Engineering Enablement by DX

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-4935a81

In 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 intentionally

Referenced:

• Measuring developer productivity with the DX Core 4

• Understand team effectiveness 

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-98b10350

Where to find Mina Tawadrous: 

• LinkedIn: https://www.linkedin.com/in/mina-tawadrous 

Where to find Justin Reock:

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

In 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 2026

Referenced:

• 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

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 questions

Referenced:

• 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

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/unamasivayam

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-course

In 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 2026

Referenced:

• DX Core 4 Productivity Framework

• Dropbox.com

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/107023

Where 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/eikalli

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-course

In 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 misunderstood

Referenced:

• 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

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-course

Where 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-in

Referenced:

• Measuring AI code assistants and agents
• QCon conferences
• DX Core 4 engineering metrics
• DORA’s 2025 research on the impact of AI
• Unpacking METR’s findings: Does AI slow developers down?
• METR’s study on how AI affects developer productivity
• Claude Code
• Cursor
• Windsurf
• Do newer AI-native IDEs outperform other AI coding assistants?

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/nathen

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-course

In 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

In this episode of Engineering Enablement, host Laura Tacho talks with Fabien Deshayes, who leads multiple platform engineering teams at Monzo Bank. Fabien explains how Monzo is adopting AI responsibly within a highly regulated industry, balancing innovation with structure, control, and data-driven decision-making.

They discuss how Monzo runs structured AI trials, measures adoption and satisfaction, and uses metrics to guide investment and training. Fabien shares why the company moved from broad rollouts to small, focused cohorts, how they are addressing existing PR review bottlenecks that AI has intensified, and what they have learned from empowering product managers and designers to use AI tools directly.

He also offers insights into budgeting and experimentation, the results Monzo is seeing from AI-assisted engineering, and his outlook on what comes next, from agent orchestration to more seamless collaboration across roles.

Where to find Fabien Deshayes: 

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

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-course

In this episode, we cover:

(00:00) Intro  

(01:01) An overview of Monzo bank and Fabien’s role  

(02:05) Monzo’s careful, structured approach to AI experimentation  

(05:30) How Monzo’s AI journey began  

(06:26) Why Monzo chose a structured approach to experimentation and what criteria they used  

(09:21) How Monzo selected AI tools for experimentation  

(11:51) Why individual tool stipends don’t work for large, regulated organizations  

(15:32) How Monzo measures the impact of AI tools and uses the data  

(18:10) Why Monzo limits AI tool trials to small, focused cohorts  

(20:54) The phases of Monzo’s AI rollout and how learnings are shared across the organization  

(22:43) What Monzo’s data reveals about AI usage and spending  

(24:30) How Monzo balances AI budgeting with innovation  

(26:45) Results from DX’s spending poll and general advice on AI budgeting  

(28:03) What Monzo’s data shows about AI’s impact on engineering performance  

(29:50) The growing bottleneck in PR reviews and how Monzo is solving it with tenancies  

(33:54) How product managers and designers are using AI at Monzo  

(36:36) Fabien’s advice for moving the needle with AI adoption  

(38:42) The biggest changes coming next in AI engineering 

Referenced:

• Monzo 
• The Go Programming Language
• Swift.org
• Kotlin
• GitHub Copilot in VS Code 
• Cursor
• Windsurf
• Claude Code
• Planning your 2026 AI tooling budget: guidance for engineering leaders

In this episode of Engineering Enablement, Laura Tacho and Abi Noda discuss how engineering leaders can plan their 2026 AI budgets effectively amid rapid change and rising costs. Drawing on data from DX’s recent poll and industry benchmarks, they explore how much organizations should expect to spend per developer, how to allocate budgets across AI tools, and how to balance innovation with cost control.

Laura and Abi also share practical insights on building a multi-vendor strategy, evaluating ROI through the right metrics, and ensuring continuous measurement before and after adoption. They discuss how to communicate AI’s value to executives, avoid the trap of cost-cutting narratives, and invest in enablement and training to make adoption stick.

Where to find Abi Noda:

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

• Substack: ​​https://substack.com/@abinoda  

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-course

In this episode, we cover:

(00:00) Intro: Setting the stage for AI budgeting in 2026

(01:45) Results from DX’s AI spending poll and early trends

(03:30) How companies are currently spending and what to watch in 2026

(04:52) Why clear definitions for AI tools matter and how Laura and Abi think about them

(07:12) The entry point for 2026 AI tooling budgets and emerging spending patterns

(10:14) Why 2026 is the year to prove ROI on AI investments

(11:10) How organizations should approach AI budgeting and allocation

(15:08) Best practices for managing AI vendors and enterprise licensing

(17:02) How to define and choose metrics before and after adopting AI tools

(19:30) How to identify bottlenecks and AI use cases with the highest ROI

(21:58) Key considerations for AI budgeting 

(25:10) Why AI investments are about competitiveness, not cost-cutting

(27:19) How to use the right language to build trust and executive buy-in

(28:18) Why training and enablement are essential parts of AI investment

(31:40) How AI add-ons may increase your tool costs

(32:47) Why custom and fine-tuned models aren’t relevant for most companies today

(34:00) The tradeoffs between stipend models and enterprise AI licenses

Referenced:

• DX Core 4 Productivity Framework
• Measuring AI code assistants and agents
• 2025 State of AI Report: The Builder's Playbook
• GitHub Copilot · Your AI pair programmer
• Cursor
• Glean
• Claude Code
• ChatGPT
• Windsurf
• Track Claude Code adoption, impact, and ROI, directly in DX
• Measuring AI code assistants and agents with the AI Measurement Framework
• Driving enterprise-wide AI tool adoption
• Sentry
• Poolside

CEO Abi Noda is joined by DX CTO Laura Tacho to discuss the evolving role of Platform and DevProd teams in the AI era. Together, they unpack how AI is reshaping platform responsibilities, from evaluation and rollout to measurement, tool standardization, and guardrails. They explore why fundamentals like documentation and feedback loops matter more than ever for both developers and AI agents. They also share insights on reducing tool sprawl, hardening systems for higher throughput, and leveraging AI to tackle tech debt, modernize legacy code, and improve workflows across the SDLC.

Where to find Abi Noda:

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

• Substack: ​​https://substack.com/@abinoda  

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-course

In this episode, we cover:

(00:00) Intro: Why platform teams need to evolve

(02:34) The challenge of defining platform teams and how AI is changing expectations

(04:44) Why evaluating and rolling out AI tools is becoming a core platform responsibility

(07:14) Why platform teams need solid measurement frameworks to evaluate AI tools

(08:56) Why platform leaders should champion education and advocacy on measurement

(11:20) How AI code stresses pipelines and why platform teams must harden systems

(12:24) Why platform teams must go beyond training to standardize tools and create workflows

(14:31) How platform teams control tool sprawl

(16:22) Why platform teams need strong guardrails and safety checks

(18:41) The importance of standardizing tools and knowledge

(19:44) The opportunity for platform teams to apply AI at scale across the organization

(23:40) Quick recap of the key points so far

(24:33) How AI helps modernize legacy code and handle migrations

(25:45) Why focusing on fundamentals benefits both developers and AI agents

(27:42) Identifying SDLC bottlenecks beyond AI code generation

(30:08) Techniques for optimizing legacy code bases 

(32:47) How AI helps tackle tech debt and large-scale code migrations

(35:40) Tools across the SDLC

Referenced:

• DX Core 4 Productivity Framework
• Measuring AI code assistants and agents
• Abi Noda's LinkedIn post
• Measuring AI code assistants and agents with the AI Measurement Framework
• The SPACE framework: A comprehensive guide to developer productivity
• Common workflows - Anthropic
• Enterprise Tech Leadership Summit Las Vegas 2025
• Driving enterprise-wide AI tool adoption with Bruno Passos
• Accelerating Large-Scale Test Migration with LLMs | by Charles Covey-Brandt | The Airbnb Tech Blog | Medium
• Justin Reock - DX | LinkedIn
• A New Tool Saved Morgan Stanley More Than 280,000 Hours This Year - Business Insider