Engineering Enablement by DX

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/reddingsetgo

In 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 thoughts

Referenced:
• 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

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.codes
Where to find Eirini Kalliamvakou: 
• LinkedIn: https://www.linkedin.com/in/eirini-kalliamvakou-1016865
• X: https://x.com/irina_kAl
Where to find Brian Houck: 
• LinkedIn: https://www.linkedin.com/in/brianhouck
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 Collin Green: 
• LinkedIn: https://www.linkedin.com/in/collin-green-97720378

In 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 challenge

Referenced:
• Etsy
• GitHub
• Microsoft
• Twilio
• Google 
• Stewart Reichling
• What is the SPACE framework and when should you use it?

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/unamasivay

In 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 takeaways

Referenced:
• 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

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-66950625

In 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 next

Referenced:
• ⁠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

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/jessicaschniepp
Where to find Nidhi Allipuram: 
• LinkedIn: https://www.linkedin.com/in/nidhi-allipuram
Where to find Brent Foster: 
• LinkedIn: https://www.linkedin.com/in/engineeringthefuture
• Website: https://brentfoster.me
Where to find Praveena Patchipulusu: 
• LinkedIn: https://www.linkedin.com/in/praveena-patchipulusu-158741

In 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 future

Referenced:
• Atlassian
• TD Bank
• Comcast Corporation
• Hewlett Packard Enterprise (HPE)
• Nationwide 
• GitHub Spec Kit
• Abi Noda