Podcasts about Chief product officer

AI platforms increasingly reward customer-focused product content over technical specifications. Lucas Tieleman, co-founder and CEO of Optiversal and former Chief Product Officer at Bazaarvoice, has helped enterprise retailers convert static product catalogs into machine-readable content that performs across traditional search and AI-driven discovery. The conversation covers why answer engine optimization depends on strong underlying SEO fundamentals, how AI is reshaping the way brands approach search visibility, and why translating product specifications into customer benefits matters more than increasing content volume.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Volume-based keyword content is quietly undermining search performance. Lucas Tieleman, CEO and co-founder of Optiversal, has helped major enterprise retailers convert static product catalogs into machine-readable content that performs across both search and AI platforms, building on experience as former Chief Product Officer at Bazaarvoice. The conversation breaks down why "sloptimization"—churning out keyword-driven posts without added value—undermines both rankings and revenue, why answer engine optimization depends on strong underlying SEO fundamentals rather than replacing them, and how transforming product data into structured, machine-readable assets drives visibility across traditional and AI-driven search.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this special episode, we are joined by Mike Sullivan, Co-Founder and Chief Growth Officer at OneDigital, and Vinay Gidwaney, OneDigital's Chief Product Officer, to discuss their new book, Workforce Intelligence: The People-First Playbook for Leading Your Company Through AI Transformation. Together, they offer a practical, pro-human framework for navigating a future where artificial intelligence becomes deeply embedded in how organizations operate.   We explore why leaders should focus on tasks rather than headcount, how AI can amplify uniquely human capabilities, and why companies may need to rethink how they manage their workforce. Mike and Vinay explain their concepts of reducible and irreducible skills, AI coworkers, workforce intelligence, collaborative AI use, and the importance of building an organizational intelligence layer. They also share practical lessons from OneDigital's own AI transformation—including why leadership activation, trust, reskilling, and a partnership between technical and non-technical leaders are essential.   This conversation offers an alternative to the prevailing narrative of AI-driven job elimination. Instead, it asks a bigger question: if AI can take on more of the work we currently do, what might humans become capable of doing next?   Key Points From This Episode: (0:00:00) Highlights. (0:00:25) Introduction. (0:01:23) Why AI adoption affects employers, employees, and financial markets—and why the conversation is relevant far beyond technology. (0:02:27) Two possible paths for companies: replacing people with AI or using AI to amplify human capabilities. (0:03:53) How PWL is already using AI to help financial planners and portfolio managers work more strategically and serve clients better. (0:06:22) Mike and Vinay's five-year partnership around deploying AI inside OneDigital. (0:09:07) The "radiating red dot": Why Mike's analysis suggested that up to 25% of OneDigital's workforce could be disrupted by AI. (0:11:09) "See faces, not headcount": The decision to pursue transformation by amplifying people rather than simply reducing jobs. (0:12:19) Why Mike and Vinay felt a responsibility to offer a more practical, human-first narrative about AI and work. (0:13:40) Vinay's realization that widespread access to AI makes human differentiation even more important. (0:17:22) Mike's first experience with an AI coworker—and the endless possibilities it unlocked for curiosity and exploration. (0:18:22) Human intelligence versus artificial intelligence: Why AI's greatest value may be its ability to help people think differently. (0:22:19) Why the future of work should be analyzed at the task level rather than through predictions about jobs disappearing. (0:23:58) The coming reskilling challenge—and why the allocation of work between AI and humans needs to be more deliberate. (0:25:12) Why Vinay believes companies that discard their human talent could lose their most important competitive differentiation. (0:26:30) Why AI transformation should be viewed as a "movie, not a snapshot," with work continually being reshaped. (0:28:10) What "workforce intelligence" means: Managing the combined intelligence of human talent and AI talent. (0:30:07) Why Mike and Vinay believe HR—not just IT—must play a central role in leading the transition to a blended workforce. (0:31:54) Reducible versus irreducible skills: Letting AI handle work that can be broken into processes while humans spend more time on judgment, experience, and other "squishy" capabilities. (0:34:10) Applying the framework to financial planning: AI for modeling and information processing, humans for judgment, relationships, and helping clients navigate life decisions. (0:37:00) How AI can reduce meeting preparation from hours to minutes while generating insights that would otherwise be missed. (0:37:48) The importance of trust and communication as employees try to understand what AI means for their future. (0:39:53) The Workforce Intelligence score: Treating AI as talent and measuring the evolving mix of human and AI work. (0:42:09) Transactional versus collaborative AI use—and why collaboration can be more amplifying for both people and organizations. (0:45:40) Why companies need agency over their AI systems and should think carefully about intelligence lock-in. (0:48:47) Codifying organizational intelligence: Building systems where human expertise improves AI, which in turn helps humans become more capable. (0:50:24) What it means to become "activated" by AI—and how using AI as a coach and teacher can expand human potential. (0:52:44) Ambient AI: Systems that continuously observe information and surface patterns when human judgment is needed. (0:54:11) The Charlotte-Denver redundancy and the challenge of making the best organizational intelligence available to everyone. (0:57:26) OneDigital's five-tier fluency model for developing AI capabilities across employees, advanced users, managers, and builders. (1:00:13) Why democratized software development creates a new governance challenge—and how AI may help manage it. (1:00:54) Why OneDigital gives AI coworkers names, faces, profiles, skill sets, and human managers. (1:04:01) Mike's belief in the dignity of work—and why employers need to approach the AI transition with humanity and care. (1:06:53) Vinay's belief in human potential and why the goal should be to expand what people are capable of doing. (1:08:56) Why organizations should avoid measuring AI success solely through cost cutting and instead consider human amplification. (1:12:41) The four questions for Monday morning: Turning big ideas about AI transformation into practical actions leaders can take immediately. (1:13:55) Why AI transformation needs leadership from the top and a partnership between someone who understands technology and someone who deeply understands the business. (1:15:40) Leadership activation: Why organizations are unlikely to change until their leaders personally experience how AI can transform their own work. (1:17:44) What we still don't know about AI—and why the guests believe we are still in the very early innings of this transformation. (1:18:55) The three-minute-mile analogy: AI may optimize human minds in ways we cannot yet imagine. (1:20:00) A final call for a pro-humanity, blended workforce—and the need to move faster in adapting to what AI makes possible.   Links From Today's Episode: Meet with PWL Capital: https://calendly.com/d/3vm-t2j-h3p Rational Reminder on iTunes — https://itunes.apple.com/ca/podcast/the-rational-reminder-podcast/id1426530582. Rational Reminder on Instagram — https://www.instagram.com/rationalreminder/ Rational Reminder on YouTube — https://www.youtube.com/channel/ Benjamin Felix — https://pwlcapital.com/our-team/ Benjamin on X — https://x.com/benjaminwfelix Benjamin on LinkedIn — https://www.linkedin.com/in/benjaminwfelix/   Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com)

What would change if a customer could return three days later through a different channel and continue the same conversation without repeating a single detail? In this episode of Tech Talks Daily, I speak with Paul Adams, Chief Product Officer at Fin, the company previously known as Intercom. Paul has spent almost 13 years with the business and provides a candid account of how it abandoned its previous roadmap, placed a company-wide bet on AI, and rebuilt its products and working practices around AI agents. Our conversation begins with Fin's move from a customer service agent toward what Paul calls a single customer agent. The idea is that customers do not care whether their request belongs to sales, service, or customer success. They want the company to understand their situation and help them complete the task. Paul explains how AI agents can bring customer history, company knowledge, operational data, and business goals into the same conversation. This could allow an agent to resolve an issue, support a purchase, recognize a valuable customer, or transfer the conversation to a human without losing the context already provided. We also examine the economics behind poor customer service. Many companies are not ignoring customers through a lack of concern. They are receiving volumes of requests that cannot economically be handled by adding people alone. Paul says some Fin customers are resolving between 70 and 90 percent of customer queries through AI. Rather than seeing entire teams disappear, he is observing employees move into customer success, knowledge management, AI supervision, and higher-touch services. The episode also provides an unusually candid account of what it took to rebuild an established SaaS company around AI. Paul describes the process as brutal. Strategies were discarded, familiar processes were removed, and some people decided the new direction was not for them. His advice is to prioritize speed, place smaller experiments in front of real customers, and learn from evidence rather than waiting for every internal condition to become perfect. Paul also recalls working on early versions of mobile YouTube and Gmail when colleagues questioned whether anyone would watch video or answer email on a phone. Those stories provide a timely warning about judging new technology by its early limitations. Could AI agents finally give customers continuity across sales, service, and support, or will internal company structures remain the greater obstacle? Listen to the conversation and share your thoughts with me.

Two identical searches now return different results across AI platforms, upending ranking logic.Lucas Tieleman, co-founder and CEO of Optiversal and former Chief Product Officer at Bazaarvoice, helps enterprise retailers convert static product catalogs into machine-readable content optimized for AI-driven discovery. He argues that answer engine optimization depends on strong SEO fundamentals rather than a separate discipline, and that success now hinges on content relevance to individual searchers rather than fixed position rankings. Tieleman outlines how brands can use performance data to validate whether their content actually serves shopper intent, shifting content strategy from volume-based production to measurable relevance.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

How much of your technology stack is being held together by people swiveling between screens, copying information, and quietly compensating for systems that cannot communicate? In this episode, I speak with John Foreman, Chief Product Officer at Clio, about what he calls the "swivel chair problem." John previously served as Chief Product Officer at Mailchimp and Podium, and now helps guide product development at a company seeking to support the complete operation of a law firm. We discuss why legal professionals have moved from understandable caution around AI toward increasingly sophisticated daily use. John explains why concerns about client confidentiality, intellectual property, model training, and data access initially slowed adoption, as well as why lawyers are now helping set the pace for responsible professional AI use. Our conversation also examines why disconnected technology stacks make AI appear far less capable. People can interpret information across documents, billing platforms, case management tools, email, and court systems. An AI system cannot perform the same work unless it receives the necessary context and access. John also explains why the familiar chatbot may be the wrong interface for many jobs. Some AI tasks should happen quietly, while work involving legal filings and client records requires structured review, accountability, and human approval. With lawyers spending an average of 62% of their time on nonbillable work, the immediate opportunity could include intake, billing, timekeeping, reviews, document processing, and filing. These lessons extend well beyond legal services. Where is the swivel chair problem hiding inside your organization? Listen to the conversation and share your thoughts with me.

Artificial intelligence has quickly evolved from a productivity tool into an active participant in many organizations' daily operations. As organizations give AI greater autonomy within their environment, they're also granting them access to sensitive systems and data. That creates a new challenge for IT and security teams: How do you enable AI to assist productivity without compromising security? Rob Allen, Chief Product Officer at ThreatLocker, joins Business Security Weekly to discuss how zero trust principles can prevent an AI breakout. Rather than relying solely on the AI tool's built-in safeguards, organizations can choose to enforce security policies using ThreatLocker. Rob will discuss how ThreatLocker can enforce AI boundaries through Allowlisting, Ringfencing™, Endpoint Firewall, and Web Content Control, with Community Policies that govern what agentic AI tools can run, do, access, and reach. Segment resources: - https://www.threatlocker.com/blog/the-principle-of-least-privilege-for-ai-agents - https://www.threatlocker.com/blog/applying-threatlocker-to-agentic-ai-tools - https://www.threatlocker.com/blog/why-the-five-eyes-alliance-sees-zero-trust-as-the-best-defense-against-agentic-ai-threats This segment is sponsored by ThreatLocker. Visit https://securityweekly.com/threatlocker to learn more about them! In the leadership and communications segment, 3 cybersecurity issues that should keep every CEO awake at night, You Don't Find Your Leadership Style. You Mentor Your Way Into It., Cybersecurity Starts With Communication – And We May Be Getting It Wrong, and more! Visit https://www.securityweekly.com/bsw for all the latest episodes! Show Notes: https://securityweekly.com/bsw-461

All sorts of cybersecurity disciplines are adopting agents to help humans save time and automate routine activities. Sai Kiran Uppu describes his work on creating a platform for agents to analyze external threat intel, examine internal systems, and present triage decisions to operators. This type of work is especially useful to orgs that deal with petabytes of data and thousands of systems. And, as Kiran notes, it's important to keep that scale from blowing up your budget or turning triage into a procession of false positives. Ideally, this kind of threat intel that's paying attention to attack trends and searching internal systems for evidence of compromise also turns into proactive defenses. We talk about some of the ways to engage developers to improve security visibility into their services and harden their designs against common attacks. After that discussion we're running two sponsored interviews from Black Hat. AI Pentesting and the Future of Cybersecurity: Black Hat interview with Chris Wallis, Founder and CEO of Intruder This segment discusses how AI addresses the long-standing gap between traditional pentesting and automated vulnerability scanning. Intruder CEO and founder Chris Wallis dives into the nuances of AI-enabled security and how these offerings will impact mid-market security teams. Segment Resources: https://www.intruder.io/platform/ai-pentesting https://www.intruder.io/blog/ai-pentesting-the-depth-of-a-pentest-on-demand https://www.intruder.io/blog/ai-web-app-pentesting-test-on-every-major-release Intruder's continuous exposure management platform helps security, IT, and engineering teams stop breaches before they start. For more information about Intruder's products and services, please visit https://securityweekly.com/intruderbh. How Menlo Security Is Securing AI Agents from Prompt Injection: Black Hat Interview with Ramin Farassat, Chief Product Officer of Menlo Enterprises are deploying AI agents like Microsoft Copilot, Google Gemini, and Claude Code faster than they can secure them, and attackers are exploiting that gap through prompt injection. Hidden instructions get buried in web pages, files, and even images that a human would never notice but an AI agent reads and acts on. Menlo Security is building Menlo Agent Runtime Security (MARS) to close that gap, running every agent session in an isolated cloud that sanitizes content before an agent can act on it. Ramin Farassat, Menlo Security's Chief Product Officer, will discuss why the exposure lives in the connectors and integrations around the model rather than the model itself, and how security teams can put controls on the agent attack surface without blocking agentic AI outright. Segment Resources: https://www.menlosecurity.com/product/ai-agent-security MARS is now available today, please visit https://securityweekly.com/menlobh Visit https://www.securityweekly.com/asw for all the latest episodes! Show Notes: https://securityweekly.com/asw-396

For twenty-five years we sent the struggling kid to the back of the room with headphones on. Todd Brekhus of Renaissance explains why that model broke personalized learning, and how teacher-facing AI hands the room back to you. Todd Brekhus is Chief Product Officer at Renaissance, General Manager of Nearpod, a former English teacher and technology director, and the creator of the myON literacy platform. We recorded this conversation at ISTE 2026. In this episode, you'll learn: - Why "personalized learning" quietly pushed teachers out of the loop, and what changes when you say "personalized teaching and learning" instead - How to pull last year's skill-level data and plan two paths before tomorrow's lesson - Why back-of-the-room intervention stigmatizes kids, and how to differentiate inside the core lesson - The case for a formative check every six to eight minutes without leaving your slides - Why Renaissance deliberately chose not to build student-facing AI, and what productive struggle has to do with it This episode is sponsored by Renaissance. Renaissance Intelligence connects the loop across assessment, curriculum, instruction, practice and proficiency, so educators can act on student needs in real time. Learn more at renaissance.com/intelligence Full show notes, resources, and the complete transcript: https://www.coolcatteacher.com/e968  If this episode gave you something you can use tomorrow, leave a rating and review wherever you're listening — it helps other teachers find the show. And share it with a teacher friend who's tired of the screen-time argument. Disclosure of Material Connection: This is a sponsored episode. Renaissance has compensated me to share information about Renaissance Intelligence. However, all opinions expressed are my own. I have personally reviewed these resources and only recommend tools I believe offer genuine value to classroom teachers. My endorsement is limited to the educational products and services discussed in this episode. I am disclosing this in accordance with the Federal Trade Commission's 16 CFR, Part 255: "Guides Concerning the Use of Endorsements and Testimonials in Advertising." The sponsor has no impact on the editorial content of this show.

Most companies bring in regulatory expertise when they're ready to file. Leland Keyt, VP of Regulatory Affairs and Quality Assurance at Apreo Health, makes the case that by then you've already absorbed costs you didn't need to, and explains what early regulatory partnership actually looks like in practice.Leland and Nick get into why risk tolerance is the thing that catches scaling companies, the hidden costs of rework beyond timeline, how regulatory input changes product decisions across geographies, and what regulators are scrutinizing more closely now.A few of Leland's key takeaways:Early means concept phase, not pre-submission. Even your IP narrative can matter years later in a submissionCompanies often run more testing than they need. Efficiency means answering the right questions with the least dataRisk tolerance is what gets companies in trouble. What's acceptable in development isn't acceptable at PMAPost-market obligations happen underneath the water, and they're a resourcing problem more than a regulatory oneRework isn't just timeline, it's product cost and a supply chain issueBiocompatibility interpretation varies by geography and can change what you develop and whenAI, usability, and management review are all getting closer regulatory attentionBuild a scalable QMS. It's the bedrock, and it isn't an afterthoughtAbout Leland KeytLeland Keyt is VP of Regulatory Affairs and Quality Assurance at Apreo Health, which is running a US pivotal study for an implantable airway scaffold for severe emphysema. She has over 20 years in medical devices across startups, mid-size companies, and multinationals, previously serving as Chief Product Officer at Biorithm and Senior Director of RA/QA at Pulmonx, where she navigated FDA's Breakthrough Device Program to a 178-day PMA approval.About The FDA GroupThe FDA Group helps life science organizations rapidly access the industry's best consultants, contractors, and candidates. Our resources assist in every stage of the product lifecycle, from clinical development to commercialization, with a focus on staff augmentation, auditing, remediation, QMS, and other specialized project work in Quality Assurance, Regulatory Affairs, and Clinical Operations. Learn more: https://www.thefdagroup.com/

Missy Tannen spent five years teaching third grade and a decade as a stay-at-home mom before she ever thought about starting a company. But when she couldn't find bedding that was high-quality, ethically sourced, and traceable, she and her husband Scott started Boll & Branch out of pure frustration. On this episode of She Pivots, Missy talks about betting the family's life savings on an idea, borrowing against their own house even while sales were taking off, and what it took to scale from a garage operation to a 200-person company without losing what made it different in the first place. She's brutally honest about the mess behind the milestones (a full batch of ivory sheets that came out green!) and about running a family business—that’s now worth over $250 million—with her husband while raising their kids. Chapters: 00:00 Welcome to She Pivots 01:32 From Teacher to Entrepreneur: Missy Tannen 09:23 Stay-at-home-mom 13:04 The Birth of Boll & Branch 26:15 The Wall Street Journal Breakthrough 33:54 Growing the Business and Family Involvement 39:24 Building a Sustainable Supply Chain 41:25 The Power of Community 42:32 The Tariff Challenge 45:54 Reflecting on Missy's Journey 47:09 Production Credits Check out Boll & Branch at www.bollandbranch.com Be sure to subscribe so you never miss a pivot story, leave us a rating (it really helps!), and share this episode with a woman in your life who you think needs a little inspiration. She Pivots is a podcast created by host Emily Tisch Sussman to highlight influential women voices, share stories of bold career moves, and inspire women with interviews about career reinvention and how personal pivots can redefine professional success. Join our Substack community! Subscribe here for exclusive content and to connect with other pivoters: shepivots.substack.com Learn more about the inspiring women in our pivoter community by following us on instagram @ShePivotsThePodcast, and check out our website shepivotspod.com for resources and updates. She Pivots is proud to be an iheart podcast.Support the show: https://www.shepivotsthepodcast.com/See omnystudio.com/listener for privacy information.

Has enterprise AI finally reached the point where impressive demonstrations are no longer enough? In this episode of Tech Talks Daily, I speak with Bruce McMahon, Chief Product Officer at CallMiner, about what he describes as the industrialization of AI: the move from experimentation and excitement toward repeatable processes, measurable ROI, better customer experiences, and technology that can operate reliably at enterprise scale. Bruce explains why business leaders are increasingly asking a much simpler question about AI: how is this going to create value? Drawing on CallMiner's experience analyzing hundreds of thousands of hours of customer interactions every day, Bruce discusses how AI can surface operational inefficiencies and customer insights that were previously difficult to identify. The opportunity is not simply generating more data. Organizations need processes that get the right insight to the right person so something actually changes as a result. We also discuss how AI is changing workforce expectations. Bruce sees curiosity and adaptability becoming increasingly valuable, particularly among technical teams. As AI takes on more routine work, employees who question outputs, experiment with new approaches, and apply human judgment can become more valuable than those who rely solely on established technical knowledge. The economics of enterprise AI present another challenge. Foundation models, capabilities, and pricing continue to change rapidly, creating questions around vendor dependency and long-term costs. Bruce explains why companies may increasingly use a mixture of commercial, open-source, fine-tuned, self-hosted, and proprietary models rather than relying on one provider for everything. Governance becomes even more important as AI agents begin interacting directly with customers. We discuss red teaming, bias testing, compliance, data protection, monitoring, and why organizations need to decide which actions can be fully automated and which decisions must remain accountable to a human. Bruce also examines how AI is changing customer experience and the BPO industry. Rather than choosing between humans and AI agents, he sees value in designing systems where both can work together, with people handling interactions requiring judgment while AI manages high-volume and repetitive work. For CIOs, CTOs, COOs, customer experience leaders, and anyone responsible for enterprise AI strategy, this conversation provides a practical look at moving beyond AI pilots and turning the technology into a dependable part of business operations.  

Building a food brand around the belief that food is healing — that's the magic ✨ On The Kara Goldin Show, I sit down with Neka Pasquale, Founder and Chief Product Officer of Urban Remedy — a company on a mission to reimagine the food system and teach the world that food is healing. We talk about how Neka's background in acupuncture, Chinese medicine, nutrition, and lifestyle medicine led her to create Urban Remedy, and how wellness retreats with her patients helped spark the idea for the brand. We also get into what it takes to scale fresh, certified organic, non-GMO, gluten-free, dairy-free, and refined sugar free meals, snacks, juices, and shakes while staying true to quality and purpose. Neka shares how Urban Remedy has grown into more than 400 Whole Foods Market locations and other major retailers, how she continues to innovate around what consumers want next, and why every ingredient in Urban Remedy products is chosen for its healing properties. If you're interested in learning what it takes to build a mission-driven food brand, scale fresh products without compromising standards, or create a company around a deeply held belief — check out the interview on my podcast. You can find it here or anywhere you listen to podcasts

Blaine Billingsley is a Member of Technical Design Staff at OpenAI whose path into the field ran through music composition, spare-change website work at college, and a decade across Gmail, Airbnb, YouTube and Slack. At OpenAI he was hired to work on Presence, then redirected to solve a more immediate problem: making ChatGPT genuinely useful as a daily tool for designers. Working with a single engineer, he built the ChatGPT product design plugin — a bridge between the raw power of Codex and the day-to-day workflow of product teams. In this conversation with Randy Silver, he talks about what design actually means when your interface is a text box, why volume beats perfection in an age of infinite iteration, and why the fundamentals of good product thinking are more durable than the tools used to apply them.Key takeaways— The designer's job at an AI company has shifted from pixels to outputs. Deciding what "good" looks like, building the criteria to evaluate it, and heuristically assessing results is now a core part of the role — one that didn't exist in the same form at Gmail or Slack.— LLMs don't replace structured creativity techniques; they scale them. The crazy eights exercise squeezes eight ideas from a room of people in eight minutes. A well-directed ChatGPT session can return 80 in the same window, alone, while you're at lunch.— Volume beats perfection. The best way to make a pot isn't to try to make the best pot — it's to make a thousand pots. That logic now applies directly to prototyping: generate at scale, stay unattached, and find the nugget in the noise.— Evals are closer to synthetic user research than quality assurance. The hardest part isn't building the rubric — it's correctly anticipating what people will actually try to do. Show it to one more person and your assumptions will immediately break.— The second 80% problem hasn't gone away. Getting to a working prototype is dramatically faster; getting that prototype to production is still gruelling — and becomes harder still when platform direction shifts mid-sprint.— Small teams with AI assistance need to protect the rituals that keep them aligned. When two people can each produce a week's worth of work in an afternoon, parallel drift becomes the real collaboration risk.— Role boundaries are dissolving, but specialisations still matter. The question is less "what is your title" and more "what does the band need right now, and can you play that part?"— Experience brings judgment; freshness brings juice. The best work often comes from junior designers unconstrained by years of accumulated assumptions — and both things need to be in the room.Chapters (00:00) Introduction(01:14) Blaine's background: from music composition to product design (02:32) What design means at OpenAI (04:21) The ChatGPT product design plugin (06:12) Deciding what to build: the early exploration (10:05) Structured creativity and LLM-powered ideation (13:37) Volume over perfection: the thousand pots approach (16:13) Designing as a two-person team (20:29) What is the job now? (23:33) Evals as synthetic user research (27:08) Testing at scale when you can't know your users (30:15) How they actually did the research (33:01) The second 80%: from prototype to production (35:06) Staying aligned without roadmaps (38:19) Demo: the product design plugin (44:52) When a prototype isn't ready to ship (48:26) Design sprints, reimagined (49:27) Advice for joining an AI-first product team (52:17) The jazz analogy: experience, freshness and your role in the band Featured LinksChatGPT for Work: https://openai.com/chatgpt Codex: https://openai.com/codex Figma: https://figma.com FigJam: https://www.figma.com/figjam Linear: https://linear.appOur HostsLily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She's currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. She's worked on a diverse range of products – leading the product teams through discovery, prototyping, testing and delivery. Lily also founded ProductTank Bristol and runs ProductCamp in Bristol and Bath.Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury's. He participated in Silicon Valley Product Group's Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He's the author of What Do We Do Now? A Product Manager's Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon's music stores in the US & UK.

Projects are, in many ways, a collection of decisions. But when work is moving fast, information is scattered, and the stakes are high, making a well-informed call isn't always straightforward. AI can pull together more context than any one person could realistically hold in their head—but that doesn't mean we should hand over the decision-making reins.Galen Low sits down with Cali Collins, Co-Founder and Chief Product Officer of Optimality, to explore how AI can support better decisions without removing humans from the equation. They dig into decision context, bias, traceability, accountability, and why the rise of AI could actually make project leaders more—not less—important.Resources from this episode:Join the Digital Project Manager CommunitySubscribe to the newsletter to get our latest articles and podcastsConnect with Cali on LinkedInVisit Optimality

We're on summer break and replaying one of our favorite episodes. This episode originally aired on September 22, 2025.A wearable can collect endless data, but it only matters if people trust it enough to change their behavior.In this replay, Oura CEO Tom Hale joins Steve and Michael to explain how the company combines clinical-grade accuracy with a consumer-friendly product. They discuss why the ring's form factor makes overnight tracking so powerful, how Oura turns physiological signals into useful guidance, and why the company is being pulled faster than expected into traditional healthcare.We cover:Why sleep data can reveal changes in the body before symptoms appearHow Oura designed a health product that supports users without judging or nagging themWhy accuracy is the foundation for trust, predictions, and behavior changeHow Oura navigates the boundary between wellness products and regulated medical devicesThe company's expansion into Medicare Advantage, women's health, metabolic health, and clinical care—About our guest:Tom Hale is the Chief Executive Officer at ŌURA and a member of its board. As the CEO he sets the company's business strategy and vision to make health a daily practice for members all over the world. He has over 30 years of experience across the technology and consumer product industry.Before joining ŌURA, Hale was president of Momentive where he drove B2B strategy and led product growth. During his time there he was instrumental in taking the company public in 2018, leading the team in key acquisitions, and driving the evolution and rebranding of the company into a multiple SAAS business. Previously, he held leadership roles at HomeAway and Linden Lab as the Chief Operating Officer and Chief Product Officer, respectively, and held executive roles at Macromedia and Adobe.Hale has a Bachelor of Arts from Harvard University and sits on the boards of Cars.com, RocketReach, and NoiseAware.—If you're enjoying the show, we would so appreciate it if you left us a review!—

What does it take to go from practicing law to becoming the Chief Product Officer of a company disrupting the legal industry with AI? In this episode of the CPO Rising series hosted by Products That Count Resident CPO Renee Niemi, LegalOn Technologies CPO Vanessa Davis will be speaking on how she made the leap from attorney to product leader, and why putting the customer first drives every major decision she makes. She also gets into how LegalOn navigated a major stack overhaul to move fully to LLMs, what the entrance of tools like Claude into legal tech means for the industry, and why DAU over MAU is her north star metric.

Football franchises run on data. Jono Luk of Sumer Sports explains how AI is changing who wins — on the field and in the stands.Full Show Notes:Most sports AI coverage focuses on stats. Jono Luk, Chief Product Officer at Sumer Sports, is thinking about something harder: measuring what a player decided, not just what they did. Sumer's computer vision tracks all 22 players up to 60 times per second, generating probabilities for every moment of every play — not to describe what happened, but to evaluate whether the best decision was made.That depth of game intelligence turns out to be useful in two places. For coaches, GMs, and scouts, it surfaces skill that box scores miss entirely. For the business side of a franchise, that same real-time game data becomes the trigger for personalized fan experiences — the right offer, the right moment, whether someone is in the stadium or watching from a sports bar. Jono calls this the before, during, and after — and argues it's the same attract-retain-monetize cycle every enterprise runs, just with a different playbook.The episode also covers what Jono wishes organizations understood before they start any AI conversation: AI isn't synonymous with LLMs. Computer vision, specialized models, and multi-component pipelines built for specific data types are doing work that frontier models simply aren't designed for. That framing matters for any enterprise buyer, not just sports franchises.What We Cover:How Sumer Sports uses computer vision to evaluate player decisions, not just outcomesWhy specialized AI models outperform general-purpose frontier models for sports analyticsThe two sides of AI in a football franchise: football operations and fan engagementHow real-time game data triggers personalized in-venue and at-home fan experiencesWhat the NIL era means for AI-assisted recruiting at college and high school levelsWhy AI in the enterprise isn't just about LLMs — and how to think about the broader toolkitThe one question Jono wants every organization to ask before deploying AIGuest Bio:Jono Luk is Chief Product Officer at Sumer Sports, a football data analytics and AI company that uses computer vision to analyze every player, every game, in real time. Before Sumer, Jono held product and technology leadership roles at Cisco. He brings a cross-industry perspective on how AI fits into larger solutions — not as a replacement for human judgment, but as the infrastructure that surfaces better decisions faster.Sumer Sports: https://www.sumersports.comJono Luk on LinkedIn: https://www.linkedin.com/in/jonoluk/ (confirm URL before publishing)Resources Mentioned:Sumer Sports: https://www.sumersports.comLopez Research: https://www.lopezresearch.com/research/

Enterprise retailers index only 40-50% of their product pages. Joe Doran, Chief Product Officer at Botify, breaks down why AI crawlers lag Google by 20 years in crawl efficiency and how JavaScript-heavy PDPs leave bots seeing just 30% of page content. The conversation covers structured product feeds as the new on-page SEO, aligning catalog data with protocols like OpenAI's Agentic Commerce Protocol and Google's Universal Commerce Protocol, and moving beyond organic traffic in isolation toward citation rate, crawl volume, and multi-touch attribution models built for zero-click discovery.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Enterprise retailers index only 40-50% of their product pages. Joe Doran, Chief Product Officer at Botify, breaks down why heavy JavaScript rendering and fragmented product data leave nearly half of retail catalogs invisible to Google and AI crawlers alike. He details the crawl budget compounding effect that shrinks indexation, the confirmation-crawl behavior LLMs use to verify and cite pages, and why structured product feeds—not visible page content—now determine agentic commerce visibility. He also makes the case for rethinking attribution beyond session-based tracking to measure multi-touch contribution across AI search, paid media, and traditional organic channels.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most CDOs talk about data products. Very few approach them the way a product manager would starting with customer need, validating demand, and building only what people will actually use.

What happens when AI search evolves into real-time intelligence for answers, agents, and enterprise workflows?In this episode of XTraw AI, Raghu Banda speaks with Saurabh Sharma, Chief Product Officer at You.com, about the future of AI-powered search, grounded answers, real-time web intelligence, and agentic workflows. The conversation explores how enterprises can move beyond traditional search and standalone AI models toward trustworthy, current, and actionable AI experiences.• How AI is changing search from links to answers, reasoning, and agents• Why grounded answers, citations, freshness, and trust are critical for enterprise AI adoption• How You.com is building real-time web intelligence and APIs to support AI applications, agents, and enterprise workflowsYou can reach @ SaurabhMy LinkedIn @ RaghuWebsite @ XTraw AI

Matt Muilenburg is the Chief Product Officer at Impel, a company building the AI platform used by thousands of dealerships to run sales, service, and fixed operations. He has spent thirty years working in dealer technology, starting at the Cobalt Group in the early internet era. He has focused on the impact of artificial intelligence on dealership operations over the last several years. Topics: 09:55 The Trade-In Lead Killing Profits. 18:40 Smallest Dealers Face Biggest Existential Risk. 21:40 First $100K Goes To Fixed Ops. 25:00 50% Of Dealers Use AI Unknowingly. 27:00 Employee ChatGPT Use Is A Legal Timebomb. 37:10 BDC Role Obsolete Within 12 Months. This episode is brought to you by: 1. Digital Dealer - Join us September 22–23 in Detroit and register today at DigitalDealer.com. Use code CDG25OFF at checkout and bring your team for a generous discount. Visit @ ⁠here⁠ for more info. 2. Uber for Business - The true cost of moving your service customers is bigger than it looks. Visit @ ⁠here⁠ to get started. 3. Impel - Meet the AI Operating System built for a new era of automotive retailing. Visit @ ⁠here⁠ for more info. Check out Car Dealership Guy's stuff: For dealers: CDG Circles ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://cdgcircles.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Industry job board ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://jobs.dealershipguy.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Dealership recruiting ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://www.cdgrecruiting.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Fix your dealership's social media ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://www.trynomad.co⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Request to be a podcast guest ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://www.cdgguest.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ For industry vendors: Advertise with Car Dealership Guy ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://www.cdgpartner.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Industry job board ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://jobs.dealershipguy.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Request to be a podcast guest ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://www.cdgguest.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Car Dealership Guy Socials: X ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠x.com/GuyDealership⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠instagram.com/cardealershipguy/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠tiktok.com/@guydealership⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠linkedin.com/company/cardealershipguy⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Threads ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠threads.net/@cardealershipguy⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠facebook.com/profile.php?id=100077402857683⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Everything else ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠dealershipguy.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Enterprise retailers index only 40-50% of their product pages. Joe Doran, Chief Product Officer at Botify, explains why exploding product portfolios and JavaScript-heavy PDPs leave nearly half of enterprise catalogs invisible to Googlebot and emerging LLM crawlers alike. The conversation covers crawl budget economics and why JavaScript rendering costs three to five times more than HTML, the gap between what bots render versus what consumers see, and how new protocols like OpenAI's Agentic Commerce Protocol and Google's Universal Commerce Protocol demand structured feed attributes—reviews, dimensions, materials—that fragmented product systems rarely deliver at scale. Doran makes the case that product feeds, not on-page content, will define discoverability over the next two years.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Vinay Gidwaney is Chief Product Officer and Mike Sullivan is Co-Founder and CEO of OneDigital, a 6,000-person, PE-backed benefits, HR, and wealth consultancy serving roughly 100,000 employers. Their contrarian bet: AI transformation has almost nothing to do with technology and everything to do with treating AI as talent. Instead of automating tasks, OneDigital built an internal hiring pipeline for AI, complete with job descriptions, an intern-to-apprentice-to-full-time promotion path, and performance improvement plans, and used it to avoid the layoffs most "AI transformation" playbooks assume are inevitable. Liam sits down with both of them to unpack the night Mike built a "disruption calculator" that showed OneDigital was on track to cut 1,800 of its 6,000 jobs, and how that all-nighter became the catalyst for a different strategy. They get into Ben, the AI coworker now handling daily conversations with 1,600 benefit consultants, why Vinay cites a claim from Lemonade's CEO that AI agents scored higher on customer empathy than human call center staff, the risk of companies "renting back" their own intelligence after gutting their workforce, and the thinking behind their upcoming book, Workforce Intelligence, releasing August 25th. Key Topics Covered Why treating AI adoption like rolling out a new CRM guarantees failure The failed early bet on automating RFPs, and why augmenting human thinking won instead The stat that proves AI adoption is personal: a manager's own AI use doubles their team's usage Building an AI "disruption calculator" overnight, and the 1,800-job number it produced OneDigital's three-part AI framework: coworkers, builders, and agents How OneDigital literally hires its AI: job descriptions, interns, apprenticeships, and performance improvement plans Meet Ben, the AI coworker fielding daily conversations with 1,600 benefits consultants Why they stay LLM-agnostic and separate the intelligence layer from the model and the harness The real risk behind AI cost metering: losing access to the intelligence your company now depends on A claim from Lemonade's CEO that AI agents beat humans on customer empathy Irreducible vs. reducible skills: how to decide what AI should do and what humans should keep The case for "faces, not headcount" and OneDigital's internal "humanity test" What's actually inside their upcoming book, Workforce Intelligence Episode Timestamps 00:00 Introduction 00:41 The thesis: AI transformation is about talent and leadership, not technology 02:44 Why treating AI adoption like a CRM rollout fails 04:29 Pitching VCs a "Workday for AI agents," and why it flopped 07:19 Why automating tasks failed, and augmenting human thinking won 11:20 The stat that changed everything: manager AI usage doubles team usage 15:04 Mike's personal epiphany and the "Claude" nickname from his family 18:10 Building an overnight "disruption calculator" with Claude and Replit 19:56 The result: a model showing 1,800 of 6,000 jobs at risk if nothing changes 21:11 Coworkers, builders, and agents: OneDigital's three-part AI framework 24:02 The AI hiring pipeline: job descriptions, interns, and apprenticeships 27:39 Meet Ben: the AI coworker now used by 1,600 benefits consultants daily 31:10 What's actually deployed: an LLM-agnostic stack with a separate intelligence layer 35:38 The metering and access risk: the Fable/White House security scare 39:56 Measuring "workforce intelligence": blending human and AI capability 50:07 Irreducible vs. reducible skills, and the Lemonade insurance AI-empathy example 53:38 The reskilling problem, and why human judgment stays irreplaceable 56:30 "Faces vs. headcount" and the company's internal "humanity test" 1:07:08 The origin of their book, Workforce Intelligence (out August 25th) 1:11:10 The risk of "renting back" your own intelligence after cutting your best people Learn more about OneDigital and the book Workforce Intelligence: https://www.onedigital.com/ Connect with Mike on LinkedIn: https://www.linkedin.com/in/mikesullivanatdigital/ Connect with Vinay on LinkedIn: https://www.linkedin.com/in/gidwaney/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices

Imagine redesigning your most important processes from a blank sheet of paper, with no legacy systems to hold you back. Alpa Lally, Chief Product Officer at Grant Thornton, shares how the company is transforming into a frontier firm by reimagining core processes instead of automating them. She unpacks how embedded analytics reshapes client conversations, why AI governance is what helps you move faster, and how to reinvent your career in the age of AI. Key Moments: From Structural Engineering to Product Leadership (01:36): Alpa explains how designing buildings taught her the vision, strategy, and foundation-up thinking she now applies to product. Becoming a Frontier Firm (06:27): Rethinking how work gets done for 13,000-plus professionals means recognizing that automating a broken process just does the wrong thing faster. Embedding ThoughtSpot Analytics (14:57): By putting insights in auditors' hands on day one, Grant Thornton shifts client conversations from data gathering to strategy. The AI Proof Gap: Governance Enables Speed (19:32): Alpa reframes governance as what lets you move faster, sharing that 78% of firms doubt they'd pass an AI governance audit. Is Product Management Dead? (24:33): The role is evolving, not dying, and AI raises the value of deciding which opportunities are actually worth pursuing. Key Quotes: “A frontier firm uses AI not just to improve how work gets done, but fundamentally rethinks how the work gets done. It's moving beyond productivity gains and creating entirely new ways to deliver value.” - Alpa Lally “The challenge is if you simply automate a broken process, you end up doing the wrong thing faster.” - Alpa Lally “What ThoughtSpot has done is [give us] our ability to embed them immediately into the flow [which] has opened up a whole level of different insights that we can offer, not to our practitioners only, but imagine an auditor showing up on day one to your client and having these insights already calculated, already delivered.” - Alpa Lally Mentions Grant Thornton's 2026 AI Impact Survey Report Wall Street is debating the AI buildout. Enterprises just answered: More than 80% say their GPUs run at half capacity or less Hers for the Taking by Tracey Newell Guest Bio Alpa Lally is the Chief Product Officer at Grant Thornton. Alpa is an experienced product management executive with several years in designing, developing, and launching consumer and business products for large to mid-sized to small organizations, fintechs, startups, and Fortune 500. She has led global cross-functional teams to drive high growth targets while staying laser-focused on solving customer and consumer problems. Alpa leverages data and analytics to drive successful business outcomes that deliver results and achieve corporate growth goals. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Enterprise retailers index only 40-50% of their product pages. Joe Doran, Chief Product Officer at Botify, breaks down why LLM crawlers—decades behind Googlebot in sophistication—struggle to render JavaScript-heavy PDPs and confirmation-crawl product data before citing it. Learn why product feeds now demand the same scrutiny as on-page SEO, how OpenAI's Agentic Commerce Protocol reshapes structured data requirements, and why citation rate and crawl volume correlations outperform synthetic share-of-voice metrics for measuring AI search visibility.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Dr Cat Hicks is a psychological scientist and the founder of Catharsis, a human-centred research consultancy that works with engineering organisations. After self-funding her first open-access study into developer motivation, she was recruited to lead the Developer Success Lab at Pluralsight — a three-year programme that generated large-scale empirical research across hundreds of companies and more than 11,000 developers. Her book, The Psychology of Software Teams, published in July 2026, brings that evidence to bear on the question her industry keeps asking: why do smart people in smart organisations so often struggle to work well together? We talk about the hidden costs of treating developers as interchangeable units, what large-scale cycle time data reveals about individual performance, and how product managers can build the conditions for teams to actually thrive.Key takeawaysThe "brains and jars" model — treating developers as interchangeable, isolated computing units — does not just feel demeaning; it actively undermines the collaborative, social work that makes software teams effective, and is linked to burnout, self-censorship, and a breakdown of trust.Cycle time data from 216 companies and over 11,000 developers shows that individual performance fluctuates dramatically week to week — it is not a stable personal trait — which means slow periods are usually an environmental problem, not a personal one.Over-production pressure (the constant pull towards short-term delivery) has measurable cascading effects: developers stop mentoring, stop surfacing problems to managers, and stop investing in long-term code quality. It can be measured and, with committed leadership, changed within weeks.AI is producing genuine identity threats, not just productivity disruption. Teams that lower the "threat temperature" — framing change as a shared technical challenge and affirming that new skills can always be learnt — unlock significantly more adaptive and curious behaviour.Metacognitive scaffolding — building structured strategies for thinking about your own thinking, not just raw output — is more trainable than cognitive capacity and is almost entirely absent from how agentic coding environments are currently designed.Superordinate goals (shared outcomes that transcend individual preferences) allow diverse teams, including those who prefer deep solo work, to collaborate productively without demanding that everyone care about the product in the same way.Chapters(00:00) Opening (01:06) Cat's background and the book (04:40) Defining a software team (07:02) Building the Developer Success Lab (10:26) The brains and jars model (13:14) What the model costs teams (17:54) Why communicating value is so hard (19:04) What change looks like in practice (23:01) Code review anxiety and cycle time research (27:55) AI's effect on developer identity (31:57) Agentic coding and cognitive fatigue (35:46) Threat, play, and psychological safety (38:19) Working across disciplines (43:04) What product managers can do (48:00) Wrap-upReferencedThe Psychology of Software Teams by Dr Cat HicksThe Programmer's Brain by Felienne HermansThe Psychology of Computer Programming by Gerald WeinbergDeveloper Success Lab (Pluralsight)DORA projectCatharsis — Dr Cat Hicks's consultancyFight for the Human — Dr Cat Hicks's newsletterDr Cat HicksLearning Opportunities Claude skill — by Dr Cat HicksDeliberate practice and chess improvement study (44,000 Chess.com players)Our HostsLily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She's currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. She's worked on a diverse range of products – leading the product teams through discovery, prototyping, testing and delivery. Lily also founded ProductTank Bristol and runs ProductCamp in Bristol and Bath.Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury's. He participated in Silicon Valley Product Group's Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He's the author of What Do We Do Now? A Product Manager's Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon's music stores in the US & UK.

Leveraging AI Efficiency and Human Insight  Shep interviews Filip Jaskólski, Chief Product Officer at Text. He talks about how customer service, empowered by AI and human collaboration, can shift from being a cost center to a revenue generator for businesses.  This episode of Amazing Business Radio with Shep Hyken answers the following questions and more:    How can customer service move from a cost center to a revenue generator?    What is the ideal balance between AI automation and human agents in customer support?    How can companies use AI to boost upselling and product recommendations?    Why should businesses transition from reactive to proactive customer service?    What metrics actually matter when evaluating AI in customer support?   Top Takeaways:    Customer service has evolved beyond being a support center. When done strategically, it can be an important contributor to customer retention and revenue.      Every interaction with customers is an opportunity to gain valuable information about what they want or need. Aside from answering questions and solving problems, successful companies use these customer signals to improve their products and services.     By analyzing and interpreting not just the words that customers use but also the intent and context behind interactions, AI becomes an essential tool for providing personalized recommendations.     The potential for upselling and cross-selling exists in every customer conversation, not just in face-to-face interactions. Automated systems are now sophisticated enough to identify when recommendations are relevant and welcome, turning customer support channels into points of sale.     The goal is not to fully automate contact centers. Instead, successful businesses strive for a balance between AI and human strengths. For example, AI can sort through large numbers of inventory to suggest products that match the customer's needs. At the same time, human agents are capable of empathy and judgment that are essential in building a lasting connection with customers.     Revenue, satisfaction, and loyalty are the biggest indicators of success in using AI to enhance customer and employee experience. Often, companies get stuck measuring how many customers interact with AI, or how fast problems are resolved while missing out on real opportunities for growth.     Plus, Shep and Filip discuss why businesses should be proactive rather than reactive when interacting with customers. Tune in!  Quote:   "With the technology that we have today, AI can help us go deeper in our relationship with customers. We can analyze so much data and context to match what our customers need to what we can offer."      About:    Filip Jaskólski is the Chief Product Officer at Text. He drives product strategy and development with a focus on practical, scalable AI-powered customer experience solutions.  Shep Hyken is a customer service and experience expert, New York Times bestselling author, award-winning keynote speaker, and host of Amazing Business Radio.    Learn more about your ad choices. Visit megaphone.fm/adchoices

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

Enterprise retailers index only 40-50% of their product pages. Joe Doran, Chief Product Officer at Botify, breaks down why indexation—not content quality or page speed—remains the most overlooked lever for organic revenue in commerce. He covers the compounding cost of JavaScript-heavy PDPs on crawl budget allocation, why LLM confirmation crawls fail to verify unrendered reviews and pricing data, and how serving non-human traffic with the same infrastructure investment as human traffic determines visibility across ChatGPT, Gemini, and Google Shopping.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI interactions are introducing behavioral risks that traditional DLP and rule-based systems were never designed to detect. In this executive conversation, Marcus Law is joined by Dan Nadir, Chief Product Officer at Theta Lake, and Irwin Lazar, President and Principal Analyst at Metrigy, to explore how generative and agentic AI are creating entirely new categories of governance risk.The discussion covers why monitoring isolated prompts and outputs is no longer enough, and why organizations need forensic-level analysis of interaction patterns over time. From behavioral drift and intent detection to the growing complexity of agentic AI, this conversation sets out what enterprises need to be thinking about — and doing — right now.Topics include: why only 58% of organizations have a proactive AI governance strategy, how traditional DLP systems miss subtle data probing and intent-based risks, what behavioral drift looks like in practice, how agentic AI blurs the line between human and machine accountability, and practical steps CIOs and CISOs can take in the next 60 to 90 days.

As organisations modernise their digital workplaces, service management and connectivity are no longer separate disciplines – they're converging into a single experience layer that shapes how people work, sell, and serve customers.In this UC Today roundtable, Christopher Carey is joined by Irwin Lazar, President and Principal Analyst at Metrigy; Chethan Visweswar, Chief Product Officer at Movius; John Finch, Global VP at RingCentral; and Mark Bunnell, Chief Operating Officer at Nuwave.Together they explore how the market is evolving, where organisations are falling behind, and what it takes to move from reactive IT to predictive, AI-driven operations.Topics covered include:

Are your meeting rooms really being used, or do they just look busy in the booking system?In this UC Today interview, Marcus Law speaks with Bill Dellara, Chief Product Officer at VOSS, about why meeting room analytics has become a critical issue for IT, workplace, and real estate leaders navigating hybrid work.Bill explains why booking data often shows intent rather than reality, and how unused or poorly utilized rooms can quietly undermine return on investment, employee experience, and workplace planning. The discussion explores the problem of “phantom space,” the rise of heavily favored “hero rooms,” and the importance of combining booking data with telemetry, occupancy signals, environmental data, and equipment usage insights.The conversation also looks at how smarter analytics can improve room recommendations, support better hybrid working decisions, and help organizations understand whether their meeting spaces are genuinely fit for purpose.

Is your organization ready to govern every conversation happening across video, voice, chat, email, SMS, and AI tools?In this Big UC Update interview for UC Today, Marcus Law speaks with Dan Nadir, Chief Product Officer at Theta Lake, about how enterprises are rethinking compliance, security, and archiving for modern communications.Dan explains how Theta Lake helps regulated organizations capture, archive, and supervise communications across collaboration platforms, including Zoom, Microsoft Teams, Webex, RingCentral, Cisco, and Workvivo. He also discusses the fast-growing role of AI governance, the risk of configuration drift, and why compliance teams need more than basic retention policies to manage today's digital communications.The conversation explores how Theta Lake's AI-native platform supports digital communications governance and archiving, detects risk across multiple channels, and helps firms prove that content has been captured and retained correctly. Dan also shares how the company is evolving its platform to support new communication types, AI interactions, legal discovery, and regulated customer needs around the world.For UC and collaboration leaders, the message is clear: as communication channels multiply, compliance needs to become more intelligent, more connected, and easier to operationalize.For more Unified Communications & Collaboration Tech News visit https://www.uctoday.com/

Enterprise retailers index only 40-50% of their product pages. Joe Doran, Chief Product Officer at Botify, analyzes AI search readiness across retail sites where 9 of 10 products contain three to seven feed errors or missing fields. The conversation covers crawl efficiency and rendering gaps that leave LLM bots seeing just 30-40% of JavaScript-dependent content, structured feed optimization for the Agentic Commerce Protocol and Universal Commerce Protocol, and a shift toward holistic, purchase-anchored attribution over session-based models.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most enterprise IT teams spend the majority of their time fighting the same fires repeatedly. BMC Helix is building the AI system that handles those fires automatically, detecting anomalies, tracing root cause through millions of asset relationships, generating remediation plans, and learning from every incident it resolves. Craig Smith sits down with Erhan Giral, VP of AI Strategy and Innovation at BMC Helix, and Ryan Manning, Chief Product Officer at BMC Helix, to explain how agentic AI is transforming IT service management from a reactive, human-driven process into something closer to a self-healing system, and why doing that at enterprise scale requires a fundamentally different architecture than most AI deployments attempt. The most technically interesting part of this conversation is where BMC Helix is headed: building "gyms", synthetic data center environments where AI agents deliberately break things and learn to fix them overnight, 24 hours a day, generating the bespoke operational training data that text-based foundation models can no longer provide. Erhan describes an architecture of specialized sub-agents, anomaly detection, log analysis, root cause analysis, remediation planning, that work in a hierarchy, passing hypotheses between each other until they converge on an answer, fine-tuned to reason the way a specific enterprise's best IT engineer would rather than the way a generic documentation page reads. For customers, the results are measurable: 25 to 50% cost reduction, fewer recurring outages, and IT staff who can finally go home at a predictable time rather than spending their nights firefighting problems that could have been prevented. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

Carolyn Woodard talks with Joe Robbins, Chief Product Officer at Food Rescue US, about a real-world AI project built from the ground up: a predictive model that flags volunteer pickups at high risk of a last-minute cancellation.Food Rescue US connects volunteers with food donors and receiving agencies through an app similar to a delivery service, coordinating about 150,000 pickups a year across 27 states. When a volunteer cancels within 24 hours of a pickup, site coordinators scramble to cover it or risk the relationship with the donor. Joe and his team partnered with a supply chain researcher at Michigan State University to build an algorithm that predicts cancellation risk and flags pickups at high-risk for cancellation. Rather than automating any decisions outright, the AI triggers an alert to the site coordinator, who checks with the volunteer about confirming the pickup or cancelling early. During the pilot and rollout the AI tool  flagged thousands of rescues that would have been last minute cancellations. Joe shares the practical lessons from a project that took a full year to scope, pilot, and improve: why good data capture has to come before any AI project, how a tightly scoped pilot funded through a grant reduced risk, and why keeping a human in the loop kept the tool trustworthy for staff and volunteers alike. He also offers a framework for nonprofits without an in-house AI researcher or technical staff to get started using AI for more than productivity tools.Joe and Carolyn discuss:How a predictive model helps Food Rescue US flag volunteer pickups at high risk of last-minute cancellation, giving site coordinators three to eight days of notice instead of a same-day scramble.Why the project deliberately keeps a human in the loop: the algorithm surfaces a risk score, but a site coordinator decides whether to act on it.Why clean, well-understood data capture has to happen before any AI project, and how to audit what data your organization already has.How building a pilot into a grant proposal made it easier to test small before rolling out more broadly.A framework for nonprofits without an in-house AI researcher: start with free assistant tools, learn what context AI needs to be useful, then look for a tightly scoped, well-documented problem to solve where you already have the data to use.What's next for Food Rescue US: using AI to take logistical pressure off site coordinators so they can focus on building community relationships.Resources Mentioned:Food Rescue US: https://foodrescue.usTech Soup AI Impact Hour interview with Joe: https://engage.techsoup.org/c/upcoming-events/ai-impact-hour-for-nonprofits _______________________________Start a conversation :)Register to attend a webinar in real time, and find all past transcripts at https://communityit.com/webinars/email Carolyn at cwoodard@communityit.comon LinkedIn on reddit/r/nonprofitITmanagementon the Community IT websiteThanks for listening. 

Le Carnet de Maxime Blot "Devenir un Artisan Hôtelier" pour 39€ seulement !Fruit de plusieurs années d'expérience sur le terrain, ce carnet signé Maxime Blot, Meilleur Ouvrier de France, offre un regard affûté sur les enjeux actuels du service hôtelier.1️⃣ Présentation de l'invité :Il y a quelques semaines, j'animais une conférence à Food Hotel Tech. Face à moi, un panel d'experts — et parmi eux, Jean-Dominique Brivet.En l'écoutant décortiquer en direct l'impact de l'IA sur la distribution hôtelière, j'ai eu une seule pensée : il faut absolument qu'il vienne dans le podcast.Fondateur d'Equaero en 2004 — quand le marketing digital hôtelier n'était encore qu'une promesse, il a passé vingt ans à aider les hôteliers à reprendre le contrôle de leur distribution.Aujourd'hui Chief Product Officer chez D-EDGE, on peut dire qu'il a les mains dedans depuis longtemps.Comment l'IA transforme-t-elle concrètement le parcours de réservation ?Quel est l'avenir des intermédiaires (OTAs) face à cette révolution ?Quelles stratégies concrètes les hôteliers doivent-ils adopter dès maintenant ?Toutes les réponses sont à retrouver dans cet épisode on ne peut plus essentiel actuellement.2️⃣ Notes et références :▶️ Toutes les notes et références de l'épisode sont à retrouver ici.3️⃣ Le sponsor de l'épisode : D-EDGED-EDGE est la seule plateforme de distribution qui couvre l'intégralité du parcours : attirer vos clients, les convertir, les fidéliser, tout en optimisant à la fois volume et marge.Reprenez la main sur votre distribution. Ce n'est pas un compromis, c'est une stratégie.Alors, contactez les experts D-EDGE de ma part !4️⃣ Chapitrage : 00:00:00 - Introduction00:02:00 - La rupture de la chaîne de valeur : du Search au Chatbot00:14:00 - L'avenir des intermédiaires et la reconquête du direct00:23:00 - La vision technologique de D-EDGE et l'IA agentique00:34:00 - Stratégie de contenu : le GEO (Generative Engine Optimization)00:42:00 - Sécurité, parcours et fondamentaux de l'accueil00:49:00 - Questions signaturesSi cet épisode vous a passionné, rejoignez-moi sur :L'Hebdo d'Hospitality Insiders, pour ne rien raterL'Académie Hospitality Insiders, pour vous former aux fondamentaux de l'accueilLe E-Carnet "Devenir un Artisan Hôtelier" pour celles et ceux qui souhaitent faire de l'accueil un véritable artLinkedin, pour poursuivre la discussionInstagram, pour découvrir les coulissesLa bibliothèque des invités du podcastMerci de votre fidélité et à bientôt !Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Today, Nicole is teaming up with U.S. Bank to unpack the state of small businesses and what entrepreneurs can do right now to get ahead.  Nicole sits down with Shruti Patel, U.S. Bank's Chief Product Officer for the Business Banking segment, to unpack the findings from the bank's fourth annual Small Business Survey and what they reveal about the state of entrepreneurship right now. Shruti breaks down why small business optimism dipped from 93% to 83% this year even as resilience holds strong, why only 3% of owners are currently considering a sale or exit, and what that signals about the massive wealth transfer coming as Boomer-aged owners hand off their businesses to the next generation over the next decade. Then the conversation turns to Gen Z founders specifically: their appetite for bold, calculated risk, and the surprising trend of delaying life milestones like marriage and family to build their businesses… a bet that, according to the data, is actually paying off in faster growth. Nicole and Shruti also get tactical: how business credit actually works (and why your personal credit score matters more than you'd think) and the real documentation lenders want to see. Plus: how small businesses are using AI to cut costs (even when the ROI math gets murky), where digital currency payments stand today, and why frictionless checkout is a bigger deal than most owners realize.    Learn more about U.S. Bank's Small Business Banking solutions at: usbank.com  Check-out the results of U.S. Bank's Small Business Survey: https://www.usbank.com/business-banking/business-resource-center/small-business-survey.html Here's what Nicole covers with Shruti: 00:00 Are You Ready for Some Money Rehab? 00:43 Meet Shruti Patel, U.S. Bank's Chief Product Officer 01:20 Inside U.S. Bank's 4th Annual Small Business Survey 02:16 Why Optimism Dropped from 93% to 83% 04:45 Why Only 3% of Owners Are Considering an Exit 05:18 The Great Wealth Transfer and Succession Planning for the Next Decade 06:31 Inside Gen Z's Bold, Calculated Approach to Risk 07:37 Delaying Marriage and Family to Build a Business 09:03 "You Can Have It All, Just Not All at Once" 11:07 How Gen Z Defines Success 12:10 The Death of the "Shark Tank" Fundraising Dream 13:45 Debt vs. Equity: Rethinking How to Finance Growth 14:17 What Banks Look for Before Approving a Loan 16:08 Do Businesses Have Their Own Credit Score? 17:38 Personal Guarantees and the Documentation You'll Need 19:06 The SBA Loan Process 19:38 How Small Businesses Are Actually Using AI to Save Money 21:16 The "Digital Target Checkout" Problem with AI Costs 22:39 Where Digital Currency and Crypto Payments Stand Today 24:15 Why Frictionless Checkout Is Everything 26:03 Inside U.S. Bank's Business Essentials Launch 29:32 U.S. Bank's Partnership with the NFL and the Financial Edge Program 33:45 Shruti Patel's Tip You Can Take Straight to the Bank All investing involves the risk of loss, including loss of principal. This podcast is for informational purposes only and does not constitute financial, investment, or legal advice. Always do your own research and consult a licensed financial advisor before making any financial decisions or investments.

Faith Forster has spent 15 years in product leadership. She was VP of product at Dex, acquired for approximately $600 million, and went on to serve as CPO at Legal, a payments and compliance platform. She now runs discovery.com, an AI-native platform that helps product leaders and teams make faster, better product decisions by connecting to their tools and synthesising competitor, customer and growth intelligence. She is also the driving force behind the Makers Manifesto — a cross-disciplinary set of values and principles for building great products in the age of AI, developed with 45 contributors from across product, engineering, design, and business leadership.We discuss:The manifesto emerged from 96 conversations with product leaders whose grasp of AI ranged from fully automated pipelines to treating it as a faster way to write PRDs — a gap that revealed an industry in urgent need of a shared reference point.Four values anchor the manifesto: purpose over possibility, value created over effort spent, learning loops over launch plans, and human accountability over full automation."Maker" was chosen over "builder" to signal that designers, engineers, salespeople, customer support staff, and founders all share equal ownership of the creation process — regardless of job title.Feature parity is no longer a defensible moat: when any competitor can replicate a capability within days, durable advantage must come from data, relationships, distribution, and business model.Product market fit can no longer be treated as a static milestone — in a market where products, competitors, and customer expectations shift simultaneously, fit must become a continuous, living part of decision-making."Done" now means adopted, not shipped. AI removes every excuse for clunky, one-size-fits-all experiences, and teams that still equate production deployment with completion are measuring the wrong thing.Making context explicit — codifying strategy in a form that agents and humans can both act on daily — is the principle teams consistently identify as their most urgent, immediate priority.Chapters00:00 Introduction 01:12 Faith's background 02:28 Origins of the Makers Manifesto 06:18 Makers Manifesto vs the Agile Manifesto 07:23 Why "maker" not "builder" 11:42 Four values and 16 principles 15:01 Purpose over possibility 19:10 Learning loops over launch plans 22:36 Who gets to be a maker 28:55 Durable advantage in the AI era 33:22 Staying close to customers 36:04 What's next for the manifesto 43:59 Wrap-upReferencedMakers Manifesto — https://makersmanifesto.orgdiscovery.com — Faith's AI-native product decision platformOur HostsLily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She's currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. She's worked on a diverse range of products – leading the product teams through discovery, prototyping, testing and delivery. Lily also founded ProductTank Bristol and runs ProductCamp in Bristol and Bath.Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury's. He participated in Silicon Valley Product Group's Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He's the author of What Do We Do Now? A Product Manager's Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon's music stores in the US & UK.

By Doug Green “We see some customers with 70% autonomous resolution—and I'm talking about real resolution, not just giving an answer, but actually doing it for you and resolving the ticket completely.” AI vendors have filled the market with promises of productivity, automation and lower operating costs. Atera is taking a different approach: putting a measurable performance commitment behind its autonomous IT agent. In this Technology Reseller News podcast, I speak with Tal Dagan, Chief Product Officer at Atera, about Robin, the company's autonomous IT agent, and what it means to move from AI experimentation to demonstrable operational outcomes. Atera began as an all-in-one professional services automation and remote monitoring and management platform. That foundation gives Robin access to both sides of an IT incident: the ticketing conversation with the employee and the device-level tools needed to diagnose and correct the problem. Dagan explains that Robin does more than suggest a troubleshooting step. When an employee reports that a computer is running slowly, Robin can run diagnostics, identify a memory-hungry browser tab or cache problem, take corrective action, confirm that performance has returned to normal and close the issue end-to-end. That distinction matters. Many AI tools provide answers or instructions, but the work still falls to the employee or technician. Robin is designed to carry out operations on managed devices and connected third-party applications, allowing the system to resolve qualifying incidents rather than simply recommend a response. Atera is backing the product with a performance guarantee for qualified customers: Robin will autonomously resolve 50% of Tier 1 and complex Tier 2 incidents within 90 days, or the applicable fees are waived. Before deployment, customers can also test selected use cases in a proof of concept using real workflows and ticket data. According to Dagan, the company is already seeing autonomous resolution rates above 50% among many customers, with some reaching approximately 70%. He emphasizes that these figures refer to complete resolution—not an AI-generated answer telling a user what to do next. The podcast also explores where people remain essential. Customers determine which actions Robin is authorized to take and where an approval or technician should enter the process. For example, Robin can verify whether an employee belongs in an email distribution group, request a manager's approval and then make the change. A more cautious organization can route the same request to a technician. Even when Robin does not close a ticket autonomously, it can gather information from the employee, the device and connected systems before handing the case to a technician. That reduces diagnostic work, shortens resolution time and gives the technician a better starting point. For MSPs, the larger opportunity is scale. Routine incidents, repetitive information gathering and approval-driven workflows consume significant technician time. Moving more of that work to an autonomous agent can enable an MSP to support additional customers without increasing headcount at the same rate, while allowing skilled staff to focus on complex problems and higher-value services. The conversation points toward a new phase of enterprise AI adoption—one in which providers are measured not by demonstrations or broad claims, but by the percentage of real work completed, the speed of response and the operational burden removed from IT teams. Listen to the podcast to learn how Atera is applying autonomous IT in the field, how customers set the boundaries for automation, and what guaranteed AI outcomes could mean for MSP growth. About Atera Atera provides an AI-powered IT management platform for enterprises and MSPs. Its autonomous IT agent, Robin, is designed to diagnose and resolve technical incidents end-to-end, reduce ticket volume and help IT teams scale operations. Guest: Tal Dagan, Chief Product Officer, Atera Tal Dagan on LinkedIn: linkedin.com/in/taldagan Atera on LinkedIn: linkedin.com/company/atera-networks

Monica Marquez helps leaders turn AI ambition into adoption by building the trust, capability, and new ways of working that make transformation real.AI changed the size of the gap, not the shape of the problem. Adoption stalls in trust, capability, and ways of working, not in the technology itself. The leaders she works with are not behind because they lack talent. They are behind because no one has handed them a system for turning their own judgment into something AI can amplify. Every leader already has a version of authentic intelligence. Her work is helping them codify it, so the artificial kind has something worth amplifying.Monica spent twenty five years inside Goldman Sachs, Bank of America, EY, and Google, building the leadership development and professional growth systems that helped established professionals advance, stay relevant, and grow as the ground shifted under them. Talent, leadership, and transformation at scale, inside organizations that do not have room for guesswork.In 2019 she left corporate to co-found Beyond Barriers with Nikki Barua, building on Nikki's bestselling book to create an AI powered professional development platform that accelerated success for women and all leaders. Today she is co-founder and Chief Product Officer of FlipWork. The throughline has never moved: she builds pathways into what comes next.Unlocking Humanity with Ancient Knowledge Hosted by John Edmonds Kozma Unimpressed Podcast offers a groundbreaking look into consciousness, ancient wisdom, and the nonconscious aspects of humanity via the Quantum Field. Hosted by John Edmonds Kozma, CEO of Bang Productions and a seasoned entertainment industry veteran with extensive experience, each episode delves deeper than typical discussions to reveal profound insights about reality, spirituality, and human potential. He has been likened to Albert Einstein for his innovative reasoning. Hosted on Acast. See acast.com/privacy for more information.

Osvald Nitski is the Chief Product Officer at Mercor, the AI-training and expert-data marketplace powering frontier-model development. Mercor last raised a $350 million Series C at a $10 billion valuation, and is reportedly in discussions for a new round at a $20 billion valuation. Mercor crossed $2BN in ARR in June; doubling from $1 billion in only four months. AGENDA:  00:04:00 Will open-source models kill the data-provider business? 00:07:00 Are enterprises still terrified of working with frontier model companies? 00:09:00 Does every company end up with its own specialised AI model? 00:10:00 Do enterprises actually have an AI ROI problem? 00:11:00 How should founders balance AI performance against exploding token bills? 00:14:00 Does AI mean product teams build 10x more—or ruthlessly simplify? 00:15:00 What does it now take to be a great product manager in an AI-native world? 00:20:00 Is the boom in AI services and forward-deployed engineers here to stay? 00:37:00 Can Mercor escape its dependence on a handful of frontier-model customers? 00:47:00 Are AI-generated code and agents creating a cybersecurity arms race? 00:56:00 When will robotics have its real "ChatGPT moment"?  

How AI Tools for Therapists Are Built: Trust, Data Privacy, and Choosing What to Adopt - An Interview with Ian Knox and Megan Toomey Ian Knox and Megan Toomey of SimplePractice take therapists behind the scenes of how AI tools for mental health are actually built, trained, and kept secure. We're in the middle of AI month, and Curt and Katie wanted to move past the buzzwords and talk with the people who actually engineer this technology. As part of the show's partnership with SimplePractice, they sit down with Ian Knox, Chief Product Officer, and Megan Toomey, Sr. Director of Clinical Support AI Product Management, to pull back the curtain on how AI tools for therapists get designed, what "training the model" really means, and how client data is handled. They get specific about what to evaluate before adopting any AI tool, from vendor trust and HIPAA compliance to data practices, and why note taking is the most mature use case while insurance, scheduling, intake, and referral matching are still emerging. Ian and Megan also take on the fear that AI-first companies want to replace therapists, and explain why clinicians have to stay at the center of care and review anything they put their name on. This is a grounded, practical conversation for any therapist trying to decide what AI belongs in their practice, and what to be cautious about, without panic or hype. In this episode, we discuss: - How an EHR decides which clinician tasks AI is mature enough to help with - What to evaluate before trusting an AI vendor with client data - Why "HIPAA compliant" is a floor, not proof of strong security - What "training the model" does and does not mean, and why SimplePractice says it is not training an LLM on your data - How transcripts are retained, and the new opt-in for de-identified data - Why you remain responsible for every AI-assisted note you sign - How to talk with clients about AI and get meaningful consent Timestamps: 00:16 - AI month, and why we wanted to talk to people who build AI 01:33 - Who Ian and Megan are 03:21 - SimplePractice's history with AI tools 05:38 - What therapists should know when adding AI to a practice 08:44 - Where clinicians can streamline with AI 12:23 - How to choose which AI features to adopt 16:10 - Why you can't fully trust AI, and best practices 19:29 - What clinicians should know about their data 21:13 - The Trust Center, and "are you here to replace therapists?" 23:55 - How AI systems are trained (the puppy analogy) 27:00 - White-label LLMs and the "golden data set" 29:11 - De-identified transcripts and the opt-in 31:40 - Current tools and the product roadmap Guest bios: Ian Knox is Chief Product Officer at SimplePractice, with prior product leadership at Expedia and Microsoft. Megan Toomey is Sr. Director of Clinical Support AI Product Management at SimplePractice, leading the team building AI tools for behavioral health clinicians, with prior experience at Microsoft and Amazon. Full show notes and transcript: mtsgpodcast.com Join the Modern Therapist Community Facebook Group: https://www.facebook.com/groups/therapyreimagined Modern Therapist's Survival Guide Creative Credits Voice Over by DW McCann: https://www.facebook.com/McCannDW/ Music by Crystal Grooms Mangano: https://groomsymusic.com/

This week, Dustin chats with Dr. Dave Duke, Chief Product Officer at McGraw Hill, about how AI is reshaping teaching, learning, and academic integrity in higher education. He highlights some new product developments for their learning tools as well as the recently released Sharpen Advantage.  Drawing on his background in psychology and product design, Dave explores how students make rational decisions about AI use, why traditional views of academic integrity are becoming more nuanced, and how institutions can better support students in developing meaningful AI proficiency for the workforce. Guest Name: Dr. Dave Duke, Chief Product Officer, McGraw Hill Guest Social: LinkedIn Guest Bio: Dave Duke, Psy.D. is the Chief Product Officer of McGraw Hill Higher Education, where he leads AI strategy, platform and content development, and product innovation. Originally trained as a Clinical Psychologist, Dr. Duke has deep expertise in learning science and has spent his entire career working at the intersection of learning and technology. Previously he served as the Chief Product Officer at ACI Learning, a global IT and cybersecurity training company supporting companies like Apple, Netflix, Boeing, GE, and Volkswagen, as well as government agencies like NASA, FDIC, and all branches of the U.S. military.   - - - -Connect With Our Host:Dustin Ramsdellhttps://www.linkedin.com/in/dustinramsdell/About The Enrollify Podcast Network:The Higher Ed Geek is a part of the Enrollify Podcast Network. If you like this podcast, chances are you'll like other Enrollify shows too!Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of The Product Podcast by Product School, Carlos González de Villaumbrosia sits down with Amit Zavery, President and Chief Product Officer at ServiceNow. The platform runs more than 75 billion workflows a year with around $15 billion in annual revenue growing over 20%. Its market cap is above $100 billion, yet the stock is down more than 30% this past year, while its AI business is on track for $1.5 billion, ahead of a $1 billion plan. Amit previously ran product and platform at Oracle for over two decades and was a VP and General Manager at Google Cloud.What you'll learn:Why the market can't yet tell AI winners from losers, and why companies that don't transform will get killedWhy the idea of one company becoming the single end-to-end enterprise orchestrator is a fallacyThe spare part approach that makes most enterprise AI projects fail, and what pacesetters do insteadWhy access is shifting from user interfaces to agents, and what taking action actually requiresHow to hold long-term conviction on platform bets while the market judges you on short-term sentimentKey takeaways:Transform or die: the market will separate AI-native platforms from legacy vendorsInteroperability beats domination in the agentic eraGovernance only wins when it accelerates innovation, not when it blocks itCredits:Host: Carlos Gonzalez de VillaumbrosiaGuest: Amit ZaverySocial Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

Steve Johnson, Founder & Chief Product Officer at Insured.io and author of the Insurance UX Survival Guide. They are a complete customer experience platform, providing all of the channels that an insured might use to interact with an insurance company, as a bolt-on solution to any carrier management system. It allows you to easily deploy a policyholder portal, inbound and outbound text messaging, AI virtual agents, FNOL, etc. It allows smaller carrier to rival the experience of larger carriers at a significantly lower cost.Steve Johnson: https://www.linkedin.com/in/steve-johnson--/Insured.io: https://www.insured.ioThe Insurance UX Survival Guide: https://www.amazon.com/Insurance-Survival-Guide-Experiences-Expectations/

In this episode, hosts Candace Gillhoolley and Frank La Vigne are joined by Neil Katz, Chief Product Officer at Valantor AI—a four-time Emmy winner whose unconventional journey spans from technology startups to award-winning journalism, and now to the forefront of enterprise AI innovation.Neil shares his unique perspective on the evolution of AI, from the early days of digital design and machine learning, to building large-scale AI and document intelligence platforms for major organizations. The conversation explores the critical challenges of knowledge extraction, document comprehension, and securing sensitive data in today's era of sovereign AI. Together, they uncover the hidden complexities behind Retrieval-Augmented Generation (RAG), discuss the importance of hybrid search strategies, and reflect on where the field is heading as enterprise needs push the boundaries of what AI can do.Whether you're a data professional, an AI enthusiast, or just curious about how language models are transforming how we understand information, you won't want to miss this candid and thought-provoking deep dive into the future of AI and data integrity.LinksNeil on LinkedIn -https://www.linkedin.com/in/neilkatz/Watch on YouTube -https://www.youtube.com/watch?v=qf8qifz_RL8Time Stamps00:00 Early career in tech and journalism04:01 Early consumer AI experiences06:49 Early AI and machine learning developments11:43 Anthropic's new findings on AI models16:15 Data sovereignty in AI systems19:27 Implementing open source AI models22:49 Breaking down documents for models25:45 Understanding the RAG system process28:47 Challenges in AI data processing31:22 Challenges in RAG with Insurance Data36:35 Understanding and managing data security39:35 Early days with OpenAI GPT40:48 Explaining vector and similarity search46:57 The evolution of computing models47:29 Computing evolution to cloud and edge

Are the platforms you rely on most for growth also the biggest barriers to understanding it?Agility requires a clear view of the entire customer journey, not just the isolated moments within a single platform. This means brands must adapt their strategies to operate effectively across the open internet, not just within its walled gardens.Today, we're going to talk about a fundamental disconnect in modern marketing: while AI is enabling a consumer journey that is more fluid and dynamic than ever, many marketers are still planning, executing, and measuring their efforts in platform-specific silos. This can create a distorted view of performance and limit a brand's ability to drive real growth.To help me discuss this topic, I'd like to welcome Todd Parsons, Chief Product Officer & President of Performance Media at Criteo. About Todd ParsonsTodd Parsons is Chief Product Officer and President of Performance Media at Criteo, where he leads the company's global product and Performance Media organization. Since joining in 2020, he has been instrumental in transforming Criteo's platform, advancing AI-powered solutions for predictive bidding, creative assembly, and merchandising, while scaling self-service activation across channels. In 2025, he assumed leadership of Performance Media to unify product, design, analytics, and go-to-market under a single vision for growth. With more than two decades of experience at the intersection of data and marketing, Todd has held leadership roles driving innovation across the industry. He previously served as Chief Product Officer at SocialCode, where he developed tools to activate and measure first-party audiences across platforms including Facebook, Amazon, and YouTube, and earlier helped establish OpenX as the first people-based programmatic marketplace. At Criteo, he continues to shape the future of commerce media, leveraging over a billion daily shopper interactions to deliver measurable outcomes for brands and retailers worldwide.Todd Parsons on LinkedIn: https://www.linkedin.com/in/toddaparsons---------- Resources ---------- Criteo: criteo.comThis show is brought to you by Criteo. Their new self-service platform, Criteo GO, makes AI-powered advertising simpler and more accessible for brands looking to drive demand, acquire customers, and scale performance. By enabling cross-channel performance across display, web, video, social, and even ChatGPT, Criteo GO helps brands reach shoppers with smarter advertising powered by real-time commerce signals.We're proud to be a media partner for #MAICON26 - Oct. 13-15! Learn how AI can power your marketing and business and help you grow smarter. Use code AGILE150 to save! https://aglbrnd.co/r/7fe458ced0f04658Reach your customers with Reddit. Spend $500 in ad spend, get $500 back in ad credit! Learn more: https://advertalize.com/r/491818c79fb1873fChaser is the only Slack-native project management platform that helps teams turn messages into tracked tasks, automate follow-ups, and maintain team-wide visibility, without adopting another tool. Now integrated with Claude and other GenAI tools. Learn more at trychaser.com and use code AGILEBRAND for a 3-month free trial (normal trial is 14 days).The most influential minds in software, AI, and engineering leadership will be at WeAreDevelopers World Congress North America, September 23-25 in San Jose. Learn more: https://aglbrnd.co/r/60a7299222a7bcf1Start building your own apps with Replit and get $20 off. Learn more: https://aglbrnd.co/r/93531742a7625a20Enjoyed the show? Tell us more at and give us a rating so others can find the show at: aglbrnd.co/r/faaed112fc9887f3Connect with Greg on LinkedIn: linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: aglbrnd.co/r/35ded3ccfb6716baCheck out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: agilebrandguide.comThe Agile Brand is produced by Missing Link. Hosted on Acast. See acast.com/privacy for more information.