2 AI software developer insights you need to know in Q2 2026: AI in developers’ workflow & the ROI measurement gap
- Stathis Georgakopoulos
- 1 hour ago
- 3 min read
You don’t need another AI hype post, so I will keep the intro part short and sweet.
Too sweet, actually, because I will be sharing the freshest possible data from the 31st wave of our independent survey, which reached more than 11,500 respondents from 95 countries around the world.
Based on the findings, we produce a 6-piece report series that delves into key developer trends for Q1 2026 and beyond.
They're all free and available here, btw.
In this post, we’ll look at 2:
AI in the Developer Workflow
The AI ROI Measurement Gap
AI in the Developer Workflow
Generative AI has entered the developer toolkit, but how deeply has it actually embedded itself into the work? This report examines how developers are using AI across their workflows: which tasks they are turning to AI for, how much of each task they are willing to hand over, and where the gap between adoption and reliance reveals the limits of current tooling.
Code generation is the most widely used AI-assisted task, with 49% of developers using AI for it, yet fewer than half of those are letting AI handle the majority of the work.
Drawing on survey data from developers actively using AI-assisted software development tools, the findings move beyond headline adoption figures to examine the texture of AI use across more than a dozen distinct development tasks. The result is a more honest picture of where AI is genuinely accelerating developer work, where it has found a willing audience for tasks developers are happy to offload, and where capability gaps are suppressing the trust needed for deeper reliance.
For organisations evaluating or expanding AI tool adoption, the data offers a practical lens for calibrating expectations, informing governance, and identifying where human oversight remains essential. For those building the next generation of AI developer tools, it maps the frontier clearly: the tasks with high demand and low trust are not side thoughts; they should be the roadmap.
Key Questions Answered in the AI in the Developer Workflow report
What tasks are developers using AI tools to assist them with?
How much of each task are developers handing over to AI tools?
Which tasks do developers trust and rely on AI tooling for?
Which tasks are the current suite of AI tooling falling short?
The AI ROI Measurement Gap
Artificial intelligence (AI) has long been embedded in technology organisations, powering systems such as search engines, recommendation algorithms, and fraud detection tools. However, it is now far more visible and strategically prioritised, with generative AI chatbots, coding assistants, and enterprise automation tools bringing it to the centre of business planning. As AI investment scales, a new pressure is emerging: the need to justify it. Boards want evidence, finance teams want numbers, and developers caught in the middle are discovering that believing AI works and being able to prove it are two very different things.
The vast majority (80%) of developers in leadership roles (technology leaders) are using AI-assisted tools, and 75% rate them as valuable.
This report examines how developers in leadership roles – hereinafter referred to as technology leaders – are experiencing and evaluating AI value today. We examine how they rate what it delivers, whether they measure it, and how rigorous those measurements are. The findings are drawn from 2,341 professional developers working in leadership positions in SlashData’s 31st global developer survey.
This report provides an overview of the headline findings. For a full deep dive, including breakdowns by role, sector, region, and agentic AI maturity level, see the full report, The state of AI ROI measurement in software teams.
Key Questions Answered in the AI ROI Measurement Gap
What share of technology leaders are using AI-assisted tools?
What share of those using AI-assisted tools are measuring their value or ROI?
How is the AI ROI structured in terms of maturity, and how does that differ based on company size?
How does AI ROI maturity level affect technology leaders’ evaluation of AI assistance?
About the author
Stathis Georgakopoulos, Head of Marketing at SlashData
Stathis leads product marketing and loves building helpful content that turns complex research into practical decisions. He focuses on setting the table for launches and campaigns, and has a soft spot for content marketing.
