Sejal Amin is the Chief Technology Officer at Priceline, where she leads product engineering, infrastructure, data, and technology operations. Pedro Gutierrez is Senior Director of Software Engineering, where he has helped drive developer experience initiatives and the adoption of Priceline's product operating model.

In this episode of the Engineering Enablement podcast, Justin Reock talks with Sejal and Pedro about Priceline's journey from a project-based organization to a product operating model and the role developer experience played in making that transformation successful. They discuss how DX metrics and developer feedback helped identify organizational bottlenecks, guide structural changes, empower engineering managers, and build trust across teams. They also explore the importance of clear communication, creating a dedicated developer experience team, and how their operating model has helped prepare Priceline for AI-driven software development.

Where to find Sejal Amin:  

• LinkedIn: https://www.linkedin.com/in/sejal-amin

Where to find Pedro Gutierrez: 

• LinkedIn: https://www.linkedin.com/in/pedro-gutierrez-b6605422

Where to find Justin Reock:

• LinkedIn: https://www.linkedin.com/in/justinreock

In this episode, we cover:

(00:00) Intro

(01:07) Meet Sejal Amin and Pedro Gutierrez

(01:47) How Priceline's developer experience journey began

(04:55) Lessons from Priceline's first developer experience surveys

(06:55) How DX improved Priceline's developer experience surveys

(09:47) Identifying the causes of organizational slowness

(12:33) How the product operating model changed the way Priceline works

(14:10) Priceline's phased rollout with DX

(18:14) How DX insights drove organizational changes

(19:33) Why Priceline improved developer experience before org change was complete

(22:18) How clear communication builds trust

(24:25) Early results from Priceline's Core Four

(25:38) Creating a culture of continuous feedback to build trust

(27:40) What has changed in the engineering manager role

(30:10) Resources for learning about the product operating model

(32:40) What Pedro learned from implementing DX

(34:51) The developer experience team

(35:59) How AI tools have impacted Priceline’s teams 

(37:20) How the product operating model supports AI-driven development

(39:13) Final advice for engineering leaders

Referenced:

• Measuring developer productivity with the DX Core 4

• Transformed: Moving to the Product Operating Model (Silicon Valley Product Group) 

• Team Topologies

• Flow Framework

• Project to Product: How to Survive and Thrive in the Age of Digital Disruption with the Flow Framework

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 

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 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

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/aabhishektibrewal

Where to find Ty Smith: 

• LinkedIn: https://www.linkedin.com/in/tyvsmith

In 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 ROI

Referenced:

• Welcome to Gas Town

• Dara Khosrowshahi (Uber CEO)

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, 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-developers

Referenced:

• AWS

• Databricks

• RICE: Simple prioritization for product managers

• Designing developer experience surveys

• GSB Preserve | View | The Curse of Knowledge

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 teams

Referenced:

• Vanguard

• Jennifer St Pierre - Dell Technologies | LinkedIn

• Mercari

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 PRs

Referenced:

• 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