WorkWave reduces workflow time by 80% with Claude Code and N-iX

WorkWave aimed to build a scalable, evidence-based approach to GenAI adoption across an engineering organization of approximately 250 people. Engineers already had access to AI tools, including Claude Code, GitHub Copilot, and Gemini, and the company wanted to turn this access into repeatable delivery practices with measurable impact.

The team focused on improving engineering throughput and pull-request flow while maintaining software quality. Before the program, average throughput was 2.8 pull requests per engineer per month, average PR cycle time was 12.4 hours, and AI-generated code represented 0% of measured output.

WorkWave also wanted to identify the development workflows where AI could create the clearest value, equip internal champions to lead adoption, and establish reliable metrics for throughput, cycle time, quality, and AI use.

N-iX designed and ran a three-month GenAI acceleration program with four WorkWave teams, representing 30 engineers. The program applied the APEX framework, N-iX’s structured methodology for enterprise GenAI enablement.

Rather than relying on a top-down rollout, N-iX used a bottom-up champion model. N-iX GenAI Adoption Leaders worked directly with one AI Champion from each WorkWave team on real production tasks. The program followed four stages:

• Capture. N-iX interviewed AI Champions across four teams and mapped their recurring workflows, time investment, and improvement opportunities. The teams identified 38 potential workflows and documented the relevant delivery bottlenecks.
• Spot. N-iX and the champions prioritized the opportunities using a shared scorecard. They shortlisted 14 workflows and selected five for pilots based on feasibility, delivery value, and potential for reuse.
• Co-implement. N-iX and WorkWave champions built AI-enabled workflows, prompts, commands, and MCP integrations. The teams tested the workflows on real production tasks and held two to five hands-on working sessions with each champion. The work included a desktop-to-web modernization platform, a scheduling service, a mobile field-operations assistant, and a shared routing service. N-iX delivered role-specific workshops on Claude Code, GitHub Copilot, and Gemini, then connected the relevant tools to WorkWave’s DX platform for automatic metrics collection.
• Demo. WorkWave champions presented results to their teams, shared before-and-after metrics, collected feedback, and identified opportunities for broader adoption. By the end of the program, the champions could extend workflows independently and support their teams’ continued use of AI.

The program ran for three months and focused on real production development work across four WorkWave teams. It supported teams building modernization, scheduling, mobile field operations, and routing capabilities.

Five AI workflows moved through pilot validation, while N-iX packaged 14 reusable workflows for wider use. The teams connected Claude Code, GitHub Copilot, Gemini, Jira MCP, Confluence MCP, and related delivery systems to the DX platform, creating a shared measurement baseline.

Eight AI Champions became autonomous practitioners within three to five weeks. They began presenting outcomes to their teams, supporting peer learning, and driving adoption independently.

The program established a foundation for scaling GenAI adoption from the initial 30 participating engineers to more than 100 engineers. WorkWave now has measurable team-level visibility into AI-generated code, throughput, PR cycle time, and quality.