#1
Dev Interrupted research: We broke the software delivery process into 14 steps across planning, requirements gathering, coding, and release. We asked participants to consider their most recent pull requests, and for each step of the process, we asked participants who completed the task: a human, AI, or both.
Coding phase shows strong AI integration
Regarding writing code, only 33% of developers rely solely on humans, while 67% use AI either wholly or partially.
AI is most dominant in writing tests, with 24% of tests being AI driven.
Even pull request descriptions have seen high AI adoption, with 53% of teams using AI assistance.
Requirements and Architecture remain largely human-led
Task creation is still 68% human-led, architecture decisions remain 64% human-driven, and the critical question of "what to build" is still 70% human-determined.
These numbers suggest that teams trust AI with implementation but hesitate to involve it in strategic decision-making.
Review and Release present the biggest bottleneck
Code review remains 51% human-only, while merge approval is 77% human-controlled.
Similarly, human workflows constrain release management, with 70% of releases being human-led.
AI can accelerate code creation, but traditional gatekeeping processes haven't evolved to handle the increased throughput.
Teams have successfully adopted AI where the risk is low and the output is measurable (coding, documentation, testing), but remain cautious about areas requiring judgment, creativity, or strategic thinking. The question for engineering leaders is whether this caution is justified or whether it's leaving significant value on the table.
Organizations like Deloitte have reported reducing unit test time by 70% through agent interactions, while others have compressed documentation cycles from weeks to days.
This level of autonomous operation extends beyond coding into areas like debugging, system administration, and even architectural analysis. However, it raises crucial questions about oversight, security, and the skills developers need to maintain as AI capabilities expand.
Implementing AI across the SDLC requires a cultural approach that embraces experimentation and healthy skepticism.
"The enthusiasts need to pull the skeptics up, and the skeptics need to pull the enthusiasts a little bit down to earth." —Birgitta Böckeler.
AI can significantly enhance quality and velocity in organizations with strong development practices.
However, AI may accelerate the creation of technical debt or security vulnerabilities in systems with underlying issues.
The sparring partner model, where AI serves as a thought partner rather than a replacement, has emerged as particularly effective.
One of engineering leaders' most significant challenges is managing expectations around AI productivity gains. The focus on percentage improvements and speed metrics can create counterproductive pressure that leads to corner-cutting and quality degradation.
Engineering leaders must also address knowledge gaps among technical teams. Many developers still don't fully understand the implications of using AI tools, from data privacy concerns to the reality that most AI interactions involve sending code to third-party servers.
"AI is becoming foundational to how we work; the real opportunity is not replacing people. It's about amplifying individuals, amplifying creativity, and improving the flow through the system." This amplification approach requires several key elements:
Transparency and ownership: Clear accountability for AI-generated outputs, with humans responsible for review and validation.
Tool selection criteria: Guidelines around which AI tools are approved for use, considering factors like data handling, security scanning, and license compliance.
Progressive experimentation: Start with low-risk use cases and gradually expand scope as teams build confidence and competency.
Continuous learning culture: Regular sharing of successes, failures, and best practices across the organization.
The conversation around AI in software development is shifting from tactical tool adoption to strategic workflow transformation. For engineering leaders, this transition requires several key considerations:
First, resist the temptation to optimize solely for speed. Organizations that see the most sustainable benefits from AI focus on overall system improvement rather than individual productivity metrics.
Second, invest in cultural change alongside technical adoption. The most successful AI implementations happen in environments that encourage thoughtful experimentation while maintaining high standards for quality and security.
Finally, think systematically about bottlenecks across the entire SDLC. The gains from AI-accelerated coding can be undermined by manual processes elsewhere in the pipeline, requiring a holistic approach to workflow optimization.
(Andrew Zigler & Ben Lloyd Pearson / June 12, 2025 / Dev Interrupted - Linear B / permalink)
#2
Paymonade.tech - Delete account feature / experience:
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