Not hitting the headline-grabbing, promised productivity gains of 10, 20, 30, 50%? You’re not alone. There’s a lot more data to prove that, for the vast majority of organizations, AI-driven productivity is yet to happen at scale.
Investment in AI tools is set to multiply over the next couple of years, yet only 5% of organizations report unlocking ROI on their AI investments. About three years into the Age of AI, only about 1% of companies consider themselves “mature” at deploying AI beyond innovation pockets.
We are thrilled to get real about these very real results in a conversation at 2:30 p.m. Eastern/11:30 a.m. Pacific on Thursday, May 7 with Octopus Deploy’s Developer Researcher, Charlotte Fleming, and Director of Developer Relations, Steve Fenton. We are set to talk about results highlighted in the AI Pulse Report and more of the latest research into AI adoption and its impact on developer productivity — and how to change your organization’s AI metrics for the better.
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One piece of this complex productivity puzzle is the different levels of developer experience with AI, with junior devs the most eager to use AI early and often. Reflecting on her previous work as a lecturer at the University of Technology, Sydney, Fleming explains,
“They appear highly productive, but it could be false, and suggest an over-reliance on AI for learning or relying on AI for foundational tasks which build the skills necessary for senior-level expertise. I taught students while AI tools were emerging in a big way, and it was a slow, gradual decline of the want and motivation to learn in a way that has traditionally built knowledge.”
That divergent experience also helps explain why 44% of developers responding to the 2025 Stack Overflow Survey said they are frustrated with the AI solutions out right now.
But part of this is that speed does not always equal value. High-performing organizations were never the ones that wrote more code — they had the right infrastructure in place and followed continuous delivery and platform engineering best practices to ensure that any code that goes to production exudes quality and purpose. If organizations weren’t able to scale before AI, they certainly can’t keep up with the pace of AI-generated and agentic code.
When it comes down to it, writing code was never the bottleneck — but it sure is putting more pressure on every person and system that has to review it. “AI everything” can no longer be a strategic priority. Instead you need to align all business and tech stakeholders to prioritize your continuous delivery pipelines in order to break down silos, and then to gain the speed of review, testing, and automation that’s necessary to ensure quality, security, reliability, and more.
Only once you’ve optimized your operational productivity can you begin to define and then unlock developer productivity at scale. And now, when fewer and fewer developers are writing code, everyone —across business and technical stakeholders — requires a single pane of glass to look through, to talk over, and to create common metrics to start to understand if any of your AI-led endeavors are actually moving any needles, particularly for your end users.
As an industry, we are well overdue for a deep conversation like this one about what we are measuring when we measure AI value and why. Don’t forget to sign up now to join us on May 7.
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Jennifer Riggins is a tech storyteller and journalist, event and panel host. She bridges the gap between business, culture and technology, with her work grounded in the developer experience. She has been a working writer since 2003, and is based…
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