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Agile and Project Management - DrunkenPM Radio
Dave Prior, Agile Trainer, Consultant and Project Manager
263 episodes
1 week ago
In this conversation, Dave Prior and Hugo Bowne-Anderson discuss the evolving landscape of AI and data science, focusing on the role of AI agents in solving business problems. Hugo shares insights on how to effectively implement AI solutions, the importance of understanding the underlying data, and the need for continuous improvement in AI systems. They also touch on the skills necessary for navigating the AI landscape, the value of collaboration between technical and non-technical teams, and the importance of assessing the value of AI projects. Hugo concludes by offering a course on building AI applications, emphasizing the iterative nature of AI development. Takeaways - Hugo emphasizes the importance of data in AI applications. - AI agents can automate tasks but require human oversight. - Understanding the problem is crucial before implementing AI solutions. - Prompt engineering remains a valuable skill alongside learning about agents. - Consultants should educate clients on practical AI applications. - AI systems should be built incrementally and iteratively. - Value assessment in AI projects should focus on efficiency and cost savings. - Continuous improvement is essential for AI systems to remain effective. - Experimentation with AI tools can lead to innovative solutions. - Collaboration between technical and non-technical teams is vital for successful AI implementation. Chapters 00:00 Introduction to Data and AI Literacy 06:14 Understanding AI Agents vs. LLMs 09:18 The Role of Agents in Business Solutions 12:21 Navigating the Future of AI and Agents 15:24 Consulting and Client Education in AI 18:37 Building Incremental AI Solutions 21:29 The Future of AI Coding and Debugging 24:32 Prototyping with AI: Challenges and Solutions 25:32 Leveraging AI for User Insights and Competitive Analysis 27:29 Understanding Value in AI Development 32:05 The Role of Product Managers in AI Integration 33:00 AI as an Instrument: The Human Element 35:33 Getting Started with AI: Practical Steps for Teams 38:51 Building AI Applications: Course Overview and Insights Links from the Podcast: Stop Building AI Agents - Here’s what you should build instead (Article) https://www.decodingai.com/p/stop-building-ai-agents Anthropic https://www.anthropic.com/engineering/multi-agent-research-system The Colgate Study https://www.pymc-labs.com/blog-posts/AI-based-Customer-Research Hugo’s Course (Starts November 3, 2025) Building AI Applications for Data Scientists and Software Engineers (with a 25% discount) https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=drunkenpm (You can use the discount code drunkenpm to get 25% off) How To Be A Podcast Guest with Jay Hrcsko https://youtu.be/vkNbgwcolIM Contacting Hugo LinkedIn https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/ Substack https://hugobowne.substack.com/ Contacting Dave Linktree: https://linktr.ee/mrsungo Dave’s Classes: https://www.eventbrite.com/cc/dave-prior-classes-4758623
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All content for Agile and Project Management - DrunkenPM Radio is the property of Dave Prior, Agile Trainer, Consultant and Project Manager and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
In this conversation, Dave Prior and Hugo Bowne-Anderson discuss the evolving landscape of AI and data science, focusing on the role of AI agents in solving business problems. Hugo shares insights on how to effectively implement AI solutions, the importance of understanding the underlying data, and the need for continuous improvement in AI systems. They also touch on the skills necessary for navigating the AI landscape, the value of collaboration between technical and non-technical teams, and the importance of assessing the value of AI projects. Hugo concludes by offering a course on building AI applications, emphasizing the iterative nature of AI development. Takeaways - Hugo emphasizes the importance of data in AI applications. - AI agents can automate tasks but require human oversight. - Understanding the problem is crucial before implementing AI solutions. - Prompt engineering remains a valuable skill alongside learning about agents. - Consultants should educate clients on practical AI applications. - AI systems should be built incrementally and iteratively. - Value assessment in AI projects should focus on efficiency and cost savings. - Continuous improvement is essential for AI systems to remain effective. - Experimentation with AI tools can lead to innovative solutions. - Collaboration between technical and non-technical teams is vital for successful AI implementation. Chapters 00:00 Introduction to Data and AI Literacy 06:14 Understanding AI Agents vs. LLMs 09:18 The Role of Agents in Business Solutions 12:21 Navigating the Future of AI and Agents 15:24 Consulting and Client Education in AI 18:37 Building Incremental AI Solutions 21:29 The Future of AI Coding and Debugging 24:32 Prototyping with AI: Challenges and Solutions 25:32 Leveraging AI for User Insights and Competitive Analysis 27:29 Understanding Value in AI Development 32:05 The Role of Product Managers in AI Integration 33:00 AI as an Instrument: The Human Element 35:33 Getting Started with AI: Practical Steps for Teams 38:51 Building AI Applications: Course Overview and Insights Links from the Podcast: Stop Building AI Agents - Here’s what you should build instead (Article) https://www.decodingai.com/p/stop-building-ai-agents Anthropic https://www.anthropic.com/engineering/multi-agent-research-system The Colgate Study https://www.pymc-labs.com/blog-posts/AI-based-Customer-Research Hugo’s Course (Starts November 3, 2025) Building AI Applications for Data Scientists and Software Engineers (with a 25% discount) https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=drunkenpm (You can use the discount code drunkenpm to get 25% off) How To Be A Podcast Guest with Jay Hrcsko https://youtu.be/vkNbgwcolIM Contacting Hugo LinkedIn https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/ Substack https://hugobowne.substack.com/ Contacting Dave Linktree: https://linktr.ee/mrsungo Dave’s Classes: https://www.eventbrite.com/cc/dave-prior-classes-4758623
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Making the Case for Middle Management with Dennis Stevens
Agile and Project Management - DrunkenPM Radio
48 minutes 59 seconds
3 weeks ago
Making the Case for Middle Management with Dennis Stevens
Dennis Stevens joins drunkenpm for a deep dive into the challenges faced by middle management in organizational change. Dennis argues that Agile and transformation efforts often fail because they treat middle managers as a roadblock, when in fact, these managers are simply victims of a badly designed system. The core idea is that companies shouldn't try to eliminate these roles but must instead design specific organizational "containers" and routines that force middle managers to leverage human nature (like self-interest and competition) to drive innovation. The goal is to shift strategy from a static plan to a dynamic process that lives in the interactions between people, ensuring all the work being done is strategically aligned, measurable, and ultimately successful. Key Takeaways Middle Management is the Constraint: Organizational change, adaptability, and innovation will either happen or stall at the middle management level. Systemic Failure: Middle managers are often marginalized as the "stepchild" and are expected to manage tasks and activities instead of creating the conditions necessary for teams to achieve outcomes. Strategy is Dynamic: Strategy doesn't happen when a leader speaks; it becomes real when people start talking to each other and applying it, which requires designing routines that create safety for delegation. Embrace Human Nature: Successful organizational design must leverage human nature, where competitiveness is the fuel, rather than relying on idealistic notions of "no ego, total alignment". Conditions Over Practices: The success of Agile is due to the environment and conditions created for the teams, not the specific practices (like stand-ups or language). A key function of management is to consciously create those conditions. Constraints Drive Innovation: Setting clear goals and enforcing constraints and consequences within the designed container will drive innovation by forcing teams to be efficient and reinvent, as opposed to operating without pressure. Key Moments 0:02:24 The Core Thesis: Stevens introduces the central argument: "If you're trying to change how an organization runs, middle management is where it will either happen or stall." 0:03:41 The Problem Defined: Stevens uses the "stepchild" analogy to describe the plight of middle managers: having "fake power," lacking strategy, and being blamed for a system that was not designed to support them. 0:08:50 The Root Cause: Stevens identifies the problem: it's not a failure of management but a failure of the organization to deeply understand the conditions necessary for teams to innovate coherently in a complex system. 0:15:26 The Anti-Commune Stance: Stevens argues against the idealistic view of self-organization, stating that to succeed at scale, a system must be built where competitiveness is the fuel, rather than expecting people "to not be people." 0:20:08 The Glue of Strategy: Stevens defines where strategy truly exists: "Strategy becomes real when people start talking to each other." He stresses the need to build routines that create safety for delegation. 0:30:46 Constraints & Innovation: Stevens explains that constraints drive innovation by forcing efficiency, while a lack of constraints leads to inefficiency and a lack of pressure to reinvent. Dennis's Article: Innovation By Design https://www.linkedin.com/pulse/innovation-design-orgwright-qeipe/?trackingId=6eh7kgcyKabru1Cdv4%2BERg%3D%3D Links from the Intro: Productivity Survival: https://www.tickettailor.com/events/markkilby/1905697 No One is Coming to Save You Amazon: https://tinyurl.com/yhdk785j Leanpub: https://leanpub.com/surfthechaos Contacting Dennis LinkedIn: https://www.linkedin.com/in/dennisstevens/ OrgWright: https://www.orgwright.com/ The Agile Network: https://tinyurl.com/2tywk29e Contacting Dave LinkedIn:https://www.linkedin.com/in/mrsungo/ Linktree: https://linktr.ee/mrsungo The Agile Network: https://tinyurl.com/y3rhnnxp
Agile and Project Management - DrunkenPM Radio
In this conversation, Dave Prior and Hugo Bowne-Anderson discuss the evolving landscape of AI and data science, focusing on the role of AI agents in solving business problems. Hugo shares insights on how to effectively implement AI solutions, the importance of understanding the underlying data, and the need for continuous improvement in AI systems. They also touch on the skills necessary for navigating the AI landscape, the value of collaboration between technical and non-technical teams, and the importance of assessing the value of AI projects. Hugo concludes by offering a course on building AI applications, emphasizing the iterative nature of AI development. Takeaways - Hugo emphasizes the importance of data in AI applications. - AI agents can automate tasks but require human oversight. - Understanding the problem is crucial before implementing AI solutions. - Prompt engineering remains a valuable skill alongside learning about agents. - Consultants should educate clients on practical AI applications. - AI systems should be built incrementally and iteratively. - Value assessment in AI projects should focus on efficiency and cost savings. - Continuous improvement is essential for AI systems to remain effective. - Experimentation with AI tools can lead to innovative solutions. - Collaboration between technical and non-technical teams is vital for successful AI implementation. Chapters 00:00 Introduction to Data and AI Literacy 06:14 Understanding AI Agents vs. LLMs 09:18 The Role of Agents in Business Solutions 12:21 Navigating the Future of AI and Agents 15:24 Consulting and Client Education in AI 18:37 Building Incremental AI Solutions 21:29 The Future of AI Coding and Debugging 24:32 Prototyping with AI: Challenges and Solutions 25:32 Leveraging AI for User Insights and Competitive Analysis 27:29 Understanding Value in AI Development 32:05 The Role of Product Managers in AI Integration 33:00 AI as an Instrument: The Human Element 35:33 Getting Started with AI: Practical Steps for Teams 38:51 Building AI Applications: Course Overview and Insights Links from the Podcast: Stop Building AI Agents - Here’s what you should build instead (Article) https://www.decodingai.com/p/stop-building-ai-agents Anthropic https://www.anthropic.com/engineering/multi-agent-research-system The Colgate Study https://www.pymc-labs.com/blog-posts/AI-based-Customer-Research Hugo’s Course (Starts November 3, 2025) Building AI Applications for Data Scientists and Software Engineers (with a 25% discount) https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=drunkenpm (You can use the discount code drunkenpm to get 25% off) How To Be A Podcast Guest with Jay Hrcsko https://youtu.be/vkNbgwcolIM Contacting Hugo LinkedIn https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/ Substack https://hugobowne.substack.com/ Contacting Dave Linktree: https://linktr.ee/mrsungo Dave’s Classes: https://www.eventbrite.com/cc/dave-prior-classes-4758623