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
Summary
In this conversation, Dave Prior and Preston Hunter discuss the challenges of transitioning to Agile methodologies, particularly focusing on the use of JIRA and visual collaboration tools like Lucid. They explore a case study of a team moving to Kanban, the integration of Lucid with JIRA, and how visual tools can simplify workflows and enhance collaboration. The discussion also covers capacity planning, managing dependencies, and the importance of creating custom views for project management. Preston shares insights on how Lucid can help teams visualize their work, engage stakeholders, and improve overall efficiency. The conversation concludes with resources for learning and support for new users of Lucid.
This podcast was recorded in video format because of the demo that was given. You can find the video here: https://www.projectmanagement.com/blog-post/78177/using-lucid-to-update-jira-with-preston-hunter
Takeaways
• Visual collaboration tools can ease the transition to Agile.
• JIRA can be overwhelming for new users; Lucid simplifies this.
• Integrating Lucid with JIRA allows for real-time updates.
• Custom views in Lucid can help manage personal and team workflows.
• Visualizing dependencies is crucial for effective project management.
• Capacity planning features help teams avoid overcommitment.
• Engaging teams with visual tools fosters collaboration and creativity.
• Lucid offers resources for new users to learn effectively.
• Conditional formatting in Lucid enhances reporting capabilities.
• Personalization of boards can reflect team culture and identity.
Chapters
03:21 Introduction to Agile and Visual Collaboration
05:13 Challenges of Implementing Kanban with JIRA
09:21 Lucid's Integration with JIRA
15:21 Enhancing Team Collaboration and Workflow Visualization
21:28 Managing Dependencies and Capacity Planning
27:21 Customizing Workflows and Agile Practices
30:48 Empowering Teams with Agency
33:58 Capacity Planning and Sprint Management
36:30 Reporting and Visual Collaboration Tools
40:28 The Future of Project Management
42:33 Enhancing Team Collaboration and Engagement
45:32 Personalizing Team Spaces
49:41 Learning and Support Resources
To learn more about Lucid go to https://lucid.co/
To contact Preston https://www.linkedin.com/in/preston-hunter/
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
