Engineering Enablement by DX

Epizody

• AI and engineering productivity: Debating the headlines 29.06.2026 39min

  In this closing panel from DX Annual, Rafe Colburn, Chief Product and Technology Officer at Etsy; Jesse Adametz, Senior Director of Engineering, Platform Engineering at Twilio; Eirini Kalliamvakou, Research Advisor at GitHub; Collin Green, Senior Staff UX Researcher at Google; and Brian Houck, Senior Principal Applied Scientist at Microsoft debate some of the biggest questions surrounding AI and engineering productivity.They discuss whether AI will reduce the need for engineers, how AI is affecting technical debt, the future role of software engineers in an agentic world, and whether organizations should mandate AI adoption. They also explore how bottlenecks are shifting across the software development lifecycle, the challenges facing junior engineers, and why learning, culture, and change management may ultimately matter more than the tools themselves.Where to find Rafe Colburn:• LinkedIn: https://www.linkedin.com/in/rafeco• Blog: https://rafe.codesWhere to find Eirini Kalliamvakou: • LinkedIn: https://www.linkedin.com/in/eirini-kalliamvakou-1016865• X: https://x.com/irina_kAlWhere to find Brian Houck: • LinkedIn: https://www.linkedin.com/in/brianhouckWhere to find Jesse Adametz: • LinkedIn: https://www.linkedin.com/in/jesseadametz • X: https://x.com/jesseadametz • Website: https://www.jesseadametz.com Where to find Collin Green: • LinkedIn: https://www.linkedin.com/in/collin-green-97720378In this episode, we cover:(00:00) Intro(01:16) Why an AI-first SDLC doesn’t mean fewer engineers (03:09) The debate over AI and technical debt(07:40) AI-generated code and the future role of engineers(14:16) Why mandating AI use doesn't necessarily lead to better outcomes(20:43) Predictions for the future of junior engineers (23:22) Where the bottlenecks are in the SDLC now(28:25) How risk influences AI use (32:38) Why the human side is the biggest AI adoption challengeReferenced:• Etsy• GitHub• Microsoft• Twilio• Google • Stewart Reichling• What is the SPACE framework and when should you use it?
• From PR throughput to product velocity: How Dropbox is rethinking productivity in the agentic era 29.06.2026 21min

  In this session from DX Annual, Uma Namasivayam, Senior Director of Engineering Productivity at Dropbox, shares how the company's developer productivity efforts evolved from improving developer experience to preparing for the agentic era.He explains how Dropbox approached AI adoption across its engineering organization, the impact it had on developer productivity, and why faster code generation is creating new bottlenecks in areas such as code review, validation, and CI/CD. He also discusses Dropbox's efforts to rethink engineering systems, measurement, and workflows, including the development of agentic tooling and new metrics designed to move beyond PR throughput and toward product velocity.Where to find Uma Namasivayam:• LinkedIn: https://www.linkedin.com/in/unamasivayIn this episode, we cover:(00:00) Intro(00:57) The beginning of Dropbox’s DX journey(02:34) AI adoption at Dropbox: what made it work  (04:46) The results of Dropbox's AI adoption efforts(05:39) What the results mean for the business (06:55) The phases of AI adoption and where they are now(08:00) The new bottlenecks(09:16) Three challenges Dropbox faces moving into agentic engineering(10:05) How Dropbox is redesigning the SDLC for agentic engineering(15:46) The new metrics that matter (19:16) Final takeawaysReferenced:• Dropbox • Developer Experience Index (DXI) | DX • DX Core 4 Productivity Framework• Cursor• Claude Code | Anthropic's agentic coding system• JetBrains • Visual Studio Code• Jira | Project Management for the AI Era | Atlassian• GitHub 
• 2x the power users: How structured AI training scaled developer productivity 29.06.2026 40min

  Indeed increased AI coding tool adoption from roughly 25% to 97% across its engineering organization, but getting engineers to use the tools was only part of the challenge.In this session from DX Annual, Michael Redding, Principal Product Manager, and Jeff Davis, VP of Core Infrastructure at Indeed, explain how the company used structured training, leadership support, and ongoing community engagement to help more than 2,000 engineers build practical AI skills. They share why an early train-the-trainer model fell short, how they redesigned their approach around hands-on learning, and what they learned about balancing adoption, measurement, and psychological safety.They also discuss the impact of the program on coding time, the role of continuous enablement after formal training ended, and how Indeed is preparing for the next phase of AI adoption, including agentic workflows and AI-powered coaching.Where to find Jeff Davis: • LinkedIn: https://www.linkedin.com/in/utjeffd Where to find Michael Redding:• LinkedIn: https://www.linkedin.com/in/reddingsetgoIn this episode, we cover:(00:00) Intro(01:05) Indeed's DX survey from January 2025(02:30) The two-part strategy to double engineering productivity(04:21) How Indeed increased AI adoption from 25% to 97%(15:40) Results from Indeed's AI training program(18:33) How Indeed sustains AI adoption and learning(23:06) What's next for AI enablement at Indeed(24:41) Q&A: How coding time was calculated(25:25) Q&A: How Indeed uses AI playbooks(26:40) Q&A: Balancing asynchronous and live AI training(28:22) Q&A: Psychological safety during AI adoption(31:44) Q&A: Why AI adoption spikes after the holidays(33:20) Q&A: The metrics Indeed tracked (35:22) Q&A: Where the time savings are going (36:54) Q&A: Reaching engineers who skipped the training(38:08) Closing thoughtsReferenced:• Indeed• Claude Code | Anthropic's agentic coding system• Cursor• Windsurf• Amp Code• The Complete Guide to Building Skills for Claude | Anthropic• Measuring developer productivity with the DX Core 4
• The future of engineering at Nationwide, Comcast, TD, and HPE 22.06.2026 36min

  In this session from DX Annual, Rebecca Fitzhugh, Lead Principal Engineer at Atlassian, moderates a panel featuring Nidhi Allipuram, Vice President, Enterprise Developer Experience and Platform at Nationwide, Jai Schniepp, Senior Director, DevX Product Management at Comcast, Brent Foster, Vice President and Head of Architecture and Strategy at TD Bank, and Praveena Patchipulusu, Vice President of Engineering at HPE.Together, they discuss how large enterprises are approaching AI adoption, what it takes to build an AI-first software development lifecycle, and how engineering leaders are balancing speed, security, governance, and developer experience. They also share their perspectives on the changing role of engineers, human accountability, and how organizations can prepare for the future of software engineering.Where to find Rebecca Fitzhugh: • LinkedIn: https://www.linkedin.com/in/rmfitzhugh • X: https://x.com/RebeccaFitzhugh Where to find Jai Schniepp:• LinkedIn: https://www.linkedin.com/in/jessicaschnieppWhere to find Nidhi Allipuram: • LinkedIn: https://www.linkedin.com/in/nidhi-allipuramWhere to find Brent Foster: • LinkedIn: https://www.linkedin.com/in/engineeringthefuture• Website: https://brentfoster.meWhere to find Praveena Patchipulusu: • LinkedIn: https://www.linkedin.com/in/praveena-patchipulusu-158741In this episode, we cover:(00:00) Intro(02:28) The AI journey across TD Bank, Comcast, and HPE(05:59) Inside Nationwide's AI-assisted development lifecycle(10:04) Reimagining the software development lifecycle with AI(11:32) Security, governance, and human accountability(15:27) Embedding security and guardrails into AI workflows(17:55) How AI is changing the role of an engineer(21:52) What developer experience looks like in the AI era(26:55) What software engineering may look like in 2030(32:47) How to prepare for the AI-driven futureReferenced:• Atlassian• TD Bank• Comcast Corporation• Hewlett Packard Enterprise (HPE)• Nationwide • GitHub Spec Kit• Abi Noda
• Uber’s journey of measuring AI impact on developer productivity 22.06.2026 41min

  As AI becomes embedded in software development, many of the metrics that engineering organizations have relied on for years are starting to break down.In this session from DX Annual, Uber's Ty Smith and Abhishek Tibrewal share how their approach to measuring AI's impact on developer productivity has evolved over time. They walk through the different phases of their measurement journey, from adoption and engagement to measuring impact, ROI, and agentic value, explaining what they chose to measure at each stage, what worked, what failed, and how their thinking changed along the way.They also discuss the role of qualitative feedback before telemetry existed, the challenge of identifying meaningful engagement signals, why "developer years saved" failed as an ROI metric, and how AI agents forced them to rethink traditional productivity measurements. Finally, they introduce Uber's emerging framework built around feature velocity and explore the unanswered questions that remain as software development becomes increasingly agent-driven.Where to find Abhishek Tibrewal • LinkedIn: https://www.linkedin.com/in/aabhishektibrewalWhere to find Ty Smith: • LinkedIn: https://www.linkedin.com/in/tyvsmithIn this episode, we cover:(00:00) Intro(01:30) Steve Yegge’s 8 stages of AI-assisted development (03:22) Uber’s shift to a generative AI-powered company (04:20) Uber’s pre-AI productivity metrics (06:55) Important questions from stakeholders that previous metrics didn’t answer (08:25) How Uber measures AI before telemetry exists(11:11) Metrics used to measure adoption(12:49) Measuring engagement(14:30) Measuring impact(16:32) The challenge of measuring AI ROI(19:32) Rethinking adoption, engagement, and impact for agentic AI(26:01) The new north star: Feature velocity (28:41) PR classification + feature velocity: the questions it can answer (33:01) What comes next and what’s still unanswered (34:30) Lessons learned and what they'd do differently(37:11) Q&A #1: How Uber defines a feature (38:50) Q&A #2: Measuring success and AI ROIReferenced:• Welcome to Gas Town• Dara Khosrowshahi (Uber CEO)
• Beyond the CLI: Agentic AI for async workloads and non-developers 22.06.2026 37min

  In this session from DX Annual, Christopher Sanson, Product Lead, AI Developer Experience, and Madison Capps, Engineering Manager, Infrastructure at Airbnb, challenge some of the most common assumptions about AI. Is AI primarily about replacing humans? Do organizations need mandates to drive adoption? And are the productivity gains really as small as some studies suggest?Using examples from Airbnb's own AI journey, they share how the company achieved widespread adoption of agentic AI through AirChat, community enablement, and internal tooling rather than top-down mandates. They also discuss the impact AI is having on developer productivity, how non-developers are increasingly using coding tools, and how teams are rethinking product development in an AI-first world.Finally, Madison takes a deeper look at the infrastructure powering Airbnb’s AI strategy, including AirChat CLI, the AirChat SDK, and AirChat Remote, along with the company’s vision for asynchronous agent workflows and the next generation of AI-powered development.Where to find Christopher Sanson:• LinkedIn: https://www.linkedin.com/in/christophersanson Where to find Madison Capps:• LinkedIn: https://www.linkedin.com/in/madison-capps-66950625In this episode, we cover:(00:00) Intro(01:37) Myth #1: AI is about replacing humans(03:22) Myth #2: You need mandates to drive AI adoption(05:21) AirChat, agentic AI, and Airbnb's adoption strategy(08:07) Myth #3: AI has little impact on productivity(09:33) Airbnb's increase in coding time and PR throughput(14:20) Myth #4: AI coding tools are just for coders(15:39) How non-developers are using coding tools(17:24) Rethinking product development in an AI-first world(20:30) Myth #5: Vibe coding isn’t coding(22:16) Unsolved problems in agentic AI tooling and how Airbnb is addressing them(26:30) Airbnb’s overall AI philosophy in practice(29:15) Using agentic AI to accelerate code migrations(30:18) AirChat SDK: How Airbnb enables teams to build AI-powered applications(33:17) AirChat Remote and asynchronous agent workflows(36:07) Predictions for what’s nextReferenced:• ⁠Airbnb• Steve Jobs’s Bicycles for the Mind • Jennifer St Pierre • Justin Reock• AI-generated merged code holds steady at ~30%• Andrej Karpathy's post on X
• Prioritization as code: An AI-supported framework for platform engineering (Eleanor Millman and Mina Tawadrous) 15.06.2026 38min

  In this session from DX Annual, Eleanor Millman, Senior Staff Product Manager, and Mina Tawadrous, Associate Director of Product Management at SiriusXM, share how their platform engineering organization developed a prioritization framework for platform engineering teams serving hundreds of developers across a complex cloud platform.They explain how they define and weight platform-specific impact factors, use developer data to refine priorities, and score projects more consistently. They also explore why prioritization debates often stem from conflicting, invisible, or outdated assumptions, and how SiriusXM began treating assumptions like code by documenting, versioning, and reviewing them in source control.Finally, they demonstrate how AI can surface assumptions, connect initiatives to existing knowledge, and support project scoring while keeping humans in the loop. Throughout the session, they offer a practical framework for making prioritization decisions more transparent, data-driven, and scalable. In this episode, we cover:(00:00) Intro(02:58) Building a platform engineering prioritization framework(04:59) The seven platform engineering impact factors(09:38) Using impact factors to score projects(13:11) Using developer data to refine priorities(16:33) Three ways assumptions fail (17:40) Assumptions as code (21:00) New problems created by assumptions as code(22:00) Using AI to surface assumptions(23:44) Building an AI-powered feedback loop(25:44) Inside the AI prioritization tool(28:18) Three steps to build your own framework(30:02) Q&A #1: Evaluating high-cost projects(31:30) Q&A #2: The cadence of iteration (32:10) Q&A #3: When the framework conflicts with a stakeholder's priorities(35:26) Q&A #4: Using the framework for non-developersReferenced:• AWS• Databricks• RICE: Simple prioritization for product managers• Designing developer experience surveys• GSB Preserve | View | The Curse of Knowledge
• Augmented, accelerated, autonomized: How Vanguard is embedding AI across the product lifecycle (Kelly Anne Pipe and Nicole Scribner) 15.06.2026 39min

  Kelly Anne Pipe is Head of Developer Experience at Vanguard, and Nicole Scribner is a Director in the firm's Chief Technology Office focused on engineering enablement and advancement.In this session from DX Annual, Kelly Anne and Nicole share how Vanguard is expanding its AI strategy beyond software engineering to the entire product development lifecycle. While the company initially focused on tools like GitHub Copilot for engineers, they found that faster coding alone did not significantly improve delivery speed. Product managers, designers, QA teams, and organizational processes were still operating at a different pace.To address this challenge, Vanguard developed a product team maturity model built around three stages: Augmented, Accelerated, and Autonomized. The framework spans six dimensions, from AI-powered delivery and AI-ready codebases to team autonomy, operations, and responsible AI.Kelly Anne and Nicole explain how Vanguard is applying the model across more than 800 product teams, the behaviors they believe will enable faster delivery, and the lessons they have learned about measurement, organizational change, dependencies, and scaling AI across the product development lifecycle.In this episode, we cover:(00:00) Intro(02:16) The state of AI one year ago at Vanguard(02:54) The engineering bubble(05:05) Building an AI maturity model for 800 product teams(08:24) Dimension 1: AI-powered product delivery(10:00) Dimension 2: AI-ready codebase(12:20) Dimension 3: Autonomous agent utilization (13:00) Dimension 4: AI-augmented operations(14:00) Dimension 5: Team autonomy and enablement(16:11) Dimension 6: Responsible AI(18:15) The people problem: role evolution (20:00) The measurement problem (22:55) Lessons learned from rolling out the maturity model (26:46) What’s ahead (30:10) Q&A #1: Getting your codebase ready for AI(32:22) Q&A #2: Audit trails and responsible AI(34:16) Q&A #3: Vanguard's maturity model progress(36:15) Q&A #4: Measuring cycle time across 800 teamsReferenced:• Vanguard• Jennifer St Pierre - Dell Technologies | LinkedIn• Mercari
• Doubling the productivity of your engineering team using AI (Brian Scanlan) 15.06.2026 39min

  Brian Scanlan is a Senior Principal Systems Engineer at Intercom, where he works on platform engineering, developer productivity, and AI adoption across the company.In this session from DX Annual, Brian shares how Intercom set out to double engineering throughput and ultimately achieved that goal in nine months. Rather than treating AI as an optional productivity tool, the company standardized on Claude Code, updated performance expectations, invested heavily in enablement, and adopted an agent-first approach to technical work.Brian explains why Intercom views Claude Code as a platform rather than a tool, how the company is building domain-specific skills and workflows for agents, and why it believes agents should eventually be able to perform any technical task a senior engineer can complete on a laptop.He also shares the data behind Intercom's AI adoption efforts, including gains in throughput, reductions in defect backlogs, improvements in code quality, and the growing use of automated pull request approvals. Throughout the talk, Brian offers a practical look at what it takes to scale AI adoption across a large engineering organization and the lessons Intercom has learned along the way.Where to find Brian Scanlan:• LinkedIn: https://www.linkedin.com/in/scanlanb• X: https://x.com/brian_scanlan • Website: https://brian.scanlan.ie In this episode, we cover:(00:00) Intro(02:54) Intercom’s goal of doubling throughput (07:30) The platform strategy (09:30) Their agent-first strategy (10:58) Evergreen capabilities vs custom tooling (12:28) How Intercom works with agents(16:43) What the data reveals about AI adoption and impact(19:20) Using session data to improve AI workflows(20:20) Cutting the defect backlog in half(22:44) Inside Intercom’s Claude Code setup(28:09) Claude Code beyond engineering(30:49) Q&A #1: Token cost (32:52) Q&A #2: Preparing for AI pricing changes(34:14) Q&A #3: Stress testing and auditing skills(36:31) Q&A #4: Criteria for agents approving PRsReferenced:• Intercom• Software? No Way. We’re an A.I. Company Now! - The New York Times• Anthropic• Snowflake• Linear• LaunchDarkly • Fin AI• Microsoft Copilot• Cursor• Claude Code | Anthropic's agentic coding system• Steve Yegge (@Steve_Yegge) / Posts / X • Honeycomb• Fin Ideas• Fin CLI | AI Agent Command Line Interface
• From AI experiments to organizational shift: Lessons from Mercari’s transformation (Michael Galloway and Snehal Shinde) 15.06.2026 44min

  Michael Galloway leads Platform Engineering at Mercari, while Snehal Shinde leads Cost and Performance Engineering. Together, they have been at the center of Mercari's effort to become an AI-native company.In this session from DX Annual, Michael and Snehal share what happened after Mercari's CEO mandated 100% AI adoption across the organization. While AI accelerated code generation and increased engineering output, the team quickly discovered that their existing dashboards could not answer a simple question: was AI actually improving productivity?They discuss how Mercari built new visibility into AI usage and software delivery, the bottlenecks they uncovered across the SDLC, why faster coding did not automatically translate into faster delivery, and the lessons they learned rolling out AI at scale. They also share how Mercari is rethinking software development around agents, feedback loops, and new ways of working.In this episode, we cover:(00:00) Intro(01:46) Mercari’s scale and engineering culture(02:51) DX awards at Mercari(03:44) Mercari’s push to become AI-native(06:34) The mandate to rethink everything(08:02) Mercari’s AI visibility problem and how they solved it(11:30) Mercari’s early findings on AI implementation(18:47) Closing the AI awareness gap at Mercari(21:11) Mapping AI opportunities across Mercari(31:32) Unpacking the results from the second rollout(34:14) Agent spec-driven development and what’s next(37:37) A multi-loop SDLC(40:50) Some hard lessons(42:55) Closing thoughtsReferenced:• Mercari• Cursor• Devin• Claude Code | Anthropic's agentic coding system• GitHub• Datadog• Tim Bozarth - Microsoft | LinkedIn• Airbnb• Jim Collins - Concepts - The Stockdale Paradox
• Designing the AI‑native engineering organization with 1Password, Microsoft and Atlassian 08.06.2026 43min

  Abi Noda is joined live at DX Annual by three engineering leaders shaping AI adoption at scale: Tim Bozarth, Corporate Vice President in Microsoft’s CoreAI division; Nancy Wang, CTO of 1Password; and Taroon Mandhana, CTO of AI and Teamwork at Atlassian. Together, they discuss how AI is changing engineering organizations, from team structures and planning cycles to hiring, governance, and measurement.The panel explores how the profile of a great engineer is evolving, why smaller cross-functional teams are becoming more effective, and what happens when product managers, designers, and customer support teams start contributing code. They also share why they are encouraging AI adoption through enablement, training, and local champions rather than mandates, and how AI is shifting more of the software development lifecycle toward planning and validation.Finally, they discuss where human judgment remains essential, how to measure adoption and manage token usage, and how to connect AI investments to business outcomes while preserving room for experimentation and learning.Where to find Nancy Wang: • LinkedIn: https://www.linkedin.com/in/wangnancyWhere to find Taroon Mandhana:• LinkedIn: https://www.linkedin.com/in/taroonmWhere to find Tim Bozarth: • LinkedIn: https://www.linkedin.com/in/tbozarthWhere to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover:(00:00) Intro(01:08) Introducing the panelists(02:16) AI’s impact on engineering team structures and planning cycles(05:00) How the role of the engineer is changing and what makes a great engineer(10:11) The opportunities and challenges of non-engineers writing code(15:26) Encouraging AI adoption without mandating it(21:25) What an AI-native SDLC looks like and why human judgment still matters(30:56) Measuring AI adoption, token usage, and ROI(37:06) How to tie AI investments to business outcomesReferenced:• DX Core 4 Productivity Framework• Microsoft • 1Password• Atlassian• Jira• Confluence• Loom• Rovo • Amazon Operating Cadence - Working Backwards
• Mapping the new SDLC at BNY: Codifying AI into every step of the delivery lifecycle (Jason Valentino) 08.06.2026 33min

  Jason Valentino is Head of Software Engineering Strategy at BNY, where he oversees developer tooling, DevEx, platform workflows, and software delivery governance across more than 8,000 engineers.In this session from DX Annual, Jason shares how BNY moved beyond AI coding assistants to rethink the entire software delivery lifecycle. He explains how his team identified bottlenecks across the SDLC, prioritized automation opportunities, and applied AI to planning, peer review, testing, change management, and compliance workflows.Jason also discusses what it takes to scale AI inside a highly regulated enterprise, including rewriting policies, partnering closely with risk and audit teams, and building a culture that encourages experimentation and rapid sharing of ideas.Where to find Jason Valentino:• LinkedIn: https://www.linkedin.com/in/jasonvalentinoIn this episode, we cover:(00:00) Intro (01:20) Early results from AI coding tools at BNY(04:08) The 3X stress test: What breaks if engineering throughput triples?(06:56) Three ways to apply AI across the SDLC: IDE and CLI tools(08:07) Using autonomous AI agents for repetitive engineering tasks(09:16) Embedding AI directly into SDLC workflows(12:27) Why leaders should encourage experimentation and “start saying yes”(15:00) Q&A: How platform and productivity teams are evolving to support AI(16:33) Q&A: Rewriting policies and controls for AI-assisted software delivery(17:52) Q&A: How AI is affecting software quality and test ownership(19:00) Q&A: What Jason is most proud of: Practical examples of AI across the SDLC(20:30) Q&A: How BNY handles duplicated work across AI initiatives(22:30) Q&A: How BNY uses AI to support regulatory and compliance work(23:30) Q&A: Automating code reviews and change tickets(25:55) Q&A: How increased AI-driven throughput is affecting on-call and reliability(27:11) Q&A: How BNY works with risk and audit partners to move quickly with AI(29:01) Q&A: How BNY scales successful AI use cases across the organization(30:42) Q&A: What Jason is most proud of after BNY’s busiest year with AIReferenced:• AI-assisted engineering: Q4 impact report• Measuring AI code assistants and agents• Measuring developer productivity with the DX Core 4• Windsurf• Claude Code by Anthropic | AI Coding Agent, Terminal, IDE• Codex | AI Coding Agent
• The current impact of AI on engineering velocity: What 400 companies are seeing (Abi Noda & Brian Houck) 08.06.2026 26min

  Recorded live at DX Annual, Abi Noda, co-founder and CEO of DX, joins Brian Houck of Microsoft to share an early look at DX’s new research on AI’s impact on engineering velocity.Drawing on data from a sample of DX customers, they discuss what companies are actually seeing as AI adoption matures. Most organizations in the study saw pull request throughput increase by 10 to 15 percent—far more modest than the 10x gains often promised in industry headlines.They explore why coding remains only a small part of developer work, where time saved by AI may be going, and the unintended consequences of moving faster, from shifting bottlenecks to “false velocity.” Abi also shares how engineering leaders are applying AI beyond coding and how DX is evolving its measurement framework to account for both human and agent productivity.Where to find Brian Houck: • LinkedIn: https://www.linkedin.com/in/brianhouck/ Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover:(00:00) Intro(00:53) What motivated DX’s research into AI’s impact on engineering velocity(02:36) How DX designed the study and selected companies(04:54) What DX’s data reveals about AI’s impact on engineering throughput(06:31) Why PR throughput was the most practical metric to publish(08:21) Why AI productivity gains are lower than many leaders expected(10:24) How an all-in culture can amplify AI productivity gains(12:35) Why it’s hard to track where AI-generated time savings are going(15:04) Unintended consequences of AI-driven productivity gains(17:12) Why leaders should look beyond coding to the rest of the SDLC(19:43) Cognitive debt and the human costs of AI-assisted development(21:33) How DX’s AI measurement framework is evolving(24:42) How to make agents more effectiveReferenced:• DX Core 4 Productivity Framework • DORA, SPACE, and DevEx: Which framework should you use?• Time Warp: The Gap Between Developers’ Ideal vs Actual Workweeks in an AI-Driven Era - Microsoft • Research• How Generative and Agentic AI Shift Concern from Technical Debt to Cognitive Debt• Measuring AI code assistants and agents
• Beyond AI tools: Evolving software engineering organizations for the agentic era 08.06.2026 29min

  Jennifer St Pierre is Senior Vice President of Developer Experience and Transformation at Dell Technologies, where she leads the strategy for how Dell’s Infrastructure Solutions Group builds, operates, and evolves software.In this session from DX Annual, Jen argues that the biggest challenge in adopting agentic AI is not the technology itself, but the people transition behind it. Drawing on lessons from earlier shifts like Agile, DevOps, and cloud adoption, she explains why organizations that treat AI as a simple tooling rollout may get compliance, but not commitment.Jen outlines five leadership imperatives for navigating the transition: building a shared understanding of why change is happening, defining a clear future state, clarifying how roles will evolve, creating psychological safety for experimentation, and aligning metrics and organizational structures with new ways of working. Throughout the talk, she emphasizes that while AI may generate code, humans remain responsible for direction, judgment, and meaning.Where to find Jennifer St Pierre: • LinkedIn: https://www.linkedin.com/in/jennifer-st-pierre-4935a81In this episode, we cover:(00:00) Intro(00:13) Why every major technology shift is ultimately a people transition(05:00) AI-generated code and the evolving role of software engineers(07:43) The importance of developing a shared understanding(12:00) Defining a clear future state and how engineering roles will evolve(19:12) How psychological safety enables experimentation and honest feedback(22:41) Why metrics and organizational structure must evolve for the age of AI(25:40) Why leaders must drive AI transformation intentionallyReferenced:• Measuring developer productivity with the DX Core 4• Understand team effectiveness 
• Assumptions as code: SiriusXM’s approach to platform prioritization 10.04.2026 50min

  Eleanor Millman, Senior Staff Product Manager, and Mina Tawadrous, Associate Director of Platform Engineering at SiriusXM, join host Justin Reock to discuss how platform teams can scale prioritization without relying on revenue.They share how SiriusXM moved beyond RICE to build a custom framework for internal platforms, using weighted factors like developer speed, reliability, cost, and trust to guide decisions across teams.The episode also explores their concept of “assumptions as code,” in which teams store and reuse assumptions in a central repository to reduce misalignment and improve decision-making, with AI helping to surface and validate those assumptions.They close with how this system is shaping SiriusXM’s 2026 prioritization approach and what it signals about a broader shift toward builder-driven product development.Where to find Eleanor Millman: • LinkedIn: https://www.linkedin.com/in/eleanor-millman-98b10350Where to find Mina Tawadrous: • LinkedIn: https://www.linkedin.com/in/mina-tawadrous Where to find Justin Reock:• LinkedIn: https://www.linkedin.com/in/justinreockIn this episode, we cover:(00:00) Intro(01:17) Mina’s role and path into platform engineering(02:03) Eleanor’s background and shift into product(03:15) Scaling prioritization across platform engineering teams(05:41) Aligning platform priorities with stakeholders(09:08) Evolving RICE into a platform-specific prioritization framework(11:33) Iterating on the prioritization framework over time(16:57) How the framework, data, and conversations drive alignment(19:06) Storing assumptions as code in a central repository(26:47) Resolving assumption conflicts with user interviews(30:47) How stored assumptions integrate with AI workflows(35:30) Standard mode and different user personas(37:20) The industry shift towards builders(41:04) The challenges of platform engineering(43:36) How SiriusXM is prioritizing in 2026Referenced:• Measuring AI code assistants and agents• SiriusXM • VMware• How SiriusXM revamped their platform and developer experience• RICE Scoring Model | Prioritization Method Overview• The evaporating cloud: A tool for resolving workplace conflict
• Measuring AI impact, assessing readiness, and new data trends 03.04.2026 38min

  In this episode of Engineering Enablement, Jesse Adametz joins Abi Noda, this time to host. Together, they explore how AI is showing up across the SDLC, not just in code generation, and how it is shifting bottlenecks across the development process. They unpack what “AI readiness” actually means in practice, and why it often comes down to developer experience fundamentals like documentation, environments, and feedback loops.They also discuss why enablement matters more than tool choice, how teams are thinking about measuring ROI, and what changes as background agents become more common. Finally, they explore how the role of the engineer may evolve, the open questions teams are still grappling with, and the challenges of non-engineers contributing to codebases.Where to find Jesse Adametz: • LinkedIn: https://www.linkedin.com/in/jesseadametz • X: https://x.com/jesseadametz • Website: https://www.jesseadametz.com/Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover:(00:00) Intro(02:12) Where AI is showing up across the SDLC(05:53) AI readiness and its link to developer experience(08:23) Why enablement, education, and experimentation matter more than tool choice(13:05) The case for a dedicated enablement team(14:50) Measuring AI ROI: challenges and tradeoffs(19:46) Background agents and token spend(24:12) Measuring agent output with PR throughput(26:58) How the engineer role might change(31:01) Specs and documentation in the age of AI(33:11) Non-engineers writing code(35:30) What’s changing in the SDLC and open questionsReferenced:• Measuring AI code assistants and agents• Lessons from Twilio’s multi-year platform consolidation• The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win• How Claude remembers your project - Claude Code Docs• specIsJustCode : r/ProgrammerHumor
• Scaling developer experience across 1,000 engineers at Dropbox 06.02.2026 39min

  Developer productivity is often framed as a tooling initiative or a morale issue. At scale, it’s a more complex socio-technical systems challenge that spans engineering foundations, leadership alignment, organizational structure, and culture.In this episode, Laura Tacho sits down with Uma Namasivayam, Senior Director, Engineering Productivity at Dropbox, to discuss how the company approaches developer experience across an organization of nearly 1,000 engineers. Uma explains why productivity must be treated as a business problem, how executive alignment enables sustained progress, and what it means to run developer experience like a product.The conversation also explores the intersection of AI and developer experience. Uma shares how Dropbox prepared its engineering systems to support AI adoption, why daily AI use depends more on habits than access, and how the company evaluates build-versus-buy decisions as AI tools struggle to scale in large environments.The episode concludes with a candid discussion of the open questions facing engineering leaders today: how to understand where AI-driven capacity actually goes, and how to connect improvements in developer experience to meaningful business outcomes in 2026.Where to find Uma Namasivayam:• LinkedIn: https://www.linkedin.com/in/unamasivayamWhere to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura’s course (Measuring Engineering Performance and AI Impact) https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro(00:45) Dropbox’s engineering org(01:59) Why developer productivity is a business problem(04:08) The role of executive sponsorship in developer productivity(06:02) How DX’s Core Four framework created a shared language(08:13) Treating developer experience as a product(11:30) How Dropbox prioritizes developer experience work(14:20) The challenge of tying developer experience to business outcomes(16:38) How AI and developer experience intersect at Dropbox(18:35) The prerequisites for AI adoption to accelerate work(20:26) How Dropbox encourages daily AI use(23:12) AI use beyond code completion(25:00) Managing AI tool demand at scale(27:56) Early results from Dropbox’s AI efforts(30:05) Progress on developer experience at Dropbox(32:55) Advice for organizations investing in developer experience(34:25) Capacity tradeoffs for developer experience(35:59) The unanswered questions around AI and capacity in 2026Referenced:• DX Core 4 Productivity Framework• Dropbox.com
• AI and productivity: A year-in-review with Microsoft, Google, and GitHub researchers 29.12.2025 41min

  As AI adoption accelerates across the software industry, engineering leaders are increasingly focused on a harder question: how to understand whether these tools are actually improving developer experience and organizational outcomes.In this year-end episode of the Engineering Enablement podcast, host Laura Tacho is joined by Brian Houck from Microsoft, Collin Green and Ciera Jaspan from Google, and Eirini Kalliamvakou from GitHub to examine what 2025 research reveals about AI impact in engineering teams. The panel discusses why measuring AI’s effectiveness is inherently complex, why familiar metrics like lines of code continue to resurface despite their limitations, and how multidimensional frameworks such as SPACE and DORA provide a more accurate view of developer productivity.The conversation also looks ahead to 2026, exploring how AI is beginning to reshape the role of the developer, how junior engineers’ skill sets may evolve, where agentic workflows are emerging, and why some widely shared AI studies were misunderstood. Together, the panel offers a grounded perspective on moving beyond hype toward more thoughtful, evidence-based AI adoption.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 Collin Green: • LinkedIn: https://www.linkedin.com/in/collin-green-97720378 • Website: https://research.google/people/107023Where to find Ciera Jaspan: • LinkedIn: https://www.linkedin.com/in/ciera • Website: https://research.google/people/cierajaspan/Where to find Eirini Kalliamvakou: • LinkedIn: https://www.linkedin.com/in/eirini-kalliamvakou-1016865/• X: https://x.com/irina_kAl • Website: https://www.microsoft.com/en-us/research/people/eikalliWhere to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura’s course (Measuring Engineering Performance and AI Impact) https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro(02:35) Introducing the panel and the focus of the discussion(04:43) Why measuring AI’s impact is such a hard problem(05:30) How Microsoft approaches AI impact measurement(06:40) How Google thinks about measuring AI impact(07:28) GitHub’s perspective on measurement and insights from the DORA report(10:35) Why lines of code is a misleading metric(14:27) The limitations of measuring the percentage of code generated by AI(18:24) GitHub’s research on how AI is shaping the identity of the developer(21:39) How AI may change junior engineers’ skill sets(24:42) Google’s research on using AI and creativity (26:24) High-leverage AI use cases that improve developer experience(32:38) Open research questions for AI and developer productivity in 2026(35:33) How leading organizations approach change and agentic workflows(38:02) Why the METR paper resonated and how it was misunderstoodReferenced:• Measuring AI code assistants and agents• Kiro• Claude Code - AI coding agent for terminal & IDE• SPACE framework: a quick primer• DORA | State of AI-assisted Software Development 2025• Martin Fowler - by Gergely Orosz - The Pragmatic Engineer• Seamful AI for Creative Software Engineering: Use in Software Development Workflows | IEEE Journals & Magazine | IEEE Xplore• AI Where It Matters: Where, Why, and How Developers Want AI Support in Daily Work - Microsoft Research• Unpacking METR’s findings: Does AI slow developers down?• DX Annual 2026
• Running data-driven evaluations of AI engineering tools 12.12.2025 37min

  AI engineering tools are evolving fast. New coding assistants, debugging agents, and automation platforms emerge every month. Engineering leaders want to take advantage of these innovations while avoiding costly experiments that create more distraction than impact.In this episode of the Engineering Enablement podcast, host Laura Tacho and Abi Noda outline a practical model for evaluating AI tools with data. They explain how to shortlist tools by use case, run trials that mirror real development work, select representative cohorts, and ensure consistent support and enablement. They also highlight why baselines and frameworks like DX’s Core 4 and the AI Measurement Framework are essential for measuring impact.Where to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura’s course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-courseWhere to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda  • Substack: ​​https://substack.com/@abinoda  In this episode, we cover:(00:00) Intro: Running a data-driven evaluation of AI tools(02:36) Challenges in evaluating AI tools(06:11) How often to reevaluate AI tools(07:02) Incumbent tools vs challenger tools(07:40) Why organizations need disciplined evaluations before rolling out tools(09:28) How to size your tool shortlist based on developer population(12:44) Why tools must be grouped by use case and interaction mode(13:30) How to structure trials around a clear research question(16:45) Best practices for selecting trial participants(19:22) Why support and enablement are essential for success(21:10) How to choose the right duration for evaluations(22:52) How to measure impact using baselines and the AI Measurement Framework(25:28) Key considerations for an AI tool evaluation(28:52) Q&A: How reliable is self-reported time savings from AI tools?(32:22) Q&A: Why not adopt multiple tools instead of choosing just one?(33:27) Q&A: Tool performance differences and avoiding vendor lock-inReferenced:Measuring AI code assistants and agentsQCon conferencesDX Core 4 engineering metricsDORA’s 2025 research on the impact of AIUnpacking METR’s findings: Does AI slow developers down?METR’s study on how AI affects developer productivityClaude CodeCursorWindsurfDo newer AI-native IDEs outperform other AI coding assistants?
• DORA’s 2025 research on the impact of AI 21.11.2025 26min

  Nathen Harvey leads research at DORA, focused on how teams measure and improve software delivery. In today’s episode of Engineering Enablement, Nathen sits down with host Laura Tacho to explore how AI is changing the way teams think about productivity, quality, and performance.Together, they examine findings from the 2025 DORA research on AI-assisted software development and DX’s Q4 AI Impact report, comparing where the data aligns and where important gaps emerge. They discuss why relying on traditional delivery metrics can give leaders a false sense of confidence and why AI acts as an amplifier, accelerating healthy systems while intensifying existing friction and failure.The conversation focuses on how AI is reshaping engineering systems themselves. Rather than treating AI as a standalone tool, they explore how it changes workflows, feedback loops, team dynamics, and organizational decision-making, and why leaders need better system-level visibility to understand its real impact.Where to find Nathen Harvey:• LinkedIn: https://www.linkedin.com/in/nathenWhere to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura’s course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro(00:55) Why the four key DORA metrics aren’t enough to measure AI impact(03:44) The shift from four to five DORA metrics and why leaders need more than dashboards(06:20) The one-sentence takeaway from the 2025 DORA report(07:38) How AI amplifies both strengths and bottlenecks inside engineering systems(08:58) What DX data reveals about how junior and senior engineers use AI differently(10:33) The DORA AI Capabilities Model and why AI success depends on how it’s used(18:24) How a clear and communicated AI stance improves adoption and reduces friction(23:02) Why talking to your teams still matters Referenced:• DORA | State of AI-assisted Software Development 2025• Steve Fenton - Octonaut | LinkedIn• AI-assisted engineering: Q4 impact report