Podcasts about Data quality

The promise of agentic AI has been massive, autonomous systems that act, reason, and make business decisions, but most enterprises are still struggling to see results.In this episode, host Chris Brandt sits down with Sumeet Arora, Chief Product Officer at Teradata, to unpack why the gap exists between AI hype and actual impact, and what it takes to make AI scale, explainable, and ROI-driven.From the shift toward “AI with ROI” to the new era of human + AI systems and data quality challenges, Sumeet shares how leading enterprises are moving from flashy demos to measurable value and trust in the next phase of AI. CHAPTER MARKERS00:00 The AI Hackathon Era03:10 Hype vs Reality in Agentic AI06:05 Redesigning the Human AI Interface09:15 From Demos to Real Economic Outcomes12:20 Why Scaling AI Still Fails15:05 The Importance of AI Ready Knowledge18:10 Data Quality and the Biggest Bottleneck20:46 Building the Customer 360 Knowledge Layer23:35 Push vs Pull Systems in Modern AI26:15 Rethinking Enterprise Workflows29:20 AI Agents and Outcome Driven Design32:45 Where Agentic AI Works Today36:10 What Enterprises Still Get Wrong39:30 How AI Changes Engineering Priorities55:49 The Future of GPUs and Efficiency Challenges -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The robotics industry is on the cusp of its own “GPT” moment, catalyzed by transformative research advances. Enter Memo, the first general-intelligence personal robot, focused on taking on your chores to give back your time. Sarah Guo sits down with Tony Zhao and Cheng Chi, co-founders of Sunday Robotics, to discuss the state of AI robotics. Tony and Cheng speak to the challenges they faced while developing their technology, the innovative glove system employed to scale real-world data collection, and the impact of diffusion policy and imitation learning. Plus, they talk about their 2026 in-home beta program and why personal robots are only a handful of years away from mass deployment. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @tonyzzhao | @chichengcc | @sundayrobotics Chapters: 00:00 – Tony Zhao and Cheng Chi Introduction 00:56 – State of AI Robotics 02:11 – Deploying a Robot Pre-AI 03:13 – Impact of Diffusion Policy  04:29 – Role of ACT and ALOHA 07:02 – Imitation Learning - Enter UMI 10:38 – Introducing Sunday 11:57 – Sunday's Robot Design Philosophy 15:05 – Sunday's Shipping Timeline 19:02 – Scale of Sunday's Training Data 23:58 – Importance of Data Quality at Scale 24:56 – Technical Challenges 27:59 – When Will People Have Home Robots? 30:48 – Failures of Past Demos 32:34 – Sunday's Demos 36:53 – What Sunday's Hiring For 39:10 – Conclusion

Join us on the latest episode, hosted by Jared S. Taylor!Our Guests: Kenneth Young, CEO at Medecision and Mike Green, Managing Partner at Excell Healthcare Advisors.What you'll get out of this episode:Strategic Union for Scalable Impact: Medecision's acquisition of Excell aims to merge technology and consulting to unlock ROI and operational change.Data Quality as the Foundation: Leaders emphasize that without clean, integrated data, AI initiatives risk failure.Enabling Clinicians to Work Top of License: AI is used to minimize administrative burden and maximize patient-focused care.AI with Purpose, Not Hype: Real-world applications, not buzzwords, are driving conversations about AI's role in healthcare transformation.Rehumanizing Healthcare: Combining AI, data, and clinical insight to ensure the right care is delivered at the right time.To learn more about:Medecision Website https://www.medecision.com/ Medecision Linkedin https://www.linkedin.com/company/medecision/ Excell Healthcare Advisors Website https://www.excellha.com/ Excell Healthcare Advisors Linkedin https://www.linkedin.com/company/excellhealthcareadvisors/ Our sponsors for this episode are:Sage Growth Partners https://www.sage-growth.com/Quantum Health https://www.quantum-health.com/Show and Host's Socials:Slice of HealthcareLinkedIn: https://www.linkedin.com/company/sliceofhealthcare/Jared S TaylorLinkedIn: https://www.linkedin.com/in/jaredstaylor/WHAT IS SLICE OF HEALTHCARE?The go-to site for digital health executive/provider interviews, technology updates, and industry news. Listed to in 65+ countries.

In this episode of Crazy Wisdom, host Stewart Alsop talks with Jessica Talisman, founder of Contextually and creator of the Ontology Pipeline, about the deep connections between knowledge management, library science, and the emerging world of AI systems. Together they explore how controlled vocabularies, ontologies, and metadata shape meaning for both humans and machines, why librarianship has lessons for modern tech, and how cultural context influences what we call “knowledge.” Jessica also discusses the rise of AI librarians, the problem of “AI slop,” and the need for collaborative, human-centered knowledge ecosystems. You can learn more about her work at Ontology Pipeline and find her writing and talks on LinkedIn.Check out this GPT we trained on the conversationTimestamps00:00 Stewart Alsop welcomes Jessica Talisman to discuss Contextually, ontologies, and how controlled vocabularies ground scalable systems.05:00 They compare philosophy's ontology with information science, linking meaning, categorization, and sense-making for humans and machines.10:00 Jessica explains why SQL and Postgres can't capture knowledge complexity and how neuro-symbolic systems add context and interoperability.15:00 The talk turns to library science's split from big data in the 1990s, metadata schemas, and the FAIR principles of findability and reuse.20:00 They discuss neutrality, bias in corporate vocabularies, and why “touching grass” matters for reconciling internal and external meanings.25:00 Conversation shifts to interpretability, cultural context, and how Western categorical thinking differs from China's contextual knowledge.30:00 Jessica introduces process knowledge, documentation habits, and the danger of outsourcing how-to understanding.35:00 They explore knowledge as habit, the tension between break-things culture and library design thinking, and early AI experiments.40:00 Libraries' strategic use of AI, metadata precision, and the emerging role of AI librarians take focus.45:00 Stewart connects data labeling, Surge AI, and the economics of good data with Jessica's call for better knowledge architectures.50:00 They unpack content lifecycle, provenance, and user context as the backbone of knowledge ecosystems.55:00 The talk closes on automation limits, human-in-the-loop design, and Jessica's vision for collaborative consulting through Contextually.Key InsightsOntology is about meaning, not just data structure. Jessica Talisman reframes ontology from a philosophical abstraction into a practical tool for knowledge management—defining how things relate and what they mean within systems. She explains that without clear categories and shared definitions, organizations can't scale or communicate effectively, either with people or with machines.Controlled vocabularies are the foundation of AI literacy. Jessica emphasizes that building a controlled vocabulary is the simplest and most powerful way to disambiguate meaning for AI. Machines, like people, need context to interpret language, and consistent terminology prevents the “hallucinations” that occur when systems lack semantic grounding.Library science predicted today's knowledge crisis. Stewart and Jessica trace how, in the 1990s, tech went down the path of “big data” while librarians quietly built systems of metadata, ontologies, and standards like schema.org. Today's AI challenges—interoperability, reliability, and information overload—mirror problems library science has been solving for decades.Knowledge is culturally shaped. Drawing from Patrick Lambe's work, Jessica notes that Western knowledge systems are category-driven, while Chinese systems emphasize context. This cultural distinction explains why global AI models often miss nuance or moral voice when trained on limited datasets.Process knowledge is disappearing. The West has outsourced its “how-to” knowledge—what Jessica calls process knowledge—to other countries. Without documentation habits, we risk losing the embodied know-how that underpins manufacturing, engineering, and even creative work.Automation cannot replace critical thinking. Jessica warns against treating AI as “room service.” Automation can support, but not substitute, human judgment. Her own experience with a contract error generated by an AI tool underscores the importance of review, reflection, and accountability in human–machine collaboration.Collaborative consulting builds knowledge resilience. Through her consultancy, Contextually, Jessica advocates for “teaching through doing”—helping teams build their own ontologies and vocabularies rather than outsourcing them. Sustainable knowledge systems, she argues, depend on shared understanding, not just good technology.

Welcome back to Intellicast! On today's episode, Brian is joined by Henry LeGard, CEO and founder of the fraud detection company Verisoul, for an in-depth discussion on the evolving state of data quality and fraud detection in market research. We kick off the podcast by having Henry share how Verisoul, a company built initially to stop banking and fintech fraud, has quickly become one of the most talked-about new players in market research. He details how the company came to be, and how they entered the market research industry in 2025 with zero clients and are now protecting nearly 100 million survey clicks per month across the industry. Henry explains how Verisoul's advanced fraud detection platform has had such a rapid impact on the industry. He shares how the team applied financial-grade fraud-prevention techniques to survey data collection, enabling them to detect and block the most sophisticated forms of digital fraud, including device spoofing, cross-continental proxy traffic, and bot farms operating at scale. From there, Brian and Henry dive into Verisoul's newly released Market Research Quality Report, which Henry describes as the largest fraud analysis ever published in the industry. For the report, the team analyzed more than 50 million sessions across 3,700 mobile apps in 190 countries, using a new proprietary technology that can identify the exact app where each respondent originated. Henry explains how this innovation was crucial in uncovering real patterns of fraud across the ecosystem, revealing which app categories generate the cleanest and riskiest respondent traffic. Brian and Henry also discuss how Verisoul's findings challenge many long-held assumptions about data quality. While larger apps and panels tend to have lower fraud rates, older respondent sources didn't necessarily perform better. The study also found that just five mobile apps accounted for more than 10% of all detected fraud, underscoring how small clusters of bad respondent sources can distort large portions of the industry's data. Later in the episode, Henry offers a perspective on the evolving sophistication of fraud, explaining that much of today's market research fraud is human-driven rather than automated. Organized groups in low-income countries are using SIM card farms, aged burner domains, proxy networks, and more to appear as legitimate U.S. or European respondents. Henry and Brian also talk about how Verisoul's recently launched transparency initiative — a “fraud or not” ranking, complete with a lighthearted tomato-throwing Easter egg for bottom performers, is designed to help the industry recognize who's getting data quality right. Rather than shaming providers, the goal is to encourage progress, reward transparency, and strengthen collaboration around fraud prevention. To close out the episode, Brian and Henry discuss Verisoul's creative marketing campaign, “We Smoke Fraud,” which debuted at the ESOMAR Congress in Prague. What started as a marketing stunt ended up earning the company some serious attention from venture capital investors. For those who live and breathe data quality, this episode is for you. Want to get a copy of Verisoul's Market Research Quality Report? Download here: https://reports.verisoul.ai/mr-qualityreport-25 Want to check out the Fraud or Not Ranking list? Take a look here: https://rankings.verisoul.ai/ If you want to learn more about Verisoul, visit their website at www.verisoul.ai You can connect with Henry on LinkedIn here: https://www.linkedin.com/in/henrylegard/ Thanks for tuning in! Want to download your copy of The Sample Landscape: 2025 Edition? Get it here:  https://content.emi-rs.com/sample-landscape-report-2025 Did you miss one of our webinars or want to get some of our whitepapers and reports? You can find it all on our Resources page on our website here. Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome to the CanadianSME Small Business Podcast, hosted by Kripa Anand. In this episode, we explore how Canadian businesses can turn data into a strategic advantage through intelligent management, AI, and digital sovereignty.Joining us is Eric Villeneuve, CEO of Technosens, a leading IT consulting company specializing in data valorization from Business Intelligence to Artificial Intelligence. Eric shares how Technosens is helping organizations harness data quality, embrace the Canadian cloud, and drive global digital transformation.Key Highlights:1. Investing in AI in Canada: How businesses can identify measurable value before adopting AI solutions.2. Data Quality for Better AI Results: Why strong data foundations are essential to accurate insights and decision-making.3. The Importance of the Canadian Cloud: How digital sovereignty safeguards compliance, security, and innovation.4. From Data to Automation: How Technosens transforms BI into AI-driven process efficiency through collaboration.5. The Future of Canadian Tech: Eric's vision for positioning Canadian expertise as a global leader in digital transformation.Special Thanks to Our Partners:RBC: https://www.rbcroyalbank.com/dms/business/accounts/beyond-banking/index.htmlUPS: https://solutions.ups.com/ca-beunstoppable.html?WT.mc_id=BUSMEWAGoogle: https://www.google.ca/A1 Global College: https://a1globalcollege.ca/ADP Canada: https://www.adp.ca/en.aspxFor more expert insights, visit www.canadiansme.ca and subscribe to the CanadianSME Small Business Magazine. Stay innovative, stay informed, and thrive in the digital age!Disclaimer: The information shared in this podcast is for general informational purposes only and should not be considered as direct financial or business advice. Always consult with a qualified professional for advice specific to your situation.

AI has changed how B2B marketers work — and how they connect.In this episode, Dr. Amy Cook, Co-Founder and CMO at Fullcast, joins Michael to explore what happens when automation starts replacing authenticity, and how today's most effective marketers are rebuilding the human side of their craft.Her framework is simple: use AI for knowledge. Leave relationships to humans.Amy brings an unusual perspective to this conversation. She has a PhD in organizational rhetoric (how companies actually persuade people), ran her own agency for 15 years, and spent her early career as a professional violinist performing with the Osmond family. That last credential matters: she learned on stage that audiences always know when something feels rehearsed instead of real.That's exactly what's happening in B2B marketing right now.Join us, as we explore where AI actually makes marketers better versus where it destroys the trust that drives revenue. You'll get frameworks you can apply immediately, data from Fullcast's State of RevOps 2025 research, and the expensive lessons that come from getting this balance wrong.What we cover

In this episode of Founder Talk, Jason Krantz, founder of Strategy Titan and Labor Titan, pulls back the curtain on what's actually happening in the world of technology, education, and work. Jason's built companies that live at the intersection of data science, human behavior, and business strategy — and he's not afraid to call out the hype.You'll learn: ✅ Why most AI pilots fail before they start ✅ How to spot the difference between hype and innovation ✅ Why the education system is built to serve itself, not its students ✅ What skills, mindsets, and strategies will still win in an AI-driven world ✅ How to future-proof your business and career by thinking differentlyIf you've been wondering “is AI a bubble?” “is college still worth it?” or “how will automation change my job?” — this episode will open your eyes. It's not fear-mongering. It's the truth about where we're headed — and how to lead when everything changes.Connect with Jason KrantzGuest LinkedIn: https://www.linkedin.com/in/jasonkrantz/Guest Website: https://www.strategytitan.com/If you are a B2B company that wants to build your own in-house content team instead of outsourcing your content to a marketing agency, we may be a fit for you! Everything you see in our podcast and content is a result of a scrappy, nimble, internal content team along with an AI-powered content systems and process. Check out pricing and services here: ⁠https://impaxs.com⁠Timecodes00:00 Introduction and Guest Welcome00:24 Discussing AI Pilot Failures01:08 Importance of Data Quality in AI02:07 Challenges in AI Implementation06:16 Advice for AI Startups06:53 AI and the Startup Journey08:29 The AI Bubble and Market Trends12:57 Crypto and Digital Currencies29:39 The Future of Coding with AI31:21 The Importance of Learning to Code32:03 Career Prospects in a Rapidly Changing Market32:53 Critical Thinking and Creativity in the Age of AI33:45 The Value and ROI of College Education36:28 The Misconceptions About Trade Jobs44:00 Winning the Battle for Talent in the Workforce48:48 The Future of Work with AI and Automation54:30 The Role of Purpose and Community in Work58:40 Conclusion and Final Thoughts

In this episode, David and Dwight dive into the critical and ever-present issue of poor data quality in HR and its cascading impact on the organization. They break the problem down into 3 key areas: recruiting, artificial intelligence, and pay transparency. They explore how recruiting often serves as the flawed entry point for employee data, discuss the dangers of training AI on biased information (which can lead to discriminatory practices like ageism), and examine the new data governance challenges posed by emerging pay transparency laws. [0:00] Introduction Today's Topic: The Impact of Poor Data Quality on HR Today  [5:24] How does poor data quality in recruiting create downstream problems? Recruiting is the first ingress of data into an organization, and it's often the least well-managed. Poor data can also originate from the company side, with issues like inaccurate job descriptions or incorrect FLSA classifications. [12:08] How can biased data lead to discriminatory AI in the hiring process? AI agents used in video interviews can reject qualified candidates based on flawed or biased training data. Companies that fail to address AI bias on the front end risk facing expensive lawsuits and will be forced to fix their systems later. [19:32] What are the data governance challenges presented by pay transparency laws? Pay transparency laws in several states require companies to disclose pay ranges for open roles also mandate that companies keep detailed records of salary structures, grade ranges, and hiring data for extended periods. Many organizations may need to update their data governance strategies to ensure they are retaining the necessary historical data to comply with these regulations and withstand potential audits. [24:31] Closing Thanks for listening! Quick Quote “As the demographic bubble for Gen X gets larger, companies can't ignore [AI bias in recruitment] anymore. They have to take it on, and that means they have to start training their artificial intelligence to not filter out all of us grays.”

On this episode of Reliability Radio, hosts Jonathan Guiney and Brendan Russ welcome Candi Robinson from IFS Ultimo to confront the biggest misconception in Enterprise Asset Management (EAM): that AI is an "easy button" that will magically fix every operational problem. With two decades of EAM experience, Candi argues that technology cannot solve a foundational problem. She reveals that many organizations are still stuck using antiquated practices, like printing work orders, while expecting to jump straight to Industry 4.0. This candid session covers: The Data Reality Check: Why 20 years of inaccurate data will shape (and potentially break) your AI-driven future. The Work Ahead: The essential steps—like implementing mobile solutions and achieving transparency—that must be completed before AI becomes useful. Ultimo's Solution: How Ultimo embeds AI into the work order process to aid technicians and enforce better data entry, driving accuracy and educating users. Single Pane of Glass: The importance of a unified platform to drive KPIs and manage compliance, especially amid changing economic and sustainability pressures. Stop debating AI and start fixing the fundamentals.

We sat down with Sara Saab (VP of Product at Prolific) and Enzo Blindow (VP of Data and AI at Prolific) to explore the critical role of human evaluation in AI development and the challenges of aligning AI systems with human values. Prolific is a human annotation and orchestration platform for AI used by many of the major AI labs. This is a sponsored show in partnership with Prolific. **SPONSOR MESSAGES**—cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economyOct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlstSubmit investment deck: https://cyber.fund/contact?utm_source=mlst— While technologists want to remove humans from the loop for speed and efficiency, these non-deterministic AI systems actually require more human oversight than ever before. Prolific's approach is to put "well-treated, verified, diversely demographic humans behind an API" - making human feedback as accessible as any other infrastructure service.When AI models like Grok 4 achieve top scores on technical benchmarks but feel awkward or problematic to use in practice, it exposes the limitations of our current evaluation methods. The guests argue that optimizing for benchmarks may actually weaken model performance in other crucial areas, like cultural sensitivity or natural conversation.We also discuss Anthropic's research showing that frontier AI models, when given goals and access to information, independently arrived at solutions involving blackmail - without any prompting toward unethical behavior. Even more concerning, the more sophisticated the model, the more susceptible it was to this "agentic misalignment." Enzo and Sarah present Prolific's "Humane" leaderboard as an alternative to existing benchmarking systems. By stratifying evaluations across diverse demographic groups, they reveal that different populations have vastly different experiences with the same AI models. Looking ahead, the guests imagine a world where humans take on coaching and teaching roles for AI systems - similar to how we might correct a child or review code. This also raises important questions about working conditions and the evolution of labor in an AI-augmented world. Rather than replacing humans entirely, we may be moving toward more sophisticated forms of human-AI collaboration.As AI tech becomes more powerful and general-purpose, the quality of human evaluation becomes more critical, not less. We need more representative evaluation frameworks that capture the messy reality of human values and cultural diversity. Visit Prolific: https://www.prolific.com/Sara Saab (VP Product):https://uk.linkedin.com/in/sarasaabEnzo Blindow (VP Data & AI):https://uk.linkedin.com/in/enzoblindowTRANSCRIPT:https://app.rescript.info/public/share/xZ31-0kJJ_xp4zFSC-bunC8-hJNkHpbm7Lg88RFcuLETOC:[00:00:00] Intro & Background[00:03:16] Human-in-the-Loop Challenges[00:17:19] Can AIs Understand?[00:32:02] Benchmarking & Vibes[00:51:00] Agentic Misalignment Study[01:03:00] Data Quality vs Quantity[01:16:00] Future of AI OversightREFS:Anthropic Agentic Misalignmenthttps://www.anthropic.com/research/agentic-misalignmentValue Compasshttps://arxiv.org/pdf/2409.09586Reasoning Models Don't Always Say What They Think (Anthropic)https://www.anthropic.com/research/reasoning-models-dont-say-think https://assets.anthropic.com/m/71876fabef0f0ed4/original/reasoning_models_paper.pdfApollo research - science of evals blog posthttps://www.apolloresearch.ai/blog/we-need-a-science-of-evals Leaderboard Illusion https://www.youtube.com/watch?v=9W_OhS38rIE MLST videoThe Leaderboard Illusion [2025]Shivalika Singh et alhttps://arxiv.org/abs/2504.20879(Truncated, full list on YT)

In this Marketing Over Coffee: Learn about clean data, AI strategy and more with Katie Robbert, CEO of Trust Insights! Direct Link to File AI Ready Data Quality Audit 6Cs of Data Quality Sora 2 video – New channel from Tim Street! Comedy Gum Dolly is still bringing it Insta360 GO Ultra is now available! […] The post Do You Have A Data Quality Problem? Foog Da Boot It! appeared first on Marketing Over Coffee Marketing Podcast.

On this week's episode of The Horizon Podcast, John Chang explores how artificial intelligence is beginning to reshape commercial real estate investing. After meeting with top institutional investors, John dives into how firms are using AI to identify high-performing trade areas, prune underperforming assets, and accelerate decision-making. He examines the strengths and pitfalls of data quality, from census and CoStar metrics to migration tracking through Placer AI, and discusses how automation could fundamentally transform investment strategy by filtering deals with higher accuracy and efficiency. John closes by connecting this evolution to his core theme—anticipating where the market will be five to ten years down the road. This is a limited time offer, so head over to aspenfunds.us/bestever to download the investor deck—or grab their quick-start guide if you're brand new to oil and gas investing. Get 50% Off Monarch Money, the all-in-one financial tool at www.monarchmoney.com with code BESTEVER Join the Best Ever Community  The Best Ever Community is live and growing - and we want serious commercial real estate investors like you inside. It's free to join, but you must apply and meet the criteria.  Connect with top operators, LPs, GPs, and more, get real insights, and be part of a curated network built to help you grow. Apply now at ⁠www.bestevercommunity.com⁠ Podcast production done by ⁠Outlier Audio⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Nike's Principal Data Engineer Ashok Singamaneni joins Benjamin and Eldad to discuss his open-source data quality framework, Spark Expectations. Ashok explains how the tool, which was inspired by Databricks DLT Expectations, shifts data quality checks to before the data is written to a final table. This proactive approach uses row-level, aggregation-level, and query data quality checks to fail jobs, drop bad records, or alert teams - ultimately saving huge costs on recompute and engineering effort in mission-critical data pipelines.

Healthcare organisations are facing the simultaneous pressures of demonstrating ROI on AI investments, addressing clinician burnout, and satisfying compliance requirements. In a new pharmaphorum podcast, Dr Jay Anders, chief medical officer for Medicomp Systems, and also host of the Tell Me Where IT Hurts podcast, discusses why he believes that the only way organisations can solve all three of those problems is by first solving data quality issues. Anders talks about this data quality crisis, and the financial and operational impacts of the current situation, and explains why providers must ensure clinical data is validated, cleaned, and optimised. You can listen to episode 208a of the pharmaphorum podcast in the player below, download the episode to your computer, or find it - and subscribe to the rest of the series – on Apple Podcasts, Spotify, Overcast, Pocket Casts, Podbean, and pretty much wherever else you download your other podcasts from.

In this episode of the Earley AI Podcast, host Seth Earley welcomes Eric Rehl, Vice President of Digital Customer Experience in North America at Schneider Electric. With over 25 years of expertise in digital strategy and customer experience, Eric has guided global organizations through complex digital transformations, always keeping business outcomes and customer needs at the core. Drawing on his deep industry knowledge, Eric shares how large enterprises can move beyond buzzwords like “digital transformation” and “AI,” instead choosing a pragmatic, data-driven approach to drive real business value.Join Seth and Eric as they discuss the evolving role of digital capabilities in business strategy, the foundational importance of high-quality data, the unique challenges faced by B2B organizations, and how AI can power truly personalized customer experiences—from the ground up.Key Takeaways:Digital transformation should be rooted in business outcomes, not technology hype; focus on the “so what” for your customer and organization.Strong, clean, accessible data is critical for scaling digital experiences and enabling AI-driven personalization—without it, even the best tools will fail.B2B companies often lag in digital maturity due to legacy data architectures and complex customer relationships, but can catch up by investing strategically in foundational capabilities.A robust digital journey relies on operationalizing and continually improving product and customer data, rather than one-off fixes.Maturity in B2B digital experiences evolves from simply “doing no harm,” to enabling ease of business, and ultimately leveraging digital platforms for growth and commercial impact.AI's promise lies in moving from segmented personalization to real-time, dynamic customer engagement powered by integrated data and knowledge.Preparing for AI-driven customer discovery means syndicating high-quality, semantically-structured content across channels—both on and off your own domain.The next frontier is operationalizing knowledge (not just product or customer data) to fuel AI tools for differentiation and problem-solving.Continuous experimentation and responsible opportunism allow organizations to discover new outcomes and business value.Insightful Quotes:"I think as you start building maturity, you're learning how to orchestrate those pieces. You're getting more of that harmonization of organizing principles across those disparate departments, across knowledge and content and customer experience and product information. And so that becomes kind of the holistic journey that you're thinking about." - Seth Earley“We always start with the outcome. Like, why are we talking about capabilities here? Why are we talking about AI? What are we actually going to do with it to get to what the business outcome we're trying to drive or the experience outcome we're trying to drive?” - Eric RehlDon't miss this in-depth conversation packed with practical advice and forward-looking insights for anyone leading or navigating digital transformation initiatives in the AI era.LinksLinkedIn: https://www.linkedin.com/in/ericrehl/Website: https://www.se.com/us/en/Thanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

Vipin Kumar, Head of CUSO IB Data Strategy and Analytics at Deutsche Bank, joins me to unpack one of the toughest problems in financial services: managing data quality in a highly regulated industry. From the outside, it might look like a box-checking exercise. In reality, it's a complex mix of legacy systems, global frameworks, regulatory controls, and the constant push to balance defensive compliance with offensive business value. Vipin makes it real with examples that connect directly to how we all experience data in daily life.Key TakeawaysData quality isn't just about accuracy—timeliness, completeness, and consistency all matter, especially when billions are on the line.Regulations push banks into “defensive” strategies, but there's growing opportunity to apply “offensive” strategies that use data for prediction, analytics, and competitive edge.Measuring effectiveness requires agreement between data producers and consumers, with preventive and detective controls working together.AI and machine learning are starting to automate checks, spot patterns, and even strengthen anti-money laundering defenses.Timestamped Highlights00:45 What data quality means in a regulated industry03:15 The challenges of managing fragmented legacy systems06:40 How producers and consumers measure effectiveness of frameworks09:30 The pizza delivery analogy for making sense of data quality14:20 Why accuracy is harder than timeliness or completeness16:50 The role of AI and machine learning in improving governance19:20 Shifting from defensive compliance to offensive strategy in banking22:40 Regulators testing AI-driven approaches to anti-money launderingMemorable Quote“Producer has preventive controls. Consumer has detective controls. True data quality happens only when both align 100%.” — Vipin KumarCall to ActionIf you enjoyed this conversation, share it with a colleague who thinks about data quality or governance. Don't forget to follow the show on Apple Podcasts or Spotify so you never miss an episode.

Stephan Donze (@sdonze CEO @AODocs), discusses the enterprise unstructured data crisis, where 80% of business data remains untapped due to legacy system limitations and the challenges of AI-powered document management at scale. We explore how AI agents can transform document workflows while maintaining trust and compliance, the architectural principles needed for cloud-native document management, and why traditional search fails in the age of generative AI.SHOW: 961SHOW TRANSCRIPT: The Cloudcast #961 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS"SPONSORS:[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcastSHOW NOTES:AODocs websiteTopic 1 - Welcome to the show, Stephan. Give everyone a quick introduction.Topic 2 - We hear all the time about unstructured data and the continual growth in the Enterprise. I've heard numbers of upwards of 80% of all corporate data is unstructured. I've worked at several companies and supported a significant number of customers over the years, and I can count on one hand how many say they have “control” of their data. How did this come to be, and is the problem as big as I think?Topic 3 - The second part of this, and this might be an even bigger problem, is how much of the data is used? Too many needles in the haystack, if you will. How does Agentic AI address this challenge, and where do traditional document management systems fail?Topic 4 - We've talked about data quality in the past on the show, and I'm wondering if this also becomes an issue. Let's say you have a bunch of draft documents leading up to the final version. Is it possible that improper version control and/or we're back to a data quality problem of finding the “final version” needle in the haystack? How does AI prevent this and also not hallucinate an answer that may not be true?Topic 5 - Some have called AI's ability to absorb and report on data just fancy search. What are your thoughts on this? Where and how does traditional search differ from Agentic AI management?Topic 6 - I also see this as being so much more than indexing and reporting on documents. There is also the concept of automation and workflows that agentic AI can improve upon. What use cases are your customers implementing?Topic 7 - Where do you think the industry will go in the next 2-3 years?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

On this episode, host Sima Vasa leads a candid panel discussion on the state of data quality with four industry leaders: Dyna Boen, EVP/Managing Director of Escalent; Jon Kay, Principal Market Research Manager of Intuit; Bob Fawson, Co-Founder of Data Quality Co-Op; and Steven Snell, EVP, Head of Research of Rep Data. Together, they explore the growing challenges of fraud, trust and integrity in research, and the solutions needed to safeguard reliable insights. Key Takeaways:00:00 Introduction.03:26 Prospect panel data quality has declined sharply.08:30 The industry faces a coordination problem in data quality.11:42 Good-looking fraud now impacts up to a quarter of respondents.18:20 Vendor tools differ greatly in detecting fraud and inattention.24:28 Respondent experience is critical to achieving trustworthy results.26:38 Brands cannot rely on flawed data for product launches or tracking.31:02 Industry change is accelerating, creating both risk and opportunity.32:54 Real progress begins when clients ask vendors the tough questions.35:06 Data quality is a game of inches, not silver bullets. Resources Mentioned: Escalent | WebsiteIntuit | WebsiteData Quality Co-Op | WebsiteRep Data | Website Thanks for listening to the “Data Gurus” podcast, brought to you by Infinity Squared. If you enjoyed this episode, please leave a 5-star review to help get the word out about the show, and be sure to subscribe so you never miss another insightful conversation.#Analytics #MA #Data #Strategy #Innovation #Acquisitions #MRX #Restech

“Most firms that are using AI are saving two to four hours per week per employee. That's not transformative. That's just doing the same thing faster.”-Alexis FinkIntroductionIn this episode of Psych Tech @ Work, Mayda Tokens (my AI co-host) and I sit down with Alexis Fink, I-O psychologist, long-time HR tech leader at Microsoft, Intel, and Meta, longtime friend and president of The Society for Industrial-Organizational Psychology (aka SIOP)!Alexis brings decades of experience at the intersection of people, organizations, and technology to the studio, offering a holistic and integrated perspective on the opportunities and challenges of AI in the workplace that is based on reality- not pure philosophy.We challenge Mayda to hang with us as we talk about all things people, technology, and the future of work. Alexis rocks it. You be the judge of how well Mayda meets the challenge. Hint: like all AI, Mayda is still a work in progress that fails sometimes, while still feeling miraculous IMHO. I mean come on- she speaks in emoji!!!Alexis leads the charge with her take on these great highlight topics:1. The Transformation of Knowledge Work AI is reshaping not just factory tasks, but the decision-making and knowledge roles once thought safe from automation.2. Organizational Design in an AI EraTrue progress requires rethinking workflows so humans and machines complement each other rather than compete.3. Data Quality and Human-Centered DesignMost raw HR data isn't fit for AI, making richer, cleaner, and more contextual data essential for real impact.4. Risk, Accountability, and Quality Control As AI takes on more autonomy, organizations must adapt proven quality management and governance principles to keep it accountable.5. The Human Problem of AI AdoptionThe hardest barriers to AI adoption aren't technical but human — fear, resistance, and behavior change.6. Looking to 2035: The Next-Gen I-O PsychologistFuture I-Os will master AI as a partner, using simulation and immersive tools while keeping work human-centered.ConclusionOur conversation underscores a central theme: AI is not even close to perfect and we need to recognize this (Mayda's responses to our questions are proof of AI gone whack!)AI's future in work won't be defined by algorithms alone, but by how organizations redesign processes, manage risk, and support people through change. For I-O psychologists, HR leaders, and technologists alike, the task ahead is clear — ensure AI is not just bolted onto old systems, but opens opportunities for true collaboration with we humans. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

Too often, AI breaks in the wild. Why? CXOTalk 890 dissects the adversarial economy with Steven C. Daffron (fintech private equity leader) and Anthony Scriffignano (distinguished data scientist), hosted by Michael Krigsman. Discover the challenges of **ai implementation** and the strategies needed to navigate the **future of work** in an AI-driven world. Stay informed with expert insights on CXOTalk. What you'll learn:How AI enables and masks adversarial behaviorMisaligned incentives, data/model drift, and biasGovernance vs. regulation; resilient metrics and KPIsInvestor/CFO implications and talent/education needs 

Join Jonathan Guiney and Brendon Russ on Reliability Radio as they welcome Louis Innocent, Director of Asset Management at EAM 360. Louis dives into a core problem facing the industry: the persistent failure of mobile adoption strategies. He explains the "why" behind this resistance—from generational knowledge gaps to poorly designed applications—and shares EAM 360's philosophy for success. Their approach prioritizes a user-friendly, offline-first experience with smart features like voice-to-text and automated work mapping. Louis also offers a powerful insight for the future: that mobile is the "last mile" for a successful AI strategy, as the quality of data captured by technicians in the field is what will ultimately enable AI's full potential.

In this episode of Quality Matters, health IT veteran and standards-editor John D'Amore joins host Andy Reynolds to unpack the deeper purpose behind the push to improve data quality. Drawing on decades of experience, from startups and academic research to national standards and consulting, John explains why the real goal isn't just clean data or seamless interoperability. It's better care, delivered more efficiently.Listen to this episode to discover:Interoperability is a Tool, Not the Goal: John compares interoperability to a power drill—valuable only when it helps achieve better care outcomes. Economic Incentives and Bipartisan Momentum: Learn why following the money reveals the true levers of change. From value-based purchasing to performance bonuses, John shows how economic feedback loops accelerate improvements in data quality. Good Pipes, Bad Water: Explore the disconnect between robust data pipelines and poor data flowing through them. John breaks down why measuring the quality of data is essential for progress. Why Quality Measurement is the Real Catalyst: John argues that quality measurement, not interoperability, is what drives meaningful change. Discover how improving data quality can yield long-term benefits across the health care ecosystem, far beyond quality improvement.This episode is essential listening for health IT leaders, quality professionals and policy makers who want to understand the deeper purpose behind the quest for data quality and how it's shaping the future of care.Key Quote:We don't really want interoperability for its own sake. Free flow of information if it's never used by the destinations isn't useful. It doesn't improve care. It doesn't bring down costs.When you go to Home Depot or Lowe's and buy a power drill, what are you really trying to buy? You want holes in the wall. Interoperability is the tool or the power drill that delivers the holes that we want, and the holes are better, more efficient care that reduces costs.I mean, can we envision a future where health care costs go down year over year? It sounds almost impossible. It sounds like a fantasy land. I think that's going to be within reach within the next 20 years. -John D'AmoreTime Stamps:(03:21) Interoperability's Incentives, Means and Ends(06:42) Good Pipes, Bad Water(09:38) Next-Generation Data Validation(12:06) Correcting Myths and Misconceptions(14:17)  Quality Drives Interoperability, Not Vice VersaDive Deeper:NCQA's Improving HEDIS Data Quality in a Digital WorldNCQA's Data Aggregator ValidationHEDIS Compliance AuditConnect with John D'Amore

In this episode, Craig Jeffery and Mayank Randev explore ISO 20022 and what it means for payments, cash reporting, and treasury strategy. They discuss how richer data and global standards offer more than compliance and open the door to automation, resilience, and better insights. How can corporates turn a required change into lasting value? Listen in to find out.  

Can we rely on government statistics to help guide our investment decisions? Confluence Associate Market Strategist and Certified Business Economist Thomas Wash joins the podcast to discuss the recent revisions to Bureau of Labor Statistics data that raised doubts about the economy and prompted the president to fire a top official.

In this episode of The Ecommerce Toolbox: Expert Perspectives, Matt Ezyk, Senior Director of Engineering, Ecommerce at Hanna Andersson, shares how heritage brands can modernize ecommerce platforms without sacrificing reliability, performance, or customer experience. Matt reveals the “silent killers” that quietly erode site conversion, from friction-filled checkout flows to cross-team misalignment, and how he's tackling them through selective modernization, API-first architecture, and a disciplined focus on site speed. He also cuts through the AI hype, explaining why clean, connected customer data is the non-negotiable foundation for any meaningful AI adoption. If you want to drive ecommerce growth while keeping your tech stack stable and your customer experience intact, you'll want to hear this conversation.

We're taking a little break this summer while Charlie and Crissy are out recharging on vacation, but that doesn't mean the content stops!While we plan some more episodes for when they return, we're revisiting some of our favorite podcast moments from the past. Whether you're new here or a longtime listener, it's a great time to catch up, reflect, and maybe even hear something you missed the first time around!Q&A: When is data quality good enough?Hear more from us:Subscribe to us on Youtube: https://www.youtube.com/channel/UCN-x5u0G03LWmU0Ds_4zR8wSubscribe to our newsletter here: https://www.cs2marketing.com/revenue-growth-architects#subscribe-to-newsletterFollow Crissy on LinkedIn: https://www.linkedin.com/in/crveteresaunders/Follow Charlie on LinkedIn: https://www.linkedin.com/in/charliesaunders/Follow Xander on LinkedIn: https://www.linkedin.com/in/xanderbroeffle/

Jacob and Rob dig into Trump's firing of the Bureau of Labor Statistics commissioner, exposing the political theater surrounding government data. They explore labor hoarding, CPI distortions, and the fragility of public statistical institutions in a polarized, AI-saturated world. The conversation spans historical context, philosophical takes on truth and data, and the coming war for proprietary information. They close with a baffling shift in U.S.-India trade policy—and a call for help understanding what the hell is going on.--Timestamps:(00:00) - Introduction (00:44) - Employment Data(02:42) - Trump's Reaction and Bureau of Labor Statistics(05:07) - Challenges in Measuring Employment(12:24) - Historical Context and Political Interference(21:06) - Impact of Data Quality on AI and Public Trust(27:29) - Speculation on the Future of Data Commoditization(29:02) - The Decline of Respect for Objective Data(31:01) - Historical Perspectives on Data and Statistics(32:36) - The French Example of Data Collection and Secularism(34:05) - The Impact of AI on Information and Truth(36:35) - The Value of Proprietary Information in the Age of AI(44:36) - Historical Parallels and the Power of Information(49:43) - US-India Trade Relations and Geopolitical Strategy(56:03) - Conclusion and Final Thoughts--Jacob Shapiro Site: jacobshapiro.comJacob Shapiro LinkedIn: linkedin.com/in/jacob-l-s-a9337416Jacob Twitter: x.com/JacobShapJacob Shapiro Substack: jashap.substack.com/subscribe --The Jacob Shapiro Show is produced and edited by Audiographies LLC. More information at audiographies.com --Jacob Shapiro is a speaker, consultant, author, and researcher covering global politics and affairs, economics, markets, technology, history, and culture. He speaks to audiences of all sizes around the world, helps global multinationals make strategic decisions about political risks and opportunities, and works directly with investors to grow and protect their assets in today's volatile global environment. His insights help audiences across industries like finance, agriculture, and energy make sense of the world.--This podcast uses the following third-party services for analysis: Podtrac - https://analytics.podtrac.com/privacy-policy-gdrp

Norbert Kremer, Ph.D., cloud solution architect and TDWI faculty member, joins host Andrew Miller to discuss data quality for AI - including the difference between data quality for AI and BI, the importance of training data for AI models, and challenges with unstructured data. Please visit Data Quality for AI for more information on Norbert's course at TDWI San Diego. ____________ More information: ·       TDWI Conferences: https://bit.ly/3XqBhGH ·       TDWI Modern Data Leader's Summits: https://bit.ly/4902fuu ·       TDWI Virtual Summits: https://bit.ly/31HJ2xr ·       Seminars: https://bit.ly/3WxQPr4 ·       More Speaking of Data Episodes: https://bit.ly/3JsQPWo Follow Us on: ·       LinkedIn - https://bit.ly/42zCZZB ·       Facebook - https://bit.ly/49uej7j ·       Instagram - https://bit.ly/3HM8x57 ·       X - https://bit.ly/3SsYu9P

Elliot Shmukler (Co-Founder and CEO at Anomalo) talks about the impact of data quality on AI, how unstructured data can be improved, and how monitoring of data lakes can help prevent model drift and give organizations confidence with predictable results.SHOW: 945SHOW TRANSCRIPT: The Cloudcast #945 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK:  http://bit.ly/cloudcast-cnotwNEW TO CLOUD? CHECK OUT OUR OTHER PODCAST:  "CLOUDCAST BASICS"SPONSORS:[DoIT] Visit doit.com (that's d-o-i-t.com) to unlock intent-aware FinOps at scale with DoiT Cloud Intelligence.[VASION] Vasion Print eliminates the need for print servers by enabling secure, cloud-based printing from any device, anywhere. Get a custom demo to see the difference for yourself.[FCTR] Try FCTR.io (that's F-C-T-R dot io) free for 60 days. Modern security demands modern solutions. Check out Fctr's Tako AI, the first AI agent for Okta, on their websiteSHOW NOTES:Anomalo websiteThe Cloudcast #598 - Data QualitySnowflake invests in AnomaloTopic 1 - Elliot, welcome back! It's hard to believe it has been 3 years since we spoke! Give everyone a brief introduction.Topic 2 - Here's the problem I see when it comes to AI adoption today. There isn't an “off the shelf” AI model with an organization's data built in; that's impossible. So, you must bring this data, often unstructured, to the model, often with mixed results. Do you agree?Topic 3 - I see data quality in two ways… the quality of the data before ingestion is one way, we want the data to be clean going in. But, we also need a way to detect, mitigate, and do a root cause analysis for quality checks along the way, correct? Give everyone an idea of what this life cycle looks like.Topic 4 - What are you seeing as the barriers to adoption? Is it the tools, the models, the need for RAG pipelines, the lack of data scientists, and AIOps?Topic 5 - We have this crossroads where proprietary data makes an organization unique, but exposing that unique data puts the organization at risk. How much of a factor does this play, and how do you advise organizations around this complex intersectionTopic 6 - There is always this concept of predictable results. This answer should be consistent and repeatable. We've seen things like model/data drift and hallucinations hinder this concept, leading to a lack of confidence in the results. How do you advise organizations to tackle this lifecycle management and predictability over time?Topic 7 - If listeners want to get started and learn more, what's the best way to get started?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

High Culture Hiring – so nennt Medizinal­cannabis-Scale-up enua sein kompromissloses Recruiting-System. Deshalb habe ich mit CEO Albert Schwarzmeier gesprochen, um herauszufinden, wie er in nur zwei Jahren von 15 auf 51 Mitarbeitende gewachsen ist – und zwar profitabel, ganz ohne Burn-rate.   Zunächst einmal: Schnelles Wachstum gelingt nur mit den richtigen Leuten. Darum definiert enua jede Rolle glasklar nach Purpose, Impact, KPIs und Culture Fit, bevor eine Anzeige live geht. Dann öffnet sich der Funnel aus LinkedIn-Posts, StepStone-Ads und einem starken Empfehlungs­programm, das bereits 30 % aller Neueinstellungen liefert.   Außerdem punktet das Team mit Tempo. HR meldet sich innerhalb von 48 Stunden im Video­call. Danach folgt der Hiring-Manager noch in derselben Woche. Schließlich trifft jede Bewerberin oder jeder Bewerber spätestens am siebten Tag eine Geschäfts­führerin oder einen Geschäfts­führer. Fällt ein Daumen im Panel nach unten, endet der Prozess sofort – daher spart enua Zeit, Geld und Nerven.   Das Konzept endet natürlich nicht mit der Unterschrift. Somit führt das Onboarding neue Kolleg:innen in den ersten 30 Tagen gezielt durch Sales, Supply Chain, Data & Quality. Dadurch verstehen sie das hoch regulierte Produkt blitzschnell und liefern schon in Woche vier erste Quick Wins. Das Ergebnis: nahezu keine Kündigungen in der Probezeit.   Zugleich bleibt Recruiting Chefsache. Albert veröffentlicht wöchentlich drei LinkedIn-Beiträge, misst Employer-Branding-KPIs und gibt jede Einstellung persönlich frei. Auf diese Weise schützt er die Kultur, obwohl das Team rasant wächst.   Hinzu kommt ein konsequentes Reporting. Jede Woche prüft das People-Team Time-to-Hire, Funnel-Conversion und Cost-per-Hire. Sobald ein Ziel verfehlt wird, reagiert enua sofort – sei es mit neuen Sourcing-Quellen oder angepassten Interview­fragen.   Mein Fazit: High Culture Hiring ist ein echter Wachstumsbooster. Wer seine Kultur messbar macht und jede Einstellung an klaren Kriterien ausrichtet, gewinnt den War for Talent – sogar in Nischen wie Medizinal­cannabis.   Daher solltest du jetzt reinhören, wenn du dein Recruiting auf High-Speed und High-Quality bringen willst. In der Episode bekommst du Alberts komplette Checkliste zum Nachbauen.  

In the data and AI space, action cannot wait. And, neither can celebration! Tune into the anniversary special of Bringing Data and AI To Life as hosts, Amy Horowitz, GVP Solutions Sales and Business Development at Informatica and Nick Dobbins, VP and Worldwide Field CTO, Informatica, along with special appearances from the podcast team - Rudra Ray, Rameez Ghouz, Stephanie Rogers and Gary Loste, celebrate one year of cutting through data and AI complexity. Together, they reflect on key insights and transformative discussions that have shaped the industry landscape, including AI governance challenges, the importance of trusted data for scaling AI initiatives, and practical strategies for modernizing data stacks. As you listen in, continue to cut through the chaos of data and AI and walk the path of clarity with us!

In “CHAINge, AI, and the Future of Freight”, Joe Lynch and Bart De Muynck, an industry expert and thought leader with over 30 years of supply chain and logistics experience across the globe, discuss how artificial intelligence and emerging technologies are reshaping the logistics landscape and what the future holds for global freight. About Bart A. De Muynck Bart De Muynck stands as an accomplished industry expert and thought leader, boasting over three decades of global supply chain and logistics experience. His distinguished career includes significant roles at major international companies such as EY, GE Capital, Penske Logistics, PepsiCo, and various tech firms. Notably, he spent eight years as a VP of Research at Gartner and recently served as Chief Industry Officer at project44. A highly sought-after speaker, Bart currently advises multiple companies and industry organizations in logistics. He chairs ASCM's CHAINge conference in Europe and North America, is the Vice Chair for Transformfest 2025, and is a member of the Forbes Technology Council, SCLA, WEF, and CSCMP's Executive Inner Circle. Born in Belgium, he now resides with his family in Texas, USA. Through Bart De Muynck LLC, he helps organizations navigate the complex technological landscape, tailoring solutions and mitigating challenges to drive operational efficiency and manage risk. His new thought leadership website, Better Supply Chains, curates high-quality content focused on leveraging technology to create more efficient, inclusive, and equitable supply chains, ultimately aiming for both better supply chains and improved individual lives. About Better Supply Chains Better Supply Chains is a new thought leadership website curated by former Gartner analyst and Industry Expert Bart De Muynck, with the primary goal of centralizing high-quality content designed to help companies significantly improve their supply chain operations. The platform is dedicated to enhancing connections by fostering networks that unite shippers, logistics service providers (LSPs), technology providers, and investors. Furthermore, it aims to empower commerce by delivering insightful information that enables businesses to refine and automate their trade processes. Crucially, Better Supply Chains also seeks to improve lives by examining the positive impacts of logistics technology (LogTech) on labor, talent development, and sustainability efforts within the industry. By actively highlighting how emerging technologies can be seamlessly integrated into supply chain organizations, processes, and people, the website strives to make supply chains more efficient, inclusive, and equitable, ultimately contributing to better operational outcomes and improved individual well-being. Key Takeaways: CHAINge, AI, and the Future of Freight In “CHAINge, AI, and the Future of Freight”, Joe Lynch and Bart De Muynck, an industry expert and thought leader with over 30 years of supply chain and logistics experience across the globe, discuss how artificial intelligence and emerging technologies are reshaping the logistics landscape and what the future holds for global freight. AI as an Enabler, Not a Replacer: A central theme is that AI's true power in supply chains lies in augmenting human intelligence rather than replacing it. Bart emphasizes that AI can automate mundane tasks, freeing up human workers for higher-value activities requiring critical thinking, problem-solving, and creativity, leading to increased productivity and improved decision-making. Focus on Practical AI Applications: The podcast will likely highlight that the effective implementation and absorption of existing technologies, particularly AI and advanced analytics, are the next big transformation in logistics. Bart's focus through "Better Supply Chains" is on practical, high-quality content that helps companies refine and automate their trade processes using proven technological solutions, not just speculative future tech. The Interconnectedness of Supply Chain Elements: Bart's extensive background and the mission of "Better Supply Chains" underscore the importance of fostering networks that unite shippers, logistics service providers (LSPs), technology providers, and investors. The "CHAINge" conference he chairs also emphasizes collaboration and interconnectivity to build more efficient and sustainable supply chains. Addressing Key Industry Challenges through Technology: The episode will touch upon how logistics technology (LogTech) can positively impact labor, talent development, and sustainability. Bart's work aims to integrate emerging technologies into supply chain organizations, processes, and people to create more efficient, inclusive, and equitable supply chains. Data Quality is Paramount for AI Success: Bart stresses that while data is the "unsung hero" in supply chain resilience and decision-making, the quality of that data is crucial. Bad or irrelevant data can be a "bad actor," highlighting the need for high-quality, real-time, and predictive insights to get ahead of disruptions. Building Resilient and Agile Supply Chains: Given Bart's expertise and the current landscape, the discussion will likely emphasize the need for supply chains to be agile and resilient in the face of geopolitical factors, trade disputes, and market uncertainties. Technology, including AI, plays a vital role in enhancing visibility, optimizing logistics, and improving decision-making in turbulent times. Investing in Talent Development alongside Technology: Bart believes that talent is a significant constraint in supply chains. The podcast will likely highlight the importance of not only embracing technology but also investing in talent development and fostering a culture of collaboration to fully leverage the transformative potential of AI and other innovations. Learn More About CHAINge, AI, and the Future of Freight Bart | LinkedIn Better Supply Chains | Linkedin Better Supply Chains CHAINge conference The Connective Tissue of the Supply Chain with Bart A. De Muynck The Logistics of Logistics Podcast If you enjoy the podcast, please leave a positive review, subscribe, and share it with your friends and colleagues. The Logistics of Logistics Podcast: Google, Apple, Castbox, Spotify, Stitcher, PlayerFM, Tunein, Podbean, Owltail, Libsyn, Overcast Check out The Logistics of Logistics on Youtube

Are healthcare providers' contact details as accurate as you think? In this podcast hosted by Chenny Solaiyappan, Curatus Chief Revenue Officer Jarrod Mandozzi speaks on the critical challenge of provider data management in healthcare. Jarrod shares insights from his extensive experience in Medicare and Medicaid programs, revealing how inaccurate provider information impacts patient care, health plan operations, and industry efficiency.

Today, we're joined Marne Martin, the CEO of Emburse whose innovative travel and expense solutions power forward-thinking organizations. We talk about:Building fast-moving & scalable businesses that can lastHow to finance and grow profitable companies to reach an exitThe challenges of finding a competitive edge as GenAI accelerates innovationTesting monetizing AI alongside conventional SaaS monetization

Bonnie Tinder is the founder and CEO of Raven Intelligence, an independent B2B peer review site that amplifies the voice of the customer. She focuses on software customers, consulting partners, and software vendors and helps identify the best partners for their needs. In this episode, Bonnie shares insights from a recent Salesforce event, exploring how AI agents, data clouds, and robotics are reshaping customer experience, software implementation, and enterprise transformation.Episode 52 | AI Agents in ActionThe Big Themes:Campaigns Are Out, Conversations Are In: Marketing is undergoing a radical transformation. Gone are the days of mass email blasts and no-reply addresses. Instead, AI is ushering in a new era of real-time, personalized engagement. Salesforce is leaning into this shift with tools that replace one-way campaigns with dynamic conversations. AI agents now tailor interactions based on behavior, preferences, and real-time context, fostering true customer intimacy at scale.Unified Data Is the Bedrock of Smart AI: No AI strategy can succeed without clean, connected data. Salesforce's Data Cloud addresses what SAP calls the “swivel chair problem” — when teams toggle between disconnected systems to piece together a customer story. AI agents can't operate effectively if data is fragmented or siloed. That's why Salesforce is investing in tools that unify sales, marketing, support, and financial data, giving AI a full-picture view of the customer journey.AI Agents Are Already Delivering Real Results: AI isn't theoretical anymore — it's working in the wild. Bonnie pointed out two standout cases: University of Chicago Medicine and Ford Pro. In healthcare, Agentforce transformed an outdated, frustrating appointment system into a streamlined digital process, improving both efficiency and patient experience. At Ford, AI agents guide customers to ideal vehicle matches with minimal input, keeping users on-site and increasing conversion.The Big Quote: “I think that buyers are looking more at the execution and fit of software, as opposed to the software brand itself. And I would say that that is a shift in the last year or so, especially now with the advent of AI and just the rapid pace that everything is moving so, less on brand, more about how are you going to offer me the complete solution and break down silos of data?” More from Bonnie Tinder:Connect with Bonnie on LinkedIn or send a message via her Acceleration Economy Analyst page. Visit Cloud Wars for more.

In this episode of Alter Everything, we chat with Alex Patrushev, Head of Product at Nebius. We discuss the gaps organizations face between data and business impact, strategies to bridge these gaps, and the role of AI in these processes. Alex explains Nebius' mission to make AI accessible, the challenges of building data centers and software from scratch, and innovative solutions like their data center in Finland. The conversation also covers key components for effectively bridging data and business impact, such as project selection, stakeholder communication, team skills, data quality, and tech stack.Panelists: Alexander Patrushev, Head of Product for AI/ML @ NebiusMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: NebiusData Version Control Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music.

Bridging the Gap Between AI and Business Data // MLOps Podcast #325 with Deepti Srivastava, Founder and CEO at Snow Leopard.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractI'm sure the MLOps community is probably aware – it's tough to make AI work in enterprises for many reasons, from data silos, data privacy and security concerns, to going from POCs to production applications. But one of the biggest challenges facing businesses today, that I particularly care about, is how to unlock the true potential of AI by leveraging a company's operational business data. At Snow Leopard, we aim to bridge the gap between AI systems and critical business data that is locked away in databases, data warehouses, and other API-based systems, so enterprises can use live business data from any data source – whether it's database, warehouse, or APIs – in real time and on demand, natively. In this interview, I'd like to cover Snow Leopard's intelligent data retrieval approach that can leverage business data directly and on-demand to make AI work.// BioDeepti is the founder and CEO of Snow Leopard AI, a platform that helps teams build AI apps using their live business data, on-demand. She has nearly 2 decades of experience in data platforms and infrastructure.As Head of Product at Observable, Deepti led the 0→1 product and GTM strategy in the crowded data analytics market. Before that, Deepti was the founding PM for Google Spanner, growing it to thousands of internal customers (Ads, PlayStore, Gmail, etc.), before launching it externally as a seminal cloud database service. Deepti started her career as a distributed systems engineer in the RAC database kernel at Oracle.// Related LinksWebsite: https://www.snowleopard.ai/AI SQL Data Analyst // Donné Stevenson - https://youtu.be/hwgoNmyCGhQ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Deepti on LinkedIn: /thedeepti/Timestamps:[00:00] Deepti's preferred coffee[00:49] MLflow vs Kubeflow Debate[04:58] GenAI Data Integration Challenges[09:02] GenAI Sidecar Spicy Takes[14:07] Troubleshooting LLM Hallucinations[19:03] AI Overengineering and Hype[25:06] Self-Serve Analytics Governance[33:29] Dashboards vs Data Quality[37:06] Agent Database Context Control[43:00] LLM as Orchestrator[47:34] Tool Call Ownership Clarification[51:45] MCP Server Challenges[56:52] Wrap up

Cribl's Field CISO Ed Bailey discusses how customers can manage the quality and quantity of data by providing intelligent controls between data sources and destinations.Topics Include:Cribl company name originCompany helps organizations screen data to find valuable insightsEd Bailey was Cribl's first customer back in 2018Data growth of 25% yearly created seven-figure cost increasesCEOs and CIOs complained about explosive data storage costsUsers demanded more data while budgets remained constrainedBailey discovered Cribl through a random Facebook advertisementCribl Stream sits between data sources and destinationsNo new agents required, uses existing infrastructure connectionsReduced data growth from 28% to 8% within yearDevelopment cycles shortened from six weeks to two weeksBailey managed global security and telemetry data systemsOperated large Splunk instance across forty different countriesTeam spent time collecting data instead of extracting valueCribl provided consistent data control plane for operationsSmart engineers could focus on machine learning solutionsMigrated from terrible SIEM to better security platformData strategy should focus on business requirements firstNot all data has the same business valueTier one: Critical data goes to expensive platformsTier two: Important data stored in cheaper lakesTier three: Compliance data in low-cost object storageSIEM costs around one dollar per gigabyte storedData lakes cost twelve to eighteen cents per gigabyteObject storage costs fractions of pennies per gigabyteAWS partnership provides scalable infrastructure for rapid growthEC2, EKS, and S3 are heavily utilized servicesCribl Search finds data directly in object storageAvoids costly data movement for search and analysisParticipants:Edward Bailey – Field CISO, CriblSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Send us a textFrom 2020-2023, DECAL received $2B+ in federal relief funds, fueling initiatives for Georgia's child care providers, workforce & families. Dive into the impact w/ insights from DECAL & Child Trends. Joining us to talk about COVID-19 relief funding and to help us follow the money is an impressive panel of guests: Shayna Funke, DECAL Director of Research Partnerships and Business Supports; Rob O'Callaghan, DECAL Director of Institutional Research and Data Quality; and from Child Trends, Dr. Dale Richards, Research Scholar and Dr. Rachel Abenavoli, Research Scientist.  Support the show

Physician executive Jay Anders discusses his article, "Health care's data problem: the real obstacle to AI success." Jay asserts that the transformative potential of artificial intelligence in health care is fundamentally dependent on the quality of the underlying clinical data. He explains that while tools like large language models and conversational AI show promise in synthesizing information and easing documentation, their reliability is compromised when fed with data from repositories often filled with inconsistencies, errors, and gaps. This can lead to an "increased workload paradox," where clinicians spend more time verifying and correcting AI-generated outputs, and a failure to produce the structured data vital for regulatory compliance, quality metrics, and analytics. Jay emphasizes that the "garbage in, garbage out" principle severely hampers interoperability and contributes to significant financial and clinical risks, including medical errors and inefficient workflows. To counter this, he advocates for robust data validation and normalization, enhancement of clinical terminologies, and the use of AI paired with evidence-based algorithms to rectify historical data issues, stressing that establishing trusted data sources is paramount before AI can truly revolutionize health care delivery. Our presenting sponsor is Microsoft Dragon Copilot. Want to streamline your clinical documentation and take advantage of customizations that put you in control? What about the ability to surface information right at the point of care or automate tasks with just a click? Now, you can. Microsoft Dragon Copilot, your AI assistant for clinical workflow, is transforming how clinicians work. Offering an extensible AI workspace and a single, integrated platform, Dragon Copilot can help you unlock new levels of efficiency. Plus, it's backed by a proven track record and decades of clinical expertise and it's part of Microsoft Cloud for Healthcare–and it's built on a foundation of trust. Ease your administrative burdens and stay focused on what matters most with Dragon Copilot, your AI assistant for clinical workflow. VISIT SPONSOR → https://aka.ms/kevinmd SUBSCRIBE TO THE PODCAST → https://www.kevinmd.com/podcast RECOMMENDED BY KEVINMD → https://www.kevinmd.com/recommended

In this episode of Alter Everything, we chat with Eric Soden and JT Morris from Alteryx partner Capitalize about the practical applications and limitations of generative AI. They discuss ideal use cases for large language models, the importance of balancing generative AI with traditional analytics techniques, and strategies for scaling AI capabilities in enterprise environments. Eric and JT also share real-world examples and insights into achieving productivity gains and ROI with generative AI, along with the importance of maintaining data quality and explicability in AI processes.Panelists: JT Morris, Senior Manager, Advanced Analytics Practice Lead @ Capitalize, @JTMorris, LinkedInEric Soden, Co-founder and Managing Partner @ Capitalize, @esoden, LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: Capitalize AnalyticsAlteryx Partners - Solution ProvidersEric's LinkedIn posts on Gen AI + AlteryxCapitalize Webinar: Alteryx +GenAI: 5 Real-World Use Cases Explained Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music and Mike Cusic for the for our album artwork.

In this episode of Identity at the Center, hosts Jeff Steadman and Jim McDonald are joined by Jerome Thorstenson, IAM Architect with Salling Group, live from EIC 2025 in Berlin! Jerome shares his insights on B2B identity, the challenges of managing access for a complex supply chain, and the importance of an identity-first approach.Discover how Salling Group, operating major labels like Target and Starbucks, handles identity for thousands of employees and external partners. Jerome dives into the complexities of balancing security, user experience, and the practicalities of implementing IGA and ABAC.From navigating the challenges of data quality and high employee turnover to the nuances of transitioning between IGA systems, this episode offers valuable insights for identity practitioners.Chapter Timestamps:00:00:00 - B2B Identity Challenges00:02:14 - Welcome to Identity at the Center from EIC 202500:04:14 - Jerome's Journey into Identity00:05:19 - Salling Group Overview00:06:57 - Securing B2B - Jerome's Presentation00:10:54 - Controlling Access in B2B00:11:41 - Identity as a Product00:14:51 - The Role of the IAM Practitioner00:16:31 - ABAC as a Game Changer00:21:00 - Language Considerations in a European Context00:22:33 - Employee Turnover Challenges00:25:07 - IGA Implementation Insights00:29:28 - Identity Fabric Discussion00:31:21 - Jerome's Caribbean Background00:34:06 - Wrap-up and Contact InformationConnect with Jerome: https://www.linkedin.com/in/jetdk/Connect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at http://idacpodcast.comKeywords:IDAC, Identity at the Center, Jeff Steadman, Jim McDonald, EIC 2025, B2B Identity, Identity First Security, IAM, Identity and Access Management, Supply Chain Security, IGA, ABAC, Attribute-Based Access Control, Role-Based Access Control, Identity Fabric, Digital Identity, Cybersecurity, Data Quality, Employee Turnover, Caribbean

Today, we're joined by Ted Elliott, Chief Executive Officer of Copado, the leader in AI-powered DevOps for business applications. We talk about:Impacts of AI agents over the next 5 yearsTed's AI-generated Dr. Seuss book based on walks with his dogThe power of small data with AI, despite many believing more data is the answerThe challenge of being disciplined to enter only good dataGaming out SaaS company ideas with AI, such as a virtual venture capitalist

Were you in Washington, DC, last week for IIeX? If not, our latest episode of Intellect has you covered! Brian Peterson and Matthew Alexander were in the nation's capital for this year's event and sat down to recap all the trends and topics. Let's dive into what the guys discussed: Data Quality Takes Center Stage One of the most talked-about issues this year was data quality, spurred on by the recent industry indictment. Sessions addressing fraud detection, transparent sampling practices, and respondent experience were standing room only. It's clear that researchers are demanding more transparency and layered approaches to improving data quality, from better survey design, better tools, and even improved respondent experience. AI and Synthetic Data: More Practical Applications and Progress AI continued to dominate the conversation, but the tone has shifted. This year, discussions focused on practical applications of how AI can assist in the research process, with a variety of presenters showing how they had integrated AI into their tools. Synthetic data was also a large topic of conversation, with several presenters showing their latest strides in synthetic data and how they are validating it. Brian and Matthew both agreed that the applications are gaining traction in qualitative research, but based on the presentations, were still cautious about its readiness for quantitative applications. What Worked (and What Didn't) From a Format Perspective Both Brian and Matthew felt Washington, D.C. was a fantastic host city—clean, easy to navigate, and close to the airport. They were also happy that the Greenbook team abandoned the headphone concept that was used in Austin. The only drawback they found was that some sessions suffered from mismatched room sizes, with popular topics overflowing beyond capacity. They speculated that because of the indictment news that broke just a couple of weeks prior, there was increased interest in some topics. Give it a listen and let us know what you think. And hey, if IIeX comes to Cincinnati next year like Brian and Matthew suggested, we'll see you there! Did you miss one of our webinars or want to get some of our whitepapers and reports? You can find it all on our Resources page on our website here. Learn more about your ad choices. Visit megaphone.fm/adchoices

A modern digital healthcare economy is impossible without a robust provider directory, which serves as the foundation for interoperability and crucial processes.  In this episode, Erin Weber, Chief Policy and Research Officer, discusses how CAQH supports provider directories, emphasizing the need for data accuracy and standardization through initiatives like universal group roster templates. She highlights the importance of interoperability and maintaining accurate data to ensure seamless care delivery and billing. Don Rucker, Chief Strategy Officer, talks about modern FHIR APIs and interoperability. He uses the analogy of domain name services on the internet and stresses the need for “computable interoperability” where data can be used in real time to improve care. They explain how the 21st Century Cures Act has impacted healthcare and how legacy systems need to be modernized. Don and Erin stress that this work is crucial for modern healthcare to evolve and deliver improved patient experiences.  Tune in and learn how these key changes are shaping the future of healthcare! Resources: Connect with and follow Erin Weber on LinkedIn. Follow CAQH on LinkedIn and visit their website. Connect with and follow Don Rucker on LinkedIn. Learn more about 1upHealth on their LinkedIn and website. Check out the latest annual CAQH Index Report here.

Randall Balestriero joins the show to discuss some counterintuitive findings in AI. He shares research showing that huge language models, even when started from scratch (randomly initialized) without massive pre-training, can learn specific tasks like sentiment analysis surprisingly well, train stably, and avoid severe overfitting, sometimes matching the performance of costly pre-trained models. This raises questions about when giant pre-training efforts are truly worth it.He also talks about how self-supervised learning (where models learn from data structure itself) and traditional supervised learning (using labeled data) are fundamentally similar, allowing researchers to apply decades of supervised learning theory to improve newer self-supervised methods.Finally, Randall touches on fairness in AI models used for Earth data (like climate prediction), revealing that these models can be biased, performing poorly in specific locations like islands or coastlines even if they seem accurate overall, which has important implications for policy decisions based on this data.SPONSOR MESSAGES:***Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/***TRANSCRIPT + SHOWNOTES:https://www.dropbox.com/scl/fi/n7yev71nsjso71jyjz1fy/RANDALLNEURIPS.pdf?rlkey=0dn4injp1sc4ts8njwf3wfmxv&dl=0TOC:1. Model Training Efficiency and Scale [00:00:00] 1.1 Training Stability of Large Models on Small Datasets [00:04:09] 1.2 Pre-training vs Random Initialization Performance Comparison [00:07:58] 1.3 Task-Specific Models vs General LLMs Efficiency2. Learning Paradigms and Data Distribution [00:10:35] 2.1 Fair Language Model Paradox and Token Frequency Issues [00:12:02] 2.2 Pre-training vs Single-task Learning Spectrum [00:16:04] 2.3 Theoretical Equivalence of Supervised and Self-supervised Learning [00:19:40] 2.4 Self-Supervised Learning and Supervised Learning Relationships [00:21:25] 2.5 SSL Objectives and Heavy-tailed Data Distribution Challenges3. Geographic Representation in ML Systems [00:25:20] 3.1 Geographic Bias in Earth Data Models and Neural Representations [00:28:10] 3.2 Mathematical Limitations and Model Improvements [00:30:24] 3.3 Data Quality and Geographic Bias in ML DatasetsREFS:[00:01:40] Research on training large language models from scratch on small datasets, Randall Balestriero et al.https://openreview.net/forum?id=wYGBWOjq1Q[00:10:35] The Fair Language Model Paradox (2024), Andrea Pinto, Tomer Galanti, Randall Balestrierohttps://arxiv.org/abs/2410.11985[00:12:20] Muppet: Massive Multi-task Representations with Pre-Finetuning (2021), Armen Aghajanyan et al.https://arxiv.org/abs/2101.11038[00:14:30] Dissociating language and thought in large language models (2023), Kyle Mahowald et al.https://arxiv.org/abs/2301.06627[00:16:05] The Birth of Self-Supervised Learning: A Supervised Theory, Randall Balestriero et al.https://openreview.net/forum?id=NhYAjAAdQT[00:21:25] VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, Adrien Bardes, Jean Ponce, Yann LeCunhttps://arxiv.org/abs/2105.04906[00:25:20] No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data (2025), Daniel Cai, Randall Balestriero, et al.https://arxiv.org/abs/2502.06831[00:33:45] Mark Ibrahim et al.'s work on geographic bias in computer vision datasets, Mark Ibrahimhttps://arxiv.org/pdf/2304.12210

Are companies starting to get concerned that AI isn't meeting expectations? Concern is a part of any new technology adoption curve, but let's explore some areas where expectations might not be meeting results. SHOW: 914SHOW TRANSCRIPT: The Cloudcast #914 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:Try Postman AI Agent Builder TodayCut Enterprise IT Support Costs by 30-50% with US CloudSHOW NOTES:Why AI isn't meeting expectations (The Artificial Intelligence Enterprise)IS THREE YEARS ENOUGH TIME FOR ANY TECHNOLOGY TO TAKE OVER THE WORLD?Costs are still high, and positive ROI is still evolvingThe technology stack and standards are still evolvingEnterprise expectations are being confused with consumer expectationsAI predictions and timelines are overly aggressiveHow is anymore measuring AI success? The AI future is already here, it's just unevenly distributed“AI First” strategies are following “Cloud First” strategies - unevenly and distributedMost people don't like to talk about the augment vs. replace issueMiscellaneous stuff:ChatGPT is the fasting growing tech ever AGI will be here at any momentFor the first 18+ months, it was only an OpenAI + NVIDIA marketAll software development will be done by AIThere will be $1B companies with 1 personGenAI, Frontier Models, Open Source Models, Agents, etc.Deep Seek, MCP, etc. FEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

Send Everyday AI and Jordan a text messageYou might not think about the supply chain every day. But every product you use or service you rely on is 100% impacted by the global supply chain. And AI is completely reshaping how it works. Join us to find out how.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Julian questions on AI and supply chainsUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:1. Role of Generative AI in Supply Chains2. Challenges in the Supply Chain Industry3. Automation and Robotics in Logistics4. Accessibility of AI Solutions5. Data Quality and ManagementTimestamps:00:00 Global Supply Chain Analytics Platform03:56 AI-Driven Procurement Insights09:57 Supply Chain Transparency and Challenges12:37 Meta & Apple Enter Robotics Race17:32 Data Classification Challenges in Industry21:22 Accessible AI: From Chatbots to Agents23:39 Navigating AI Disruption in Product Suites27:02 Data Management and Security EssentialsKeywords:Generative AI, global supply chain, artificial intelligence, machine learning, large language models, data extraction, ERP systems, data classification, supply chain analytics, predictive analytics, scenario planning, robotics, automation, ChatGPT, business impact, ESG compliance, supply chain insights, procurement officer, spend management, minority supplier spend, data quality, predictive insights, scenario analysis, enterprise resource planning, SAP, Oracle, Coders, generative AI applications, supply chain transformation, generative AI impact, technology disruption. Ready for ROI on GenAI? Go to youreverydayai.com/partner