Podcasts about responsible ai

AI is no longer a future trend. It is reshaping how organizations lead, innovate, and create impact today, and nonprofits are no exception.  DeAnna Hoskins is the President and CEO of JustLeadershipUSA, an organization led by formerly incarcerated people. She also served as a senior policy advisor during the Obama administration. Her experience in government and nonprofit leadership gives her a unique view on how technology, policy, and community work can come together to create positive change. During the conversation, DeAnna explains why nonprofits should invest in AI training before adopting new tools. She mentions that AI should support people, not replace them, and that human oversight is always important. She also talks about the need to reduce bias in AI, to create clear guidelines for its use, and to continue learning as the technology evolves. Furthermore, DeAnna discusses the role of philanthropy in funding AI education and helping organizations build the skills they need.  In this episode, you will be able to:  Recognize the value of AI training. Use AI with human oversight. Build responsible AI guidelines. Identify AI bias and risks. Create long-term AI strategies. Use AI to support teams. Understand AI funding needs. Get all the resources from today's episode here.  Support for this show is brought to you by Donor Perfect. Our friends at Donor Perfect really understand fundraising on so many levels. Stay aligned while working online with a seamless and secure payments experience for your donors and your team. Empower donors to give where they are, whenever they like, automate data entry, and process online, monthly, and mobile payments, and accept payments over the phone. Connect with me:  Instagram: https://www.instagram.com/_malloryerickson/ Facebook: https://www.facebook.com/whatthefundraising YouTube: https://www.youtube.com/@malloryerickson7946 LinkedIn: https://www.linkedin.com/mallory-erickson-bressler/ Website: malloryerickson.com/podcast Loved this episode? Leave us a review and rating here: https://podcasts.apple.com/us/podcast/what-the-fundraising/id1575421652 If you haven't already, please visit our new What the Fundraising community forum. Check it out and join the conversation at this link. If you're looking to raise more from the right funders, then you'll want to check out my Power Partners Formula, a step-by-step approach to identifying the optimal partners for your organization. This free masterclass offers a great starting point.

Join us for an insightful episode of The Brand Called You as host Ashutosh Garg speaks with Jyothika Raju, Co-Founder and Program Director of ImpactAI Foundry.Discover how Jyothika's journey—from studying engineering in Bengaluru to leading initiatives at the intersection of artificial intelligence, public policy, and social impact—has shaped her vision for responsible AI.In this conversation, she explores:What responsible and accountable AI truly meansThe challenges NGOs and nonprofits face when adopting AIWhy capacity building is more valuable than technology dependencyPractical AI solutions already transforming the social sectorThe importance of collaboration between governments, universities, startups, and nonprofitsActionable advice for young professionals pursuing careers in AI for social good

Join us for an insightful episode of The Brand Called You as host Ashutosh Garg speaks with Jyothika Raju, Co-Founder and Program Director of ImpactAI Foundry.Discover how Jyothika's journey—from studying engineering in Bengaluru to leading initiatives at the intersection of artificial intelligence, public policy, and social impact—has shaped her vision for responsible AI.In this conversation, she explores:What responsible and accountable AI truly meansThe challenges NGOs and nonprofits face when adopting AIWhy capacity building is more valuable than technology dependencyPractical AI solutions already transforming the social sectorThe importance of collaboration between governments, universities, startups, and nonprofitsActionable advice for young professionals pursuing careers in AI for social goodWhether you're passionate about artificial intelligence, nonprofit innovation, public policy, or ethical technology, this episode offers valuable insights into building AI that creates meaningful and lasting impact.

Reggie Townsend: Making Responsible AI Irresistible Responsible AI has a design problem. Too often, it is treated as a compliance exercise, a policy document, or a late-stage control, when it should be the operating system that enables organizations to innovate with confidence. In this episode of Scouting for Growth, Sabine VanderLinden welcomes back Reggie Townsend, Vice President of AI Ethics, Governance and Social Impact at SAS, to explore one compelling idea: making responsible AI irresistible. Drawing on decades of experience helping enterprises embed trustworthy AI into their operations, Reggie explains why governance must evolve from abstract principles to practical systems people actually use. As organizations race to deploy generative and agentic AI, the conversation has shifted. Responsible AI is no longer just about avoiding harm—it is about creating the conditions for innovation, resilience, and long-term competitive advantage. Boards are asking tougher questions. Regulators are raising expectations. Employees increasingly need guidance they can apply in real-world decisions, not just policies they acknowledge once a year. This conversation is essential listening for CEOs, board directors, Chief Risk Officers, compliance leaders, AI product teams, and founders navigating the transition from responsible AI intentions to responsible AI execution.   KEY TAKEAWAYS One of the biggest insights I took from this conversation is that responsible AI is fundamentally a design challenge. We have spent years writing principles and policies, yet many organizations still struggle to translate those aspirations into everyday decisions. Reggie reminded me that if governance feels complicated, disconnected, or burdensome, people will naturally work around it. Our challenge as leaders is to make responsible behavior the easiest path rather than the hardest one. I was also struck by how governance is becoming inseparable from business strategy. As generative and agentic AI systems begin making increasingly autonomous decisions, governance can no longer be treated as a legal or compliance function operating at the edge of the organization. It has to become part of product design, procurement, operations, and executive decision-making. Trust is no longer something we communicate after deployment; it is something we engineer from the beginning. Another important theme was the preservation of human agency. While AI can dramatically enhance productivity and decision-making, Reggie reminds us that organizations must remain intentional about where humans stay accountable. The future is unlikely to be defined by replacing people with AI, but by designing systems where humans and intelligent machines complement one another in transparent and meaningful ways. However, perhaps my greatest takeaway is that responsible AI should become a competitive advantage rather than a regulatory obligation. Organizations that embed governance in their innovation will move with greater confidence because customers, regulators, employees, and investors will increasingly reward trust. Making responsible AI irresistible is ultimately about making good governance so practical, intuitive and valuable that people actively choose to adopt it—and that may prove to be one of the most important leadership capabilities of the next decade.   BEST MOMENTS "Responsible AI isn't about slowing innovation. It's about creating the confidence to innovate at scale." – Reggie Townsend "If responsible AI feels like extra work, we've designed it wrong. We have to make it irresistible." – Reggie Townsend "Governance shouldn't be something you visit once a year. It should be embedded into every decision, every workflow, and every system we build." – Reggie Townsend "The question isn't whether AI will make decisions. It's whether we've designed those decisions to preserve human agency." – Reggie Townsend "Trust isn't something you add after deployment. It's something you architect from the very beginning." – Reggie Townsend "We're moving from governing models to governing systems of intelligence—and that's an entirely different challenge." – Reggie Townsend "The organizations that thrive won't be the ones that adopt AI the fastest. They'll be the ones that build trust the fastest." – Reggie Townsend "Responsible AI is no longer just an ethics conversation. It's becoming a leadership conversation, an operational conversation, and ultimately a competitive advantage." – Sabine VanderLinden "As AI becomes more autonomous, our responsibility as leaders becomes even more intentional." – Sabine VanderLinden "The future belongs to organizations that can turn responsible AI from a policy into a practice." – Sabine VanderLinden   ABOUT THE GUEST Reggie Townsend is Vice President of AI Ethics, Governance and Social Impact at SAS, where he leads SAS' global AI Ethics, Governance and Social Impact organization. His remit includes the company's Data & AI Ethics Practice, AI & Society initiatives, AI Governance Advisory, Standards, Regulations & Risk Intelligence programs, and Accessible & Adaptive AI efforts. He drives SAS' work on trustworthy, human-centric innovation across products, policies, and partnerships.  Reggie is recognized as one of the clearest voices in responsible and trustworthy AI. He has served as a member of the White House National AI Advisory Committee, sits on the board of EqualAI, and works at the intersection of responsible innovation, enterprise governance, social impact, and emerging AI regulation. In this conversation, he explores how organizations can turn AI governance from a source of friction into a driver of adoption, accountability, and growth.    ABOUT THE HOST Sabine VanderLinden is a corporate strategist turned entrepreneur and the CEO of Alchemy Crew Ventures. She leads venture-client labs that help Fortune 500 companies adopt and scale cutting-edge technologies from global tech ventures. A builder of accelerators, investor, and co-editor of the bestseller The INSURTECH Book, Sabine is known for asking the uncomfortable questions—about AI governance, risk, and trust. On Scouting for Growth, she decodes how real growth happens—where capital, collaboration, and courage meet. If this episode sparked your thinking, follow Sabine VanderLinden on LinkedIn, Twitter, and Instagram for more insights. And if you're interested in sponsoring the podcast, reach out to the team at hello@alchemycrew.ventures

Subscribe now for an ad-free experience. Danny and Derek welcome to the show journalist and author Robert Wright to talk about the potential and pitfalls of the AI revolution. They discuss how neural networks learn, the differences between predictive and generative AI, machine consciousness, job displacement, the environmental costs of data centers, how capitalism is shaping the AI race, and the need for international regulation. Read Robert's new book The God Test: Artificial Intelligence and Our Coming Cosmic Reckoning. Learn more about your ad choices. Visit megaphone.fm/adchoices

Danny and Derek welcome to the show journalist and author ⁠Robert Wright⁠ to talk about the potential and pitfalls of the AI revolution. They discuss how neural networks learn, the differences between predictive and generative AI, machine consciousness, job displacement, the environmental costs of data centers, how capitalism is shaping the AI race, and the need for international regulation.Read Robert's new book The God Test: Artificial Intelligence and Our Coming Cosmic Reckoning.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Ready to build intelligent cloud applications? The AWS Certified AI Practitioner credential validates fundamental cloud AI expertise. In this episode of TechTalks, InfosecTrain delivers an exam-oriented walkthrough to help you pass on your first attempt. The "course titled" AWS Certified AI Practitioner Training accelerates your study through real-world architectures, Amazon Bedrock, SageMaker, prompt optimization, and token management.

In this episode of The Product Podcast by Product School, Carlos González de Villaumbrosia sits down with Jeff Kunins, Chief Product Officer and Chief Technology Officer at Axon, the company that created the Taser and the body cameras federal agencies wear. Axon ingests more video per year than YouTube, and with a market cap of approximately $32.9 billion and $2.78 billion in revenue, growing 33% year over year, it is one of the highest-growth companies in the S&P 500. What you'll learn:How law enforcement agencies are using AI inside body cameras and Tasers to save lives, not just hit metrics.Why Axon declared a public moratorium on facial recognition AI for six years and what finally changed.How Axon embeds external activists and researchers directly into product manager squads as a design input, not a compliance process.Building first-party AI models for real-time license plate detection while using foundation LLMs for everything else.Key takeaways:Axon created the Taser and the body cam, and now ingests more video per year than YouTube. Most people have never heard of them.Build only what you must to be differentiated. Everything else, license from the best available source.Ethics review is not a compliance burden. When embedded in the product lifecycle, external critics help you see around corners and design better products.Credits:Host: Carlos Gonzalez de VillaumbrosiaGuest: Jeff KuninsSocial Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

Ryan Bishara and Christian Lau team up on an AI and data playbook that delivers an engaging, penalty free experience for LAFC employees and fans on and off the pitch.   Ryan, Christian and Kimberly discuss LAFC's innovative ethos; building an analytics and data program from the pitch up; LAFC's AI plays; making mistakes and continuous learning; keeping AI onside with responsive governance; prioritizing joy and the human experience; if convenience and privacy can co-exist; staying accessible and engaging fans; and how analytics/AI continue to play out in a rapidly evolving ecosystem.  Ryan Bishara and Christian Lau serve as the EVP of Revenue & Strategy and Chief Technology Officer, respectively, at the LA Football Club (LAFC).  A transcript of this episode is here.

What does responsible AI actually look like once you move beyond the headlines and start deploying it inside highly regulated businesses? In this episode, I speak with Richa Kaul, Founder and CEO of Complyance, about one of the biggest challenges facing enterprise AI today: building systems that people can trust. As companies race to adopt AI across every part of the business, governance, risk management, and compliance are no longer back-office functions. They are becoming central to every conversation about innovation. Richa shares the personal experiences that inspired her to build Complyance, from her work in public sector technology and legal AI to her long-standing passion for data privacy. We discuss why trust has become one of the defining themes of enterprise AI and why businesses must think beyond their own AI initiatives to also understand the risks introduced by third-party vendors. One of the most interesting parts of our conversation focuses on the difference between compliance and risk. Rather than viewing compliance as a box-ticking exercise or a cost center, Richa explains why AI has brought risk discussions directly into the boardroom. Business leaders are now asking deeper questions about how customer data is handled, how AI decisions are governed, and what safeguards need to exist before new technologies are deployed at scale. We also explore how AI is changing governance itself. Traditional compliance has often relied on manual reviews and simple pass-or-fail checks, but Complyance is applying agentic AI to introduce greater context and human-like judgment into governance workflows. Richa explains how that approach is helping reduce manual effort while allowing teams to focus on higher-value risk decisions rather than repetitive administrative work. Our conversation also covers practical advice for companies introducing AI into regulated environments. From evaluating third-party vendors and defining acceptable risk thresholds to adopting emerging AI standards and maintaining transparency throughout the process, Richa offers thoughtful guidance for leaders trying to balance innovation with accountability. Along the way, we also discuss Complyance's recent $20 million Series A investment led by Google Ventures and what that recognition means for the company's mission to modernize governance, risk, and compliance with AI. If your business is investing in AI while trying to strengthen trust, transparency, and responsible innovation, this episode offers a timely look at how governance is evolving alongside the technology itself. As AI becomes embedded into more business processes, how is your company building trust while still giving teams the freedom to innovate?

Today's episode of the Punk CX podcast features a chat I had with Katja Forbes, Author, Advisor & Keynote Speaker, about her new book: Machine Customers: The Evolution has Begun: How AI that buys is changing everything. We talk about what exactly a machine customer is, what proportion of both B2B and B2C transactions are likely to be driven by machine customers in five years time, if we are seeing Doc Searls' Vendor Relationship Management (VRM) brought to life with this, what sort of agents will there be, who will provide them and what happens to “shopping”…..so many questions! This interview follows on from my recent interview – Responsible AI isn't an optional layer, it must be foundational – Interviews from Pegaworld 2026 Pt2 – and is number 593 in the series of interviews with authors and business leaders who are doing great things, providing valuable insights, helping businesses innovate and delivering great service and experience to both their customers and their employees.

AI is impacting every part of our lives—and we need to start paying more attention.Listen to this insightful conversation with Dr. Steven Wang, CEO of Venture for Canada, whose work sits at the intersection of AI, entrepreneurship and public policy.We discuss:How AI is transforming the workforce and traditional careersHow to prepare the next generation for an AI-driven economyWhat governments, businesses, and citizens must do to ensure AI benefits everyoneWhy entrepreneurship skills—and empathy!—offer competitive advantagesIf you're looking for strategies to future-proof your career, you don't want to miss this conversation.00:00 Preview00:59 Episode introduction03:36 About Dr. Steven Wang05:27 Steven's backstory08:16 Why AI governance matters more than AI technology11:01 What does “Responsible AI” mean?18:26 Why every young person needs an entrepreneurial mindset23:32 How can fresh graduates overcome the experience gap?28:14 Why buying a business may be smarter than launching one in the age of AI31:58 The human skills that AI cannot replace38:52 Smart ways to use AI during a job search46:27 How “reverse mentorship” can transform a workplace17:40 How to prepare young people for an AI-ubiquitous future30:42 Dr. Steven Wang's Purposeful Empathy storyEpisode #9 of Empathy in the Age of AI, a special 25-part series: https://tinyurl.com/exyw2nua CONNECT WITH STEVEN✩ LinkedIn https://www.linkedin.com/in/stwang12/ ✩ Website https://ventureforcanada.ca/✩ Instagram https://www.instagram.com/venture4canada/ CONNECT WITH ANITA✩ Email purposefulempathy@gmail.com ✩ Website https://www.anitanowak.com✩ Buy a copy of Purposeful Empathy http://tiny.cc/PurposefulEmpathyCA✩ LinkedIn https://www.linkedin.com/in/anitanowak/✩ Instagram https://tinyurl.com/anitanowakinstagram✩ Podcast Audio https://tinyurl.com/PurposefulEmpathyPodcast✩ Bluesky https://bsky.app/profile/anitanowak.bsky.socialSHOW NOTES✩ From Brain Drain to Talent Circulation: Why Canada Needs a Diaspora Strategy https://opencanada.org/from-brain-drain-to-talent-circulation-why-canada-needs-a-diaspora-strategy/✩ The Perils of Using AI to Replace Entry-Level Jobs https://hbr.org/2025/09/the-perils-of-using-ai-to-replace-entry-level-jobs Video edited by Jad Misri, Green Horizon Studio

KI diskriminiert nicht von selbst – sie wiederholt unsere Muster. Alex Ulbricht von Fujitsu hat über 400 Keynotes zu AI-Fairness gehalten. In dieser Folge geht es um den Amazon-Recruiting-Fall, um Crashtest-Dummies und Medikamentenforschung, um Fairness by Design und um den Disparate Impact Ratio, mit dem du Bias in einer einfachen Formel testen kannst. In dieser Folge erfährst du: → Warum KI keine Vorurteile erfindet, sondern historische Muster reproduziert → Was der Amazon-Recruiting-Fall von 2014 bis heute lehrt → Wie ein Bias-Audit abläuft – und was Intersectional Bias bedeutet → Warum Fairness von Anfang an ins Design gehört (wie Barrierefreiheit) → Wie du mit dem Disparate Impact Ratio in einer Formel auf Bias testest Über den Gast: Alex Ulbricht ist seit über zwölf Jahren bei Fujitsu und war dort die jüngste Vertriebsleiterin im Datacenter-Sales. Heute ist sie Keynote-Speakerin zum Thema AI-Fairness und hat über 400 Vorträge gehalten. MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für richtig gute Podcasts.

Chaitra Vedullapalli from Women in Cloud discusses the future of local commerce, the impact of AI, and the importance of adaptive leadership in the economic landscape. Chaitra discusses a topic that should be on every business leader's radar: the shift toward local commerce and how AI, community partnerships, and economic access are reshaping the way we buy, sell, and connect. Key TakeawaysWomen in Cloud's mission and accomplishmentsThe shift towards local commerce and trust-buildingThe role of AI in personalization and scalabilityEcosystem orchestration and powered marketplacesResponsible AI use and human element in technologyChapters00:00Introduction to Chaitra and Her Mission02:36Women in Cloud: Empowering Women in the AI Economy05:16The Next Trillion Dollar Opportunity: Local Commerce08:06Community Powered Marketplaces and Economic Engagement10:58The Role of AI in Local Commerce13:59Human Element in AI and Commerce16:39Hot Takes on Current Trends and Future Opportunities22:13Closing Thoughts and Call to ActionConnect with Chaitra and Women in Cloudhttps://www.linkedin.com/in/chaitrav/ https://in.linkedin.com/company/women-in-cloud

For nearly 160 years, the International Telecommunication Union has helped the world communicate across borders, from the telegraph to the telephone, television, satellite, the internet, and now AI. In this episode of Tools and Weapons, Brad Smith sits down with Doreen Bogdan, Secretary-General of the ITU, to discuss why connectivity remains one of the world's most important foundations for opportunity. The conversation explores the 2.2 billion people who are still unconnected, the estimated $2.8 trillion needed to connect the world by 2030, and the partnerships required to reach the hardest-to-connect communities. Doreen shares stories from the field, including a refugee camp in Chad where a small computer center gives people access to learning, health care, financial tools, and family connections. Brad and Doreen also discuss the rise of AI for Good, the challenge of scaling solutions that address real-world needs, and the role of global cooperation in shaping responsible AI governance. From early warning systems that can help save lives during natural disasters to digital skilling and infrastructure investment, this episode examines how technology can create opportunity when access, trust, and partnership come together. Listen to the full episode and join the conversation about building a more connected and inclusive digital future.

AI adoption is no longer just a policy conversation. For many organizations, the bigger question is how to move faster without creating avoidable risk.In this episode of The Tech Trek, Amir Bormand sits down with Aimee Cardwell, CIO and CISO in residence at Transcend, to talk about responsible AI deployment, the tension between speed and control, and how leaders should think about security, compliance, productivity, and customer experience as AI moves through the enterprise.Aimee brings a rare view across the CIO, CISO, and board lens. The conversation gets into why blocking AI often backfires, how prompt redaction can help teams move faster safely, where companies should draw the line on risk, and why some teams may need to rethink old assumptions about tech debt, code ownership, and modernization.Practical Takeaways• Responsible AI depends on the lens. Security, compliance, business, board, and technology teams may all define it differently.• Blocking employee AI usage can create worse outcomes. People may use shadow tools anyway, or teams may fall behind in productivity.• Prompt redaction and enterprise agreements can give teams room to experiment while reducing exposure of sensitive data.• Moving fast is not the same as releasing half finished customer experiences. Bad AI tools can train customers to distrust the entire interaction.• AI may change how teams think about tech debt, refactoring, and whether some legacy systems should be rebuilt instead of patched forever.Timestamped Highlights00:00 Responsible AI deployment and why the definition changes by role02:35 Aimee explains the CIO, CISO, and board perspectives on AI adoption05:14 Why companies that block AI may create shadow usage and slower teams06:52 Prompt redaction as a practical way to let employees experiment safely10:40 How AI risk changes when the data exposure model is different from traditional insider theft15:10 Why releasing poor AI customer experiences can damage trust21:50 Using shared enterprise prompts to raise the quality of AI output across engineering teams26:20 How AI could change the way teams approach security debt and code modernizationOne Line That Stuck“The conversation has flipped, and it is really how can I get the company to go faster.”Pro Tips• Start by identifying what truly makes your business defensible. Not every asset carries the same risk.• Give employees safe paths to use AI instead of pretending they will not use it.• Build shared prompts with engineering standards, approved tools, and company context so teams do not start from scratch every time.• Ask whether old assumptions still hold. Some decisions made sense when changes were expensive, slow, or risky. AI may change that equation.Subscribe to The Tech Trek for more conversations on how modern technical teams are building, hiring, operating, and adapting around AI, data, platform, product, and engineering execution.#ai #agentic #techleadership #engineeringleadership 

Today's episode of the Punk CX podcast is Part Two of a two-parter featuring a series of chats I had with Pega executives while at Pegaworld in Las Vegas a couple of weeks ago. In this episode, I talk with Matt Healy, Senior Director, Product Strategy & Marketing at Pega, and Tara DeZao, Senior Product Marketing Director at Pega. Some of the things we cover include the big themes and takeaways from the event, the future of customer service, ethical AI, the customer engagement blueprint and the new and exciting agentic capabilities of Customer Engagement Studio. This interview follows on from my recent interview with Ken Stillwell, COO & CFO at Pega and Peter Lacroix, Head of Low-Code, Achmea, a long-time customer of Pega's, called Moving from Experimental Pilots to Proven CX Outcomes – and is number 592 in the series of interviews with authors and business leaders who are doing great things, providing valuable insights, helping businesses innovate and delivering great service and experience to both their customers and their employees. #PegaPartner

Get in touch - leave me a messageWhat if AI's biggest climate impact isn't chatbots, but cutting real energy waste in buildings, grids, and factories?In this episode of Climate Confident, I'm joined by Philippe Rambach, Chief AI Officer at Schneider Electric, to unpack one of the sharpest tensions in climate tech today: AI is increasing electricity demand, but used well, it may also be one of the tools we need for decarbonisation, emissions reduction, and a faster energy transition.You'll hear why Philippe argues that the real opportunity is not in chasing every shiny new model, but in applying AI to physical systems: reducing peak demand, optimising building energy use, supporting grid operators, and helping companies move from pilots to production. We dig into Schneider Electric's work on using AI to cut energy waste, including the striking claim that in some energy-saving applications, the carbon emitted to run the model can be dwarfed by the energy saved.We also get into the hard bits people love to ignore because apparently spreadsheets and wishful thinking are still considered strategy in some quarters. Why do so many AI pilots fail to scale? Why does domain knowledge matter as much as technical skill? How should businesses think about responsible AI, privacy, policy, net zero, and the operational realities of electrification?This is a practical conversation about AI for energy, not AI theatre.

Maitreya Shah disables harmful notions and aspires to a world in which AI systems honor the humanity and agency of disabled persons rather than using them as a shield.   Maitreya and Kimberly discuss digital tech done well; how society views disabled persons; engaging people with disabilities as leaders and developers; ableist narratives; why ‘fixing' disabilities misses the mark; confusing accessibility with AI for Good; whitewashing bad behavior with assistive tech; the false dichotomy between access and privacy; disability as a diverse identity; the high stakes for AI reliability and trust; the deepening digital divide; the dearth of disability data and resources; entrenched societal biases; and asking rather than deciding for people with disabilities. Maitreya Shah is a lawyer and researcher working at the intersection of tech policy and disability rights. Maitreya current serves as the Technology Policy Director at the American Association of People with Disabilities (AAPD). Related Resources: To Regulate Artificial Intelligence Effectively We Need to Confront Ableism (Article) Maitreya Shah (Profile) A transcript of this episode is here.

In this episode of UC Today, host Kristian McCann sits down with Laura Maffucci, Head of HR at G-P, to explore one of the biggest questions facing enterprise leaders today: is AI delivering on its promises, or are organizations beginning to question the return on their investments?According to G-P's latest research, a significant number of executives report that AI investments have failed to meet expectations, raising concerns about ROI, productivity gains, and long-term value. But does this signal a problem with AI itself, or with how organizations are approaching it?Laura shares a candid assessment of the current state of enterprise AI, explaining why many companies are chasing AI initiatives without clearly defining the problems they are trying to solve. Key Discussion Points:AI investment challenges: Why many executives say AI spending is failing to deliver the expected ROI.The "supervision tax": How AI can create additional work when organizations rely on poorly implemented solutions.Responsible AI adoption: Why reimagining business processes matters more than simply adding AI tools to existing workflows.Trust, governance, and data quality: How enterprises can improve AI outcomes by using verified information sources and protecting confidential data.Next Steps:Follow the latest developments in employee engagment at UC Today

City leaders are on the front lines of data use, but most lack visibility into the federal data landscape, what's available, what's changing, and how federal policy decisions affect local outcomes. This gap delays emergency response, misdirects resources away from high-need neighborhoods, and undermines AI systems that depend on accurate data and community trust. Host Stephen Goldsmith speaks with Denice Ross,  Director of Federal Data Policy at the Federation of American Scientists, about the relationship between local and federal data, what city CDOs should prioritize, and why cities have untapped power to shape federal data policy. In this episode, you'll learn: The often-hidden relationship between local data needs and federal data infrastructure How to identify and access the federal data your city should be using Why now is the time to prepare for Census 2030 and protect funding How community participation in data decisions prevents disparities and builds legitimacy for AI systems How local data leaders can advocate effectively during federal policy windows Guest: Denice Ross –  Director of Federal Data Policy at the Federation of American Scientists; former United States Chief Data Scientist Listener Survey: bit.ly/datasmartpod Music credit: Summer-Man by Ketsa About Data-Smart City Solutions Data-Smart City Solutions, housed at the Bloomberg Center for Cities at Harvard University, is working to catalyze the adoption of data projects on the local government level by serving as a central resource for cities interested in this emerging field. We highlight best practices, top innovators, and promising case studies while also connecting leading industry, academic, and government officials. Our research focus is the intersection of government and data, ranging from open data and predictive analytics to civic engagement technology. We seek to promote the combination of integrated, cross-agency data with community data to better discover and preemptively address civic problems. To learn more visit us online and follow us on LinkedIn.

Why the most effective communicators help people see not just what's changing, but why it matters to them.For Sinéad Bovell, effective communication isn't just about explaining what's coming next—it's about giving people the confidence and agency to engage with it.Bovell is a futurist, founder of the tech education company WAYE, and an expert advisor to the United Nations AI Advisory Body. Known for making complex topics accessible to broad audiences, she has spent years helping leaders, organizations, and young people understand the implications of artificial intelligence and other transformative technologies. Her approach starts with a simple principle: meet people where they are and connect big ideas to what matters in their lives. “If you scare people too much, if you disempower them, [and] they do unsubscribe from the very activities you need them to lean into.”In this episode of Think Fast, Talk Smart, Bovell joins host Matt Abrahams to discuss how to communicate complexity without overwhelming people and why skills like adaptability and judgment are becoming more valuable in the age of AI. From making emerging technologies more accessible to building trust through relevance and empathy, they discuss what it takes to help audiences engage with change rather than fear it.To listen to the extended Deep Thinks version of this episode, please visit FasterSmarter.io/premium.Episode Reference Links:Sinéad BovellConnect:Premium Signup >>>> Think Fast Talk Smart PremiumEmail Questions & Feedback >>> hello@fastersmarter.ioEpisode Transcripts >>> Think Fast Talk Smart WebsiteNewsletter Signup + English Language Learning >>> FasterSmarter.ioThink Fast Talk Smart >>> LinkedIn, Instagram, YouTubeMatt Abrahams >>> LinkedIn Chapters:(00:00) - Introduction (01:00) - Explaining Complex Ideas (03:48) - The Future of Soft Skills (06:52) - Talking About AI Without Fear (10:33) - Storytelling for Young Audiences (12:46) - Reaching Young Audiences (15:01) - Career Pivots & Reinvention (16:53) - Becoming a Better Communicator (18:59) - The Final Three Questions (25:09) - Conclusion

Kelly Anne Pipe is Head of Developer Experience at Vanguard, and Nicole Scribner is a Director in the firm's Chief Technology Office focused on engineering enablement and advancement.In this session from DX Annual, Kelly Anne and Nicole share how Vanguard is expanding its AI strategy beyond software engineering to the entire product development lifecycle. While the company initially focused on tools like GitHub Copilot for engineers, they found that faster coding alone did not significantly improve delivery speed. Product managers, designers, QA teams, and organizational processes were still operating at a different pace.To address this challenge, Vanguard developed a product team maturity model built around three stages: Augmented, Accelerated, and Autonomized. The framework spans six dimensions, from AI-powered delivery and AI-ready codebases to team autonomy, operations, and responsible AI.Kelly Anne and Nicole explain how Vanguard is applying the model across more than 800 product teams, the behaviors they believe will enable faster delivery, and the lessons they have learned about measurement, organizational change, dependencies, and scaling AI across the product development lifecycle.In this episode, we cover:(00:00) Intro(02:16) The state of AI one year ago at Vanguard(02:54) The engineering bubble(05:05) Building an AI maturity model for 800 product teams(08:24) Dimension 1: AI-powered product delivery(10:00) Dimension 2: AI-ready codebase(12:20) Dimension 3: Autonomous agent utilization (13:00) Dimension 4: AI-augmented operations(14:00) Dimension 5: Team autonomy and enablement(16:11) Dimension 6: Responsible AI(18:15) The people problem: role evolution (20:00) The measurement problem (22:55) Lessons learned from rolling out the maturity model (26:46) What's ahead (30:10) Q&A #1: Getting your codebase ready for AI(32:22) Q&A #2: Audit trails and responsible AI(34:16) Q&A #3: Vanguard's maturity model progress(36:15) Q&A #4: Measuring cycle time across 800 teamsReferenced:• Vanguard• Jennifer St Pierre - Dell Technologies | LinkedIn• Mercari

LEXINGTON, Ky. (June 11, 2026) – [THIS IS AN ENCORE EPISODE.] Artificial intelligence is moving fast — and Kentucky lawmakers are working to make sure the state can take advantage of new tools without sacrificing transparency, privacy or public trust. On this episode of 'Behind the Blue', Kentucky State Senator Amanda Mays Bledsoe — a Lexington native and University of Kentucky alum — joins host Kody Kiser to talk about her path into public service, what she's hearing from constituents in Senate District 12, and how she views UK's land-grant mission of service to communities across the Commonwealth.  Bledsoe represents parts of Fayette County along with Woodford, Mercer and Boyle counties. In the conversation, she points to infrastructure — including roads and aging water and wastewater systems — as a major concern for the region, while also highlighting the role higher education, signature industries and health care play in central Kentucky's future.  The interview also explores Bledsoe's emerging leadership on technology policy, including Kentucky Senate Bill 4, which she describes as a framework for "responsible AI governance" within state government. Bledsoe explains that the goal is not to regulate every minor use of technology, but to establish guardrails for higher-risk, decision-making tools — including creating transparency around where and how AI is used, and building oversight to ensure accountability.  "AI is not spellcheck," Bledsoe said, emphasizing the need for stronger scrutiny when government systems generate new outputs or influence decisions. She also discusses concerns around deceptive AI-generated political content and the importance of ensuring voters can trust what they see — particularly in the final days leading up to an election.  Looking ahead, Bledsoe points to a wide range of challenges and opportunities — from consumer protection and privacy to safeguarding minors online — and says Kentucky will likely need to keep refining its approach as the technology evolves. She also describes how institutions like UK can help shape the state's AI future through research, workforce preparation and teaching students to be critical, responsible users of these tools. 'Behind the Blue' is available via a variety of podcast providers, including Apple Podcasts, YouTube and Spotify. Subscribe to receive new episodes each week, featuring UK's latest medical breakthroughs, research, artists, writers and the most important news impacting the university. 'Behind the Blue' is a production of the University of Kentucky. Transcripts for most episodes are now embedded in the audio file and can be accessed in many podcast apps during playback. Transcripts for older episodes remain available on the show's blog page.  To discover how the University of Kentucky is advancing our Commonwealth, click here. This interview has been edited for time and clarity.

Most home service companies don't have a lead problem. They have a follow-up problem. In this episode of Service Business Mastery, Tersh Blissett and Joshua Crouch sit down with Kevin Wu, founder of Leaping AI, to discuss how AI voice agents and automation are helping home service businesses solve some of their biggest call center challenges. From missed calls and slow follow-up times to after-hours booking and lead nurturing, Kevin shares how AI is being used as a practical tool to support customer service teams, improve speed-to-lead, and create better customer experiences without replacing people. The conversation also explores the future of AI-powered call centers, how contractors can automate repetitive tasks, and why business owners should focus on using AI to eliminate bottlenecks instead of simply cutting payroll. What You Will Learn in This Episode Why speed-to-lead is still one of the biggest challenges in home services How AI voice agents help capture missed opportunities The difference between replacing employees and supporting employees Why call centers struggle with consistency and process adherence How AI can improve appointment scheduling and follow-up The role of AI in call monitoring and quality control Why data quality inside your CRM matters more than ever How AI can help clean and organize customer databases The future of automated outbound campaigns The risks of AI abuse and spam calling How responsible AI adoption can improve customer experience Why contractors should start experimenting with AI today If you're looking for ways to improve call handling, increase booked appointments, and build a more efficient service business, this episode is packed with practical insights. Timestamps 00:00 Challenges with contractor responsiveness 03:11 Kevin introduces himself 08:58 Kids' interest in trade careers 09:45 Issues with Customer Support Responsiveness 14:00 AI improving call center efficiency 18:33 Solving data analysis with AI 20:55 Improving business metrics accuracy 25:37 Implementing EOS for business owners 29:31 Focusing on business priorities 32:00 AI for persistent customer follow-up 35:27 Responsible AI use in marketing 39:33 Data export issues in QuickBooks 40:45 Using AI to clean CRM data 43:35 Trying something new for 30 minutes Follow the Host and Guest Tersh Blissett: https://www.linkedin.com/in/tershblissett/ Joshua Crouch: https://www.linkedin.com/in/josh-crouch/ Kevin Wu: https://www.linkedin.com/in/kevin-wu-452a6393/ Connect with Us • LinkedIn - https://www.linkedin.com/company/service-business-mastery • TikTok - https://www.tiktok.com/@servicebusinessmasterypodcast • Facebook Group - https://www.facebook.com/groups/servicebusinessmasterypodcast • Instagram - https://www.instagram.com/servicebusinessmasterypodcast This episode is kindly powered by: UpFrog: upfrog.com  MarketStorm is an AI-powered advertising platform. Results vary by market, budget, and campaign configuration: https://marketstorm.ai/  Get Your 14-Day Free Trial with CallRail!: https://www.callrail.com/sbmpod CompanyCam: https://companycam.com/  Breezy: Capture 25-30% more clients with Breezy AI Agents. Use code 'SBM' to book a demo and get $500 on us: https://getbreezyapp.com/schedule-demo PhoneTAP: Your calls hold the key to growing your business. PhoneTAP gives you instant AI analysis, real customer lifetime value, and tools to coach your team. Learn more: phonetap.ai/demo   

Courtney Radsch reports on the political and economic impact of synthetic media and the stultifying consequences of our increasingly low-quality, high-fat media diet.  Courtney and Kimberly discuss the range of journalistic endeavors; synthetic media's entrée on the scene; disinformation vs. propaganda; competing with AI in the marketplace of ideas; content verification, labeling and trust; how synthetic media depends on and undermines journalism; information as a social, political and economic concern; embedded AI ideologies; equating regulation with censorship; information warfare; cognitive liberty in an age of corporate dominance; infrastructure and intent; the need for bright line protections, pluralism and independent oversight.Dr. Courtney Radsch, PhD is the Director of the Center for Media and Digital Governance (formerly CJL) and a non-resident Fellow at the Brookings Institution.  An award-winning journalist, scholar, diplomat, and human rights advocate, Courtney was recently named one of the 100 Brilliant Women in AI Ethics.Related Resources:Same Gatekeepers, New Tollbooths: Mapping the AI Content Licensing Market (CMDG Research Report)The Algorithm Loses Its Immunity (Article)The Pentagon Wants Its Panopticon (Article)The Battle for Cognitive Liberty in the Age of Corporate AI (Tech Policy Press)A transcript of this episode is here.

Nishat Mehta, CEO of Lexitas, joins Jennifer Simpson Carr to discuss how AI and technology-enabled litigation services are reshaping the legal industry. From deposition summaries and case triage to client pressure, access to justice, and the future of the billable hour, the conversation explores why law firms can not treat technology adoption as optional. Nishat offers law firm leaders a grounded and strategically honest look at where AI is creating real value. To our listeners: Audio issues during this recording impacted its sound quality, but we're publishing the episode because Nishat's insights are too valuable not to share.

Part two of the conversation with Natsue Ishida dives into the intricacies of governance in Japan and how you can come across opportunities to do pro bono work that expand your area of expertise. Don't miss the beautiful reading of a poem that has had a big impact on Natsue at the end of the episode. If you enjoyed this episode and it inspired you in some way, we'd love to hear about it and know your biggest takeaway. Head over to Apple Podcasts to leave a review and we'd love it if you would leave us a message here!In this episode you'll hear:The difference between Chief Legal Officer (CLO) and General Counsel (GC)Managing what's best for the company and respecting the founder Finding unique ways to contribute to the governance community in Japan and globallyWhat Natsue would say to her younger self Her favourite book and poemAbout NatsueNatsue is a seasoned General Counsel and senior executive with over 20 years of international experience advising senior management, Boards of Directors, and Audit Committees across Asia, North America, and Europe. She brings extensive expertise in legal strategy, IP, compliance, risk management, corporate governance, global subsidiary management, M&A, and data privacy.Her career spans highly regulated industries—including financial services, medical devices, automotive, and gaming—where she has consistently guided organisations through complex regulatory landscapes while shaping strong cultures of compliance and ethical leadership.Natsue is a widely recognised award winning thought leader, deeply committed to advancing excellence within the legal profession.On the weekends, she enjoys rock climbing (shower climbing in the summer). She would like to do more photography (medium format film photo printing), maybe when she retires, if the technology still exists!Connect with NatsueLinkedIn: https://www.linkedin.com/in/theflyingcat/ LinksPart 1: ⁠https://www.catherineoconnelllaw.com/podcast/season-12-ep2-natsue-ishida⁠  Global Council for Responsible AI https://gcrai.ai/ Connect with Catherine LinkedIn https://www.linkedin.com/in/oconnellcatherine/Instagram: https://www.instagram.com/lawyeronair

In this episode of The Buzz with ACT-IAC, we are in conversation with Ratima Kataria, VP of Health and IT Strategy at ICF. We talk about her career journey from satellite communications and semiconductors into federal health, including serving on the government side during COVID-19, and how high-stakes environments shaped her leadership values. Kataria explains ICF's work helping federal agencies modernize at the intersection of enterprise modernization, data strategy, and responsible AI adoption amid fragmented data, legacy platforms, and demand for AI-enabled services. She describes a “think big, start small” approach focused on mission-aligned tech strategy, data governance and interoperability, platform consolidation, and scaling trusted AI use cases.Become a Member | ACT-IAC Summary - A Hole in One with ACT-IACSubscribe on your favorite podcast platform to never miss an episode! For more from ACT-IAC, follow us on LinkedIn or visit http://www.actiac.org.Learn more about membership at https://www.actiac.org/join.Donate to ACT-IAC at https://actiac.org/donate. Intro/Outro Music: See a Brighter Day/Gloria TellsCourtesy of Epidemic Sound(Episodes 1-159: Intro/Outro Music: Focal Point/Young CommunityCourtesy of Epidemic Sound)

Most growing companies are held together by spreadsheets that nobody fully understands — built by someone who left three jobs ago, maintained by someone who doesn't know why it exists, and quietly critical to daily operations. In this episode, Jeff Mains sits down with Garrett Fritz, co-founder of MetaCTO, a fractional CTO firm that helps mid-market companies transform outdated operational processes into custom, scalable software.Garrett breaks down why so many organizations are trapped in the "if it ain't broke, don't fix it" mindset, how AI has lowered the barrier to custom software without eliminating the need for expertise, and when it actually makes sense to build your own tool versus buying off-the-shelf SaaS. He also shares how internal tools can evolve into white-labeled revenue generators — and the most common mistake founders make when they try to take that leap too fast.Whether you're drowning in manual processes, questioning your SaaS spend, or wondering how to implement AI responsibly, this episode delivers a practical, no-hype roadmap.Key Takeaways4:37 — **The #1 operational inefficiency Garrett sees:** Hundreds or thousands of employees running mission-critical operations on a spreadsheet built a decade ago by someone who's since been promoted — and nobody knows why it has the formulas it has. 6:15 — **What "turning spreadsheets into apps" actually means:** MetaCTO embeds in the business, decodes the spreadsheets, understands the workflows, and builds working software that can replace the internal process — or be taken to market as a SaaS product. 7:54 — **Profitable from day one:** Because Garrett and his partner came with a thick Rolodex from 15–20 years in tech leadership, MetaCTO launched with clients already lined up — no burning cash to find product-market fit. 13:27 — **70% of AI POCs never see the light of day:** The excitement dies when teams realize how much effort is involved. MetaCTO's focus is getting those 90%-done prototypes all the way to the finish line. 18:34 — **Build custom vs. buy SaaS — the real decision framework:** After 2–4 weeks embedded in a business, MetaCTO looks at licensing costs, actual feature utilization (often just 2% of the SaaS product), man-hours wasted, and growth trajectory to determine the ROI break-even point. 28:25 — **Niches win:** SaaS isn't dead — it's narrowing. The companies gaining ground are building hyper-specific tools for specific industries (think: Procore, but only for commercial plumbers) where the UI, reports, and workflows are built around exactly how that niche operates. 31:33 — **The #1 mistake when productizing internal software:** Not talking to the second customer. Your problems aren't always everyone else's problems. Validate outside your organization before building for market, or you risk six months of rework when the deltas turn out to be core to the platform. 33:40 — **How to actually quantify the ROI of custom software:** Bake usage analytics into every product from day one. Track utilization, time on platform, transactions processed, and revenue generated — then compare to the man-hour cost baseline captured during discovery. 39:14 — **Responsible AI implementation starts with one rule: Resist "Accept All."** Don't grant admin tokens to AI agents for convenience. Suffer through permissions early so you don't face irreparable reputation or business damage when a bad actor exploits an over-permissioned agent. 41:22 — **The smartest first step for any leader feeling stuck:** Use AI tools like Replit to build a prototype with fake data. Don't try to connect it to real systems — just use it to force yourself through the problem-solving process. Come to the conversation with a working wireframe and you'll skip weeks of expensive discovery.Tweetable QuotesAt the heart of it is some Excel spreadsheet that some employee made 10 years ago — and it is critical to the operation." — Garrett Fritz"70% of AI proof of concept projects have never seen the light of day. It's pretty common to get excited about something and then realize, oh, this is a lot more effort than we thought." — Garrett Fritz"You can't just give a layman a chainsaw and expect to be a carpenter. A little bit of finesse and experience goes a long way." — Garrett Fritz"The niches win. The companies gaining ground are building hyper-specific tools for specific industries — where the UI, reports, and workflows are built around exactly how that niche operates." — Garrett Fritz"We never build it and run away. And as you can imagine, anyone who's created a piece of software has never said 'I'm done' either." — Garrett Fritz"Resist 'Accept All.' Give the AI admin access for convenience, and you're one bad actor away from irreparable damage to your business." — Garrett Fritz"AI is most valuable when it's applied to real business friction — not just trendy experiments or chatbots. Nobody needs another one of those." — Jeff MainsSaaS Leadership Lessons1. Familiarity is the enemy of efficiency. The "if it ain't broke, don't fix it" mentality keeps organizations locked in spreadsheet-driven operations for years — sometimes decades. The pain point has to get big enough to justify change, but by then the cost of switching is enormous. Don't wait for a crisis to modernize.2. The barrier to custom software has dropped — but expertise still matters. AI tools like Replit and Lovable have made it possible for non-developers to prototype software. But there's a massive gap between a 90%-done prototype and a production-ready, secure, maintainable application. Knowing what you're doing still matters.3. Don't buy features you'll never use. Most enterprise SaaS customers use 2% of the product's functionality — but pay for 100% of the license. When your team is only using 2% of the product and only 50% of the people who should be using it actually are, you're compounding inefficiency at every layer.4. Build for the second customer before you build for the market. If you think your internal tool has market potential, validate it with people outside your organization before investing further. Your problems are not automatically everyone else's problems. The cost of discovering core delta requirements after six months of development is enormous.5. Measure everything from day one. Custom software that doesn't have baked-in usage analytics is a black box. You can't demonstrate ROI, you can't justify ongoing investment, and you can't make intelligent roadmap decisions. Instrument every product with utilization metrics, transaction data, and performance monitoring from the start.6. AI governance isn't optional — it's the first conversation. The most dangerous thing you can do is grant your AI agents broad permissions during development and never revisit it. Treat AI like a junior employee: define its scope, limit its access, and require human approval for anything with downstream consequences. Someone always has to be the final buck.Guest Resourcesgarrett@metacto.comhttps://metacto.com/https://www.linkedin.com/in/grfritz/https://www.linkedin.com/in/grfritz/Episode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

Somewhere in your organization, an AI decision is sitting on someone's desk right now. Who owns it? In most mid-market companies, nobody does — or rather, it's landed on the IT leader who was already doing three other jobs.In this episode of The Catalyst, we follow Jeremy Wight, CTO of CareMessage — a patient engagement platform serving 22 million low-income patients across the US — who had to write his organization's AI policy himself. No committee. No playbook. Just the weight of getting it right for some of the most vulnerable people in the healthcare system.Alongside Jeremy, we hear from Reid Blackman, author of The Ethical Nightmare Challenge and founder of Virtue, who argues that the standard policy-first approach to AI governance is already broken — and offers a framework any team can implement in weeks, not years. Olivia Gambelin, AI ethicist and author of Responsible AI, reframes the vendor selection question entirely: it's not about auditing their product, it's about whether their values align with yours. And Anthony Vinci, former intelligence officer and author of The Fourth Intelligence Revolution, draws an unexpected parallel — between the integrity required of a spy with no rulebook, and the integrity required of an IT leader doing the same.====This episode is brought to you by HPE.From AI to data center and network modernization, HPE delivers a cloud-like experience right on your own infrastructure — the full portfolio, from one partner. softchoice.com/technology-partners/hewlett-packard-enterprise ====In this episode:Why the policy-first approach to AI governance is broken — and what to do insteadA practical three-question framework any team can implement this weekHow to evaluate AI vendors by values alignment, not just product capabilityWhat it actually looks like when one IT leader has to make these calls alone — with 22 million patients on the lineFeatured guests: Jeremy Wight (CTO, CareMessage) • Reid Blackman (Founder/CEO, Virtue) • Olivia Gambelin (AI Ethicist & Author) • Anthony Vinci (CEO, VICO) • Craig McQueen (VP Microsoft Practice, Softchoice)#AIEthics #ResponsibleAI #ITLeadership #AIGovernance #TheCatalyst #Softchoice #MidMarket #HPE===Show Notes & ResourcesGuestsJeremy Wight, CTO — CareMessage: caremessage.orgReid Blackman, Founder/CEO — Virtue: reidblackman.com • The Ethical Nightmare Challenge (book, April 2025) • Ethical Machines (HBR Press, 2022)Olivia Gambelin, AI Ethicist: oliviagambelin.com • Responsible AI: Implement an Ethical Approach in Your Organization • Values Canvas framework — free download at oliviagambelin.comAnthony Vinci, CEO — VICO: anthonyvinci.com • The Fourth Intelligence Revolution (Henry Holt, 2025) • VICO forecasting platform: vico.aiCraig McQueen, VP Microsoft Practice — Softchoice, a World Wide Technology CompanySponsorHPE via Softchoice: softchoice.com/technology-partners/hewlett-packard-enterpriseSoftchoice AI & Ethics resources: softchoice.com/EASThe Catalyst by Softchoice is the podcast dedicated to exploring the intersection of humans and technology. 

98% of patients welcome AI in their care — and still want a human in charge. That tension ran through the OECD and Spanish Ministry of Health conference on scaling AI in health (Madrid, late May 2026), and it frames this episode of Faces of Digital Health. Out of 38 OECD countries, only seven have a formal AI strategy and just over a tenth run workforce upskilling programmes — the ambition is outrunning the institutions meant to govern it. Host Tjaša Zajc brings together voices from across the conference to ask what actually has to change: regulation, trust, who gets a seat at the table, and the parts of the agenda nobody is funding. Featuring: - Eric Sutherland — Senior Economist, OECD - Aferdita Bytyqi — Executive Director & Founding Partner, Digital Transformations for Health Lab (DTH-Lab) - Erza Selmani — Research Fellow, DTH-Lab - Valentina Strammiello — Executive Director, European Patients Forum (EPF) - Dr Ricardo Baptista Leite — CEO, HealthAI (the Global Agency for Responsible AI in Health) - Dr Persephone Doupi — Senior Medical Officer, Finnish Institute for Health and Welfare; President, European Federation for Medical Informatics (EFMI) What the conversation covers: - Why trust — not capability — is the binding constraint on health AI adoption - The OECD readiness gap: AI strategies, HTA frameworks and workforce upskilling - How patients really feel about AI: consent forms, transparency, and keeping clinicians central - Why youth health and wellbeing keep getting left out of AI governance frameworks - Five recommendations to make the EU AI Act work for health and competitiveness - Coordinating the EU AI Act, MDR/IVDR and the European Health Data Space - Health technology assessment and reimbursement as the real barriers to scale - AI literacy and prevention: the most underweighted lever in the room Chapters: 0:10 — Welcome: AI in Health & the 2026 OECD Conference in Madrid 0:25 — Key Stats: Only 7 of 38 OECD Countries Have a Formal AI Strategy 2:10 — Eric Sutherland (OECD): We're Not Using Data as Effectively as We Could 3:11 — Afrodita & Erza (DTH Lab): Youth Health Is Missing from AI Governance Frameworks 5:12 — Valentina Stramello (EPF): 98% of Patients Are Positive About AI, But Trust Requires Transparency 7:14 — Dr. Ricardo Baptista Leite (Health AI): 5 Recommendations to Fix EU AI Policy for Health 10:53 — Persephone Doupi (EFMI): We Must Prioritize AI Literacy and Shift Healthcare Toward Prevention —

0:30 - Midterms 13:03 - Sheridan Gorman's parents at status hearing for their daughter's killer on sanctuary pols in IL 34:23 - Henry Nowak's father outside courthouse post-conviction of son's killer 51:47 - Menahem Merhavy, senior fellow at the Harry Truman Institute at the Hebrew University of Jerusalem, breaks down Why Iran’s regime did not collapse 01:04:29 - In-depth History with Frank from Arlington Heights 01:08:06 - Platner 01:20:47 - Pistols and Pilates 01:25:48 - Wirepoints founder Mark Glennon on what it would take to get a Spencer Pratt-like candidate in Chicago. 01:43:09 - Targeting speeders in NYC 02:06:29 - David Krueger, assistant professor in Robust, Reasoning, and Responsible AI at the University of Montreal and founder of Evitable, warns that the risks posed by artificial intelligence are real—and cannot be ignored. Follow David on X @DavidSKruegerSee omnystudio.com/listener for privacy information.

From McDonald's drive-thru meltdown to hiring and banking algorithms discriminating against women, AI failures are already happening in 2025.In this video, I break down how to implement Responsible AI that protects your brand, your customers, and your future.You'll learn: the real-world AI disasters and what caused them, how to build an AI governance framework that actually works, 4 critical steps every organization must take today, how to create transparency, accountability, and trust in your AI systems.Because Responsible AI isn't just a buzzword- it's your survival skill.Looking to go from chaos and unpredictability to resilience in the world of AI? Start here with The Predictability Factor newsletter at The Monica Talks Cyber (https://www.monicatalkscyber.com).

How do you stay in control of AI while still benefiting from everything it can do? As AI becomes more powerful, many leaders are focused on getting AI to do more work. But perhaps the better question is: How do you make AI more effective without letting it influence your thinking, your brand, or your business direction? In this episode of Grounding AI, Donna Peterson explores why leaders must remain the decision-makers while using AI as a tool to strengthen expertise, deepen customer relationships, and improve business outcomes. You'll learn why AI should enhance your knowledge rather than replace it, how to train AI around your company's goals and values, and the questions every leader should ask before using AI-generated recommendations. If you're responsible for marketing, sales, leadership, customer relationships, or business strategy, this episode offers practical guidance for maintaining control while leveraging AI effectively. In This Episode: Why AI naturally influences decisions and direction The difference between getting AI to do more and getting AI to be more effective How leaders can maintain ownership of their expertise Why your brand voice must remain stronger than AI suggestions The importance of building AI libraries and organizational knowledge Questions to ask before every AI prompt How to keep customer relationships at the center of AI adoption A simple test to determine if AI is helping or leading your business Key Takeaway Before every prompt, ask yourself: What am I trying to accomplish? Why does it matter to my audience? What action do I want people to take? Does this align with our company values and goals? The more direction you provide, the more valuable AI becomes. Connect With World Innovators // Website: www.worldinnovators.com Subscribe for weekly conversations on AI, leadership, trust-building, marketing strategy, and practical business growth. *** Reach out to dpeterson@worldinnovators.com if you'd like help building a marketing strategy that builds relationships and/or AI training for individuals or full teams.*** Visit www.worldinnovators.com for more resources on building stronger marketing and leadership strategies.*** Subscribe to the Grounding AI podcast for weekly insights into marketing, leadership, and the future of AI.

What does “responsible AI” look like in practice? In one of our most engaging episodes of the year, host Will Francis speaks with  Gordon Ryan, Senior Managing Consultant and Design Process Lead at Sopra Steria, about the growing impact of AI on work, business, and society, and the hidden trade-offs behind its adoption. From the future of design and marketing to productivity and identity, Gordon shares his perspective on where AI could take us next, and whether we're building the kind of future we want. Gordon's top 3 tips for responsible strategic use of AI: Reflect on what you value most in your work: Identify the parts of your role that feel meaningful and uniquely human Use AI intentionally: Focus on solving real problems instead of adopting tools simply because they're available Think beyond productivity: Consider how AI could improve wellbeing, relationships, creativity, and quality of life at work The Ahead of the Game podcast is brought to you by the Digital Marketing Institute and is available on YouTube, Apple Podcasts, Spotify, and all other podcast platforms. And if you enjoyed this episode, please leave a review so others can find us. If you have other feedback or would like to be a guest on the show, email the podcast team!  Timestamps: 0:01:57 – What systems-oriented design means 0:04:31 – UX design, digital experiences and systems thinking 0:05:10 – Will AI replace designers? 0:08:24 – Creativity, craft and the human side of design 0:09:24 – Are companies adopting AI without a clear strategy? 0:11:37 – The “Wild West” of AI inside organizations 0:14:41 – Gordon's most practical uses of AI today 0:16:00 – Using AI to analyze complex environmental and forestry data 0:18:36 – The human impact of automation and lost relationships 0:21:13 – What ethical AI really means beyond compliance 0:22:41 – Productivity, profit and the future of work 0:26:12 – Why business growth can't continue forever 0:30:18 – Is Gordon optimistic or skeptical about AI? 0:31:30 – AI, inequality and the environmental crisis 0:34:59 – Reconciling AI's benefits with its environmental impact 0:36:00 – Could AI enable shorter working weeks? 0:39:36 – The future of marketing and behavioral manipulation 0:45:50 – Marketing, persuasion and ethical responsibility 0:46:37 – How to use AI more mindfully 

In this episode of the Healthy Wealthy & Smart Podcast, Dr. Karen Litzy, PT, DPT, welcomes Dr. Minal Patel and Brijraj Bhuptani of Spry Therapeutics. We explore how AI is transforming clinical workflows, documentation, and patient care in physical therapy. We cut through the hype to understand what responsible AI integration really means for clinicians and practice owners.   Key topics   The origins of Spry and the real-world problems AI aims to solve in healthcare How AI-powered documentation like Spry's Scribe tool works in practice The importance of transparency, data security, and reliability in healthcare AI Balancing customization and standardization with AI tools The role of AI in addressing clinician burnout and administrative burden Future pathways: AI's potential to standardize workflows while respecting individual practice styles Practical steps for clinicians and practice owners to start exploring AI in their clinics Evolving perceptions of AI's impact on human interaction and empathy in therapy   Timestamps   00:00 - Introduction to AI in clinics and why it matters 02:16 - The story behind Spry's inception and industry pain points 04:44 - How COVID accelerated the need for smarter workflows 09:11 - Overcoming practice ownership inertia toward new technology 12:06 - The role of AI-powered documentation and clinician workflows 18:15 - How Spry's AI listens and transcribes in real-time during therapy 24:09 - Protecting note integrity and avoiding homogenized documentation 27:51 - The impact of admin overload on clinician burnout and patient trust 36:17 - Building trust in AI with transparency and data access 40:48 - The future of AI: opportunities and responsibilities for practice owners 43:20 - Responsible AI and industry responsibility for ethical tech deployment 47:40 - Clarifying probabilistic AI and ensuring reliable clinical outputs 48:43 - Lightning round: quick takes on practice management and AI mindset 55:09 - How to connect with the experts and learn more about Spry   Resources & Links Spry Brij Bhutani - LinkedIn Dr Minal Patel - LinkedIn AI-powered documentation in healthcare: a look at Spry's approach   More About Dr. Patel: Dr. Minal Patel PT, DPT, OCS is a seasoned Physical Therapist with over 17 years of clinical and non-clinical expertise. She has held pivotal roles within rehab organizations including leadership and innovation for both in-person and digital services. Dr. Patel holds a Doctor of Physical Therapy from Midwestern University, and is an Orthopedic Certified Specialist. As Director of Clinical Solutions at SPRY, Dr. Patel leads the development and implementation of innovative care strategies that bridge the gap between clinical excellence and operational efficiency. With a deep background in physical therapy and healthcare operations, Dr. Patel brings a clinician-first perspective to building solutions that streamline workflows, optimize patient outcomes, and enhance revenue cycle performance. At SPRY, Dr. Patel works closely with product, engineering, and customer success teams to ensure the platform supports the real-world needs of outpatient therapy practices. Their work focuses on translating clinical insight into scalable technology—empowering providers to deliver high-quality care while navigating complex payer and compliance environments. Prior to joining SPRY, Dr. Patel held leadership roles in multi-site rehab networks and has been instrumental in driving clinical innovation, EMR optimization, and value-based care initiatives. She is passionate about elevating the role of therapists in the broader healthcare ecosystem through data-driven, patient-centered tools. More About Brijraj: Brijraj (Vaghani) Bhuptani is co-founder and chief executive officer of SPRY Therapeutics, Inc., inventor of rehab therapy's first fully integrated, AI-powered EMR. As CEO, Brij drives company and product strategy as he leads the organization in the commercialization of rehab therapy's only AI-first software platform. Before SPRY, Brij co-founded and served as chief executive officer of Birds Eye Systems, the creator of major mass transit platform Ridlr. This enterprise was acquired by Ola, one of the world's largest ride-hailing companies, where Brij then served as chief technology officer. Prior to Birds Eye Systems, Brij applied his engineering background to solving some of the most pressing technology concerns facing large media and wireless firms, including Qualcomm and Sears India. For more information on SPRY, visit www.sprypt.com, and follow the company on LinkedIn Jane Sponsorship Information: Book a one-on-one demo here Mention the code LITZY1MO for a free month   Follow Dr. Karen Litzy on Social Media: Karen's Instagram Karen's LinkedIn   Subscribe to Healthy, Wealthy & Smart: YouTube Website Apple Podcast Spotify SoundCloud Stitcher iHeart Radio

Companies are making the exact same mistake with AI that they made with cloud. FICO's CIO Mike Trkay breaks down why 95% of companies are failing at AI alignment, why "automating a bad process faster" is the #1 trap, and why regulated industries are already abandoning LLMs in favor of focused language models.   Key takeaways: • Only 5% of AI pilots make it to production — and MIT's research backs it up • The lift-and-shift parallel: cloud costs went up for the same reason AI ROI is missing • Why the LLM-to-focused-language-model shift mirrors cloud-native vs lift-and-shift • What "AI native" actually means (and why chatbots aren't it)   Chapters 00:00 The 5% Alignment Problem 01:09 Only 7% Even Measure If Their AI Works 02:15 "You're Just Doing a Bad Process Faster" 06:29 LLM Repatriation and the Rise of Focused Language Models -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Understand how to close the gap between AI experimentation and enterprise production. Shub Agarwal, Founder of the AI Trust Lab at USC and author of Successful AI Product Creation: A Nine-Step Framework, shares his AI product management framework for taking enterprise AI strategy from demo to production, drawing on two decades of product leadership at Amazon and Fortune 50 firms. He breaks down why experimentation must tie directly to business OKRs, the four mindset shifts leaders need to scale AI responsibly, and how the AI Trust Lab is building a benchmark evaluation framework for AI model trust and governance. Key Moments: Why 80% of AI Projects Never Reach Production (02:13): Shub traces the root cause of stalled AI programs to a missing system for moving from demo to deployment. Most teams have no repeatable path to production. Shub's Nine-Step Framework for Building AI Products (06:00): Most AI projects start with a cool model instead of a painful problem. Shub walks through the three phases of his framework: discovery, execution, and excellence. The Case Against "Fix Your Data First" (12:41): Conventional wisdom says clean your data before building AI. Shub challenges that, arguing modern LLMs offer far more flexibility with imperfect data. Four Mindset Shifts for Scaling Enterprise AI (16:35): Shub outlines the four shifts separating organizations that scale AI from those that stall, from measuring AI performance differently to embedding trust from day one. Inside Shub's AI Trust Lab at USC (23:54): Major foundation models are already being benchmarked on trust and safety. Shub explains the lab's mission to build a standardized evaluation framework for AI model governance. Why Enterprise AI Governance Needs Multiple Disciplines (28:36): AI models can be sycophantic, manipulative, or lack candor. Shub argues that building trustworthy AI demands an interdisciplinary approach. Key Quotes: “I think the fundamental problem that organizations are facing today… is not that they have a lack of experimentation in the demo aspect. The challenge is they don't know how to take those demos to production, and that is where I saw the gap.” - Shub Agarwal “I do think data is the fuel for AI… But I think today organizations are crippled by this ‘fix your data, and then we'll build AI', and they never build AI. They never build use cases that are adding value.” - Shub Agarwal “There's no FICO scores for models, so I decided to create one. I built this lab… bringing the computer scientists, the researchers, the applied AI researchers, the policy, and the communication people together to think of what is trust, define it, and ultimately measure and evaluate it.” - Shub Agarwal Mentions USC AI Trust Hub Successful AI Product Creation: A Nine-Step Framework by Shub Agarwal Four Steps to Epiphany: Successful Strategies for Products That Win by Steve Blank Masters of Scale podcast with Reid Hoffman Guest Bios  Shub Agarwal is an associate professor of professional practice at the University of Southern California, an industry executive, and an advisor to start-ups and academic institutions. He holds an MBA from the University of California, Los Angeles (UCLA), and an MS from Carnegie Mellon University (CMU). He is the author of two books: Solve Catch-22 of Product Management and Successful AI Product Creation: A 9-Step Framework. He has made significant contributions to the fields of artificial intelligence and machine learning, holding several U.S. and global patents for his work, and is also a published author of several technical research papers. With around two decades of extensive experience in product management and leadership, his journey has been marked by a relentless pursuit of leveraging AI technologies to create impactful products that redefine industry standards. His industry experience includes leadership roles at Amazon, Silicon Valley start-ups, and other Fortune 50 firms. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Olga Goriunova rejects digital abstractions as mirror images of ourselves and reflects on why we concern ourselves with representations that aren't concerned about us.  Olga and Kimberly discuss how cultural imagination is shaped by technology; digital subjects as unnatural constructs; the distance between individuals and their digital profiles; banal categorization and subjective truth; how statistics and ML changed the concept of the ideal; the limits of digital subjects; extreme individuation and aspiring to become our digital reflections; how current predictions create future realities; why the ideal digital subject isn't concerned with you; and thinking critically about what we desire and why. Olga Goriunova is a cultural theorist working at the intersection of technology, philosophy, and aesthetics. A Professor of Media Arts at Royal Holloway, University of London, Olga is the author of the critically acclaimed book Ideal Subjects: The Abstract People of AI. Additional Resources: Aksioma: Institute for Contemporary Art Book Lecture  Olga Goriunova Academic Profile  A transcript of this episode is here. 

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the critical definition and requirements for navigating Enterprise AI. You’ll learn how to distinguish between consumer-grade tools and the strict standards required in regulated industries. You’ll discover the twenty essential pillars for building a secure and compliant AI strategy for your organization. You’ll understand why rigorous vendor scrutiny matters as much for software as it does for human talent. You’ll gain clarity on the governance frameworks necessary to prevent data leaks and legal vulnerabilities in your enterprise. 00:00 – Introduction 03:15 – Defining Enterprise AI vs. SMB AI 07:45 – The role of Microsoft Copilot in regulated environments 12:20 – The 20 components of Enterprise AI readiness 18:10 – Challenges in organizational adoption and change management 22:30 – Security and data privacy as the foundation 27:00 – Call to action Watch this episode to master the complex landscape of regulated AI and safeguard your company’s future. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-enterprise-ai-101.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: In this week’s In Ear Insights, we are talking about Enterprise AI 101. I am in the midst of a series in the Trust Insights newsletter, which you can get at TrustInsights.ai/newsletter. Part one was last week on seven different aspects of enterprise AI. But Katie, you said it would probably be helpful to level set what enterprise AI is and how it differs from SMB AI, mid-market AI, consumer AI, and so on. Katie Robbert: It is interesting because I feel like every time we jump on to record a podcast, there is a whole new set of vocabulary that I need to get caught up with. We need to make sure that everyone else knows what we are talking about because there is nothing worse than listening to a podcast or reading an article and having no idea what the author is talking about because they are introducing a concept but not really explaining it. I wanted to take this episode to talk about what enterprise AI is. Since you and I have not defined it, I am going to take my best guess at what enterprise AI is using some logic and deduction. I could be wrong, and that is why I think it is worth covering. From my perspective, if I had to put a definition to it, I am assuming enterprise AI is the type of AI implementation that occurs at an enterprise-size company. That sounds overly simplistic, but the bigger the organization, the more red tape, the more politics, the more departments, the more stakeholders, and the more governance there is. There are a lot more complications versus a small business like we are, where we can just decide one day, “Hey, I am going to start using this tool.” There are no real hurdles to go through. Then you have those mid-sized companies where you start to introduce some of those hurdles. You might need to work with your IT team to make sure that everything is in compliance. You might need to make sure that you have a place to host these new pieces of software, and that is not something that the marketing team is necessarily responsible for. Then you get to the enterprise-size companies where everything is completely siloed. Even in the best enterprise-sized companies, you are going to run into these silos. Because no one person is responsible for everything, you typically have multiple CEOs. Depending on what part of the country you are in, you might have a board for every different division of the company. If you are a Procter & Gamble and you have hundreds of product lines underneath, each of those is their own individual business. Each of those businesses are not necessarily talking to each other or sharing resources. That is my logical guess at what enterprise AI is. Christopher S. Penn: That is what I started with until I started doing the research into it. I realized that is not what it is. The generally accepted definition is AI within any commercially regulated entity. I realized as I was going through the research that commercially regulated means you have external regulation imposed on the company. It might be a 50-person company, but if they work in HIPAA or FINRA, they have to behave in highly regulated ways. Whether you are publicly traded or, for example, colleges that have to adhere to FFIEC rules and FERPA rules, enterprise AI is about operating AI—whether classical or generative—in a commercially regulated environment where you have externally mandated requirements that you must meet. Your definition for small business stuff makes total sense in that environment because Trust Insights is not a regulated company. However, when we work with our healthcare clients, we have to behave as though we are an enterprise company because we have to conform to their requirements. Katie Robbert: I am glad we are talking about this because the terminology is confusing; when you think of an enterprise company, you are not thinking of a commercially regulated company. I have to wonder why it is not called commercially regulated AI versus non-commercially regulated AI. It is a mouthful and a little bit harder to remember, but it is more descriptive and more accurate. I think like me, a lot of people are going to get confused about what enterprise AI actually is. Christopher S. Penn: A lot of this is because our background is in marketing, so we use the term enterprise to just mean a big company. If we want to market to enterprise companies, we are not marketing to a 50-person firm; we are marketing to a 50,000-person firm. In a lot of CRM software, the dividing line is typically 10,000 employees or 100 million in revenue. This is especially relevant because you see a lot of AI companies like Anthropic and OpenAI in a fight with Microsoft to try and gain a foothold into those enterprises. Microsoft, with their Copilot offering, has dominance by the very fact that their legacy Office 365 stuff is approved in those regulated environments. Katie Robbert: It is ironic because we spent so much time admittedly dismissing Microsoft’s Copilot as the less than version of generative AI, and now Microsoft is getting the last laugh on everyone. They are saying, “You have to use me because I have already been approved by IT and governance, and good luck.” You are stuck with whatever I decide to give you. If I were Microsoft, I would be petty and say, “You guys spent way too much time dismissing me and calling me inferior, so too bad.” Christopher S. Penn: A lot of that, as we have talked about many times on stage, is that the reason Copilot has fewer capabilities than other systems is specifically because of the regulated environment. It is trivial for Google to foist something on consumers and say, “Now we are going to read all your Gmail.” That does not fly in a regulated industry. Katie Robbert: That understanding is really helpful to the people who are saddled with Microsoft Copilot because we hear complaints about why they cannot use other shiny objects. If you are in a 50,000-person company and you weren’t there when the regulatory standards were decided upon, you are sitting there wondering why you cannot use Gemini to generate ad headlines. Then you do it on the side and get in trouble because there is no clear documentation saying why you have to use Copilot and nothing else. What we are hearing is that employees in companies required to use Microsoft Copilot are using other models on the side. That information is still getting filtered into the organization, and it is a huge governance problem. Christopher S. Penn: Completely. In enterprise AI, there are 20 different components to being ready. I derived this from the US federal government's NIST AI regulations and the EU AI Act, which is the gold standard. Katie Robbert: I want to see if you can get all 20. Christopher S. Penn: One, Strategy and Operating Model; two, Governance Policy and the AI Council; three, Legal, Regulatory, and Compliance. Katie Robbert: Are you reading this off a screen? Christopher S. Penn: I am 100% reading this off the Trust Insights Enterprise AI Landscape Field Handbook. Katie Robbert: Fine, continue. Christopher S. Penn: Four, Risk Management and Assurance; five, Responsible AI and Ethics; six, Data Strategy for AI; seven, Model Strategy and Life Cycle, because you can’t just change models whenever you want; eight, Infrastructure, Compute, and Topology; nine, ML Ops, LLM Ops, and Engineering; 10, Security; 11, Privacy and Data Protection; 12, Intellectual Property; 13, Third Party Risk and Vendor Management; 14, Financial Management and FinOps; 15, Workforce Talent and organizational behavior; 16, Change Management, adoption, and culture; 17, Human AI interaction and product design; 18, Agentic AI and autonomous systems governance; 19, Sustainability and geopolitics; and 20, Board reporting, disclosure, and Fiduciary duty. Katie Robbert: I just heard a whole lot of new job opportunities listed. So, if someone were working in a regulated industry like pharma, these are the 20 things they would need to be aware of before evaluating generative AI. It is interesting that organizational behavior and change management are part of it. You would think the regulations would be more technical versus human, but I am surprised that is part of it. Christopher S. Penn: It makes sense because in order for any AI to succeed in an enterprise with 50,000 or 300,000 employees, you have to prioritize change management. Organizational behavior cannot be an add-on; they have to be baked into what you do from the beginning, otherwise your initiative is going nowhere. Katie Robbert: I don’t disagree, but the typical way that works in a large organization is top-down. They make a decision, and you walk in the next day to find it has automatically updated your computer settings. Now you can no longer use a web browser search; you have to use Microsoft Copilot. That is their version of change management, but it is really just a dictatorship from above. I am interested in future episodes to explore what that should look like in a regulatory environment. Christopher S. Penn: We have known for two years that adoption is the hardest part. Deployment is easy compared to adoption. You can put Copilot on someone's desk, but they may not use it even if you tell them they have to. It comes back to how you get them to see the benefits. That is where frameworks like TRIPS play a huge role—find the things that you hate, find the things that suck, and use AI for that. Get that one thing off your plate. Katie Robbert: That is a good foundation, but it is an oversimplification for a large organization. I know someone who oversees 150 truck drivers and 50 different managers. The layers are so deep. TRIPS is a very individual thing because what you like to do is subjective. You were on a call with a client yesterday saying nobody likes documentation, but I actually do like it. My scoring would look different than yours. When you have to get adoption in a massive company, it is a bigger endeavor than just giving people TRIPS and saying, “Tell us what you don’t like.” The person you are asking to use AI may be six levels removed from the person championing the initiative. Christopher S. Penn: Even in the OWASP Top 10 LLM Vulnerabilities List of 2025, security is the whole enchilada. Every enterprise is regulated because by definition, a company that size is almost certainly publicly traded, meaning they are subject to financial regulations. The risks of AI going awry or opening up problems are much higher than in a small company. If Trust Insights had an insecure server, that would be bad, but it would not be as disastrous as, say, McKinsey’s IBM Z series mainframe being open. Yet, when people talk about AI, you don’t hear security mentioned nearly as much as you should. Katie Robbert: It is true. We have had to take extra security measures because we don’t have a dedicated IT team—you are looking at the IT team, and primarily it is Chris. We don’t have any wiggle room to set things up haphazardly. We have to do it right from the start. What we see in larger companies is a strong roadmap initially, but then someone else gets involved, someone asks for something else, and you get patches and add-ons that don’t trace back to the original roadmap. By the end, you are wondering what the original goal was. The bigger the organization gets, the harder it is to maintain control. It becomes a snowball effect. Christopher S. Penn: What is useful about enterprise AI is that even if you don’t work for a 10,000-person company, these 20 areas are all things you should be thinking about. Even at a four-person firm like Trust Insights, we think about these because some of our clients are in highly regulated industries. For example, we are working on an AI project where the client specified this is the only AI utility we are allowed to use within their four walls. Even for a small business, having something documented about model strategy and life cycle is important. As of the day we are recording this, Google Gemini 3.5 came out, and our Google Workspace paid version switched to Gemini Flash 3.5. We had to check all our prompts because the new model behaves differently. Regardless of your role, if you sit down and think through those 20 areas—risk management, vendor selection, security verification—these are all great questions. Katie Robbert: There is a good starting place for this. You can find our downloads at TrustInsights.ai/StrategicToolkit. There is also a free version at TrustInsights.ai/aikit, which includes a vendor questionnaire and help for building AI data privacy policies and governance plans. We have already templated these things out. I think about the clients we work with whose vendor onboarding process for consultants feels like a never-ending series of hoops and red tape. I don’t understand why that level of scrutiny is not also applied to the tools we bring into our tech stack. We are renting space in those tools and freely giving them our data. Those companies now have our data and will use it for their own benefit. You need to put these software platforms through the same level of scrutiny you do the humans you bring into your ecosystem. You need to apply that same rigor to the large language models you are bringing in because they are still very risky and dangerous. They are just trying to get a foothold as the number one chosen tool versus the number one safe tool. Christopher S. Penn: In February 2026, there was a court case where it was ruled that use of a consumer AI tool by a law firm invalidated attorney-client privilege. The judge ruled that this is no longer privileged information. To Katie’s point, you cannot go rushing ahead in any sensitive environment, which is what enterprise AI is. You have to be doing your homework. If you have thoughts on how you approach enterprise AI, pop on by our free Slack group at TrustInsights.ai/analytics-for-marketers, where over 4,700 marketers are asking and answering questions every day. Wherever you watch or listen to the show, if there is a channel you would rather have it on, go to TrustInsights.ai/tipodcast. Thanks for tuning in; we will talk to you on the next one. Katie Robbert: Want to know more about Trust Insights? 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. Our 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. Trust Insights also offers expert guidance on social media analytics, marketing technology, 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 a 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 our focus on delivering actionable insights, not just raw data. We are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet we excel at explaining complex concepts clearly through compelling narratives and data storytelling. This commitment to clarity and accessibility extends to our 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 are 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.

Sichere dir jetzt deinen Platz in unserem kostenlosen KI Spotlight “Mental Overload endlich adé” am 28. Mai um 11:00 Uhr! Wir stecken mitten in einer technologischen Disruption: Über 80% der Unternehmen weltweit nutzen bereits KI-Tools. Jetzt steht der nächste riesige Sprung kurz bevor: Agenten und Agentic AI.In dieser Folge mit unserer Dozentin und KI-Beraterin Zamina Ahmad erfährst du:Agentic AI: Was autonome KI-Agenten wirklich von einfachen Custom GPTs unterscheidet.Der Klarna-Effekt: Warum reine Automatisierung ohne Empathie scheitert und wie die perfekte hybride Zusammenarbeit aussieht.Rollenverschiebung: Warum du in Zukunft weniger selbst codierst oder textest, sondern zur strategischen Qualitätsprüferin wirst.Human in the Loop: Wie du durch klare Quality Gates und Feedback-Schleifen die Kontrolle über deine Governance behältst.Experimentierräume: Wie du als Führungskraft psychologische Sicherheit schaffst, um gemeinsam statt einsam mit KI zu lernen.Prozessliebe: Mit welcher einfachen Frage du deine tägliche Arbeit mithilfe von KI heute komplett neu erfinden kannst.Zamina teilt ihre Geheimtipps für KI im Alltag, erklärt, warum gerade Frauen jetzt die KI-Zukunft aktiv mitgestalten müssen und weshalb die Zukunft hybrid ist.Keywords: Agentic AI, KI-Agenten, Vibe Coding, Prozessautomatisierung, Künstliche Intelligenz 2026, Human in the Loop, Responsible AI, Governance, Experimentierkultur, Female Leadership, Vera Strauch, Zamina Ahmad.+++Alle Links und Details findest du hier.Du willst 2026 deine Karriere selbst erzählen? Dann melde dich jetzt bei der Female Leadership Academy 2026 an und gestalte deine Leadership Karriere mit uns.Du brauchst mehr Infos? Melde dich hier zum Newsletter an.+++ Hosted on Acast. See acast.com/privacy for more information.

What do the American people actually expect from companies deploying AI — and are corporate leaders listening?In this episode of One Vision Podcast, Theodora Lau sits down with longtime friend Tyler Spalding, Chief Marketing, Communications & Engagement Officer at JUST Capital, to unpack the organization's latest research on how the public, investors, and corporate executives view AI's impact on society, jobs, and the economy.They dig into the perception gap between public sentiment (66%) and corporate optimism (94% of investors and 90% of corporate leaders see AI as a net positive), and what that gap means for business leaders navigating workforce decisions, reskilling investments, and responsible AI deployment.The conversation also explores the tension between AI-driven efficiency gains and the human cost of disruption — from layoffs framed as AI transformation and the anxiety facing the next generation entering the workforce, as well as the importance of defining and incentivizing responsible AI through consistent, comparable standards guided by public expectations.

We are reckoning with truths that are a bit uncomfortable. AI is not just a tool, but something people are relating to - personally. For kids and teens, we're finding that that can be extremely dangerous.In this episode, Shannon Peavey speaks with Tracy Pizzo Frey, a veteran technology leader including 11 years at Google where she oversaw Responsible AI for Google Cloud. As a consultant, she worked with Common Sense Media to develop a system for assessing the risks of large language models and AGI, with a particular focus on kids and teens.Tracy brings deep technical experience and knowledge of how technology shapes behavior. Drawing on lessons from social media and emerging research, she explores what feels different about AI.For young people who are still developing social and emotional skills, these interactions may have unique implications. AI systems are responsive and engaging, but they do not challenge users or help them navigate real-world complexity in the same way humans do. Over time, that difference may influence how teens build coping skills, relationships, and a sense of self.Through her work with Common Sense Media, Tracy has evaluated leading AI systems and reached some important conclusions. Today's models are not designed to serve as mental health companions for kids or teens, even though many young people are already engaging with them in ways that resemble emotional support.Tracy shares how these assessments were created, what they measure, and what they reveal about the current state of AI safety. She offers a grounded perspective on building & using these technologies responsibly, especially when younger users are already deeply involved..00:20 AI's effect on kids 02:07 Why harms are specific to kids & teens 04:50 AI is not a search engine07:27 Kids & teachers are often earliest adopters 08:06 Tech companies know more than they let on 09:15 Common Sense Media's risk assessment project09:57 Let's not repeat mistakes 11:24 Tracy's involvement13:11 Set your charter 14:50 Bring diverse, multi-disciplinary teams 16:25 Why psychological safety is important 17:38 Distilling masses of information into risk assessments18:48 Why hype matters20:30 How the team looked at social media 23:20 Early assessment of potential harms25:10 Character.ai as precursor to interaction with LLMs26:00 ‘Everything in the whole wide world'27:13 Why kids are different32:18 The danger of so-called frictionless relationships33:02 The best way to test36:50 Some surprising findings39:08 How tech can reshape a worldview41:02 There are good people, but - business models43:30 Know the tradeoffs45:07 The fact-to-fiction scale46:30 Some positivity48:00 Books, lawsuits, and resources

What do the American people actually expect from companies deploying AI — and are corporate leaders listening?In this episode of One Vision Podcast, Theodora Lau sits down with longtime friend Tyler Spalding, Chief Marketing, Communications & Engagement Officer at JUST Capital, to unpack the organization's latest research on how the public, investors, and corporate executives view AI's impact on society, jobs, and the economy.They dig into the perception gap between public sentiment (66%) and corporate optimism (94% of investors and 90% of corporate leaders see AI as a net positive), and what that gap means for business leaders navigating workforce decisions, reskilling investments, and responsible AI deployment.The conversation also explores the tension between AI-driven efficiency gains and the human cost of disruption — from layoffs framed as AI transformation and the anxiety facing the next generation entering the workforce, as well as the importance of defining and incentivizing responsible AI through consistent, comparable standards guided by public expectations.

Kevin Werbach speaks with long-time responsible AI leader Rumman Chowdhury the current environment, in which substantive standards and oversight efforts for AI are taking shape amid a larger anti-regulation wave. Chowdhury distinguishes sharply between frontier labs, where the posture is largely "AI at all costs," and the non-tech enterprises she works with, who are wrestling with how to scale governance bodies that originally reviewed single AI implementations to hundreds of systems, third-party procurement questions, and agentic workloads. She describes the current evaluations market as immature on nearly every dimension, and explains why generic benchmarks rarely translate to enterprise contexts like insurance or auto manufacturing. The conversation then turns to AI's impact on work and education. Her concern is that companies pursuing short-term efficiency by cutting entry-level hiring will face what MIT researchers Caosun and Aral call the "augmentation trap," in which workers' cognitive skills atrophy while new workers never develop them. She offers "discernment" as her 2026 word of the year, discribing the skill -- more than just critical thinking -- we must cultivate and defend. Her new podcast and forthcoming book, Thinking About Thinking, argues that our notion of intelligence was built for an Industrial Revolution workforce we are now automating away. Dr. Rumman Chowdhury is the founder of Humane Intelligence PBC, building modular, tool-agnostic AI evaluation infrastructure for enterprise and real-world contexts. She co-founded the nonprofit Humane Intelligence in 2022 and served as its CEO until 2025. She previously was Director of the Machine Learning Ethics, Transparency, and Accountability team at Twitter, founder of the algorithmic audit platform Parity, and Global Lead of Responsible AI at Accenture, where she built one of the first enterprise-level bias detection tools. She has served as U.S. Science Envoy for AI and as a Responsible AI Fellow at Harvard's Berkman Klein Center, and holds a doctorate in political science from the University of California, San Diego. Transcript Virginia SB 384 / HB 797 — Independent Verification Organization legislation (Fathom) The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading Open to Debate: Will AI Make Work Obsolete? Why AI evals need to reflect the real world (Transformer)

In this episode, we're joined by Georgina Bulkeley, Director of Financial Services at Google Cloud, to unpack how AI is transforming banking and financial services. With over 25 years in financial institutions, she shares what it takes to innovate in a highly regulated industry and how concepts like agentic commerce are paving the way for autonomous money movement. We also explore how AI is shifting from a risk to a powerful tool for fraud prevention, risk management and personalized financial advice at scale, along with the balance between innovation and responsible AI. Georgina closes with her perspective on what the industry will look like in five years and the biggest misconceptions leaders still have about AI.

Mel Sellick readies for AI by going beyond literacy to address the psychological, cognitive, and relational capacities required to ensure AI works for humans.Mel and Kimberly discuss AI literacy vs. human readiness; the contours of human vulnerability; AI as a social actor; collective understanding and emotional regulation; instrumental AI dependency; the non-reciprocal nature of AI; the spectrum of relationality; human flourishing; attention, agency and alternate futures; positive friction in human systems; supportive social structures; cognitive offloading and debt; self-reflection and calibrating human needs.Mel Sellick is an applied psychologist specializing in Human-AI interaction. The Founder of the Future Human Lab, her Human Readiness Framework has shaped conversations in IEEE, UNESCO, Oxford, MIT, Harvard and beyond.Additional Resources:Future Human Lab: https://www.futurehumanlab.com/ IEEE Organizational Readiness for Human-AI Interaction (Chair, SA-P7023) https://standards.ieee.org/ieee/7023/12394/Oxford AI in Education Hub (AIEOU): https://aieou.web.ox.ac.uk/ Harvard AI for Human Flourishing Council: https://hfh.fas.harvard.edu/ai-human-flourishing A transcript of this episode is here.

What if the real power of AI in healthcare isn't the technology itself, but how we apply it responsibly and intentionally? In this episode, Ajoy Ranga, Chief Digital Officer of Healthcare at UST Global, and Ashok Chennuru, Chief Data & Digital AI Transformation Officer at the Digital Platforms and Artificial Intelligence Office at Elevance Health/Carelon, discuss how their partnership between UST and Elevance Health is leveraging AI, data, and digital transformation to improve healthcare outcomes and consumer experience. They emphasize that scaling AI responsibly requires strong governance, human oversight, and a clear stance against using AI to deny care. Both highlight that high-quality, actionable data is foundational, but must be practical, cost-effective, and usable even when imperfect. Ultimately, they stress that success in healthcare innovation comes from starting with user experience, rapidly prototyping solutions, and fostering a mindset of continuous learning and experimentation. Tune in to hear how Elevance Health and UST are balancing innovation with responsibility to unlock AI's true potential in healthcare! Resources: Connect with and follow Ajoy Ranga on LinkedIn. Follow UST Global on LinkedIn and visit their website! Connect with and follow Ashok Chennuru on LinkedIn. Follow Elevance Health on LinkedIn and visit their website!