo Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

Content's value is in the intelligence it brings, regardless of what system it's found in. But there is a lot of enterprise content across many, many systems.On the Mostly Unstructured Podcast, KeyMark CMO Clay Tuten sits down with Mike Askren, VP of Product at Hyland, on how document management and ECM are becoming an intelligence layer for agentic AI, and the right size and scale problems to tackle with agents.Topics explored: Why has enterprise value moved from storing and securing content to extracting intelligence from it? How content federation connects AI services to content across on-prem, cloud, and hyperscaler systems. What an enterprise context engine does, and why the relationships between documents matter more than the engine itself. Why agentic governance matters so much. Monitoring, coaching, and shutting down agents that hallucinate or run on stale instructions. Why the highest-ROI AI work comes from the processes that are least exciting, but have the highest volume of occurrence. Questions this episode answers: What is the intelligence layer in enterprise content management? How much enterprise data is unstructured, and why does it matter for AI? What is content federation and why is it needed for enterprise AI? What is agent governance and how is it different from data governance? How do you get ROI from AI without replacing your existing systems? Where should a CIO start when moving ECM into an AI intelligence layer? What is intelligent document processing (IDP) and how does it relate to agentic automation? Subscribe for more AI talk on content intelligence, IDP, and agentic AI from the team at KeyMark, or reach out if anything caught your ear.Timestamps:00:00 – From storage to intelligence: the ECM shift01:58 – What "unstructured content" really means03:01 – Mike's role at Hyland and content federation04:11 – The content-fueled agentic enterprise06:45 – Why 70–90% of enterprise data goes untapped08:03 – Agentic governance and context you can trust09:25 – Human-in-the-loop feedback and coaching agents10:22 – The control tower: monitoring and stopping agents12:03 – Agents as digital employees13:45 – Advice for CIOs under pressure15:23 – Start small: the attainable win, not the moonshot18:39 – Where the ROI actually hides19:47 – Practical outcomes: claims, HR, government21:03 – First steps into the intelligence layer24:45 – From IDP to agentic automation to new workflows27:19 – Slow down, ask questions

Today, we are kicking off a new series entitled The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.Today's episode is entitled AI Security is API Security, and joining us is Tim Erlin, VP of Product Marketing at Wallarm. We discuss the foundational link between AI security and API security, digging into the role that APIs play in the dev, deployment, and operations of AI. We explore how they contribute to the risk profile of AI transformation projects, and how securing APIs is critical for successful AI transformation.QuestionsWhen people hear “AI security,” they often think first about models, prompts, or training data. Why do you argue that AI security starts with APIs?Where do you see organizations underestimating API risk as they move AI projects from pilot to production?How does the rise of AI agents change the stakes for API security compared with traditional application architectures?What are the most common API security assumptions that break down once AI systems begin taking action autonomously?Wallarm's ThreatStats research points to APIs as a major overlap point for AI vulnerabilities and exploited vulnerabilities. What does that tell us about where attackers are likely to focus?How should security leaders think differently about authentication, authorization, and API abuse when the “user” may be an AI agent rather than a human?What is one practical step teams can take today to strengthen API security before AI adoption expands further?Once you accept that AI security depends on APIs, what do organizations actually need to discover before they can protect it?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/tim-erlin/Full AbstractIn the first episode of the AI Control Loop series, Tim Erlin, VP Product at Wallarm, examines why AI security and API security are the same problem approached from different angles, and what organizations need to discover before they can protect either one.Every AI model needs data to act on. Every AI agent needs services to call. Every AI workflow needs integrations to function. The connective tissue running through all of it is APIs, which means the security posture of any AI system is inseparable from the security posture of the APIs underneath it.That link is not theoretical. APIs are already the most targeted attack surface in enterprise environments, and AI is making that problem significantly larger. Agents that act autonomously on behalf of users do not just consume APIs the way traditional applications do. They discover them, invoke them dynamically, chain them across workflows, and do all of it at a speed and scale that makes human review impractical. The authentication assumptions, rate limiting strategies, and abuse detection models that worked for human-driven API traffic were not designed for this, and the gaps are not subtle.Most organizations moving AI from pilot to production are underestimating how much of their AI risk surface is actually API risk surface. Shadow APIs that were never inventoried, overpermissioned integrations that made sense for a human user but not for an autonomous agent, authentication patterns that cannot distinguish a legitimate AI session from an abused one. Securing AI at the foundational level means answering the API question first: what APIs does the AI touch, what can it do through them, and what would an attacker be able to reach if any part of that surface were compromised.Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://click.cash.app/ui6m/mt82fpxl #CashAppPod. Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. See terms and conditions at https://cash.app/legal/us/en-us/card-agreement. Cash App Green, overdraft coverage, borrow, cash back offers and promotions provided by Cash App, a Block, Inc. brand. Visit http://cash.app/legal/podcast for full disclosures.* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

SUMMARY: If the cost of public AI continues to rise, because of various market shortages, should CIOs start looking at backup plans to better own their AI journeys and futures?SHOW: 1035SHOW TRANSCRIPT: The Enterprise AI Show #1035 TranscriptSHOW VIDEO: https://youtu.be/ngBBpP2LgdoSHOW SPONSORS:ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoOutShift by Cisco - “Scaling Out Superintelligence”  The Internet of Cognition architectureSHOW NOTES:THESIS: Between pending IPOs (Wall St. demands), high user-demand, GPU/TPU shortages, Data Center shortages, Model prices increasing (open models fading away), the cost of using AI is going to get more expensive over time. Should CIOs start thinking about a Backup plan to their current AI adoption that has lower cost alternatives?Topic 1 - Assuming you could get access to GPUs/TPUs/Accelerators, and suitable data center space to host them, what would be your thinking as a CIO if you felt like you needed to own some aspect of your AI roadmap/journey? Topic 2 - Assuming the normal “Shadow AI” backlash that you'd receive for offering something that wasn't “frontier” level, how would you go about trying to communicate that within your organization?Topic 3 - What metrics or KPIs would you initially target to try and get buy-in that your approach was acceptable and moving towards the company goals?FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

Enterprise AI agents fail consistently in production, not because of model limitations, but because they lack a live, temporally aware context layer grounded in the actual current state of the business. In this episode, Ravi Marwaha, Chief Operating Officer & Chief Technology Product Officer at Arango, explores how treating context as infrastructure—rather than a data pipeline problem—enables agents to reason accurately, explain their decisions, and deliver measurable outcomes across customer support, semiconductor engineering, and clinical trial site selection. The discussion covers five practical frameworks for CIOs and chief data officers on building real-time, explainable context layers on top of existing enterprise systems, without ripping and replacing current infrastructure. This episode is sponsored by Arango. To learn how to improve landing page conversion and use self-qualification systems to identify high-intent leads, download Emerj's free PDF report, "B2B AI Lead Generation Guide," at emerj.com/aig2

Neste episódio do Economia & Negócios, José Inácio Pilar e Patricia Chaccur discutem sobre campanhas de marketing que não apenas fracassaram, mas geraram perda de reputação e prejuízos bilionários para marcas icônicas.Jaguar, Tesla, Havaianas e Nike são alguns dos casos que mostram como decisões equivocadas de branding podem gerar rejeição do público, boicotes e destruir legados. Contando com a experiência da Patricia Chaccur, que ocupou posições de liderança à frente da comunicação de marcas globais como Nike e Avon, a análise destes casos recentes revela os principais fatores que levam uma marca a perder confiança e conexão com seus consumidores.Em um ambiente polarizado e sensível a questões culturais, mudanças bruscas de posicionamento e campanhas desconectadas do público-alvo podem custar décadas de construção de marca. Entre os temas abordados: * O colapso da Jaguar após seu controverso reposicionamento * A crise de imagem da Tesla associada ao comportamento de Elon Musk * O boicote histórico enfrentado pela Bud Light *O caso Havaianas e a polêmica do “pé direito” * A crise de identidade da Nike e os erros recentes envolvendo a Seleção Brasileira. * O que empresas e profissionais podem aprender com campanhas que deram errado.#Marketing #ErrosDeMarketing #Branding #Nike #Tesla #Jaguar #Havaianas #Negocios #EstudoDeCaso #CriseDeImagem

In this episode of Future Finance, Paul Barnhurst and Glenn Hopper sit down with Dave Trier, CEO of ModelOp, to discuss how enterprises can govern, manage, and operate AI at scale. Dave shares insights on implementing AI responsibly, tracking ROI, managing risks, and creating an enterprise-wide AI portfolio that drives value while ensuring compliance and governance.Dave Trier leads ModelOp with a focus on customer value, product innovation, and enterprise execution. With over 20 years in data science, AI, analytics, cloud, and enterprise software, he brings technical expertise and a pragmatic leadership style, helping CIOs, CTOs, and AI leaders deploy AI effectively across organizations .In this episode, you will discover:How enterprises can scale AI responsibly and reliablyThe CFO's role in AI oversight and portfolio managementMeasuring AI value through ROI, usage, and internal feedbackDistinctions between AI governance and traditional data governanceImportance of change management and structured AI adoptionDave provides a framework for enterprise AI adoption, emphasizing disciplined management, measurable impact, and alignment with regulatory and operational requirements. This episode is essential for finance and tech leaders looking to integrate AI at scale while ensuring oversight, efficiency, and business value . Follow Dave:Website: https://www.modelop.com/LinkedIn: https://www.linkedin.com/in/davidetrier/Follow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn: https://www.linkedin.com/in/thefpandaguyFollow QFlow.AI:Website - https://bit.ly/4i1EkjgFuture Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.In Today's Episode:[00:00] – Trailer[02:38] – AI Compliance & Governance Challenges[04:35] – Distinction Between AI & Data Governance[07:28] – Measuring AI Value & ROI[12:41] – Treating AI as a Portfolio of Investments[15:05] – Change Management & Enterprise Adoption[17:39] – Wild West of AI & Need for Rigorous Processes[18:54] – CFO Oversight in AI Implementation[21:00] – Closing Remarks

Most enterprises are renters, not owners, of their technology and AI. Raffi Krikorian, Chief Technology Officer of Mozilla, explains why dependence on a handful of closed model providers means losing control over model behavior, pricing, and your own data.In CXOTalk episode 920, Krikorian lays out where open-source AI actually wins in the enterprise, how lock-in happens quietly, and what CIOs and CTOs should do about it now. Krikorian draws on his experience building infrastructure at Twitter and running the self-driving division at Uber to ground the discussion in real engineering and economic tradeoffs, not hype.YOU'LL DISCOVER✅ Why 85% of enterprises believed they could switch AI vendors, but only about 30% actually could when they tried✅ The "renters vs. owners" framing and what it means to control your AI destiny✅ Why Krikorian wants data "protected by architecture, not legal handshakes"✅ How Pinterest reportedly saved on the order of $10 million in a single quarter by switching from closed to open models✅ Why IT is becoming "the HR team for agents," and the read/write "dangerous triangle" of agentic permissions✅ The case for recording your prompts and running your own evaluations instead of trusting public benchmarks✅ Why roughly 70% of enterprise GPUs sit idle, and the missing "LAMP stack for AI" that could put them to work✅ How closed "validation machines" can quietly steer answers toward sponsored outcomes⏱️ TIMESTAMPS (estimated, verify before publishing)0:00 Renters vs. owners: who controls enterprise AI2:26 The risks of depending on closed model makers6:23 How lock-in happens and where open source fits9:53 Regression testing and building your own evals13:24 Pricing instability and the post-IPO cost question23:31 Governance: IT as HR for AI agents32:38 Can a small organization own its AI stack end-to-end?38:47 Validation machines, trust, and sponsored answers43:39 Keeping humans at the center, not in the loop47:23 Can open source beat big tech in AI?51:39 Inside Mozilla.ai: Otari, CQ, Octanus, Thunderbolt55:21 The "rebel alliance" strategy

AI implementation in higher education is often framed as a technology question. California State University treated it as change management with technology as the catalyst, rolling out ChatGPT Edu to 22 universities in 18 months while running the largest AI survey ever conducted at a single university system. In this episode of the Changing Higher Ed® podcast, Dr. Drumm McNaughton speaks with Dr. Leslie Kennedy, Assistant Vice Chancellor for Academic Technology Services at the California State University Office of the Chancellor, about how the system designed and executed its generative AI implementation and what the Ahead of the Curve survey of 94,060 respondents reveals about AI adoption, faculty engagement, and student behavior. Drawing on her work co-leading the academic side of CSU's GenAI initiative, Kennedy explains the governance structure that made the rollout possible, the campus-level training infrastructure that scaled adoption across 22 universities, and the survey findings that pushed back on common assumptions about cheating, faculty resistance, and AI access gaps. This conversation is especially relevant for presidents, provosts, boards, and CIOs evaluating how to move from AI policy discussions to systemwide implementation. Topics Covered: The sequencing model behind CSU's 18-month AI rollout Findings from the largest AI survey ever conducted at a single university system Why faculty are the only group reporting both positive and negative AI impact How CSU funded faculty-led innovation through the AI Educational Innovations Challenge The communication challenges of running AI implementation across 22 independent campuses What CSU plans next: hackathons, embedded credentials, and domain-specific tools Real-World Examples Discussed: The AI Educational Innovations Challenge received 417 faculty applications against an expected 50, with 63 funded at $3M ChatGPT Edu deployment across all 22 CSU campuses, now at 225,000 active accounts Student hackathons run with IBM Watson, AWS, NVIDIA, and Cal Poly partners across multiple disciplines Faculty-led podcasts (My Robot Teacher from Cal Maritime and Unfixed from Chico State) that built peer-to-peer training resources Three Key Takeaways for Leadership: Sequencing matters more than budget or technology. Faculty resolution first, governance second, enterprise tool third, training and funded experimentation in parallel. Faculty carry more complexity than staff or students in AI implementation, and need different support, training cadence, and communication than other groups. Communication is a continuous operating discipline, not a launch campaign. The technology changes faster than any single training cycle. This episode offers a practical view of what large-scale AI implementation actually looks like in higher education, and why the institutions getting it right are treating it as change management work supported by technology rather than a technology rollout in search of governance. Read the transcript: https://changinghighered.com/https://changinghighered.com/csu-chatgpt-edu-rollout-lessons-higher-ed-leaders/ #GenerativeAI #HigherEducation #HigherEducationPodcast

For the 194th episode of the Healthcare IT Today Podcast, we are back with another episode of everyone’s favorite game – buy or sell! In case you’ve forgotten or this is your first buy or sell episode, we set out a list of hot topics and trends in healthcare to discuss whether we believe the topic or trend is true/is going to happen (aka, we ‘buy’ it), or if we think it is not true/will not happen (aka, we ‘sell’ it). For this episode, we are doing a special conference edition, focusing on the trends we’ve heard from all of the different conferences we’ve both attended recently! Here's a preview of the topics and trends we discuss in this episode: Health IT budgets are shrinking. Vendor consolidation is still a high priority for CIOs. Value-based care is the key to rural health’s success and survival. Healthcare AI will not replace people. Now, without further ado, we’re excited to share with you the next episode of the Healthcare IT Today podcast. We publish a new Healthcare IT Today podcast every ~2 weeks. Thanks to our friends at Healthcare Now Radio, you’ll be able to listen to the latest episodes of Healthcare IT Today on their radio station for the first two weeks. Then, we’ll be publishing each episode as a podcast and YouTube video here after it finishes on the radio. You can also subscribe to the Healthcare IT Today podcast on any of the following platforms: Apple Podcasts Google Podcasts Stitcher Podcast Radio TuneIn Spotify iHeartRadio Pandora Thanks for listening to Healthcare IT Today and if you enjoy the content we’re sharing, please rate the podcast on your favorite podcasting platform. Along with the popular podcasting platforms above, you can Subscribe to Healthcare IT Today on YouTube. Plus, all of the audio and video versions will be made available to stream on HealthcareITToday.com. If you work in Healthcare IT, we’d love to hear where you agree and/or disagree with the perspectives we shared. Feel free to share your thoughts and perspectives in the comments of this post, in the YouTube comments, with @Colin_Hung or @techguy on Twitter, or privately on our Contact Us page. Let us know what you think of the podcast and if you have any ideas for future episodes. Thanks so much for listening! Listen to Our Latest Episodes:

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

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Dave wraps Season 7 of Reboot IT with Gretchen Steenstra, VP, Client Strategy at DelCor, and a discussion on how organizations can get unstuck and find success with their technology initiatives. Gretchen shares insights from her experience working across multiple organizations, exploring the balance between strategy and execution, the importance of adoption and change management, and how CEOs, CIOs, and business leaders each contribute to achieving success. Themes and Topics:The Balance Between Strategy and ExecutionOrganizations often lean too heavily toward either visionary strategy or detailed execution; success requires both.Strategy sets direction, but execution delivers results and the two must continuously inform each other.Periodic “zoom out” moments are essential to ensure execution is still aligned with strategic goals.Alignment Across the OrganizationTechnology is no longer separate. IT plans must align directly with organizational strategy.CIOs play a critical role in connecting leadership vision with operational reality.Alignment requires continuous communication between leadership, staff, and partners.The Role of the CEO and LeadershipCEOs should connect high-level vision to day-to-day operations without getting lost in the details. Leadership can remove blockers, balance priorities, and champion initiatives effectively. Leadership alignment helps teams understand how their work contributes to the organizational mission.The CIO as a Translator and NegotiatorCIOs act as intermediaries between business needs, technology teams, vendors, and security requirements.They must balance usability and security, often negotiating trade-offs between the two.CIOs also serve as escalation points when projects stall or teams hit roadblocks.Adoption and Change ManagementThe real work begins after launch. Long-term adoption determines success.Organizations often underinvest in post-launch behaviors and process changes.Preventing “backslide” requires ongoing reinforcement and attention to how people actually work.Building Tech Literacy and Cross-Functional CollaborationBusiness leaders must develop a baseline understanding of how systems connect and interact.Cross-functional planning (marketing, finance, IT, membership) helps surface risks early.Mapping the full customer journey, from awareness to transaction to delivery, improves outcomes.

Send us Fan MailJennifer Smith is the CEO of Scribe and a former McKinsey consultant turned venture capitalist who interviewed over 1,200 enterprise CIOs before founding her company. Recorded live from the floor of HumanX 2026, this lightning round covers how AI is helping organizations finally understand how work actually happens inside their teams.Jennifer and host Dan Turchin explore why even Fortune 500 leaders don't truly know how their companies operate, and what that means for AI transformation.What You'll Learn Why process visibility comes before any AI initiativeHow Scribe maps workflows across 600,000+ companiesResponsible AI and building a data-driven cultureHumans and agents working side by sideHelping people spend more time on the work they love 

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

If your leadership team is always busy but never quite focused on the right things and you're still the one who has to sort out which initiatives actually matter, this episode is for you.Teresa Duran, Chief Information Officer at Tree Top Inc., is an award-winning technology executive with over two decades of experience leading large-scale transformations across multiple industries, including biotech, pharma, and nonprofit. She was recognized with the 2023 SeattleCIO ORBIE Award for leading the largest transformation in Make-A-Wish America's 43-year history.In this conversation, Teresa unpacks why organizations, even well-resourced ones, keep chasing the wrong priorities, and how the CEOs and CIOs who actually move fast build simple, repeatable filters so their leaders can make better calls without waiting for direction from the top.You'll learn:- Why "too many initiatives" is a leadership structure problem, not a workload problem and the top-five filter that cuts the noise fast- How to tell the difference between what's important and what actually has impact at the company level- What effective decision-making looked like when Teresa's team delivered a three-year technology roadmap in one year, during COVID- Why bringing your technology leader into M&A and divestiture planning early reduces risk and increases your company's attractiveness to buyers- How to prepare your leadership bench to stay ahead of AI disruption without chasing every tool that hits the marketIf your regional managers or functional leaders are still running to you every time something feels uncertain, or your company is spending money on tools and projects that aren't moving the needle, this episode gives you a practical framework to change that.Listen now. Then share this with another CEO or operator who's tired of sorting through the noise so their team doesn't have to.

In this episode of the Finovate podcast, host Greg speaks with Oren Buskila, CEO and co-founder of Cobalt, a FinovateSpring 2026 Best of Show winner. Cobalt has developed enterprise architectural intelligence specifically designed for financial institutions, addressing a critical challenge in modern banking: the lack of understanding of complex system dependencies.Banks operate on enormously intricate systems comprising their own code and dozens of third-party vendor applications, yet most institutions don't fully comprehend how these systems interconnect and depend on one another. This knowledge gap leads to significant problems, including development slowness—with banks spending 70% of their IT budgets on maintenance rather than innovation—and costly production failures that can result in millions of dollars in direct and indirect costs when changes are deployed without full visibility into system dependencies.Cobalt's solution automatically maps both IT systems and business processes in real-time by scanning existing data sources including event logs, API logs, code, and database tables. The platform creates a comprehensive topology map that aligns business processes with their underlying technical infrastructure, allowing banks to see exactly which systems support which business functions and understand the full impact of any proposed changes. This "bank on a page" architecture view enables technical teams to anticipate the consequences of modifications before implementation, preventing failures and ensuring safer deployments. The platform maintains a live, continuously updated view of the system architecture by taking frequent snapshots that can be compared to detect changes and investigate issues, a capability that was previously impossible with manual architecture mapping methods.The demo at FinovateSpring resonated strongly with attendees, particularly technical leaders like CIOs and CTOs from medium and large banks who recognized the transformative potential of having complete visibility into their systems. The presentation also attracted significant interest from venture capitalists and leaders from smaller banks and credit unions, the latter group seeing opportunities to introduce agentic AI into their operations by first mapping existing business processes. Looking ahead, Cobalt positions itself as the essential architectural layer for AI-driven development in banking, as the industry moves toward having AI agents generate, maintain, and modify code at unprecedented velocities—a shift that will require the contextual understanding and change management capabilities that Cobalt provides.More info:Cobalt AI: https://www.getcobalt.ai/; https://www.linkedin.com/company/getcobalt/Oren Buskila: https://www.linkedin.com/in/oren-buskila/Greg Palmer: https://www.linkedin.com/in/gregbpalmer/Finovate: https://www.finovate.com; https://www.linkedin.com/company/finovate-conference-series/FinovateSpring: https://informaconnect.com/finovatespring/#Finovate #FinovateSpring #Banking #banks #creditunions #personalization #data #communitybanks #AI #backoffice #corebanking #digitaladoption #podcast #fintechpodcast #financialservices #innovation #digitraltransformation #fintech #finserv #modernization

For twenty years the security playbook started in the same place, find a vulnerability, prioritize it, and patch it. Doug Merritt, CEO of Aviatrix and former CEO of Splunk, thinks that playbook is quietly breaking, and his explanation has nothing to do with anyone being careless. The economics of offense changed underneath us, and most security programs are still funded as if they did not.Why this conversation mattersDoug has sat in two seats that give this argument weight. At Splunk he evangelized detect and respond, and now at Aviatrix he is arguing that detect and respond, while still important, is no longer enough on its own. That is not a vendor pivot so much as an honest reading of the incentives, and it lands differently coming from someone who built a business on the previous era. If you are a practitioner watching AI rewrite the attacker's cost curve, or a leader trying to defend a prevention-heavy budget to a board, this conversation reframes where the money should actually go.Key takeawaysOffense became a compute problem, and that is permanent. Finding and exploiting a vulnerability is a search task, and the cost per token has been deflating faster than Moore's Law. That is why this is a structural shift rather than a few headline demos, and why throwing compute at offense keeps getting cheaper and faster.Patching has a ceiling that offense does not. Every patch carries the risk of breaking something, so testing, deployment, and organizational friction cap how fast defenders can move. When vulnerability discovery scales freely and patching cannot, "find more and patch faster" turns into a race you are structurally set up to lose.The interesting question is not how they got in, it is where they went. Attackers increasingly arrive with valid credentials and move through the trust graph that runs across cloud services and CI/CD pipelines, including malware injected into trusted repositories. Once they look legitimate inside the environment, lateral movement and egress are where the real damage happens.Cloud rewarded velocity, and security paid the bill. Cloud providers made identity default-deny because someone has to own and pay for a workload, but they left networking wide open because their economic engine is developer velocity and security reads as friction. New agentic frameworks inherit that same wide-open default, connected to the internet with little oversight.A strong identity stance is necessary and not sufficient. Identity answers whether someone is allowed to act, not whether the action is an attack, which is why attackers log in rather than hack in. Human, agent, and workload identities are genuinely different, and workload identity in particular has been underserved.Containment is about blast radius, not about keeping everyone out. The mindset shift is to accept that breaches will occur and to govern every path a workload can take, so an incident stays local and recoverable. Done well, containment holds firm whether or not anyone has detected the attack yet.Blast radius has to become a boardroom metric. Doug's argument is that CISOs, CIOs, CEOs, and boards should be able to answer how reachable anything is from anything else, and treat that number as something to drive down deliberately rather than discover after an incident.AI is the reason containment is finally workable. The historic blocker to micro-segmentation was cognitive load across tens or hundreds of thousands of workloads. AI is strong at synthesis and pattern matching, which makes a staged path of observe, discover, monitor, and then enforce realistic, ideally starting with the internet-exposed workloads that have no filtering at all.