Engineering Enablement by Abi Noda

DX
Engineering Enablement by Abi Noda

This is a weekly podcast focused on developer productivity and the teams and leaders dedicated to improving it. Topics include in-depth interviews with Platform and DevEx teams, as well as the latest research and approaches on measuring developer productivity. The EE podcast is hosted by Abi Noda, the founder and CEO of DX (getdx.com) and published researcher focused on developing measurement methods to help organizations improve developer experience and productivity.

  1. The biggest obstacles preventing GenAI adoption — and how to overcome them

    ٦ يونيو

    The biggest obstacles preventing GenAI adoption — and how to overcome them

    In this episode, Abi Noda speaks with DX CTO Laura Tacho about the real obstacles holding back AI adoption in engineering teams. They discuss why technical challenges are rarely the blocker, and how fear, unclear expectations, and inflated hype can stall progress. Laura shares practical strategies for driving adoption, including how to model usage from the top down, build momentum through champions and training programs, and measure impact effectively—starting with establishing a baseline before introducing AI tools. Where to find Laura Tacho:  • LinkedIn: https://www.linkedin.com/in/lauratacho/ • Website: https://lauratacho.com/ Where to find Abi Noda: • LinkedIn: https://www.linkedin.com/in/abinoda  In this episode, we cover: (00:00) Intro: The full spectrum of AI adoption (03:02) The hype of AI (04:46) Some statistics around the current state of AI coding tool adoption (07:27) The real barriers to AI adoption (09:31) How to drive AI adoption  (15:47) Measuring AI’s impact  (19:49) More strategies for driving AI adoption  (23:54) The Methods companies are actually using to drive impact (29:15) Questions from the chat  (39:48) Wrapping up Referenced: DX Core 4 Productivity FrameworkThe AI adoption playbook: Lessons from Microsoft's internal strategyMicrosoft CEO says up to 30% of the company's code was written by AI | TechCrunchViral Shopify CEO Manifesto Says AI Now Mandatory For All EmployeesDORA | Impact of Generative AI in Software DevelopmentGuide to AI assisted engineeringJustin Reock - DX | LinkedIn

    ٤٢ من الدقائق
  2. DORA’s latest research on AI impact

    ٢٣ مايو

    DORA’s latest research on AI impact

    In this episode, Abi Noda speaks with Derek DeBellis, lead researcher at Google’s DORA team, about their latest report on generative AI’s impact on software productivity. They dive into how the survey was built, what it reveals about developer time and “flow,” and the surprising gap between individual and team outcomes. Derek also shares practical advice for leaders on measuring AI impact and aligning metrics with organizational goals. Where to find Derek DeBellis:  • LinkedIn: https://www.linkedin.com/in/derekdebellis/ Where to find Abi Noda: • LinkedIn: https://www.linkedin.com/in/abinoda  In this episode, we cover: (00:00) Intro: DORA’s new Impact of Gen AI report (03:24) The methodology used to put together the surveys DORA used for the report  (06:44) An example of how a single word can throw off a question  (07:59) How DORA measures flow  (10:38) The two ways time was measured in the recent survey (14:30) An overview of experiential surveying  (16:14) Why DORA asks about time  (19:50) Why Derek calls survey results ‘observational data’  (21:49) Interesting findings from the report  (24:17) DORA’s definition of productivity  (26:22) Why a 2.1% increase in individual productivity is significant  (30:00) The report’s findings on decreased team delivery throughput and stability  (32:40) Tips for measuring AI’s impact on productivity  (38:20) Wrap up: understanding the data  Referenced: DORA | Impact of Generative AI in Software DevelopmentThe science behind DORAYale Professor Divulges Strategies for a Happy Life Incredible! Listening to ‘When I’m 64’ makes you forget your ageSlow Productivity: The Lost Art of Accomplishment without BurnoutDORA, SPACE, and DevEx: Which framework should you use?SPACE framework, PRs per engineer, AI research

    ٤٠ من الدقائق
  3. Setting targets for developer productivity metrics

    ٩ مايو

    Setting targets for developer productivity metrics

    In this episode, Abi Noda is joined by Laura Tacho, CTO at DX, engineering leadership coach, and creator of the Core 4 framework. They explore how engineering organizations can avoid common pitfalls when adopting metrics frameworks like SPACE, DORA, and Core 4. Laura shares a practical guide to getting started with Core 4—beginning with controllable input metrics that teams can actually influence. The conversation touches on Goodhart’s Law, why focusing too much on output metrics can lead to data distortion, and how leaders can build a culture of continuous improvement rooted in meaningful measurement. Where to find Laura Tacho:  • LinkedIn: https://www.linkedin.com/in/lauratacho/ • Website: https://lauratacho.com/ Where to find Abi Noda: • LinkedIn: https://www.linkedin.com/in/abinoda  In this episode, we cover: (00:00) Intro: Improving systems, not distorting data (02:20) Goal setting with the new Core 4 framework (08:01) A quick primer on Goodhart’s law (10:02) Input vs. output metrics—and why targeting outputs is problematic (13:38) A health analogy demonstrating input vs. output (17:03) A look at how the key input metrics in Core 4 drive output metrics  (24:08) How to counteract gamification  (28:24) How to get developer buy-in (30:48) The number of metrics to focus on  (32:44) Helping leadership and teams connect the dots to how input goals drive output (35:20) Demonstrating business impact  (38:10) Best practices for goal setting Referenced: DX Core 4 Productivity FrameworkEngineering Enablement PodcastDORA’s software delivery metrics: the four keysThe SPACE of Developer Productivity: There’s more to it than you thinkDevEx: What Actually Drives ProductivityDORA, SPACE, and DevEx: Which framework should you use?Goodhart's law Nicole Forsgren - Microsoft | LinkedInCampbell's law Introducing Core 4: The best way to measure and improve your product velocityDX Core 4: Framework overview, key design principles, and practical applicationsDX Core 4: 2024 benchmarks - by Abi Noda

    ٤٣ من الدقائق
  4. The AI adoption playbook: Lessons from Microsoft's internal strategy

    ١٨ أبريل

    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 productivity Referenced: 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

    ٢٩ من الدقائق
  5. Gene Kim on developer experience and AI engineering

    ٤ أبريل

    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

    ٣٩ من الدقائق
  6. Getting Airbnb’s Platform team to drive more impact: Reorganizing, defining strategy, and metrics

    ٧ مارس

    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 links Follow 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

    ٣٣ من الدقائق
  7. You have developer productivity metrics. Now what?

    ٢٢ فبراير

    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

    ٣٩ من الدقائق
  8. Leveraging sentiment data, driving org-wide action, and executive engagement

    ٣١ يناير

    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

    ٢٥ من الدقائق
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This is a weekly podcast focused on developer productivity and the teams and leaders dedicated to improving it. Topics include in-depth interviews with Platform and DevEx teams, as well as the latest research and approaches on measuring developer productivity. The EE podcast is hosted by Abi Noda, the founder and CEO of DX (getdx.com) and published researcher focused on developing measurement methods to help organizations improve developer experience and productivity.

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