Engineering Enablement by Abi Noda

• The AI adoption playbook: Lessons from Microsoft's internal strategy

  Brian Houck from Microsoft returns to discuss effective strategies for driving AI adoption among software development teams. Brian shares his insights into why the immense hype around AI often serves as a barrier rather than a facilitator for adoption, citing skepticism and inflated expectations among developers. He highlights the most effective approaches, including leadership advocacy, structured training, and cultivating local champions within teams to demonstrate practical use cases. Brian emphasizes the importance of honest communication about AI's capabilities, avoiding over-promises, and ensuring that teams clearly understand what AI tools are best suited for. Additionally, he discusses common pitfalls, such as placing excessive pressure on individuals through leaderboards and unrealistic mandates, and stresses the importance of framing AI as an assistant rather than a replacement for developer skills. Finally, Brian explores the role of data and metrics in adoption efforts, offering practical advice on how to measure usage effectively and sustainably.Where to find Brian Houck: • LinkedIn: https://www.linkedin.com/in/brianhouck/ • Website: https://www.microsoft.com/en-us/research/people/bhouck/ Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover:(00:00) Intro: Why AI hype can hinder adoption among teams(01:47) Key strategies companies use to successfully implement AI(04:47) Understanding why adopting AI tools is uniquely challenging(07:09) How clear and consistent leadership communication boosts AI adoption(10:46) The value of team leaders ("local champions") demonstrating practical AI use(14:26) Practical advice for identifying and empowering team champions(16:31) Common mistakes companies make when encouraging AI adoption(19:21) Simple technical reminders and nudges that encourage AI use(20:24) Effective ways to track and measure AI usage through dashboards(23:18) Working with team leaders and infrastructure teams to promote AI tools(24:20) Understanding when to shift from adoption efforts to sustained use(25:59) Insights into the real-world productivity impact of AI(27:52) Discussing how AI affects long-term code maintenance(29:02) Updates on ongoing research linking sleep quality to productivityReferenced:DX Core 4 Productivity FrameworkEngineering Enablement PodcastDORA MetricsDropbox Engineering BlogEtsy Engineering BlogPfizer Digital InnovationBrown Bag Sessions – A GuideIDE Integration and AI ToolsDeveloper Productivity Dashboard Examples

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• Gene Kim on developer experience and AI engineering

  In this episode, we’re joined by author and researcher Gene Kim for a wide-ranging conversation on the evolution of DevOps, developer experience, and the systems thinking behind organizational performance. Gene shares insights from his latest work on socio-technical systems, the role of developer platforms, and how AI is reshaping the shape of engineering teams. We also explore the coordination challenges facing modern organizations, the limits of tooling, and the deeper principles that unite DevOps, lean, and platform engineering.Mentions and links:Phoenix ProjectDecoding the DNA of the Toyota Production SystemWiring the Winning OrganizationETLS VegasFind Gene on LinkedInDiscussion points:(0:00) Introduction(2:12) The evolving landscape of developer experience(10:34) Option Value theory, and how GenAI helps developers(13:45) The aim of developer experience work(19:59) The significance of layer three changes(23:23) Framing developer experience(32:12) GenAI’s part in ‘the death of the stubborn developer”(36:05) GenAI’s implications on the workforce(38:05) Where Gene’s work is heading

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  38:40
• Getting Airbnb’s Platform team to drive more impact: Reorganizing, defining strategy, and metrics

  In this episode, Airbnb Developer Productivity leader Anna Sulkina shares the story of how her team transformed itself and became more impactful within the organization. She starts by describing how the team previously operated, where teams were delivering but felt they needed more clarity and alignment across teams. Then, the conversation digs into the key changes they made, including reorganizing the team, clarifying team roles, defining strategy, and improving their measurement systems. Mentions and linksFollow Anna on LinkedInFor A deeper look into how our Engineers and Data Scientists build a world of belonging, check out The Airbnb Tech BlogDiscussion points:(0:00) Intro(1:40) Skills that make a great developer productivity leader(4:36) Challenges in how the team operated previously(10:49) Changing the platform org’s focus and structure(16:04) Clarifying roles for EM’s, PM’s, and tech leads(20:22) How Airbnb defined its infrastructure org’s strategy(28:23) Improvements they’ve seen to developer experience satisfaction(32:13) The evolution of Airbnb’s developer experience survey

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  32:58
• You have developer productivity metrics. Now what?

  Many teams struggle to use developer productivity data effectively because they don’t know how to use it to decide what to do next. We know that data is here to help us improve, but how do you know where to look? And even then, what do you actually do to put the wheels of change in motion? Listen to this conversation with  Abi Noda and Laura Tacho (CEO and CTO at DX) about data-driven management and how to take a structured, analytical approach to using data for improvement.Mentions and Links:Measuring developer productivity with the DX Core 4Laura’s developer productivity metrics courseDiscussion points:(0:00) Intro(2:07) The challenge we’re seeing(6:53) Overview on using data(8:58) Use cases for data-engineering organizations(15:57) Use cases for data - engineering systems teams(21:38) Two types of metrics - Diagnostics and Improvement(38:09) Summary

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  39:09
• Leveraging sentiment data, driving org-wide action, and executive engagement

  In this episode, David Betts, leader of Twilio’s developer platform team, shares how Twilio leverages developer sentiment data to drive platform engineering initiatives, optimize Kubernetes adoption, and demonstrate ROI for leadership. David details Twilio’s journey from traditional metrics to sentiment-driven insights, the innovative tools his teams have built to streamline CI/CD workflows, and the strategies they use to align platform investments with organizational goals.Mentions and links:Find David on LinkedInMeasuring developer productivity with the DX Core 4Ask Your Developer by Jeff Lawson, former CEO of TwilioDiscussion points:(0:00) Introduction(0:49) Twilio's developer platform team(2:03) Twilio's approach to release engineering and CD(4:10) How they use sentiment data and telemetry metrics(7:27) Comparing sentiment data and telemetry metrics(10:25) How to take action on sentiment data(13:16) What resonates with execs(15:44) Proving DX value: sentiment, efficiency, and ROI(19:15) Balancing quarterly and real-time developer feedback

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  24:33

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