What matters most for embedding AI:
How you adapt

Over the last 15 months, we’ve researched 500+ engineers and 200+ leaders to understand the impact of AI on engineering teams. In this whitepaper, part two of our research, we look at the specific tactics that organizations are using to get more from their AI tooling.

Part one is called What matters most for AI rollouts: How you lead. That whitepaper looked at the impact of AI on developer productivity, codebase quality, and wellbeing.

Here in part two, we use new survey and interview data to dive deep into what leaders are doing to set their teams up for success with AI.

Our research shows:

• The importance of building a learning organization – one that has systems to support continuous learning – to keep up with the pace of change in AI
• What outcomes organizations want from AI, and how they’re measuring them
• The gap between adapting your codebase versus your processes to work better with AI
• How engineering roles are evolving with AI and what this could mean for the future

More about the research:

Over the last 15 months, we’ve researched 500+ engineers, and 200+ leaders, all to understand the impact of AI on engineering teams.

For our first AI impact whitepaper, we followed 500+ engineers at 4 organizations over 10 months, combining telemetry data, survey responses, and one-on-one interviews to understand the impact of AI tooling. This second whitepaper adds an industry survey to give us a breadth of insights – in total, covering hundreds of organizations with 428 survey responses (about half from ICs, half from leaders) and 29 one-on-one interviews.

Most organizations now have AI tooling, but many are still figuring out how to enable the ongoing learning required to keep up with AI, measure impact, and adapt their teams for what’s ahead. This second whitepaper focuses on the tactical side – what strategies organizations are using to get more from their AI tooling – as well as emerging changes for the field.

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