The Challenge
AI-enabled engineering exposed a new constraint
Eudonet faced an ambitious mandate: reimagine and modernize the platform by embedding AI across the entire software engineering lifecycle, from product discovery to operations. With primary investors placing speed of software delivery at the top of the agenda, AI-accelerated development was not a nice-to-have; it was a board-level priority. With over 300 roadmap items to ship and progress measured against a regular board meeting cadence, the pressure to move fast was relentless.
Applying Claude Code to the engineering team alone helped – but Plandek’s Lead Time to Value metric revealed a deeper problem. When developers moved faster, the bottleneck simply shifted upstream to the product definition part of the PDLC. Product managers were still working at their original pace, using tools disconnected from AI, and weren’t able to get work ready for development soon enough. The constraint hadn’t been removed; it had moved to a different part of the PDLC. Plandek helped uncover this for Eudonet.
The Solution
Transforming product and engineering into an AI-native system
With Plandek revealing that product definition had become the constraint, Eudonet redesigned how both product and engineering teams worked. Eudonet invested in a 30-hour AI training program spanning both product and engineering — differentiated by role rather than one-size-fits-all:
Product managers: Git fundamentals, MCP, and AI agents for product workflows
Developers: advanced Claude Code usage, agent orchestration, and specification co-authoring
The rollout was staged deliberately – Week 1 on MCP, Week 2 on AI agents – treating AI adoption like an agile transformation rather than a big-bang change.
The team also confronted a structural limitation: their roadmap tool had no API, making AI integration impossible. Their solution – build a replacement. Using AI to build with AI, they created a local web app reading from Markdown files in a shared Git repository. All specs and discovery documents moved into this single source of truth, giving Claude full project context at any time.
Plandek continued to track how work flowed across the SDLC, showing whether changes were removing the constraint and where new software development bottlenecks were emerging.
The Results
Delivering more value, faster
Plandek measured the following improvements across Eudonet's software delivery process:
Key Learnings
AI Exposes the Constraints You Didn’t Know You Had
The most significant outcome of Eudonet’s AI programme wasn’t a metric – it was a revelation. Before the transformation, development was the bottleneck: product managers could define requirements faster than engineers could build. After it, that constraint had completely inverted.
Not adopting AI across every stage of the SDLC accentuates inefficiencies borne out of interdependencies between the different stages. Making one function faster with use of AI only shifts the delivery bottleneck, and in Eudonet’s case, showed that product teams couldn’t keep up with the increased rate of delivery of the development teams. Plandek’s data surfaced this shift in real time, giving Eudonet the evidence to act on it rather than discover it months later.
This exercise has helped Eudonet rethink how they run Agile, as the natural next step to maturing into an AI-native organization. They are now able to do the following:
Less granular upfront task breakdown – AI handles decomposition that previously required detailed specs
Developers take halfway specifications and complete the detail themselves
Closer developer engagement with business processes, reducing the hand-off cost between product and engineering
Discovery remains with the product team, but the bar for ‘ready to build’ has fundamentally shifted
The lesson for any organization on this journey: measure the whole system, not just the part you’re transforming. The bottleneck will move – and you want to know where it went.
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