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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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
Redefining Project Success with Dennis Stevens - March 2025
Agile and Project Management - DrunkenPM Radio
33 minutes 49 seconds
6 months ago
Redefining Project Success with Dennis Stevens - March 2025
Summary
In this conversation, Dave Prior and Dennis Stevens explore the evolving definition of project success as outlined in PMI's recent report. They discuss the implications of this new definition, emphasizing the need for continuous transformation in organizations and the role of project managers in delivering value. The conversation also touches on the importance of reducing friction in project management, the impact of AI on the profession, and the necessity for project managers to navigate the political landscape of project success. Ultimately, they highlight the need for project managers to adapt to a rapidly changing environment and the skills required to thrive in this new landscape.
Takeaways
• A successful project delivers value worth the effort and expense.
• Transformation is continuous, not stepwise.
• Project success must include stakeholder perception and value delivery.
• Project managers need to focus on reducing friction in processes.
• AI will change the role of project managers significantly.
• Friction is not the same as constraints; it impedes progress.
• Project success should be measured by the ability to adapt and deliver value.
• The role of project managers is evolving to include more strategic responsibilities.
• Continuous evaluation is essential for project success.
• Navigating politics is crucial for achieving project success.
Links:
PMI Article on Reframing Project Success https://www.pmi.org/blog/reframing-project-success
Agile for Project Managers - What is Project Success? w/ Dennis Stevens, Jesse Fewell, Jenny Tarwater, Sally Sloley, Jimi Fearless, and Semira Allen and (on The Agile Network) https://theagilenetwork.com/content/1d2a0a72-4d33-4145-b80a-3728f45b1947/item/0
Redefining Project Success and How it Impacts Project and Product Leaders with Lenka Pincot, Mike Griffiths and Dave Prior (on The Agile Network) https://theagilenetwork.com/content/926c08d0-52c7-4444-8f2a-732ac6acea61/item/0
Contacting Dennis
Website: https://www.google.com/search?client=safari&rls=en&q=orgwright&ie=UTF-8&oe=UTF-8
LinkedIn: https://www.linkedin.com/in/dennisstevens/
The Agile Network Profile: https://theagilenetwork.com/celebrity/fa0c1ba5-2373-4750-a3a2-86e37bc3ab03
Chapters
00:18 Introduction to Project Success Redefined
03:25 Transformation in Project Management
06:14 The New Definition of Project Success
09:21 Challenges in Project Management
12:08 Reducing Friction in Project Success
15:16 The Role of Project Managers in Value Delivery
18:39 The Politics of Project Management
22:18 Evolving Role of Project Managers
25:08 AI and the Future of Project Management
28:08 The Changing Landscape of Project Management
30:18 Concluding Thoughts and Future Conversations
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
