As engineering organizations expand, they inevitably accumulate process layers that reduce development. Any engineering leader who has built an organization that exceeds a certain size knows this pattern: First of all, the basic chaos, and very quickly cross-team dependencies require coordination of meetings, and ultimately, you find yourself considering managing it all securely like a secure framework. I once found myself running engineering organizations using a 3D organization matrix (not computed with separate product organizations). result? VPS was frustrated by slowing down, engineers blamed “process overhead” on delays and grind the crawl under the weight of bureaucracy.
For those who have been there, the process tax on innovation is real and expensive. AI now provides an escape route, rather than making engineers faster first-order effects faster, but through profound second-order effects that fundamentally reshape how engineering organizations work.
Beyond productivity: Organizational impact
While much of the focus is on AI’s ability to accelerate individual coding tasks, the more transformative potential lies in how it reduces the need for organizational complexity. By enhancing a single capability, AI systematically eliminates many coordination problems that processes are designed to solve.
Consider the ideal of a “full stack engineer”. Historically, this is often more aspiring than reality in organizations of scale, often creating parallel organizational structures for Scrum teams. Today, AI has greatly changed this equation. Engineers can effectively work in unfamiliar parts of the code base or technology stack and bridge knowledge gaps in real time. result? Teams require fewer handovers, thus reducing coordination overheads that plague large organizations.
This feature extension also extends to the architecture. Instead of waiting for a formal building review meeting, engineers can use AI as their initial “school partner” to develop and refine ideas. Engineers can interact with AI to challenge assumptions, identify potential problems and strengthen proposals before involving human auditors. In many cases, these AI-assisted recommendations can be shared asynchronously, often completely eliminating the need for formal meetings. The architecture is still properly reviewed, but there are no calendar delays and coordination headaches.
Quality assurance provides another opportunity for process simplification. Traditional development cycles involve multiple handovers between development and quality inspections, and errors trigger new review and rework cycles. AI compresses this cycle by helping developers integrate comprehensive testing, including integration and end-to-end testing. By identifying problems ahead of time and more reliably, AI reduces the back and forth of traditionally slowing down releases. Teams can use fewer round trips to maintain high quality standards.
Perhaps most importantly, these individual capacity enhancements are enabling organizational simplification. Now, teams that used to rely on multiple groups can now operate more autonomously. Projects that once needed several professional teams could increasingly be handled by smaller, more self-sufficient groups. The meticulous scaling framework that many large organizations often reluctantly adopt is no longer needed when teams have AI to scale up their capabilities.
15 Minute Rules: Reimagine the Agile Process
These transformations create opportunities to simplify the traditional chaotic process. Consider adapting to AI-enhancing teams’ personal productivity “2-minute rule”: “If it takes less than 15 minutes to correctly prompt the AI agent to implement something, do this right away instead of putting that task through the entire backlog/planning process.”
This method greatly improves efficiency. While working on AI, engineers can focus on other priorities. If the AI solution is insufficient, they can create a proper user story for the backlog. With proper integration, small improvements are constantly made without rituals, while larger efforts still benefit from proper planning.
The pattern we see hint at the advent of a new software development model, a way of retaining the principle of human-centered agility while eliminating most of the process overhead accumulated over the years.
Leadership in the AI-enhanced engineering era
For engineering leaders, this transformation requires a basic rethinking of organizational design. As teams grow, reflections increase processes, specialization and coordination mechanisms may no longer be the right approach. Instead, leaders should consider:
- Invest heavily in AI capabilities that expand the range of effective skills for individual engineers
- Challenging assumptions about the size and specialization of necessary teams
- Try simplified process models to reduce effects by using AI coordination
- In addition to traditional development indicators, it is necessary to measure and optimize to reduce “process time”
Thriving organizations will be the enablers of not only productivity tools, but fundamentally simple organizational structures. By developing hierarchies, reducing handovers and eliminating coordination overheads, AI offers the potential to combine entrepreneurial speed with problem-solving capabilities of large engineering organizations.
After two decades of process complexity in software development, AI may eventually allow us to return to the original spirit of the Agile Manifesto: interactive assessment and interaction of individuals and tools. The future of engineering is not only faster, but also simpler.
