How Arcoro doubled AI adoption and reported 26% velocity gains with DevClarity

Arcoro, a provider of human resources management (HR) software for construction and specialty trades, needed to accelerate innovation without proportional headcount growth.

The engineering team had access to AI coding tools—primarily GitHub Copilot and Claude Code—but with developers focused on shipping product every day, adoption had been organic rather than intentional. Most were using AI only in passing, and without structured workflows or shared best practices, the tools weren't yet delivering on their potential.

This gap became clear during Arcoro's modernization initiative—migrating legacy applications to a modern front-end architecture. Early proof-of-concept migrations using basic AI prompts still required significant developer time due to major architectural differences between the legacy and target frameworks. Without repeatable workflows, migration at this pace would bottleneck the entire modernization roadmap.

DevClarity partnered with Arcoro on an AI Jumpstart engagement—a structured program combining foundational training, hands-on work on the CoreHR migration, and team-wide knowledge sharing. The goal: move AI adoption from occasional use to repeatable workflows the team could apply across the modernization backlog.

DevClarity began with foundational AI coding training for the full engineering team, then worked alongside a smaller group on the CoreHR migration to build and test reusable workflows against real constraints. The engagement wrapped with organization-wide knowledge sharing sessions so every team could apply what was learned.

The engagement included:

• • Foundational training. The engineering team completed training on AI coding best practices and environment setup. DevClarity established documented coding guidelines and reusable prompts tailored to Arcoro's tech stack.
• • Structured migration process. Developed a systematic workflow for migrating legacy pages to the modern architecture—ranging from straightforward pages to complex multi-step workflows—with built-in checkpoints at each stage.
• • Plan-first workflow. Required a written migration plan, reviewed and approved by engineers, before any code changes. Human review at each step caught AI assumptions before they became bugs.
• • Verification and iteration. Created a three-step verification process (document legacy behavior → verify plan → verify migration) that kept quality high. When the AI made incorrect assumptions, the team updated their guidelines to prevent repeat mistakes—building institutional knowledge over time.
• • Knowledge sharing. Conducted sessions across the engineering organization to demonstrate the workflow, share reusable artifacts, and teach practical patterns for breaking complex tasks into manageable steps.

Throughout the engagement, every challenge became a documented improvement. The team also established a "thin slice" strategy for complex pages, making plans more manageable and validation more straightforward.

Based on a self-reported survey, Arcoro's engineering team saw 41% faster completion of repetitive tasks, 26% higher overall weekly output, and AI coding time more than doubled. Most developers now use AI tools daily, with the majority using AI for more than half their coding time.

Beyond the headline metrics, the engagement produced durable changes across the organization:

• • Confidence in AI output up 36%. Developers moved from skepticism to trust, with structured workflows making AI results more predictable and reviewable.
• • AI integration in development processes up 31%. The team adopted reusable prompts, plan-first workflows, and automated code review as part of daily practice.
• • Reusable migration workflow. The CoreHR migration approach became immediately applicable across Arcoro's entire modernization backlog. The team can now apply the same process systematically to remaining legacy applications.
• • All prompts, guidelines, and workflow documentation committed to the repository. The team is building on the foundation rather than starting over—extending workflows as they encounter new edge cases.

What Arcoro received. Arcoro left the engagement with shared training across the engineering organization, reusable workflows for migration and verification, and documented guidelines embedded in their repositories. The result was not just a short-term productivity lift, but a foundation the team can extend as modernization work continues.