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In this episode, I’m joined by DX CTO Laura Tacho to talk about what’s really holding back AI adoption in engineering teams. It’s not the technical challenges—it’s fear, unclear expectations, and the disconnect between hype and reality. Laura shares practical strategies for making progress, from modeling usage at the leadership level to creating space for experimentation. We also talk about how to measure impact effectively—including why it’s critical to establish a baseline before introducing AI tools, so you can track real changes over time.
Some takeaways:
Obstacles to AI adoption
The hype: Some of the claims written on LinkedIn and other places online overstate the impact of AI coding tools.
The cost of AI tools.
Technical barriers aren’t holding back adoption—cultural and human resistance are.
Some AI adoption stats
Top-end organizations report that 60-70% of developers are using code assistance either daily or weekly.
Less than 60% of code is written with AI at Microsoft.
There is a steady increase in the adoption of AI tools.
Strategies for driving AI adoption
Work against the hype by showing what the real impact of AI tools is. Methods for demonstrating impact include: self-reported, telemetry-based, direct signals from developers, indirect signals using hard data, and quality measures.
There isn’t a special set of metrics for AI adoption: We still care about quality, developer experience, and business impact.
Model and encourage AI use from the top down.
Remove the fear and stigma from using AI: Make employees reassured that using AI isn’t cheating.
Carve out time for employees to experiment with AI tools.
Have clear conversations around expectations around AI use.
Host webinars, in-person trainings, and office hours.
Champions programs: Identify the early adopters to help evangelize and drive adoption with their peers.
Use DX’s Guide to AI assisted engineering
DORA’s recent AI report shows a 451% increase in adoption among companies with an acceptable use policy.
Key measures from the DX Core 4 productivity framework
For quality: Measure change failure rate before and after introducing AI tools.
For speed: Measure PR throughput.
For quality: Survey developers. Many report improved code readability at companies with high AI adoption.
Always stay on top of developer experience.
In this episode, we cover:
(00:00) Intro: The full spectrum of AI adoption
(03:02) The hype of AI
(04:46) Some statistics around the current state of AI coding tool adoption
(07:27) The real barriers to AI adoption
(09:31) How to drive AI adoption
(15:47) Measuring AI’s impact
(19:49) More strategies for driving AI adoption
(23:54) The Methods companies are actually using to drive impact
(29:15) Questions from the chat
(39:48) Wrapping up
Where to find Laura Tacho:
• LinkedIn: https://www.linkedin.com/in/lauratacho/
• Website: https://lauratacho.com/
Where to find Abi Noda:
• LinkedIn: https://www.linkedin.com/in/abinoda
Referenced:
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