Tech Explained: Here’s a simplified explanation of the latest technology update around Tech Explained: Why Pilots Succeed — But Performance Doesn’t Scale in Simple Termsand what it means for users..
By late 2025, many engineering leaders had the same experience: developers were faster than ever, but overall delivery performance barely changed.
The 2025 DORA report captures the disconnect succinctly: Speed-ups on individual tasks rarely translate into overall performance improvement unless teams address systemic constraints.
Harvard Business Review reached the same conclusion: early wins from AI stall once teams attempt to scale them because workflows around the tools remain unchanged. AI accelerates tasks; delivery requires transforming systems.
This reality is well known inside high-performing engineering organizations, and it is a challenge Cong Nguyen, CEO of Synodus emphasizes: “AI is not the shortcut. It’s the amplifier. It scales whatever engineering discipline or lack of discipline you already have.”
The teams that prepared their architecture, feedback loops, and governance have seen meaningful impact. The teams that didn’t found AI adding noise, not velocity.
This is the central truth engineering leaders must confront: AI does not solve systemic bottlenecks. It exposes them.
Lessons from Real Engineering Teams
The industry’s most experienced practitioners increasingly agree that the real work is not in the model, but in the integration.
Christian Runge, Senior Director of Engineering for AI at the financial technology company SimCorp, has seen this firsthand while overseeing AI adoption across a globally distributed engineering organization. SimCorp’s pilot outcomes hinged on one factor: workflow maturity. “For our use cases, the most work is not on the AI part. It is on integrating it in our platform and workflows to create production grade, secure, integrated, high quality solutions.”
The teams with the most guided, stable workflows, fast feedback loops and independent software architecture saw the fastest and most reliable gains when pairing these foundations with AI-assisted coding and agentic systems. Teams with less of this foundation had a harder time to validate suggestions, maintain quality, or benefit from automation.
This pattern mirrors case studies across the industry. Adidas reported 20–30% productivity gains in teams with loosely coupled architecture and rapid feedback loops. Teams tightly coupled to
legacy ERP systems saw little improvement. Booking.com found AI’s impact uneven until developers learned how to give explicit instructions and structured context, after which merge requests and satisfaction increased.
All three cases point to the same conclusion: AI works when engineering fundamentals work. It cannot compensate for architectural debt or fragmented workflows.
As Christian notes, adoption barriers are rarely technical: “There are always discussions about priorities… it’s more a choice of when to do it than if it’s possible.” The real constraint is organizational readiness, not AI capability.
Building the AI-Native Engineering System
If AI is an amplifier, then engineering leaders need a model for scaling impact without introducing chaos.
One of the most practical frameworks emerging today is the Two-Lane Model, articulated by Tim Kitchen, CEO of Coding the Future with AI: “AI-native engineering only works when teams separate the system into two lanes. Delivery Lane: shared tools, repeatable workflows, predictable outcomes. Innovation Lane: a safe space for experimentation without breaking delivery. Both must exist—but cannot be mixed. This separation prevents chaos, improves consistency, and accelerates learning.”
The model explains why so many pilots stall at scale. When experimentation enters the delivery pipeline, quality breaks, trust erodes, and teams conclude prematurely that “AI isn’t ready.” In reality, the workflow wasn’t ready.
This aligns with what Synodus sees across modern engineering organizations:
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AI reshapes skills and behaviors, not roles.
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Senior engineers spend more time on architecture, constraints, and workflow design.
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Leaders shift from task assignment to system orchestration, ensuring AI, automation, and human judgment reinforce one another.
As Christian frames it: “AI is not deterministic and will not work perfectly—at least not in its current form. For the foreseeable future, I mostly see it as another abstraction layer on coding and the tools we use.”
AI is becoming the environment engineering happens in. The teams who thrive will be those who treat workflow design as a first-class engineering discipline and give AI something coherent to accelerate.
About Synodus & Our Webinar Series
To hear how leading engineering teams are turning AI pilots into real delivery performance, join Synodus’ webinar series “AI in Software Delivery: From Pilots to Performance.”
Episode 1 — Building the AI-Native Team (06/01/2026) Featuring Tim Kitchen, CEO & Founder, Coding the Future with AI
Episode 2 — Measuring AI’s Real Impact in the SDLC ( 22/01/2026) Featuring Christian Runge, Senior Director of Engineering, AI at SimCorp
Synodus is a performance-led engineering partner serving BFSI, healthcare, and regulated sectors. We focus on building reliable systems and applying AI responsibly to unlock meaningful, measurable business outcomes.