59| Get Better at Getting Better: Leveraging AI to Elevate Human Learning [with Nathen Harvey]
Chain of Learning: Leadership Strategies for Continuous Improvement and Transformational Change
AI for Learning: Play, Optionality, and Collaboration
They discuss using AI for experimentation, creative exploration, and preparing smarter questions for experts.
AI is everywhere. And its use and capabilities are accelerating every day.
But is AI actually helping us get better at getting better?
Or is it just amplifying the friction, bottlenecks, and complexity that already exists in our workflows and processes?
In this episode, Nathen Harvey, leader of the DORA Research team at Google, explores how AI is reshaping not just how we work, but how we can use it to elevate human work, collaborate as teams, and reach better outcomes.
Drawing on new findings from the DORA 2025 report on AI-assisted software development, we dig into what truly drives high performance – regardless of your industry or work – and how AI can either accelerate learning or amplify bottlenecks.
If you lead or work on any kind of team you’ll discover how to use AI thoughtfully, so it supports learning and strengthens the people-centered learning culture you’re trying to build.
YOU’LL LEARN:
- How AI accelerates learning—or intensifies friction—based on how teams use it
- Why AI magnifies what already exists, and why stronger human learning habits matter more than stronger tools
- The seven DORA team archetypes—and how to quickly spot strengths, gaps, and next steps for more effective collaboration
- How to use team characteristics to target where AI (or any tech) will truly move the needle and support continuous improvement
- How the Toyota Production System / lean principle of jidoka—automation with a human touch—guides us to use AI to elevate human capability, not replace it
ABOUT MY GUEST:
Nathen Harvey, Developer Relations Engineer, leads the DORA team at Google Cloud. DORA enables teams and organizations to thrive by making industry-shaping research accessible and actionable. Nathen has learned and shared lessons from some incredible organizations, teams, and open source communities. He is a co-author of multiple DORA reports on software delivery performance and is a sought after speaker in DevOps and software development.
IMPORTANT LINKS:
- Full episode show notes with links to other podcast episodes and resources: ChainOfLearning.com/59
- Check out my website for resources and ways to work with me KBJAnderson.com
- Connect with Nathan Harvey: linkedin.com/in/nathen
- Follow me on LinkedIn: linkedin.com/in/kbjanderson
- Learn more about DORA: dora.dev/publications
- Join the DORA community: dora.community
- Download my free KATALYST™ Change Leader Self-Assessment: KBJAnderson.com/katalyst
- Learn more about my coaching, trusted advisor partnerships, and leadership learning experiences: KBJAnderson.com
TIMESTAMPS FOR THIS EPISODE:
03:04 What DORA is and how it’s used as a research program for continuous improvement
04:31 AI’s primary role in software development as an amplifier where organizations are functioning well and where there’s friction
05:53 Using AI to generate more code in software engineering
07:03 Danger of creating more bottlenecks when you try to speed up processes
07:44 Importance of a value stream to understand the customer journey
10:41 How value mapping creates visibility across silos so others see different parts of the whole process
10:55 The process of gathering information for the State of AI Assisted Software Development report
12:20 Finding seven team characteristics based on a survey of 5,000 respondents and learning how to leverage the results to improve performance
14:18 Examples of several team characteristics and how it applies over various industries
16:33 The negative impact of focusing on the wrong process that impacts the throughput
17:00 Focusing at different types of waste to prevent undue pressure on people
17:51 What DORA has found in having a tradeoff in having fast and stable production pushes vs. working slow and rolling back changes
18:50 Three big things you need to improve throughput and quality
19:44 Why the legacy bottleneck team archetype is unstable with elevated levels of friction
21:22 Why harmonious high achievers deliver sustainable high quality work without the burnout
22:37 How the report findings are being used to help improve organizations
23:42 Seven capabilities of the DORA AI Capabilities Model in amplifying the impact of AI adoption to improve team and product performance
26:27 The capability of executing in small batches to see the process through to fruition
28:52 How to leverage AI to elevate human work vs machine work
30:58 The benefits of AI in making new skills accessible, but does not make anyone experts in a specific skill
31:44 Leveraging AI to help you complete tasks that would’ve taken longer
32:43 Using AI to elevate creative thinking, but doesn’t replace your thoughts
33:56 Ability to ask AI “dumb” questions to improve collaboration across teams
34:49 Creating an experiential learning experience where there's not a step-by-step path on how to reach outcomes
37:08 Importance of collaboration when moving from point A to point B
37:35 The difference between trainers and facilitators
39:03 Using the DORA report to form a hypothesis for your next experiment in whether a process is working
39:55 Two ways to start leveraging AI to accelerate learning
40:23 Importance of using AI and learning through use
40:58 Benefits of having a conversation with someone who introduces friction to your work
44:21 The concept of jidoka in designing systems that empower humans to do their best thinking and work
45:22 Questions to ask yourself as your reflect on the role of AI in your organization
Submit your questions to John Shook here:
https://kbjanderson.com/ask/
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