2x the power users: How structured AI training scaled developer productivity

Key takeaways

Structured, community-driven training significantly boosts AI tool adoption among engineers.
Hands-on experience and ongoing engagement are critical for sustaining AI skills in teams.
Voluntary participation and positive reinforcement foster better learning outcomes compared to mandates.
Tracking completion and usage data helps maintain momentum and identify areas needing support.
Continual iteration and embracing frontier models are essential to keeping AI enablement effective.

Summary

The Background and Challenge of AI Adoption at Indeed

Indeed aimed to boost the percentage of engineers using AI coding tools from 25% to near-universal adoption. Initially, they faced a significant challenge as only a quarter of engineers actively used these tools weekly despite substantial investment and encouragement. The goal was to reduce overhead work, increase the efficiency of innovation, and ultimately improve overall engineering productivity, including reducing coding time and fostering effective AI usage.

Strategies for Driving AI Adoption and Enablement

Indeed employed a multi-faceted approach starting with an initial train-the-trainer model that proved insufficient, leading to a redesigned strategy focusing on structured, hands-on training and community engagement. They established AI coding ambassadors to promote usage, eventually transitioning to a broader 'AI coding essentials' program that enabled all engineers to learn and practice AI skills through comprehensive, accessible content. Emphasis was placed on asynchronous learning complemented by live forums and community activities to foster ongoing learning and sharing.

Implementation and Outcomes of Training Programs

The firm saw remarkable growth in AI tool usage, reaching 97%, with significant impact on reducing engineers' coding time by around 35%. The training involved real-world exercises, community recognition, and continuous engagement through webinars, office hours, and Slack channels. They tracked progress through tools like Jira and dashboards, emphasizing completion and effective use to sustain high engagement and results. They noted that the success was facilitated by voluntary participation, community building, and leadership support, rather than mandates.

Maintaining Momentum and Future Plans for AI Enablement

Going forward, Indeed plans to iterate on their training models, incorporate AI-powered coaching, and expand AI literacy beyond software engineering to other R&D functions. They recognize the rapid pace of AI model development mandates ongoing re-evaluation of their tools and techniques. Their focus is to keep training current, promote continuous learning, and address bottlenecks such as code review delays while exploring new delivery modes, including AI as a coach to support engineers.

Reflections and Lessons Learned

The hosts highlight the importance of community, psychological safety, and tailored engagement strategies in driving adoption. They stress that continuous measurement, feedback, and iteration are vital as AI technology evolves. Their experience underscores that effective enablement depends on making learning accessible, fostering peer sharing, and aligning training incentives with organizational goals.

Show notes

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