Measurement Frameworks for AI Impact

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