Podcasts about product delivery

Grab the Business System Audit Checklist: https://linktw.in/XfctuS Book a call with my team: https://linktw.in/aqROSR In this video, I'm breaking down the 5 systems that actually scale a business and how to install them in the right order over the next 90 days without slowing down your operations. Learn how these systems stack, why order matters, and how to build a well-oiled business (the right way). Connect with me: LinkedIn: https://www.linkedin.com/in/stacytuschl/ Instagram: https://www.instagram.com/stacytuschl/ Facebook: https://www.facebook.com/stacytuschl/ Newsletter: https://www.linkedin.com/newsletters/7396626889408274432/ 00:00 - Intro 01:55 - Product Delivery 03:43 - Client Acquisition 06:52 - Client Onboarding 09:19 - Hiring & Attraction 11:34 - Back-End Operations #businessoperations #systemsthinking #scalingsystems

Finding product–market fit isn't the finish line—it's base camp. In this episode, Hannah Clark sits down with Amit Shah, COO of Virta Health, to unpack what really happens after you've “made it” and the mountain suddenly gets steeper. Virta is scaling a fully virtual metabolic health clinic in a fast-moving healthcare landscape, and Amit brings a rare perspective shaped by nearly a decade inside the company—and a career that started in operations, not product.Amit shares how Virta navigated rapid growth while building a scalable product organization, why simplicity becomes harder (not easier) as you scale, and how they make deliberate choices about where humans add the most value versus where AI should step in. If you're wrestling with org design, prioritization, tech debt, or the ethics of AI in real-world products, this conversation offers a grounded look at what it takes to scale without losing your north star.Resources from this episode:Subscribe to The CPO Club newsletterConnect with Amit on LinkedInCheck out Virta Health

Vibe Coding verändert gerade, wie Produkte entstehen. Produktmenschen bauen selbst. Ideen werden direkt im Code sichtbar. Dokumente, Übergaben und lange Abstimmungen verlieren an Bedeutung. In dieser Folge sprechen Oliver und Tim darüber, was diese Entwicklung für Product Owner, Developer und die Zusammenarbeit im Team bedeutet. Sie zeigen, warum Vibe Coding sich so befreiend anfühlt. In kürzester Zeit entsteht funktionierende Software. Lernen passiert unmittelbar. Hypothesen lassen sich ausprobieren, anpassen oder direkt verwerfen. Gleichzeitig werfen sie einen kritischen Blick auf die Risiken. Wenn Entscheidungen direkt im Code getroffen werden, rückt Product Delivery stark in den Vordergrund. Nutzerfeedback und strukturierte Product Discovery geraten leicht ins Hintertreffen. Bauchgefühl ersetzt dann schnell echte Erkenntnisse. Auch die Frage nach Verantwortung spielt eine zentrale Rolle. Wenn Produkt und Umsetzung in einer Hand liegen, verschwimmen klassische Rollengrenzen. Das kann effizient sein, birgt aber Risiken für Qualität, Wartbarkeit und langfristige Kosten – vor allem ohne Sparring. Trotzdem sehen Oliver und Tim große Chancen. Product Owner entwickeln mehr technisches Verständnis. Developer profitieren von klareren, greifbaren Ideen. Diese Nähe kann Zusammenarbeit stärken – wenn sie bewusst gestaltet wird. Am Ende bleibt eine entscheidende Frage: Nutzen wir Vibe Coding, um schneller Lösungen zu bauen – oder um schneller herauszufinden, welche Probleme wirklich relevant sind?

Gil Broza joins our hosts, Jim Sammons and Rich Visotcky, for the first episode of the Product Fields podcast in 2026! In this conversation, we discuss the evolving landscape of agility and product management in the age of AI. Together, we explore how AI has transformed product delivery, the importance of accountability, and the need for leaders to adapt their strategies to ensure effective team dynamics. Too often, we have seen companies go full-on into AI without any strategy or understanding of the consequences. Through our discussion, we dive into the balance between leveraging AI for efficiency while maintaining critical thinking and human oversight, and the need for a thoughtful approach to integrating AI into work processes.00:00:00 Intro00:02:04 Agility Beyond Tech: Adapting Principles for Non-Tech Teams 00:03:44 The Relevance of the Agile Manifesto, Values, and Principles 00:08:44 AI's Impact on Product Delivery and Management 00:12:15 Going Back to Principles in the Age of AI 00:16:52 Accountability in the Age of AI 00:28:03 The AI Industrial Revolution: Trust and Human Connection 00:33:58 The Atrophy of Skills in the Age of AI 00:35:07 The Impact of AI on Communication and Authenticity 00:45:38 The Dangers of Over-Reliance on AI 00:47:27 Fundamentals in the Age of AI 00:50:45 The Danger of Agency and AI 00:53:01 The Future of Work and AI Integration 00:57:46 Quantity vs. Impact 01:01:20 Closing Connect with Product Fields:

As we close out 2025, Peter and Dave are making predictions about what's coming in 2026, especially around AI, organizational change, and how teams actually work.They cover five key predictions:AI moves from tools to organizational capability: Organizations that invest in literacy, governance, and data foundations will pull ahead of those just sprinkling AI on top and hoping for the best.Critical thinking beats prompt engineering: The real competitive advantage won't be writing clever prompts. It'll be knowing when to pause, think through the problem, and decide if you even need the AI in the first place.Product delivery becomes non-negotiable: After 20 years of pushing Agile principles, AI might finally force organizations to actually adopt them (even if they're reluctant to call it "Agile").Businesses return to fundamentals: Just like the dot-com bubble, we're heading toward a moment where the market will care more about revenue, customers, and sustainability than hype.Reskilling becomes a structural investment: Organizations will need to figure out what roles actually look like in an AI-enabled world and invest in growing their people, not just replacing them.At the end, Peter and Dave pick which prediction is hardest to measure (spoiler: it's critical thinking) and commit to revisiting these in March to see how wrong they were.If you've been wondering where all this AI stuff is actually heading, this episode cuts through the noise with grounded, practical predictions you can actually use.Related episodes:AI and Knowledge Management with Derek Crager: https://www.buzzsprout.com/1643821/episodes/17360635Product vs. Process Innovation: https://www.buzzsprout.com/1643821/episodes/7953100There Are No Safe Bets in Business Anymore: https://www.buzzsprout.com/1643821/episodes/17433034Reach out: feedback@definitelymaybeagile.com

Viele Produktteams sind ständig beschäftigt – aber nicht immer wirksam. In dieser Episode spricht Oliver Winter mit Produktcoach Tim Herbig über echte Fortschritte in der Produktentwicklung. Ausgehend von seinem Buch „Real Progress“ teilt Tim, wie Teams durch klare Produktstrategie, bewusste Entscheidungen und lernorientiertes Arbeiten echten Impact schaffen. Statt sich in Methoden zu verlieren, gilt es, Verbindungen zu schaffen – zwischen Product Discovery, Product Delivery und Produkt Strategie. Eine Folge für alle, die sich weniger Output, aber mehr Outcome und Impact wünschen.

In dieser Podcastfolge spricht Tim mit Björn Schotte, Mitgründer und Geschäftsführer von Mayflower, darüber, wie tiefgreifend der Einfluss von AI auf Produktentwicklung bereits ist – und wie sich die Arbeit von Product Ownern, Entwicklerinnen und Organisationen verändert. Björn bringt dabei nicht nur seine Erfahrungen aus der agilen Softwareentwicklung ein, sondern zeigt, wie AI und Context Engineering die Produktarbeit grundlegend neu definieren. AI verändert nicht einfach nur Prozesse. Sie verschiebt den Fokus. Wo früher monatelange Diskussionen über Features und Architekturen nötig waren, entstehen heute funktionsfähige Prototypen in wenigen Stunden. Systeme, die sich selbst kontextbezogen steuern, liefern Vorschläge, testen Varianten und verbessern kontinuierlich die Ergebnisse. Damit rückt die eigentliche Frage in den Mittelpunkt: Wie kann man als Produktmensch diese Geschwindigkeit und künstliche Intelligenz sinnvoll nutzen, um echten Mehrwert für Nutzerinnen und Nutzer zu schaffen? Björn beschreibt aufgrund seiner Erfahrung, dass AI längst über das reine „Vibe Coding“ hinausgeht. Die Zukunft liegt für ihn im Context Engineering – also darin, KI-Systemen Zugang auch zu relevanten Unternehmensdaten, Prozessen und Werkzeugen zu ermöglichen. So können sie nicht nur auf Zuruf Code generieren, sondern eigenständig sinnvolle Entscheidungen im Entwicklungsprozess treffen. Diese agentischen Systeme lernen aus Daten, messen Auswirkungen und schlagen eigenständig Verbesserungen vor. Damit entsteht ein Kreislauf, in dem Produktentwicklung zu einem lernenden, sich selbst optimierenden Prozess wird. Für Product Owner bedeutet das eine Veränderung ihrer Rolle. Nicht mehr jede Entscheidung wird im Expertengremium getroffen. Stattdessen verlagert sich Verantwortung dahin, wo Arbeit tatsächlich passiert – in die Teams und an die Schnittstelle zwischen Mensch und Maschine. Die Aufgabe verändert sich: vom Entscheider hin zum Orchestrator. Jemand, der den Rahmen schafft, in dem Menschen, Daten und AI-Systeme gemeinsam wirken können. Ein Beispiel: Mayflower entwickelt Voice-AI-Lösungen, die in Echtzeit mit Menschen sprechen, lernen und kontextbezogen reagieren. Damit verschwimmen die Grenzen zwischen Development, AI und User Experience. Produktentwicklung wird zu einem Dialog – zwischen Mensch, Maschine und Nutzer. Auch in der Modernisierung von Legacy-Systemen zeigt sich der Einfluss von AI: Software kann heute automatisch dokumentiert, getestet und migriert werden. Entwicklerinnen werden nicht ersetzt, sondern durch AI aufgeladen – schneller, präziser und mit deutlich höherer Testabdeckung. Doch die größte Herausforderung liegt nicht in der Technologie, sondern in der Organisation. Klassische Hierarchien und lange Entscheidungswege bremsen aus. Wer den Einfluss von AI auf Produktentwicklung wirklich nutzen will, muss Strukturen verändern – Budgetierung, (Entscheidungs-)Verantwortlichkeiten, Zusammenarbeit. Entscheidungen gehören dahin, wo die Arbeit geschieht. Dorthin, wo AI, Daten und Menschen gemeinsam im Produktentwicklungsprozess lernen. Am Ende dieser Entwicklung steht kein Kontrollverlust, sondern eine neue Form moderner Produktentwicklung. Eine, die Geschwindigkeit mit Lernfähigkeit verbindet. Für Product Owner heißt das: Jetzt ist die Zeit, sich mit AI auseinanderzusetzen – nicht theoretisch, sondern praktisch. Wer versteht, wie AI die Arbeit erleichtert, kann schneller handeln, bessere Produkte bauen und mehr Wirkung erzielen. Das Gespräch knüpft wunderbar an unsere vorletzte Podcast-Folge mit Ben Sufiani "Ist Vibe Coding relevant für die Produktentwicklung?" an. Wer weitere Fragen an Björn Schotte hat oder mit ihm ins Gespräch kommen möchte, erreicht ihn am Besten über ein LinkedIn-Profil oder per Mail (bjoern.schotte@mayflower.de. Wie verändert AI deinen Arbeitsalltag in der Produktentwicklung? Wir freuen uns, wenn du deine Erfahrungen aus der Praxis mit uns in einem Kommentar des Blog-Artikels teilst.

You can build the best product in your category and still fall behind. Why? Because the market doesn't reward “best”—it rewards best at business. In this episode, I unpack the pattern I've seen in conversations with very smart founders: they perfect the thing, but neglect the system that sells the thing.We start with the two brains of your business:Product/Delivery drives retention.Marketing & Sales drive attention and acquisition.Being great at #1 is necessary—but insufficient in a noisy, novelty-driven world where people discover the loud before the great.Then we dig into three traps that smart people fall into:The Technician's Curse: When numbers dip, you “improve the product” instead of the pipeline (offer → traffic → show → close). We'll install a simple weekly scorecard and fix the bottleneck first.The Projection Trap: Assuming customers and staff think like you do. We replace assumptions with customer interviews, plain-English messaging, and clear “definitions of done.”The “I'll Figure It Out” Fallacy: Brains and hustle aren't enough without context and reps. Borrow them—via mentors, playbooks, and proven scripts—so you make right moves faster.Finally, we make you best at business: choose a focused market, sharpen your promise and proof, show your mechanism, and adopt a weekly operating cadence—one growth action every morning, one bottlenecked metric every week, one small test at a time.You'll leave with a playbook to turn smart into scale—and the three actions to run this week so your best product finally wins.Connect with Chris Cooper:Website - https://businessisgood.com/

[AAA] In 'Access All Areas' shows we go behind the scenes with the crew and their friends as they dive into complex challenges that organizations face—sometimes getting a little messy along the way.This week, we address the ‘big rocks' that can obstruct or delay successful outcomes in organizational transformations. Dave, Esmee, and Rob are joined by Jasmin Booth, Head of Product Delivery to discuss the transformation to being a (digital) product based organization.TLDR05:22 Access All Areas: This third episode focuses on the products we build that drive outcomes.06:52 Conversation with Jasmin about our digital products37:06 What makes it better to be in a product centric organization? 54:00 Conclusion of the seven Big Rocks and how to smash them59:00 Going on the Blue Bell railway HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/with Jasmin Booth: https://www.linkedin.com/in/jasminbooth15/ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:  https://www.linkedin.com/in/louis-corbett-087250264/'Cloud Realities' is an original podcast from Capgemini

How do you scale product delivery without losing agility? In this podcast hosted by Cassio Sampaio, Okta Auth0 VP and Chief of Staff Lauren McCarthy will be speaking on strategic program management and organizational growth. Lauren shares insights from her 25-year career on balancing process, tools, and team collaboration to drive successful product development across different organizational stages.

When Michelle joined DraftKings seven years ago, she wasn't stepping into the business it is today, she was jumping into a tech company that was about to take off.  Michelle, now a Senior Manager on the Program Delivery team, has been part of DraftKings' transformation from a startup to a global leader in sports entertainment. She's one of a few teammates who has helped build our Engineering team from the beginning.    Listen to more of her career journey. Want to learn more? Check out our jobs page.   

If your organization is laser-focused on epics, features, and user stories, you may want to shift your attention to something else that can have an even greater impact on your product or solution. Michele Lanzinger, strategic advisor at Scaled Agile, explains what and why in this episode. Like what you hear? Connect with Michele on LinkedIn. Explore SAFe courses here.

Global Agile Summit Preview: Unifying Strategy, Discovery, and Delivery in Product Development With Roman Pichler In this BONUS Global Agile Summit preview episode, we explore a crucial topic that's shaping how we approach product development—sometimes in ways that serve us well and sometimes in ways that hold us back.  There's a growing trend in our industry to explicitly separate strategy, discovery, and delivery into distinct activities or even different teams. On the surface, this seems logical: strategy decides the right thing to do, discovery figures out how to do it, and delivery gets it done. But is this division actually helping us? Or is it creating barriers that make great product development harder? The Origins of Product Discovery "I think it's partly based, at least on Marty Cagan's work, and his insight that many teams are very much focused traditionally on delivering outputs, on writing code. And I think his original intention was to say, 'Let's not worry about creating outputs. Let's also make sure that what we creating makes sense.'" Roman Pichler shares insights on how the concept of product discovery emerged as a reaction to teams being overly focused on outputs rather than outcomes. He explains that conceptually distinguishing between product strategy, discovery, and delivery can be helpful—much like organizing clothes into different sections of a wardrobe. However, in reality, these activities must be connected, informing and guiding each other rather than existing as sequential steps. The Risks of Separating Product Strategy, from Discovery, and from Delivery "If we have a group of people who takes care of strategic decisions, a different group focusing on product discovery, and another group—the tech team—who focuses on product delivery, and those groups don't talk as much as they could and should do, then suddenly we have a sequential process and handoffs." One of the primary challenges with separating strategy, discovery, and delivery is the risk of creating handoffs between different teams. Roman highlights how this sequential approach can slow down value creation, lead to knowledge loss, and increase the likelihood of introducing mistakes. This separation can create barriers that ultimately make product development more difficult and less effective. In this segment, we refer to the podcast interview with Tim Herbig on the concept of Lateral Leadership, and how that is critical for product people. Integrating the Work Streams "What I usually use as a visualization tool is three work streams: a strategy work stream, a discovery work stream, and a delivery work stream. The strategy stream guides the discovery stream. The discovery stream guides the delivery stream, and then the delivery stream informs the discovery stream, and the discovery stream informs the strategy stream." Rather than seeing strategy, discovery, and delivery as separate phases, Roman suggests visualizing them as parallel work streams that continuously inform and guide each other.  This approach recognizes that strategy work doesn't just happen at the beginning—it continues throughout the product lifecycle, adapting as the product evolves. By integrating these work streams and ensuring they're interconnected through feedback loops, teams can create a more cohesive and effective product development process. The Power of Collaboration "The important thing is to make sure that the different areas of work are not disjointed but interlinked. A key element to make that work is to use collaboration and teamwork and ensure that there aren't any handoffs, or avoid handoffs as much as possible." Collaboration and teamwork are essential to successfully integrating strategy, discovery, and delivery. Roman emphasizes the importance of bringing product people—who understand customer needs, business models, and stakeholder relationships—together with tech teams to foster innovation and create value. This collaborative approach helps overcome the challenges that arise from treating these activities as separate, sequential steps. Building an Extended Product Team "Form a big product team, a product team that is empowered to make strategic decisions and consists not only of the person in charge of the product and maybe a UX designer and a software developer, but also key business stakeholders, maybe somebody from marketing, maybe somebody from sales, maybe a support team member." Roman advocates for forming an extended product team that includes not just product managers, designers, and developers, but also key business stakeholders. This larger team can collectively own the product strategy and have holistic ownership of the product—not just focusing on discovery or delivery. By empowering this extended team to make strategic decisions together, organizations can ensure that different perspectives and expertise inform the product development process. Practical Implementation: Bringing it all Together "Have regular meetings. A specific recommendation that I like to make is to have quarterly strategy workshops as a rule of thumb, where the current product strategy is reviewed and adjusted, but also the current product roadmap is reviewed and adapted." Implementing this integrated approach requires practical mechanisms for collaboration. Roman recommends holding quarterly strategy workshops to review and adjust the product strategy and roadmap, ensuring they stay in sync with insights from development work. Additionally, he suggests that members of the extended product team should attend monthly operational meetings, such as sprint reviews, to maintain a complete understanding of what's happening with the product at both strategic and tactical levels. Moving Beyond Sequential Thinking "Unfortunately, our software industry has a tendency to make things structured, linear, and assign ownership of different phases to different people. This usually leads to bigger problems like missing information, problems discovered too late that affect 'strategy', but need to be addressed in 'delivery'." One of the challenges in adopting a more integrated approach is overcoming the industry's tendency toward linear, sequential thinking. Roman and Vasco discuss how this mindset can lead to issues being discovered too late in the process, after strategic decisions have already been made. By embracing a more iterative, interconnected approach, teams can address problems more effectively and adapt their strategy based on insights from discovery and delivery. About Roman Pichler Roman Pichler is a leading product management expert specializing in product strategy, leadership, and agility. With nearly 20 years of experience, he has coached product managers, authored four books, and developed popular frameworks. He shares insights through his blog, podcast, and YouTube channel and speaks at major industry conferences worldwide. You can link with Roman Pichler on LinkedIn and check out the resources on Roman Pichler's website.

We're taking a critical look at the Double Diamond model, aka. Dual Track Development, aka. Dual Track Agile.This widely-adopted model might be leading leadership and/or teams astray, so we're taking some time to explore its limitations in real-world applications. From the misconception of linear progression to the crucial importance of keeping customers involved throughout the process, Brian tries to convince Om that the model needs significant rethinking!Other things we discuss are:Why the "messy middle" is where the real magic happensHow to properly involve your whole team in both discovery and deliveryThe importance of continuous customer involvementWhy organizational support is crucial for success#ProductManagement #AgileMethodology #ProductDevelopment #Leadership #ProductStrategyproduct management, agile methodology, product development, leadership, team development, double diamond, product discovery, product delivery, agile coaching, product strategy= = = = = = = = = = = =Watch on YouTubeSubscribe on YouTubeAppleSpotify= = = = = = = = = = = =Toronto Is My Beat (Music Sample)By Whitewolf (Source: https://ccmixter.org/files/whitewolf225/60181)CC BY 4.0 DEED (https://creativecommons.org/licenses/by/4.0/deed.en)

“Architecture should be leading the delivery, not just the parts … And when I say delivery, I don't mean the tech delivery. I mean the overall business product delivery.” This episode explores the evolution of digital transformation and the role of architecture in enabling product-centric thinking and Agile delivery. Adam talks to Ravi Purushothaman, Director, Head of Enterprise Agility, and Logan Daigle, Director of Business Transformation, both with NTT Data. The three dive into topics including the importance of architects leading the delivery process, the challenges of transforming legacy “brownfield” environments versus building new “greenfield” solutions, and the need for a holistic, product-oriented approach. Ravi and Logan also share how they think leaders can best support product-centric thinking in any organization undergoing a digital transformation. Like what you hear? Connect with Ravi and Logan on LinkedIn. Explore SAFe courses here.

n dieser Podcastfolge widmen sich Oliver & Tim dem Thema Produktrisiken und beleuchten, welche Herausforderungen Product Owner im Hinblick auf die Risikobetrachtung meistern sollten. Jede Produktentwicklung beinhaltet Risiken mit denen man sich auseinandersetzen und bewusst mit ihnen umzugehen muss. Als Product Owner liegt es im Kern ihrer Verantwortung, mögliche Risiken frühzeitig zu erkennen und Strategien zu entwickeln, um diese zu minimieren. Die beiden sprechen über die Einteilung von Produktrisiken in vier Kategorien: Usability-Risiken (Nutzbarkeit für den Kunden), Value-Risiken (Mehrwert für den Kunden), Business Viability-Risiken (wirtschaftliche Tragfähigkeit) und Feasibility-Risiken (Machbarkeit). Es ist entscheidend, als Product Owner ein Bewusstsein für diese unterschiedlichen Risikobereiche zu entwickeln. Das Verständnis der Kundenbedürfnisse und die fortlaufende Evaluation des Marktes helfen, mögliche Value-Risiken zu reduzieren. Denn nur ein Produkt, welches tatsächlich einen Mehrwert bietet, hat langfristig Bestand. Bei den Business Viability-Risiken liegt der Fokus auf der wirtschaftlichen Tragfähigkeit des Produkts. Ein Produkt mag den Nutzern gefallen und technisch machbar sein, dennoch kann es an einem rentablen Geschäftsmodell scheitern. Es ist dabei von entscheidender Bedeutung, die strategische Ausrichtung des Unternehmens zu berücksichtigen und sicherzustellen, dass das Produkt langfristig den wirtschaftlichen Erfolg unterstützt. Ein wichtiger Aspekt, der in dieser Folge angesprochen wird, ist die Notwendigkeit, über rein technische Risiken hinaus auch ethische Aspekte zu berücksichtigen. Hier kommen Tim und Oliver auf das sogenannte ethische Risiko zu sprechen, bei dem es darum geht, ob ein Produkt moralisch vertretbar ist und im Einklang mit den ethischen Grundsätzen der Organisation steht. Kontinuierliche Product Discovery und die enge Zusammenarbeit mit Stakeholdern können dabei helfen, Produktrisiken frühzeitig zu identifizieren und durch gezielte Tests und Experimente zu mindern. Produktideen werden in der Entstehungsphase auf Annahmen geprüft und in Hypothesen überführt, um auf Basis der Ergebnisse Entscheidungen zu treffen, bevor es in die Product Delivery geht. Dabei kann die Zusammenarbeit in einem sogenannten „Product Trio“ aus Product Owner, Designer und Engineers wertvolle Perspektiven für die Risikobetrachtung eröffnen. Diese Folge bietet praxisnahe Einblicke und viele anschauliche Beispiele, wie Product Owner im täglichen Umfeld Produktrisiken bewerten und Strategien entwickeln können, um Unsicherheiten zu managen und die Erfolgsaussichten ihrer Produkte zu steigern.

In this Ask a PST session, Agile Director, Executive Coach and Professional Scrum Trainer Jay Rahman answers listener questions and offers advice about their Scrum challenges and obstacles. Jay offers strategies for avoiding and recovering from product delivery failures. Jay emphasized the importance of alignment across leadership, management, and teams, and the need for business and technology to work together. He highlights the critical role of Retrospectives in identifying and addressing issues, and the necessity of senior leadership's accountability. Jay also stresses the importance of Product Owners being fully engaged and available, and the value of using OKRs to foster a culture of accountability. He shares practical examples, such as cross-skilling teams to reduce dependencies and leveraging stakeholder metrics to prioritize requirements. Tune in for a thought provoking session!

Michon Williams is my special guest in this episode, sharing her career journey, emphasizing an early interest in technology and explaining her role as Chief Technology Officer (CTO) at Walmart Canada.Michon highlights the importance of balancing innovation and stability, operating on three horizons: stability, incremental improvements, and long-term disruptions. We discuss the need for local solutions in Canada due to unique market requirements, the rapid pace of innovation in retail, particularly post-pandemic, and the role of AI in enhancing efficiency and customer experience. Michon advises retailers to foster a culture of experimentation and continuous learning and encourages women in tech to be curious and proactive in their career development. About MichonMichon was recruited to Walmart Canada as Vice President of Product & Delivery to steward their accelerated investment in technology in 2021. Promoted to Chief Technology Officer, Michon and her team are responsible for core systems strategy and delivery, technology infrastructure and operations, infosec and architecture across stores, pharmacies, supply chain, merchandising, core data platforms, associate tools and enterprise technology (including real estate). Michon is a long-time advocate of the environment and inclusion and leads programs in support of Green Technology and to promote and advance Diversity in Technology. About MichaelMichael is the president and founder of M.E. LeBlanc & Company Inc, a senior retail advisor, keynote speaker and media entrepreneur. He has been on the front lines of retail industry change for his entire career. He has delivered keynotes, hosted fire-side discussions and participated worldwide in thought leadership panels, most recently on the main stage in Toronto at Retail Council of Canada's Retail Secure conference with leaders from The Gap and Kroger talking about violence in retail stores, keynotes on the state & future of retail in Orlando and Halifax, and at the 2023 Canadian GroceryConnex conference, hosting the CEOs of Walmart Canada, Longo's and Save-On-Foods Canada. Michael brings 25+ years of brand/retail/marketing & eCommerce leadership experience with Levi's, Black & Decker, Hudson's Bay, Pandora Jewellery, The Shopping Channel and Retail Council of Canada to his advisory, speaking and media practice.Michael also produces and hosts a network of leading retail trade podcasts, including the award-winning No.1 independent retail industry podcast in North America, Remarkable Retail, Canada's top retail industry podcast; the Voice of Retail; Canada's top food industry and the top Canadian-produced management independent podcasts in the country, The Food Professor, with Dr. Sylvain Charlebois. Rethink Retail has recognized Michael as one of the top global retail influencers for the fourth year in a row, Coresight Research has named Michael a Retail AI Influencer, and you can tune into Michael's cooking show, Last Request BBQ, on YouTube, Instagram, X and yes, TikTok.Available for keynote presentations helping retailers, brands and retail industry insiders explaining the current state of the retail industry in Canada and the U.S., and the future of retail.

In this episode our host Behrad Mirafshar dives into the essentials of launching a successful MVP Design Sprint, breaking down each crucial step for aspiring entrepreneurs and product teams. Behrad kicks off the discussion by introducing the MVP Sprint Service, emphasizing how it accelerates the process of bringing an idea to life. He then outlines the assets necessary for a smooth sprint, from design tools to team collaboration platforms, ensuring you're fully equipped to start strong.Moving forward, Behrad highlights the importance of a well-defined product strategy, stressing that it is the foundation for every successful product. He explains how to effectively translate this strategy into user stories, which guide the development process and keep teams aligned with the overall vision. The episode also touches on the critical role of branding guidelines, showing how they ensure consistency and strengthen the product's identity.Finally, Behrad underscores the significance of thorough discovery and research in the early stages of product development. He explains how these elements inform decision-making and reduce risks, ultimately leading to a more refined and user-centered product. This episode is a must-listen for anyone looking to streamline their product development process and bring their ideas to market with confidence.Learn more about Bonanza Studios here.

In this episode, join Brad Blizzard, Vice President, Logistics Operations and Product Delivery at Bridgestone, and Bob Boehm, Vice President of Operations at DHL Supply Chain, as they discuss their careers in logistics, industry changes, autonomous guided vehicle (AGV) implementation and our partnership between Bridgestone and DHL.  

In this episode, you'll discover:Defining your chiropractic USP for humanity (Unique Success Proposition) We have a SIMPLE and ELEGANT solution - don't confuse people You have a COMPELLING story - Are you telling it? Applying the principle of the “Hard-Easy” in practice It's never about price - it's always about VALUEEpisode Highlights00:57 - Gratitude for the in-person training and the power of the product they deliver.06:33 - Combining conversion and retention for better results.08:32 - The importance of understanding the core principles of chiropractic and their unique value proposition.10:48 - Reinvigorating the chiropractic process through a renewed understanding of the product's power.13:05 -  The importance of an immersive environment for chiropractors to level up and improve their skills.17:41 - The transformative concepts and takeaways from a recent event, including the importance of engaging people's hearts before their heads.19:31 - Removing interference to the flow of nerve impulses, likening it to CPR and the Heimlich maneuver.22:13 - The importance of addressing patients' belief systems for successful chiropractic care.25:06 - Addressing patients' belief systems before introducing chiropractic care.27:54 - Dr. Bobby chats with Dr. Andrew Powell from Success Partner, Better Balance Orthotics about a unique product that enhances patient outcomes through proprioception rather than traditional arch support. These orthotics stimulate foot muscles, improving posture, balance, and gait. Initially designed for scoliosis, they've been found to benefit people of all ages. Dr. Powell discusses how implementing these orthotics can boost patient results and significantly increase a practice's revenue. Discover the effectiveness of the product and its potential to boost profitability. Resources MentionedTo learn more about the REM CEO Program, please visit:  http://www.theremarkablepractice.com/rem-ceoBuild your dream team with Chiro Match Makers. Learn more at https://chiromatchmakers.com/For more information about Better Balance Orthotics please visit: https://betterbalanceorthotics.com/Subscribe to our newest podcast "Build Your Remarkable Practice" here: https://podcasts.apple.com/us/podcast/build-your-remarkable-practice-for-chiropractors/id1734107477  Schedule a Brainstorming call with Dr. PeteDr. Stephen's LinkedInDr. Peter's LinkedInThe Remarkable CEO WebsiteDr. Stephen's Book – The Remarkable Practice: The Definitive Guide to Build a Thriving Chiropractic Business

Building products with GenAI brings powerful new capabilities but also a whole new set of uncertainties. Teams can't rely on best practices because the technology is changing so quickly and users are cautiously adopting change. Designing and shipping products can no longer be thought about as a linear process.Alexandra Holness, Senior Lead Product Designer at Klaviyo, joins to share lessons, cautions, and a path forward to help product teams build AI products that customers want. She sees that successful product teams will depend on designer, data scientists, engineers working more closely than ever because it is very hard to predict how customers will use models until you've shipped them.Topics discussed:* How she created her role leading AI design * Assumptions the team had about how to leverage AI * What works and doesn't from a design perspective* AI models being so nascent that its hard to design a UX* Designers-data-engineers working together in new ways* Building AI products is very different than traditional * Building effective AI products requires culture change* Why you need to test out potential futuresHave questions? Join the conversation https://www.linkedin.com/company/designofai/Subscribe to the Design of AI podcast for more in-depth resources for product teams. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com

On today's episode, I had the pleasure of chatting with Justin Davis VP of Product Delivery at Point Predictive. Justin built an amazing career started from Call-Center and is now VP of Product Delivery. He worked with all types of fraud, he was the youngest manager in DCU and he holds a CFE certificate. We spoke about his journey and ongoing trends and you have to listen to it.

Today's episode will explain the application of a product roadmap and we will include the determination of which components go to which releases. Next, we talk about the role of a business analyst in adaptive or predictive, plan-based approaches. Finally, we cover the acceptance criteria (the action of defining changes based on the situation) and how to decide if a project/product is ready for delivery based on a requirements traceability matrix or product backlog. --- Support this podcast: https://podcasters.spotify.com/pod/show/vets2pm/support

Customer-centricity is essential for successful product delivery. Head of Product, Sue Anderson shares how Trade Me keeps its customers at the heart of the product delivery process, and how the renowned New Zealand website constantly strives to do better. This episode is a must-listen for product or business leaders looking to embrace a more customer-led mindset across their organization and teams.

With social chasms widening and climate change intensifying, it has never been more important to reframe what it means for businesses to “do good” in an ever-changing world. Today, we're joined by Clay Brown from B Lab Global to discuss how we can transform the global economy from a system that profits a few to one that benefits all people, communities, and the planet.As Head of Standards, Certification, and Product Delivery for B Lab, Clay plays a critical role in evolving, growing, and scaling new and existing programs, products, and tools, including the B Corp Certification, which B Lab is perhaps best known for. In this episode, he outlines the evolving B Corp Certification standards and explains how B Lab is adapting and advancing its performance requirements to not only meet the needs of the moment but also optimize the movement for the future.For full show notes, visit: https://www.lifteconomy.com/blog/clay-brown/The spring cohort of the Next Economy MBA is officially open! Save 20% when you register before 1/29 with our early-bird sale ➡️ https://lifteconomy.com/mba

In this episode of Dreams With Deadlines, host Jenny Herald interviews Tim Herbig, a seasoned expert on Objectives and Key Results (OKRs). They unpack the deeper facets of this management tool that's taking the corporate world by storm:Key Things Discussed: Journey with Tim as he retraces his formative years with OKRs, shedding light on the challenges of top-down implementations. Emphasizing utility over form, he makes a case for the importance of day-to-day applicability of OKRs in organizations. Delve into the resistance faced by product teams towards OKRs, especially when established strategies are in place. Tim's pragmatic approach champions the integration of OKRs with current team practices, ensuring they bring tangible value to daily operations. Navigate the intricate relationship between OKRs and product strategy. With a spotlight on the essence of strategy, Tim guides listeners on making strategic choices that can be tracked effectively with OKRs, bridging the gap between grand visions and actionable metrics. Show Notes [00:00:31] Discovering OKRs: A Personal and Professional Evolution. Tim Herbig's journey with OKRs began at XING, evolving through challenges and insights. He underlines the issue of top-down OKRs without clarity and stresses their day-to-day utility. [00:02:37] Bridging the Gap: Ensuring OKRs Reflect Everyday Utility and Purpose. Tim Herbig dissects the outcomes vs. outputs debate, emphasizing OKRs' bridge role between mission and daily tasks. OKRs should be flexible tools, reflecting real team tasks and challenges. [00:05:44] Marrying OKRs with Established Practices: Pragmatism Over Dogma. Jenny and Tim explore why product teams resist OKRs. Tim advocates for intertwining OKRs with current practices like sprint planning, emphasizing their practical day-to-day value. [00:08:56] Making Pragmatic Choices: How OKRs Bridge Product Strategy to Everyday Work. The dialogue between Jenny Herald and Tim Herbig delves deep into the intersection of OKRs and product strategy. Tim highlights recognizing strategy's essence, making impactful choices, and then tracking progress with OKRs. [00:14:06] Marrying OKRs with Product Discovery: From Outcomes to Behaviors. Jenny and Tim discuss tracking team/user behaviors and predicting product success using OKRs. [00:20:54] Embracing 'Better' Practices in OKRs Over 'Best' Practices. Jenny Herald and Tim Herbig delve into the nuances of better practices (adaptive and relative methods) as opposed to rigid best practices, highlighting five critical 'better practices' for effective and practical application of OKRs. [00:24:11] Aligning OKRs with Organizational Capabilities and Structures. Jenny Herald prompts Tim Herbig to share insights on the challenges faced by organizations when their desired objectives do not align with their current capabilities or structures. Tim elaborates with stories that exemplify the discrepancies between organizational structures and the application of OKRs. [00:28:52] Aligning the Cadence of Product Delivery with Outcomes. Jenny Herald and Tim Herbig discuss the challenge of synchronizing the cadence of product delivery with the desired outcomes. They ponder on the nuances of how teams should approach measuring meaningful progress, especially when direct results may not be immediately evident. [00:30:07] The Art of Developing Leading Indicators in Product Delivery. Jenny Herald and Tim Herbig delve deeper into the concept of leading and lagging indicators in relation to key results. They discuss the challenges, conceptual considerations, and the dynamism of creating proxies and leading indicators. [00:35:37] Quick-Fire Questions for Tim: What is your dream with a deadline? Tim's dream with a deadline is to do a solo travel to Tel Aviv in the next two years. When someone says they failed with OKRs previously and want to try again, what's your advice? Tim advises them to first clarify why they want to use OKRs in the first place. What's a good reason for using OKRs? The ideal reason is to enable team autonomy and outcome thinking. A more pragmatic reason is to ensure people work on the right things and maintain strategic focus. Which book largely shaped how you think? Both in general and related to OKRs. For general thinking, "Radical Acceptance" by Tara Brach influenced him the most. Regarding OKRs, he credits "Radical Focus" by Christina Wodtke and "OKRs at the Center" co-written by Natalija and Sonja. Relevant links: “Radical Acceptance,” book by Tara Brach “The Courage to Be Disliked: How to Free Yourself, Change your Life and Achieve Real Happiness,” book by Ichiro Kishimi and Fumitake Koga “Radical Focus,” book by Christina Wodtke, American businessperson and specialist in the area of design thinking, information architecture and Management Science (specializing in objectives and key results (OKR) and team productivity.) “OKRs At The Center: How to use goals to drive ongoing change and create the organization you want,” by Natalija Hellesoe and Sonja Mewes Linked Better Practices over Stacked Best Practices About the Guest:Tim Herbig is a seasoned Product Management Coach and Consultant. He is passionate about helping product teams develop better practices to measure the progress of their decisions. Tim masterfully connects Strategy, OKRs, and Product Discovery. Tim has worked on solving hard business problems and driving user behaviors in diverse product contexts.Follow Our Guest:Website | LinkedIn | YouTubeFollow Dreams With Deadlines:Host | Company Website | Blog | Instagram | Twitter

On Today's Episode: You'd be hard-pressed to find someone in the solar industry who hasn't heard of, worked for, competed against, or just stood in awe at the company today's guest built w/his brother. Borrego Solar is an institution in the solar industry here in the USA alongside such names as REC Solar, SunPower, and SolarCity. The Hall brothers and their 3rd co-founder have unlocked something special that has created one of the most enduring (and prolific) solar companies of our time. And I finally got the opportunity to dig deep with Mike Hall, the 20+ yr CEO of this industry leader, to get the goods on just what makes this company tick and how they've had such staying power and dominance through the years. The clean energy industry is experiencing rapid growth and transformation, yet it faces significant challenges that must be overcome to reach its full potential. Two of the most pressing challenges include connecting renewable energy resources to the electric grid and dealing with inefficiencies in the procurement process for large-scale solar and battery projects. The complexity of interconnecting diverse energy sources, along with the lack of real-time data and analytical tools in procurement, creates barriers that hinder the industry's progress.Mike Hall, CEO and co-founder of Borrego Solar and Anza Renewables (which recently spun out of Borrego), has been at the forefront of tackling these challenges for more than 2 decades. With Borrego Solar, he has been instrumental in pioneering the residential, C&I and even community solar markets, especially in developing and financing projects and unlocking the value in virtual net metering projects in California and the Northeast. These innovative efforts have paved the way for connecting more renewable energy resources to the grid, the careers of thousands of #SolarWarriors, and countless prosperous former-employee-led startups along the way. Through Anza Renewables, Mike is addressing the procurement inefficiencies by aggregating real-time data from over 90% of the US market and utilizing advanced algorithms to optimize buyers' choices based on lifetime value rather than just upfront costs.By focusing on these two distinct challenges - Development & Product Delivery, Mike's leadership and innovation are helping to shape a more resilient and sustainable clean energy landscape. Through Borrego Solar, the connection of renewable energy resources to the grid has become more accessible, opening up new opportunities for growth in the community solar market. Meanwhile, Anza Renewables is transforming the way large-scale solar and battery projects are procured, making the process more efficient, transparent, and financially rewarding for buyers. Together, these efforts are not only solving immediate problems but also laying the groundwork for a more robust clean energy future.Listen in as Mike finally pulls back the veil on 20 years of solar industry entrepreneurship as leader of one of the most iconic and economically impactful companies of our time. His anecdotes of learning from sports, harnessing failure, and riding out the solarcoaster are both entertaining and instructive. If you want to connect with today's guest, you'll find links to his contact info in the show notes on the blog at https://mysuncast.com/suncast-episodes/.SunCast is presented by Sungrow, the world's most bankable inverter brand.You can learn more about all the sponsors who help make this show free for you at...

Alamelu Radhakrishnan reveals her best frameworks for making great decisions quickly without fear in one of our favorite ELC Annual sessions last year. She covers her threefold approach to knowing when to make a decision, pitfalls & anti-patterns to avoid throughout the decision-making process, and strategies for delegating and avoiding decision fatigue, all while working around fear of failure & empowering other decision-makers to act with confidence. Alamelu also shares some interesting decision-making concepts including first principles, the 40% to 70% guide, and more.Interested in topics like this, and beyond? #ELCAnnual2023 is happening 8/30 & 8/31! You can get your ticket to join your peers, check out all our speakers + explore additional topics at sfelc.com/annual2023ABOUT ALAMELU RADHAKRISHNANAlamelu is a technology leader, operator, and advisor with experience at scale and in high-growth environments across eCommerce, energy, and professional services. She's excited to start her next chapter with Homebase as VP, Engineering leading Product Delivery, helping small and medium businesses maximize their potential.Previously, she was Chief of Staff to the CTO at Shopify, supporting the Engineering organization, leading the teams responsible for building the systems, technology, and technical programs that power Shopify. Prior to Shopify, Alamelu has worked with some of Canada's most innovative product and consulting agencies, leading engineering and delivery teams and helping organizations leverage technology to create maximum impact.Alamelu finds joy in solving business problems through technology, strives for organizational excellence, and is passionate about supporting and sponsoring underrepresented folks in the industry. Alamelu lives in Toronto, and loves food, travel, the outdoors, and horror movies."Any decision you make is better than not making a decision. Most of the decisions that we make in our job are reversible decisions, but the time that we lose by not making a decisionis irreversible. The opportunity cost of that time, you're never gonna get that back.”- Alamelu Radhakrishnan   Join us at ELC Annual 2023!ELC Annual is our flagship conference for engineering leaders. You'll learn from experts in engineering and leadership, gain mentorship and support from like-minded professionals, expand your perspectives, build relationships across the tech industry, and leave with practical proven strategies.Join us this August 30-31 at the Fort Mason Center in San FranciscoFor tickets, head to https://sfelc.com/annual2023SHOW NOTES:Introducing Alamelu @ Shopify (2:54)Why we make decisions as eng leaders (4:11)Understand your top priority & its influence on decision-making (11:05)Common pitfalls & anti-patterns to avoid in the decision-making process (12:28)Tips for making a successful decision stick (16:20)Avoiding decision fatigue & learning to delegate when possible (19:51)Decision-making, autonomy, & navigating fear of failure (22:41)Audience Q&A: decision-making artifacts & organizational aspects (25:39)When your gut feeling contradicts your framework / decision (28:39)Examples of Alamelu's first principles concept (30:15)Approach for knowing if you're not delegating enough (31:49)Decision-making in remote environments (33:20)How to delegate without being perceived as disengaged (34:54)The “40% to 70%” guide & knowing when to make a decision (36:40)Alignment vs. consensus (37:32)Techniques for gaining team / individual buy-in (39:59)When you're on the receiving end of an overly abstracted problem (41:30)Strategies for empowering decision-makers as an eng leader (43:31)This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/

In this episode of The Fully booked Photographer, the Business Success Academy team discusses the importance of client satisfaction.   They emphasise that ‘fast' is the only thing that beats ‘free' in people's minds, and over-delivering is the key to success. The team also stresses the importance of setting expectations with clients on when they want their products delivered, especially during critical times such as birthdays and weddings. The team discusses the ultimate level of customer service, which includes understanding the pain points of the client and servicing them in line with their values. They highlight the power of creating moments and understanding your client avatar to build a successful photography business.   In this episode you will hear: How to ensure client satisfaction How to manage your client expectations during critical times The ultimate level of customer service and how can you align it with your client's values Why is it important to understand your client avatar What to do to create a great client experience   For more information about this episode head to https://discover.thefullybookedphotographer.com/sync9keystraining-713   The Fully Booked Photographer is the podcast that will help you grow your photography business by teaching you how to improve your marketing to get better leads, increase conversations with your ideal clients and generate more profit for your photo-based business, whether that is through eliminating the seasonality of your sessions or filling up the calendar of your studio. This show is brought to you by the industry experts from the Business Success Academy, Ronan Ryle - Board of Directors of the PPA, Professional Photographers Of America; Photography-marketing funnel specialist Jonathan Ryle; 7-figure entrepreneur, including a successful 3rd generation photography business, Bradley Bulmer; and published author and successful children's studio owner in Tampa Jeanine McLeod.   Tune in to this show for real-world experience, outside perspective, industry knowledge and mentorship that is usually only accessible to members of BSA's Photography Marketing Accelerator and listen to the business growth tactics that generate highly targeted leads and bookings for your photography brand.   Through this fun, educational, inspirational, innovative and high-energy show, The Fully Booked Photographer aims to share the mission of Creating A Healthier Society Through Photography.

At NAB Show in Las Vegas, we learned about a new capability for Filmic Pro: LUT (Look Up Table) support. Luke Vander Pol, Manager, Quality and Product Delivery, tales about how this will improve the already amazingly powerful and versatile iOS video app so that it is even more in line with professional video workflows.  This edition of MacVoices is supported by The MacVoices Slack. Available all Patrons of MacVoices. Sign up at Patreon.com/macvoices. Show Notes: Links: Guests: Support:      Become a MacVoices Patron on Patreon     http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:     http://macvoices.com      Twitter:     http://www.twitter.com/chuckjoiner     http://www.twitter.com/macvoices      Mastodon:     https://mastodon.cloud/@chuckjoiner      Facebook:     http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:     http://www.facebook.com/macvoices/      MacVoices Group on Facebook:     http://www.facebook.com/groups/macvoice      LinkedIn:     https://www.linkedin.com/in/chuckjoiner/      Instagram:     https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes     Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

At NAB Show in Las Vegas, we learned about a new capability for Filmic Pro: LUT (Look Up Table) support. Luke Vander Pol, Manager, Quality and Product Delivery, tales about how this will improve the already amazingly powerful and versatile iOS video app so that it is even more in line with professional video workflows. This edition of MacVoices is supported by The MacVoices Slack. Available all Patrons of MacVoices. Sign up at Patreon.com/macvoices. Show Notes: Links: Guests: Support:     Become a MacVoices Patron on Patreon     http://patreon.com/macvoices     Enjoy this episode? Make a one-time donation with PayPal Connect:     Web:     http://macvoices.com     Twitter:     http://www.twitter.com/chuckjoiner     http://www.twitter.com/macvoices     Mastodon:     https://mastodon.cloud/@chuckjoiner     Facebook:     http://www.facebook.com/chuck.joiner     MacVoices Page on Facebook:     http://www.facebook.com/macvoices/     MacVoices Group on Facebook:     http://www.facebook.com/groups/macvoice     LinkedIn:     https://www.linkedin.com/in/chuckjoiner/     Instagram:     https://www.instagram.com/chuckjoiner/ Subscribe:     Audio in iTunes     Video in iTunes     Subscribe manually via iTunes or any podcatcher:     Audio: http://www.macvoices.com/rss/macvoicesrss     Video: http://www.macvoices.com/rss/macvoicesvideorss

In today's fast-paced world, we often prioritize the "how" of building things - the methods, tools, and resources we use to deliver results. However, in our eagerness to achieve our goals, we sometimes lose sight of the "why" - the deeper purpose and meaning behind our actions. In this episode, we'll delve into the delicate balance between the "how" and "why" and explore how we can avoid overemphasizing the process at the expense of the value we're trying to deliver. So, join us as we discuss practical strategies for staying focused on our goals while honoring our actions' deeper purpose.Join Shawna Cullinan, Jörg Pietruszka,  Diana Larsen,  Sheila Eckert, Sheila McGrath, Hendrik Esser, Ray Arell, and all the callers to the monthly live event as we explore topics related to Agile.  For details on the next live event, please visit  acnpodcast.org.(00:00) Introduction(03:07)  Why vs. How(49:54) Wrap upThe Agile Coaching Network podcast is licensed under CC BY-NC-ND 4.0, and we rely on the support of our listeners to keep the ACN going. The ACN is made possible by the support of its listeners. To learn more about how to support the show, please visit acnpodcast.org. Support the show

Scrum is not a product management framework. But it can be tremendously valuable for product people: It can help you make the right product decisions and deliver great products if it's correctly applied. In this podcast episode, I share ten tips to help you maximise value delivery with Scrum.

In this episode, I talk with the CEO and founder of an organization that has been applying AI to help them develop products. Will AI help you develop your products faster? Come and see. Grant Hey, everybody, welcome to another episode of ClickAI Radio. So today I have this opportunity to speak with one of those brains out there in the market that's being disruptive, right? They're making changes in the industry in terms of not only the problems are solving, but it's the way in which they're solving the problems using AI very fascinating. Anyway, everyone, please welcome Paul Ortchanian here to the show. Paul Hi, nice. Nice, nice of you, happy to be here on the show.  Grant Absolutely. It's very good to have you here today. When I was first introduced to you. And I started to review your material what it is that your organization has put together as fascinated with the approach because I have a product development background and in in the software world. AI was late comer to that right meaning over generations when I saw the approach that you're taking to that I'm interested to dig more into that. But before we do that big reveal, could you maybe step back and talk about the beginning your journey? What got you on this route? And this map, both in terms of product development, and technology and AI itself? Paul Yeah, absolutely. So I started out as an engineer, headed down to San Francisco in the early 2000s. And, and I was more of a thinker than an actual engineer, or just be the type of guy who would figure things out by themselves. But if you were to ask me to really do things that the real things engineers do, you know, creativity was there, but not the solutioning. So being in San Francisco was a humbling experience, I guess, Silicon Valley, you get to see some really, really good engineers. So I had to make a shift in my career. And since I had a passion for user experience, the business aspect, product management was a great fit a function I didn't really understand. And I got to learn and respect, and did that for about 10 years.  In the mid 2000s, and 10s, I basically moved back to Montreal for family reasons and cost of living, of course in San Francisco. And I started a company called Bank Biddick, which in French stands for public bath. And the idea is that most what I realized in Canada was that people here in accelerators, incubators and, and startups just didn't understand what product management was. So they didn't really understand what they do and how they do it. And I saw a lot of organizations being led by the marketing teams, or the sales team and being very service oriented and not really product LED.  So basically, it basically stands for public bath, which means every quarter, you want to basically apply some hygiene to your roadmap, you have a galaxy of ideas, why not go out there and just, you know, take the good ones and remove the old ones and get rid of the dirt. And we started with that premise. And we put we said, well, what does a product manager do on a on a quarterly basis? Because a lot of the material you'll read out there really talks about, you know what product managers should do in terms of personas and understanding the customer's data and this and that, but nobody really tells you which order you should do it. Right. If that was my initial struggle as a product manager, do you try to do it all in the same day and then you realize that there's not enough time? So the question is like in a one quarter 12 week cycle, as my first three weeks should be about understanding the market shifts the industry, the product competitors and and the users and then maybe in the next three weeks working with leadership on making sure that there is no pivots in the organization or there are some some major strategic changes and then going into analyzing the DIS parking lot of ideas and figuring out which ones are short term and re and making business cases in order to present them for, for the company to make a decision on What to do next on the roadmap.  So there is a process and we just call that process SOAP, which goes in line with our public bath theme. So the idea was like, let's let's give product managers SOAP to basically wash their roadmap on a quarterly basis. And, and that's what being public does. And we work with over 40 organizations today so far, on really implementing this product LEDs process within their organizations, we work with their leaders on identifying a product manager within the organization and making sure that marketing support sales, the CFO CEO really understand how to engage with them what to expect from them, and how product manager can add value to to the organization. And so they just doesn't become, you know, this grace towards them as many features as you can pump out, right. Grant Oh, boy, yeah. Which, which is constant problem. The other thing that I've noticed, and I'm wondering if, and I'm sure that your SOAP methodology addresses this, it's the problem of shifting an organization in terams of their funding model, right? They'll come from sort of these project centric or service centric funding styles, and then you've got to help them through that shift to a different funding model round products. You guys address that as well. Paul Yeah, we address that a lot. One of the things we always tell them is if you are a service professional services firm, and you know, I have no issues basically calling them that. If and I asked them like do you quantify staff utilization in percentages, like 70% of our engineers are being billed? Right? Do we basically look at the sales team? How many new deals do they have in terms of pipeline? Are we looking at on time delivery across those, so double use that to serve the sales team closed? And what is our time and technical staff attrition, that usually tends to be identifiers of you being a service firm? And we often ask them, well, let's let's make the shift, when we identify one little initiative that you have that you want to productize because they all these service firms, really all they want is recurring revenue, then the service is tough, right?  That you constantly have to bring in new clients. So this recurring revenue, the path to recurring revenue is, you know, being able to say, Okay, I'm going to take two engineers, one sales person, one marketing person, one support person, and a product manager. And those guys collectively will cost me a million dollars a year, and I'm going to expect them to basically bring me $3 million in recurring revenue. That means that they're, they're no longer going to be evaluated on staff utilization, they're no longer going to be evaluating the number of deals they're bringing in. And they're, they're really going to be evaluated on how are they releasing features? Are they creating value for those features? are we increasing the number of paid customers? And are we basically, you know, staying abreast in terms of competitors and market industry changes.  And so that's a complete paradigm shift. And that transition takes a while. But the first seed is really being able to say, can you create an entity within your organization where the CFO accepts that those engineers are dedicated and no longer being, you know, reviewed in terms of their utilization rate in terms of their know how much they're billing to customers? Once they do that shift in the recipe is pretty easy to do. Grant Yeah. So it's become easy. So the thing to I've seen and experienced with, with product and product development is the relationship of innovation to product development. And so I see some groups will take innovation, and they'll move that as some separate activity or function in the organization, whereas others will have that innate within the product team itself. What have you found effective? And does self addressed that? Paul Yeah, I mean, we always ask them the question of what how are you going to defend yourself against the competition with the VCs that have to call their moat, right? And that defensibility could be innovation, it could also be your global footprint, or, you know, it could be how you operationalize your supply chain make things really, really cheap, right? Every company can have a different strategy. And we really ask them from the get go. We call this playing the strategy, we'll give them like eight potential ways a company can, you know, find strategies to differentiate themselves? And the first one is first the market?  And the question is, it's not about you being first to market today. But do you want to outpace your curlier closest rivals on a regular basis? And if so, you know, you need an r&d team and innovation team who is basically going to be pumping out commercializable features or r&d work. And then we always give him the two examples, the example of Dolby Dolby being completely analog in the 70s, but really banking on their r&d team to bring him to the digital age and from the digital age to set top boxes to Hollywood and now into Netflix compression, right?  So they basically put their R&D team as the leader to basically keep them a step ahead of their competition. But it but on the other hand, we also Welcome, you know, talk about Tesla, where Tesla is basically doing the same thing, but they're not doing it for intellectual property like Dolby, they're not suing anybody are actually open sourcing it. But there's a reason behind it where that open sourcing allows them to basically create the, you know, what we call the Betamax VHS issue, which is making sure that there's compatibility across car manufacturers for Tesla parts and overproduction of parts that are Tesla just to increase their supply chain, right? So we ask them, Do you want to be that company, if you don't want to be that company, then there's other ways for you to basically create defensibility, it could be regulatory compliance, if your industry requires it, you can go global, you can go cross industry, you can basically create customer logins, how just how SAP and Salesforce love to basically just integrate workflows with like boots on the ground, professional services certified teams, right?  And or you can basically review your process and make sure just like Amazon, that you're creating robots to do human work in order to just basically do it cheaper than anybody else. So there's ways of doing it. And I would say that if you were in AI space, especially, you know, it's important to make sure that, you know, are you really trying to innovate through AI, because you can get a lot of researchers doing a lot of things, but that's not really going to help you create commercializable ideas. So from the get go, the leadership team needs to, you know, at least make a hedge a bet on, you know, expansion, innovation, or creating efficiencies and just, you know, decide and let the product management team know in which direction they're gonna go planning on going for the next six years. Please. Grant I love your last comment there, Paul about about getting the leadership team involved. It seems that many times in organizations, this challenge of making the change sticky, right, making it last making it resonate, where people truly change their operating model, right, they're going to start operating in a different way, their roles and responsibilities change, what is the order in which things get done all of those change, when they start moving both into this AI space, but you know, product driven just by itself, even without AI has its own set of challenges? So here's the question I have for you. As you move companies through this transformation, that's part of your business, right? You are transforming the way companies operate and bring about better outcomes. How do you make those changes sticky? Because this is a cultural change? What is it you guys have found it's effective? Paul Or it goes back to our name public bath and SOAP, right? Because the idea is, you take a bath on a regular basis hygiene is something you do regularly, right? So we ask these organization, if we give you a process where you know exactly what the product management team is going to do with you with the leadership team in order to prioritize your next upcoming features, then can you do it in a cyclical way, every quarter, you need the product manager do the exact same process of revisiting the competitors, the industry, the market, as well as like the problems that you have with your premature customers, bringing it back to the organization, asking if the strategy is still about expansion, innovation, efficiencies, identifying new ideas, clearing up the parking lot of bad ideas, etc, and eventually making the business case for the new features in order for them to make a commitment. So if we do this in a cyclical way, then the product role becomes the role of what I'd like to call the CRO, which is the chief repeating officer, because all the product manager is doing is repeating that strategy and questioning the CEO, are we still on? Are we pivoting or if we pivot?  What does that mean? And if you're doing it on a three month basis, what that allows your company to do is to make sure that the marketing and sales and support team are going along with what the engineering team is going to be delivering. So this is what I usually see most product organization where a decision has been made that the engineers are going to be building a particular feature, the sales and marketing team just waits for the engineers to be Code Complete. And once a code completes, done, they're like, Okay, now we're gonna promote it. But my question is that it's too late. Right? You really need so I always show the talk about Apple, how Apple would basically go out in front of millions of people and just say, here's the new iPhone 13. And we came up with a new version of Safari, and we're updating our iOS and we're doing a 40 Other changes. And the next thing you want considered an Apple store and you know, everything has changed. The marketing has changed the guys that the doing the conferences, and the lectures and the training are all talking about the new supplier, the new iPhone, and you ask yourself, How did how did Apple know and to organize the marketing support and sales team in that in such a way that the day that the announcement has been done? Everything is changed. So that means that it's not just the engineering team's responsibility to get to Code Complete.  It is a collective responsibility where marketing support and sales are also preparing for the upcoming releases. And and the only way you can get that type of alignment is If every three months these these parties, technology, product, CEO, CFO, sales, marketing and support can get together and make a clear decision on what they're going to do, and be honest enough of what they're not going to do, and then work collectively together on making sure that that those are being delivered and prepared in terms of the size of the promotion that we're going to do, and how are we going to outreach how's the sales collateral going to change? How is the support team going to support these upcoming features. And so everybody has work to do in that three months timeframes. So and then that if we can get to that cyclical elements, I think most companies can create momentum. And once that momentum has is generating small increments of value to the customers, then you base start start building, what I like to call reputational capital, with the clients, with the customers with the prospects. And eventually anything you release the love, and everything you release adds value. And eventually everybody loves everything you're doing as an organization become that, you know, big unicorn that people want to be. Grant Yeah, so the net of that is, I believe what you said as you operationalize it. Now there's it gets integrated into everyone's role and responsibility. It's this enterprise level cross functional alignment that gets on a campus. And the cadence is, in your case, you'd mentioned quarterly, quarterly sounds like that's been a real real gem for you. I've seen some organizations do that in shorter timeframes and some much longer. It sounds like yeah, at least quarterly is that a good nugget that you find there?  Paul Yeah, quarterly works, because you know, markets are set in a quarter way they operate in that way the you want results on a quarterly basis in terms of sales in terms of engagement, etc. But what's important is that which you know, a lot of engineering teams like to work agile or Kanban. And in a quarter in a 12 week timeframe, you could fit, I'd say, Let's see your Sprint's are three weeks, you could fit for sprint for three weeks variance, or you could fit six 2-week sprints. But I feel that if you were to shorten it, then the marketing team and sales teams supporting might not have enough time to prepare themselves for Code Complete, the engineers might be able to deliver but then the product manager gets overwhelmed because doing an industry research, competitor research etc. Every, say month and a half or two months just becomes overwhelming for them. Because things don't change enough in two months for them to be able to say, Oh, look, this competitor just came up with that. And now we need so so I think three months is enough time for the world to change for, you know, country to go to war for COVID to come over and just destroy everything. So pivot decisions are usually can pretty good to do on a on a quarterly basis.  Grant Yeah, that's good. That's, I think COVID follow that rule. Right. Hey, I have a question for you around AI. So how are you leveraging AI in the midst of all this? Can you talk about that? Paul Yeah, absolutely. So what we noticed is a lot of organizations who have products, so SaaS products, or any type of product, IoT products, etc, they're generating data. I mean, it's it comes hand in hand with software development. So all that data is going into these databases are and nobody knows what to do with them. And eventually, you know, they want to start creating business intelligence, and from business intelligence, AI initiatives have just come about, it's very normal to say, You know what, with all this data, if we were to train a machine learning module, we would be able to recommend the best flight price or the best time for somebody to buy a flight, because we have enough data to do it. So so we're not working with AI first organizations who are here we have, our entire product is going to be around AI, we're just trying to work with organizations that have enough data to warrant 1-2-3, or four AI initiatives and an ongoing investment into those. So the best example I like to talk about is the Google Gmail suggestive, replies, right, which is adding value to the user needs AI in the back, end a lot of data.  But ultimately, it's not that Gmail isn't AI product, it simply has AI features in it. So and when organizations start identifying AI or machine learning, predictive elements to their product, then we go from engineering being a deterministic function, which is if we were to deliver this feature, then customers will be able to do that to a probabilistic function where Let's experiment and see what the data can give us. And if this algorithm ends up really nailing it, we will achieve this result. But if it doesn't, then do we release it? Do we not release it?  What's the and then it gets a little bit hairy because product managers just lose themselves into it. Oftentimes, they'll release a feature and the sales team would just ask them to pull it out right away because it has not met the expectations of a customer or two. And ultimately, like what we ask product managers to do is work with leadership on really it Identifying a few key elements that are very, very important to just just baseline before you were to begin an AI project. And those are pretty simple. It's, it's really like, are you trying to create to have the machine learning module? Make a prediction? Are you or are you trying for it to make a prediction plus pass judgment? Are you trying to make it a prediction, a judgment and take action? Right? Decision automation, which is what you know, self driving cars do, will will see biker, they will make a prediction that it's a biker will make a judgment that it's indeed a biker, and we'll take action to avoid the biker, right?  But when you when you're creating ml projects, you can easily say, you know, we're just going to keep it to prediction, right? Like this machine is going to predict something and then a human will make judgment and the human will take action. There's nothing wrong in doing that. So just setting the expectations for from the get go in terms of are we basically going to predict judge or take action? That's number one. And then the next question is whatever that we decide if it's just prediction, is that worth guessing? And who doesn't have guessed today, if it's a human? Is that how accurate is that human? Let's quantify. So this way we can compare it against what this machine is going to do? What is the value the company gets out of that gas being the right gas? And what's the cost of getting it wrong? So oftentimes, we forget that humans to get it wrong to and if humans get it wrong, there are huge consequences to organizations that will overlook but as soon as machine learning does the same thing, we're ready to just cancel hundreds of $1,000 of investment.  Grant Yeah, that's right. Yeah, we tossed it out. So the use case, I'm assuming would be you would leverage AI to say enhance a product managers abilities to either predict outcomes of some product development activities, or releases or things like that, would that be a kind of use case where he looked apply? Paul Well, not a product managers, I would say the product manager, we'd look at it software, let's take the software of a website that tries to predict your if people qualify for a mortgage loan, for example, right? So you have enough data at that point to be able to automate, what's the underwriting process that humans do of validating whether or not somebody's eligible for loan? Well, we could take all that data and just make a prediction of that person's fit for a particular loan. Now, if we were to say, well, it's just going to be the prediction, but we're not going to give this person the loan, we're still going to ask a human being to pass judgment that that prediction was the correct one, and then take action to give or not give him a loan.  So let's say that's the machine learning module, we're going to add to our to our feature. Now, the question is how this underwriting department in the past 10 years, how often did they really screw up that, you know, and issued loans to people that were that couldn't pay their loan, right? And realize it's 40%? Were like, Wow, 40%? Could this machine learning be as accurate as damn plus one, right? And, and then we ended up realizing that yeah, this, whatever we delivered is 33% accurate, and not 40% plus one accurate now is it still worth putting out there we spent $100,000 into it, and then you know, then it's up to the product manager to basically be able to put this thing in place and say, but look, you know, underwriting is a nine to five job currently in our business, and it cost us this much money.  On the other hand, if there's this machine learning is 33% accurate, but it's actually doing it 24/7 365 days a year, and it's only going to improve from 33 to 40. And if it goes above 40, then we the savings for our organization are this much money. So it is really the product managers job to be able to not only talking about the business KPIs, but also the what the AI machine learning KPIs we need to achieve and what the impact of that would be if we get it right. And I think that the biggest issue we have as product managers in the AI space is if we were to go and do this all there everything that we need to create AI, like the day data ops, selecting the data, sourcing it, synthesizing it, cleaning it, etc. The model ops, which, you know, comes down to multiple algorithms, training those algorithms, evaluating tuning them, and then the operationalization. If you do all these steps, and you get to 80 to 20% accuracy, and your target is at 70% accuracy, right? What do you do with it?  Because you had to do all this work anyways, it cost you tons of money and time. And so how do we get the leadership team to say this AI initiative has enough value for us that we're willing to live with the consequences of it getting it wrong, or we're willing to actually have it supported by human for the next six months to a year until we basically trains itself and gets better? So it's how do you get this openness from from from a leadership team? Because what I've often find delivering AI projects is every time you deliver an AI project, and it's misunderstood in terms of its output, and everybody thinks it has to be 100% accurate, the second and goes wrong. It's the political drama that you have to go through in order to keep it alive. is just it's just overwhelming, right? So miners will set those expectations up front and tool, the product managers with the right arguments to make sure that they the expectations are set correctly. Grant Have you ever worked with or heard of the company called digital.ai? Are your familiar with them? digital.ai, maybe not. Anyway, they have been working in a similar space as you but not so much of the product management level. What they're doing, though, is they're, they're looking to apply AI to the whole delivery function. So so you can you see, the product manager is above this, and is making sort of these KPIs and other estimate activities and the planning out. But then there are all these functions under there that of course, do the delivery of the product. And so they're working on the tooling spectrum, I think they acquired I think, was five different companies like in the last nine months, that they're integrating these and then building this AI seam or layer across that data across delivery with that purpose and intent to do that predictive not not only backwards analysis activities around AI, but predictive, which is what's the probabilities, I might run into the problem, or some problem with this particular release, right, of this product, right, that we're about to send out, now might be an interesting group for you to get connected with. Paul Yeah, I know, it's funny, because we're there. There's a local company here in Montreal that does the same thing. It's really about like data scientists are really expensive, and they're really hard to find, and there's a shortage of them. So, you know, the lot of organizations are trying to find like a self serve AI solution where you can build your AI using their AI. But ultimately, what they're doing is taking your data and delivering 123 or 10 versions of the machine learning module, it's up to you basically, judge which one is going to work the best for you, but they actually operationalize it, put it out there for you, and really automate the whole thing. So this way, you're not dependent on humans, I love that I really love that I think your organization should have one of those. But that still means that there's a dependency from the for the product manager to know that it's, it's data, like end to end, be able to clean it be able to tag it and then feed it to the to these machines, right? And I think that part is also misunderstood. Because Do we have enough data? Is there bias in the data and all that needs to be understood and figure it out? Because, you know, you could say like, Hey, we put it to this big machine. And we ended up with a 20% accuracy on the best ml that it out, put it, but that's still not good enough? Because we're trying, we're aiming for 87? And what does it mean? What do we need to do to basically get it to 87? We're gonna have to review the data bringing some third party data, you know, and it's, and that's, that costs a lot as well. So, yeah, Grant Do you think AutoML solutions play a role here like, Aible, I don't know if you're familiar with that platform, you know, that the goal is to try to reduce the amount of dependency that's needed on the data science. Scientists themselves, right. And but it's, it's still doesn't remove all of the data cleansing part, but it does help take care of some of the certainly the low level data science requirements, you think you think that's a viable solution in this area?  Paul I think it is. I mean, it's, you know, we went from rule based AI, where data scientists had to do good old fashioned AI, which was a feature engineering, right? Putting the rules themselves to machine learning AI, where, you know, we had to train the data that we needed, were so dependent on these data scientists. And now we're getting to v3, where we have these tools. And you know, there's a data dependency, but there, they also don't have such a high dependency on data scientists are and you know, figuring our algorithms and etc, we could just basically have these prepackaged algorithms that could basically output us any types of solution. What I tend to like, I've seen this a lot in a lot of companies. There's some companies that are very, very industry specific, right? So they're providing AI for E-commerce to be able to provide better search with predictive elements based on the person's browsing history. I mean, I, I'm not sure, but the ones that are providing every ML imaginable, so you could use it for supply chain, or you could use it for something else. I know it's dependent on data. But again, these algorithms, you can't have all the algorithms for all scenarios.  Even if it's supply chain, some person has perishables and there's ordering bananas and the other person is ordering, I don't know water coolers, and those, those don't have the same rules, right. You know, so it's, it's important to just, I think that maybe in the coming years, we'll have a lot of companies that are really going cross industry, just like we're in E-commerce, the other ones that are med tech, the other ones are, etcetera, the tools are the same. I mean, more or less the same, the customers are gonna get used to basically having these UI is that I'll give you your input the data in and then these emails come out, and then you choose which one and they give you probability you can retrain them and all that stuff. And I think that it's just going to get to a point where we're going to have these product managers who are now responsible of kind of training the Machine Learning Module themselves, you know if it's going to be the product manager, or if it's going to be some other function, where I think it does definitely fit inside the product managers? Grant Well I do is, I think it's because they still need to have what we would call the domain knowledge and in this domain of building products, yeah, AI, at least at least in this phase of the life of AI, where we are today for the foreseeable future. I think the product manager needs to be involved with that. Sure. So. Paul It comes down to intuition, right, somebody has to have like to build that intuition about what a model is relying on when making a judgment. And I think that, you know, with product managers, the closest one really, maybe in bigger organizations, it's the person who's managing analytics and data, but in smaller startup organization, I can definitely see the product manager putting that  Grant Yeah, absolutely. Paul, I really appreciate you taking the time. Here today on this been fascinating conversation. Any last comments you want to share? Paul We have tons of articles that talk about so we're very open source as an organization. So if you want to learn more about this, we have about 70 articles on our website. Just go to BainPublic.com and just click on "Articles" and you could just, you know, self serve and basically improve as a product manager in the AI space. Grant Excellent, fascinating, love, love the conversation, your insight and the vision where you guys are taking this I think you're gonna continue to disrupt everyone. Thanks for joining another episode of ClickAI Radio and until next time, check out BainPublic.com. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook visit ClickAIRadio.com now.  

In this episode, I talk with the CEO and founder of an organization that has been applying AI to help them develop products. Will AI help you develop your products faster? Come and see. Grant Hey, everybody, welcome to another episode of ClickAI Radio. So today I have this opportunity to speak with one of those brains out there in the market that's being disruptive, right? They're making changes in the industry in terms of not only the problems are solving, but it's the way in which they're solving the problems using AI very fascinating. Anyway, everyone, please welcome Paul Ortchanian here to the show. Paul Hi, nice. Nice, nice of you, happy to be here on the show.  Grant Absolutely. It's very good to have you here today. When I was first introduced to you. And I started to review your material what it is that your organization has put together as fascinated with the approach because I have a product development background and in in the software world. AI was late comer to that right meaning over generations when I saw the approach that you're taking to that I'm interested to dig more into that. But before we do that big reveal, could you maybe step back and talk about the beginning your journey? What got you on this route? And this map, both in terms of product development, and technology and AI itself? Paul Yeah, absolutely. So I started out as an engineer, headed down to San Francisco in the early 2000s. And, and I was more of a thinker than an actual engineer, or just be the type of guy who would figure things out by themselves. But if you were to ask me to really do things that the real things engineers do, you know, creativity was there, but not the solutioning. So being in San Francisco was a humbling experience, I guess, Silicon Valley, you get to see some really, really good engineers. So I had to make a shift in my career. And since I had a passion for user experience, the business aspect, product management was a great fit a function I didn't really understand. And I got to learn and respect, and did that for about 10 years.  In the mid 2000s, and 10s, I basically moved back to Montreal for family reasons and cost of living, of course in San Francisco. And I started a company called Bank Biddick, which in French stands for public bath. And the idea is that most what I realized in Canada was that people here in accelerators, incubators and, and startups just didn't understand what product management was. So they didn't really understand what they do and how they do it. And I saw a lot of organizations being led by the marketing teams, or the sales team and being very service oriented and not really product LED.  So basically, it basically stands for public bath, which means every quarter, you want to basically apply some hygiene to your roadmap, you have a galaxy of ideas, why not go out there and just, you know, take the good ones and remove the old ones and get rid of the dirt. And we started with that premise. And we put we said, well, what does a product manager do on a on a quarterly basis? Because a lot of the material you'll read out there really talks about, you know what product managers should do in terms of personas and understanding the customer's data and this and that, but nobody really tells you which order you should do it. Right. If that was my initial struggle as a product manager, do you try to do it all in the same day and then you realize that there's not enough time? So the question is like in a one quarter 12 week cycle, as my first three weeks should be about understanding the market shifts the industry, the product competitors and and the users and then maybe in the next three weeks working with leadership on making sure that there is no pivots in the organization or there are some some major strategic changes and then going into analyzing the DIS parking lot of ideas and figuring out which ones are short term and re and making business cases in order to present them for, for the company to make a decision on What to do next on the roadmap.  So there is a process and we just call that process SOAP, which goes in line with our public bath theme. So the idea was like, let's let's give product managers SOAP to basically wash their roadmap on a quarterly basis. And, and that's what being public does. And we work with over 40 organizations today so far, on really implementing this product LEDs process within their organizations, we work with their leaders on identifying a product manager within the organization and making sure that marketing support sales, the CFO CEO really understand how to engage with them what to expect from them, and how product manager can add value to to the organization. And so they just doesn't become, you know, this grace towards them as many features as you can pump out, right. Grant Oh, boy, yeah. Which, which is constant problem. The other thing that I've noticed, and I'm wondering if, and I'm sure that your SOAP methodology addresses this, it's the problem of shifting an organization in terams of their funding model, right? They'll come from sort of these project centric or service centric funding styles, and then you've got to help them through that shift to a different funding model round products. You guys address that as well. Paul Yeah, we address that a lot. One of the things we always tell them is if you are a service professional services firm, and you know, I have no issues basically calling them that. If and I asked them like do you quantify staff utilization in percentages, like 70% of our engineers are being billed? Right? Do we basically look at the sales team? How many new deals do they have in terms of pipeline? Are we looking at on time delivery across those, so double use that to serve the sales team closed? And what is our time and technical staff attrition, that usually tends to be identifiers of you being a service firm? And we often ask them, well, let's let's make the shift, when we identify one little initiative that you have that you want to productize because they all these service firms, really all they want is recurring revenue, then the service is tough, right?  That you constantly have to bring in new clients. So this recurring revenue, the path to recurring revenue is, you know, being able to say, Okay, I'm going to take two engineers, one sales person, one marketing person, one support person, and a product manager. And those guys collectively will cost me a million dollars a year, and I'm going to expect them to basically bring me $3 million in recurring revenue. That means that they're, they're no longer going to be evaluated on staff utilization, they're no longer going to be evaluating the number of deals they're bringing in. And they're, they're really going to be evaluated on how are they releasing features? Are they creating value for those features? are we increasing the number of paid customers? And are we basically, you know, staying abreast in terms of competitors and market industry changes.  And so that's a complete paradigm shift. And that transition takes a while. But the first seed is really being able to say, can you create an entity within your organization where the CFO accepts that those engineers are dedicated and no longer being, you know, reviewed in terms of their utilization rate in terms of their know how much they're billing to customers? Once they do that shift in the recipe is pretty easy to do. Grant Yeah. So it's become easy. So the thing to I've seen and experienced with, with product and product development is the relationship of innovation to product development. And so I see some groups will take innovation, and they'll move that as some separate activity or function in the organization, whereas others will have that innate within the product team itself. What have you found effective? And does self addressed that? Paul Yeah, I mean, we always ask them the question of what how are you going to defend yourself against the competition with the VCs that have to call their moat, right? And that defensibility could be innovation, it could also be your global footprint, or, you know, it could be how you operationalize your supply chain make things really, really cheap, right? Every company can have a different strategy. And we really ask them from the get go. We call this playing the strategy, we'll give them like eight potential ways a company can, you know, find strategies to differentiate themselves? And the first one is first the market?  And the question is, it's not about you being first to market today. But do you want to outpace your curlier closest rivals on a regular basis? And if so, you know, you need an r&d team and innovation team who is basically going to be pumping out commercializable features or r&d work. And then we always give him the two examples, the example of Dolby Dolby being completely analog in the 70s, but really banking on their r&d team to bring him to the digital age and from the digital age to set top boxes to Hollywood and now into Netflix compression, right?  So they basically put their R&D team as the leader to basically keep them a step ahead of their competition. But it but on the other hand, we also Welcome, you know, talk about Tesla, where Tesla is basically doing the same thing, but they're not doing it for intellectual property like Dolby, they're not suing anybody are actually open sourcing it. But there's a reason behind it where that open sourcing allows them to basically create the, you know, what we call the Betamax VHS issue, which is making sure that there's compatibility across car manufacturers for Tesla parts and overproduction of parts that are Tesla just to increase their supply chain, right? So we ask them, Do you want to be that company, if you don't want to be that company, then there's other ways for you to basically create defensibility, it could be regulatory compliance, if your industry requires it, you can go global, you can go cross industry, you can basically create customer logins, how just how SAP and Salesforce love to basically just integrate workflows with like boots on the ground, professional services certified teams, right?  And or you can basically review your process and make sure just like Amazon, that you're creating robots to do human work in order to just basically do it cheaper than anybody else. So there's ways of doing it. And I would say that if you were in AI space, especially, you know, it's important to make sure that, you know, are you really trying to innovate through AI, because you can get a lot of researchers doing a lot of things, but that's not really going to help you create commercializable ideas. So from the get go, the leadership team needs to, you know, at least make a hedge a bet on, you know, expansion, innovation, or creating efficiencies and just, you know, decide and let the product management team know in which direction they're gonna go planning on going for the next six years. Please. Grant I love your last comment there, Paul about about getting the leadership team involved. It seems that many times in organizations, this challenge of making the change sticky, right, making it last making it resonate, where people truly change their operating model, right, they're going to start operating in a different way, their roles and responsibilities change, what is the order in which things get done all of those change, when they start moving both into this AI space, but you know, product driven just by itself, even without AI has its own set of challenges? So here's the question I have for you. As you move companies through this transformation, that's part of your business, right? You are transforming the way companies operate and bring about better outcomes. How do you make those changes sticky? Because this is a cultural change? What is it you guys have found it's effective? Paul Or it goes back to our name public bath and SOAP, right? Because the idea is, you take a bath on a regular basis hygiene is something you do regularly, right? So we ask these organization, if we give you a process where you know exactly what the product management team is going to do with you with the leadership team in order to prioritize your next upcoming features, then can you do it in a cyclical way, every quarter, you need the product manager do the exact same process of revisiting the competitors, the industry, the market, as well as like the problems that you have with your premature customers, bringing it back to the organization, asking if the strategy is still about expansion, innovation, efficiencies, identifying new ideas, clearing up the parking lot of bad ideas, etc, and eventually making the business case for the new features in order for them to make a commitment. So if we do this in a cyclical way, then the product role becomes the role of what I'd like to call the CRO, which is the chief repeating officer, because all the product manager is doing is repeating that strategy and questioning the CEO, are we still on? Are we pivoting or if we pivot?  What does that mean? And if you're doing it on a three month basis, what that allows your company to do is to make sure that the marketing and sales and support team are going along with what the engineering team is going to be delivering. So this is what I usually see most product organization where a decision has been made that the engineers are going to be building a particular feature, the sales and marketing team just waits for the engineers to be Code Complete. And once a code completes, done, they're like, Okay, now we're gonna promote it. But my question is that it's too late. Right? You really need so I always show the talk about Apple, how Apple would basically go out in front of millions of people and just say, here's the new iPhone 13. And we came up with a new version of Safari, and we're updating our iOS and we're doing a 40 Other changes. And the next thing you want considered an Apple store and you know, everything has changed. The marketing has changed the guys that the doing the conferences, and the lectures and the training are all talking about the new supplier, the new iPhone, and you ask yourself, How did how did Apple know and to organize the marketing support and sales team in that in such a way that the day that the announcement has been done? Everything is changed. So that means that it's not just the engineering team's responsibility to get to Code Complete.  It is a collective responsibility where marketing support and sales are also preparing for the upcoming releases. And and the only way you can get that type of alignment is If every three months these these parties, technology, product, CEO, CFO, sales, marketing and support can get together and make a clear decision on what they're going to do, and be honest enough of what they're not going to do, and then work collectively together on making sure that that those are being delivered and prepared in terms of the size of the promotion that we're going to do, and how are we going to outreach how's the sales collateral going to change? How is the support team going to support these upcoming features. And so everybody has work to do in that three months timeframes. So and then that if we can get to that cyclical elements, I think most companies can create momentum. And once that momentum has is generating small increments of value to the customers, then you base start start building, what I like to call reputational capital, with the clients, with the customers with the prospects. And eventually anything you release the love, and everything you release adds value. And eventually everybody loves everything you're doing as an organization become that, you know, big unicorn that people want to be. Grant Yeah, so the net of that is, I believe what you said as you operationalize it. Now there's it gets integrated into everyone's role and responsibility. It's this enterprise level cross functional alignment that gets on a campus. And the cadence is, in your case, you'd mentioned quarterly, quarterly sounds like that's been a real real gem for you. I've seen some organizations do that in shorter timeframes and some much longer. It sounds like yeah, at least quarterly is that a good nugget that you find there?  Paul Yeah, quarterly works, because you know, markets are set in a quarter way they operate in that way the you want results on a quarterly basis in terms of sales in terms of engagement, etc. But what's important is that which you know, a lot of engineering teams like to work agile or Kanban. And in a quarter in a 12 week timeframe, you could fit, I'd say, Let's see your Sprint's are three weeks, you could fit for sprint for three weeks variance, or you could fit six 2-week sprints. But I feel that if you were to shorten it, then the marketing team and sales teams supporting might not have enough time to prepare themselves for Code Complete, the engineers might be able to deliver but then the product manager gets overwhelmed because doing an industry research, competitor research etc. Every, say month and a half or two months just becomes overwhelming for them. Because things don't change enough in two months for them to be able to say, Oh, look, this competitor just came up with that. And now we need so so I think three months is enough time for the world to change for, you know, country to go to war for COVID to come over and just destroy everything. So pivot decisions are usually can pretty good to do on a on a quarterly basis.  Grant Yeah, that's good. That's, I think COVID follow that rule. Right. Hey, I have a question for you around AI. So how are you leveraging AI in the midst of all this? Can you talk about that? Paul Yeah, absolutely. So what we noticed is a lot of organizations who have products, so SaaS products, or any type of product, IoT products, etc, they're generating data. I mean, it's it comes hand in hand with software development. So all that data is going into these databases are and nobody knows what to do with them. And eventually, you know, they want to start creating business intelligence, and from business intelligence, AI initiatives have just come about, it's very normal to say, You know what, with all this data, if we were to train a machine learning module, we would be able to recommend the best flight price or the best time for somebody to buy a flight, because we have enough data to do it. So so we're not working with AI first organizations who are here we have, our entire product is going to be around AI, we're just trying to work with organizations that have enough data to warrant 1-2-3, or four AI initiatives and an ongoing investment into those. So the best example I like to talk about is the Google Gmail suggestive, replies, right, which is adding value to the user needs AI in the back, end a lot of data.  But ultimately, it's not that Gmail isn't AI product, it simply has AI features in it. So and when organizations start identifying AI or machine learning, predictive elements to their product, then we go from engineering being a deterministic function, which is if we were to deliver this feature, then customers will be able to do that to a probabilistic function where Let's experiment and see what the data can give us. And if this algorithm ends up really nailing it, we will achieve this result. But if it doesn't, then do we release it? Do we not release it?  What's the and then it gets a little bit hairy because product managers just lose themselves into it. Oftentimes, they'll release a feature and the sales team would just ask them to pull it out right away because it has not met the expectations of a customer or two. And ultimately, like what we ask product managers to do is work with leadership on really it Identifying a few key elements that are very, very important to just just baseline before you were to begin an AI project. And those are pretty simple. It's, it's really like, are you trying to create to have the machine learning module? Make a prediction? Are you or are you trying for it to make a prediction plus pass judgment? Are you trying to make it a prediction, a judgment and take action? Right? Decision automation, which is what you know, self driving cars do, will will see biker, they will make a prediction that it's a biker will make a judgment that it's indeed a biker, and we'll take action to avoid the biker, right?  But when you when you're creating ml projects, you can easily say, you know, we're just going to keep it to prediction, right? Like this machine is going to predict something and then a human will make judgment and the human will take action. There's nothing wrong in doing that. So just setting the expectations for from the get go in terms of are we basically going to predict judge or take action? That's number one. And then the next question is whatever that we decide if it's just prediction, is that worth guessing? And who doesn't have guessed today, if it's a human? Is that how accurate is that human? Let's quantify. So this way we can compare it against what this machine is going to do? What is the value the company gets out of that gas being the right gas? And what's the cost of getting it wrong? So oftentimes, we forget that humans to get it wrong to and if humans get it wrong, there are huge consequences to organizations that will overlook but as soon as machine learning does the same thing, we're ready to just cancel hundreds of $1,000 of investment.  Grant Yeah, that's right. Yeah, we tossed it out. So the use case, I'm assuming would be you would leverage AI to say enhance a product managers abilities to either predict outcomes of some product development activities, or releases or things like that, would that be a kind of use case where he looked apply? Paul Well, not a product managers, I would say the product manager, we'd look at it software, let's take the software of a website that tries to predict your if people qualify for a mortgage loan, for example, right? So you have enough data at that point to be able to automate, what's the underwriting process that humans do of validating whether or not somebody's eligible for loan? Well, we could take all that data and just make a prediction of that person's fit for a particular loan. Now, if we were to say, well, it's just going to be the prediction, but we're not going to give this person the loan, we're still going to ask a human being to pass judgment that that prediction was the correct one, and then take action to give or not give him a loan.  So let's say that's the machine learning module, we're going to add to our to our feature. Now, the question is how this underwriting department in the past 10 years, how often did they really screw up that, you know, and issued loans to people that were that couldn't pay their loan, right? And realize it's 40%? Were like, Wow, 40%? Could this machine learning be as accurate as damn plus one, right? And, and then we ended up realizing that yeah, this, whatever we delivered is 33% accurate, and not 40% plus one accurate now is it still worth putting out there we spent $100,000 into it, and then you know, then it's up to the product manager to basically be able to put this thing in place and say, but look, you know, underwriting is a nine to five job currently in our business, and it cost us this much money.  On the other hand, if there's this machine learning is 33% accurate, but it's actually doing it 24/7 365 days a year, and it's only going to improve from 33 to 40. And if it goes above 40, then we the savings for our organization are this much money. So it is really the product managers job to be able to not only talking about the business KPIs, but also the what the AI machine learning KPIs we need to achieve and what the impact of that would be if we get it right. And I think that the biggest issue we have as product managers in the AI space is if we were to go and do this all there everything that we need to create AI, like the day data ops, selecting the data, sourcing it, synthesizing it, cleaning it, etc. The model ops, which, you know, comes down to multiple algorithms, training those algorithms, evaluating tuning them, and then the operationalization. If you do all these steps, and you get to 80 to 20% accuracy, and your target is at 70% accuracy, right? What do you do with it?  Because you had to do all this work anyways, it cost you tons of money and time. And so how do we get the leadership team to say this AI initiative has enough value for us that we're willing to live with the consequences of it getting it wrong, or we're willing to actually have it supported by human for the next six months to a year until we basically trains itself and gets better? So it's how do you get this openness from from from a leadership team? Because what I've often find delivering AI projects is every time you deliver an AI project, and it's misunderstood in terms of its output, and everybody thinks it has to be 100% accurate, the second and goes wrong. It's the political drama that you have to go through in order to keep it alive. is just it's just overwhelming, right? So miners will set those expectations up front and tool, the product managers with the right arguments to make sure that they the expectations are set correctly. Grant Have you ever worked with or heard of the company called digital.ai? Are your familiar with them? digital.ai, maybe not. Anyway, they have been working in a similar space as you but not so much of the product management level. What they're doing, though, is they're, they're looking to apply AI to the whole delivery function. So so you can you see, the product manager is above this, and is making sort of these KPIs and other estimate activities and the planning out. But then there are all these functions under there that of course, do the delivery of the product. And so they're working on the tooling spectrum, I think they acquired I think, was five different companies like in the last nine months, that they're integrating these and then building this AI seam or layer across that data across delivery with that purpose and intent to do that predictive not not only backwards analysis activities around AI, but predictive, which is what's the probabilities, I might run into the problem, or some problem with this particular release, right, of this product, right, that we're about to send out, now might be an interesting group for you to get connected with. Paul Yeah, I know, it's funny, because we're there. There's a local company here in Montreal that does the same thing. It's really about like data scientists are really expensive, and they're really hard to find, and there's a shortage of them. So, you know, the lot of organizations are trying to find like a self serve AI solution where you can build your AI using their AI. But ultimately, what they're doing is taking your data and delivering 123 or 10 versions of the machine learning module, it's up to you basically, judge which one is going to work the best for you, but they actually operationalize it, put it out there for you, and really automate the whole thing. So this way, you're not dependent on humans, I love that I really love that I think your organization should have one of those. But that still means that there's a dependency from the for the product manager to know that it's, it's data, like end to end, be able to clean it be able to tag it and then feed it to the to these machines, right? And I think that part is also misunderstood. Because Do we have enough data? Is there bias in the data and all that needs to be understood and figure it out? Because, you know, you could say like, Hey, we put it to this big machine. And we ended up with a 20% accuracy on the best ml that it out, put it, but that's still not good enough? Because we're trying, we're aiming for 87? And what does it mean? What do we need to do to basically get it to 87? We're gonna have to review the data bringing some third party data, you know, and it's, and that's, that costs a lot as well. So, yeah, Grant Do you think AutoML solutions play a role here like, Aible, I don't know if you're familiar with that platform, you know, that the goal is to try to reduce the amount of dependency that's needed on the data science. Scientists themselves, right. And but it's, it's still doesn't remove all of the data cleansing part, but it does help take care of some of the certainly the low level data science requirements, you think you think that's a viable solution in this area?  Paul I think it is. I mean, it's, you know, we went from rule based AI, where data scientists had to do good old fashioned AI, which was a feature engineering, right? Putting the rules themselves to machine learning AI, where, you know, we had to train the data that we needed, were so dependent on these data scientists. And now we're getting to v3, where we have these tools. And you know, there's a data dependency, but there, they also don't have such a high dependency on data scientists are and you know, figuring our algorithms and etc, we could just basically have these prepackaged algorithms that could basically output us any types of solution. What I tend to like, I've seen this a lot in a lot of companies. There's some companies that are very, very industry specific, right? So they're providing AI for E-commerce to be able to provide better search with predictive elements based on the person's browsing history. I mean, I, I'm not sure, but the ones that are providing every ML imaginable, so you could use it for supply chain, or you could use it for something else. I know it's dependent on data. But again, these algorithms, you can't have all the algorithms for all scenarios.  Even if it's supply chain, some person has perishables and there's ordering bananas and the other person is ordering, I don't know water coolers, and those, those don't have the same rules, right. You know, so it's, it's important to just, I think that maybe in the coming years, we'll have a lot of companies that are really going cross industry, just like we're in E-commerce, the other ones that are med tech, the other ones are, etcetera, the tools are the same. I mean, more or less the same, the customers are gonna get used to basically having these UI is that I'll give you your input the data in and then these emails come out, and then you choose which one and they give you probability you can retrain them and all that stuff. And I think that it's just going to get to a point where we're going to have these product managers who are now responsible of kind of training the Machine Learning Module themselves, you know if it's going to be the product manager, or if it's going to be some other function, where I think it does definitely fit inside the product managers? Grant Well I do is, I think it's because they still need to have what we would call the domain knowledge and in this domain of building products, yeah, AI, at least at least in this phase of the life of AI, where we are today for the foreseeable future. I think the product manager needs to be involved with that. Sure. So. Paul It comes down to intuition, right, somebody has to have like to build that intuition about what a model is relying on when making a judgment. And I think that, you know, with product managers, the closest one really, maybe in bigger organizations, it's the person who's managing analytics and data, but in smaller startup organization, I can definitely see the product manager putting that  Grant Yeah, absolutely. Paul, I really appreciate you taking the time. Here today on this been fascinating conversation. Any last comments you want to share? Paul We have tons of articles that talk about so we're very open source as an organization. So if you want to learn more about this, we have about 70 articles on our website. Just go to BainPublic.com and just click on "Articles" and you could just, you know, self serve and basically improve as a product manager in the AI space. Grant Excellent, fascinating, love, love the conversation, your insight and the vision where you guys are taking this I think you're gonna continue to disrupt everyone. Thanks for joining another episode of ClickAI Radio and until next time, check out BainPublic.com. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook visit ClickAIRadio.com now.  

In this episode of the AFSA Extra Credit Podcast Dan chats with Justin Davis, VP of Product Delivery and Frank McKenna, Chief Strategist with Point Predictive about the uptick in fraud as we come out of the pandemic and the economy remains turbulent. They also talk about what kind of fraud we're really seeing in the market. Hint? It's not a concerted effort by criminals.  Justin Davis Frank McKenna PointPredictive.com

‘Build the new' is one of the many phrases Nick Thompson uses to define L192's mission. This exciting team based out of our Washington DC base specialise in collaborating with idea generators, technology innovators and market leaders.  Together, SES combines expertise in network technology, automation, cloud and productization with customers to create prototypes, test, launch and monetize new, disruptive services. Nick, responsible for Product Delivery at SES Networks, shares why and what the team gets up to.  Satellite Stories podcast is presented by SES Senior Creative, Kristina Smith-Meyer. To find out more visit the “Sparking Innovation with L192” webpage on http://ses.com/ (ses.com) (under Insights section). On this webpage you can watch the roundtable films and read more on L192, its mission and why it matters to our customers and partners.

Listen to our podcast to learn how observability provides organizations with continuous and complete telemetry, enabling faster, automated problem identification and resolution. 

Break down barriers, build a shared culture and deliver excellent products with globally distributed teams. Explore how on this product delivery and management-focused Shine Podcast episode.

1. Agility The teams' ability to react to changes in requirements, development time issues and organizational pivots. You can also consider this as reactiveness to change. 2. Predictability This accounts for commitments made v/s what is actually delivered at a fixed point in time. If the team commits to delivery N number of items in the to-do list in X days, predictability is how close to N the team reaches on an average. It is of course impossible in real world scenarios to delivery N items as was committed since engineering complexities, process dependancies and people factors come into play. There is also the matter of our inability to estimate the size of the work to be done. 3. Efficiency Efficiency looks at how many work packets or items were delivered by the team v/s how much they could have delivered. It is obviously hard to say how much they could have delivered, but you can look at it as a relative (across methods) area. A perfectly efficient team utilizes all their time in delivering items. This is of course impossible as the same factors that plague predictability plague this area too.

In this episode, we talk to Deborah Simpier and Justin Kilpatrick, co-founders of Althea, an Internet Service Provider (ISP) platform and blockchain that enables the coordination of multi-stakeholder networks. Althea is built for a fast and affordable, locally run internet. Althea's unique cooperative vision for the internet brings peering from the data center to the field. Empowering communities to build multi-stakeholder networks faster and more affordably than legacy telecom models. The core technology behind Althea is a price-aware routing protocol and blockchain-based payment system that debits and credits funds based on a router's bandwidth usage. Althea decouples the service and infrastructure layers of Internet delivery and coordinates transparent and programmatic revenue sharing. Deborah's Twitter (https://twitter.com/DeborahSimpier) Justin's Twitter (https://twitter.com/ttk314) We spoke to the team about their journey with Althea, and: Open source & Linux Engineering & impacting the world Bridges & the philosophy behind building them Mesh & bandwidth Payment channels & TCR Cryptography Working nights & delivering products Routing & decentralization Delivering products Tools & libraries for web3 Economic incentives The projects and people that have been mentioned in this episode: | Tendermint (https://tendermint.com/) | Cosmos (https://cosmos.network/) | IBC (https://ibcprotocol.org/) | Althea (https://althea.net/) | Gravity Bridge (https://www.gravitybridge.net/) | Ethereum (https://www.ethereum.org/) | BTC (https://www.bitcoin.org/) | Spank chain (https://spankchain.com/) | XDAI (https://www.xdaichain.com) | If you like what we do at Citizen Cosmos: Stake with Citizen Cosmos validator (https://www.citizencosmos.space/staking) Help support the project via Gitcoin Grants (https://gitcoin.co/grants/1113/citizen-cosmos-podcast) Listen to the YouTube version (https://youtu.be/KEZCOQ5vTAk) Read our blog (https://citizen-cosmos.github.io/blog/) Check out our GitHub (https://github.com/citizen-cosmos/Citizen-Cosmos) Join our Telegram (https://t.me/citizen_cosmos) Follow us on Twitter (https://twitter.com/cosmos_voice) Sign up to the RSS feed (https://www.citizencosmos.space/rss) Special Guests: Deborah Simpier and Justin Kilpatrick.

Todays episode will be another installment of the "Meet the Team" series, where we introduce you to the influencers within Envision and the greater WorldStrides organization. For this episode we are joined by Matt Pollard. Matt is one of the newest member of the Product Delivery team after moving over from our sales and delivery team in the fall of 2021. He is currently serving in the role of Program Manager of our National Security, Intensive Law & Trial and the Global Young Leaders Conference (Matt made the switch after this episode was recorded).

Read the full Show Notes and search through the world's largest audio library on Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes. Luis and his colleagues were working on a new product that they would introduce in the market. While preparing the team, they decided to start changing some things that would also affect the wider organization. In this episode, we explore how the change we bring to an organization is not necessarily “external”, but can be created by internal triggers and affect plans, teams and ultimately the business! About Luis Carvalho Luis is an enthusiast for all things related with organizations, teams, structures and ways of working. He has been working in large scale consumer products for most of his professional life, worked with people of many backgrounds, cultures and locations and made many friends in the process. He loves traveling, food and getting to know people. You can link with Luis Carvalho on LinkedIn.

We welcome Jonathan Nolen, Senior VP of Engineering and Product at LaunchDarkly, to talk about his 100+ people Product Delivery team - a term coined by the company, where Engineering, Product, and Design come together. Key Takeaways: Tech companies should shift their mindset from building and shipping software to delivering services to customers. Having Engineering, Product, and Design as one team with shared goals is key for all companies delivering services. Impact is not just about visibility. Your teams need to articulate what their impact is, preferably in one sentence. Structuring your orgs right is necessary for scaling, but finding the right individuals is essential for success. Deep dive into the topics discussed in this episode at go.developingleadership.co/ep17 Join the discussion and follow us on twitter @ devleadership_   Developing Leadership is a podcast presented by Athenian. We are introducing the world of engineering to metrics and data that improve processes and help teams. If you want to learn more about data-enabled engineering, go to athenian.co

Twitter CEO ✅ Microsoft CEO ✅ Google CEO ✅ Learn how you can succeed and follow in the footsteps of so many successful Indians in the USA and India. Pallavi Srinivasa is a Senior Director of Product Management at Cisco and is responsible for $9B Enterprise Switching business from a Product Delivery perspective. // MENU // 00:00 ▶️ Pallavi introduction & background 08:13 ▶️ Why are Indians successful in the tech industry? 11:05 ▶️ Q&A (Indian people and humility, culture, advice for young people) 20:10 ▶️ What do you look for in a candidate 23:27 ▶️ Tips for candidates for an interview 26:30 ▶️ Be yourself, try something, do what's right for you 30:30 ▶️ Imposter syndrome and advice (don't overthink) 34:43 ▶️ The job market in India 37:41 ▶️ Advice for LinkedIn and social media 40:28 ▶️ Advice for someone wanting to move to Canada/USA 43:36 ▶️ Advice for someone looking to work for big tech companies 45:36 ▶️ Keeping record of all work done & distinguished engineers 48:37 ▶️ Choosing between technical vs personality candidates 50:28 ▶️ Are degrees/certifications important? 52:48 ▶️ Advice for women getting into the tech business/industry 01:03:05 ▶️ Conclusion, closing thoughts and advice // Connect with Pallavi // LinkedIn: https://www.linkedin.com/in/pallavisr... Twitter: https://twitter.com/Mayaloka // Connect with David // Discord: https://discord.com/invite/usKSyzb Twitter: https://www.twitter.com/davidbombal Instagram: https://www.instagram.com/davidbombal LinkedIn: https://www.linkedin.com/in/davidbombal Facebook: https://www.facebook.com/davidbombal.co TikTok: http://tiktok.com/@davidbombal YouTube: https://www.youtube.com/davidbombal // MY STUFF // https://www.amazon.com/shop/davidbombal // SPONSORS // Interested in sponsoring my videos? Reach out to my team here: sponsors@davidbombal.com india indian ceo indian jobs jobs Sundar Pichai Parag Agrawal Satya Nadella interview tips women in tech why indians succeed in the usa indians smart cricket indian heros cisco twitter google microsoft Please note that links listed may be affiliate links and provide me with a small percentage/kickback should you use them to purchase any of the items listed or recommended. Thank you for supporting me and this channel! #india #indiajobs #indiasuccess

Dr Mark Messmer, VP of Breeding and Product Delivery at CoverCress, Inc, is breeding a well-known winter annual weed into a cash crop that benefits farmers' bottom line and the environment. In this episode of the Plant Breeding Stories podcast, Mark explains why this particular project brought him out of retirement after a long, successful career in the seed industry. We learn about the challenges that the CoverCress team has to overcome to breed wild pennycress, a winter annual weed, into a commercial crop. He also discusses the potential markets for this new oilseed crop, including biofuel, animal feed and food production uses, and gives us a glimpse of what it takes to commercialize a new crop through the eyes of a startup company. To find out more go to https://covercress.com https://www.linkedin.com/in/mark-messmer-53451873/ Transcript - www.PBSInternational.com/podcast

Jessica attended the university of Arizona, and was pursuing a normal cited career path. At the age of 27 she lost the majority of her central vision to Leber's Hereditary Optic Neuropathy. She struggled for a short time as she adjusted to her vision loss, but once she embraced her situation her career took off. --- Send in a voice message: https://anchor.fm/careersfortheblind/message