Hands-On AI Coding Workshops Drive Up to 50% Time Savings for a Global Technology Company

About Our Client

The Client is a French technology company with over 20 years of industry experience, known for releasing several groundbreaking digital tools that have achieved global recognition.

Uncertainty Between AI Hype and Engineering Reality

The Client has in-house development teams that engineer and support all its software products. As GenAI coding assistants gained popularity, the Client got intrigued by the technology but remained skeptical about its real-world applicability — particularly in complex product environments where code quality, maintainability, and architectural consistency matter more than speed alone. The Client lacked clarity about where the technology could deliver value (e.g., whether it could support core development tasks or was limited to simple boilerplate) and how to use it effectively.

The challenge, therefore, was twofold: to validate whether AI-assisted coding could deliver tangible value in a real development setting, and to guide the team toward practical adoption across both technical and non-technical roles. To address this, the Client turned to ScienceSoft, relying on our deep experience in AI strategy consulting and adoption.

Expert Workshops Turn AI Curiosity Into Practical Adoption

ScienceSoft’s architect experienced in facilitating enterprise adoption of AI-assisted coding tools acted as a consultant.

The consultant assessed the Client’s needs, concerns, codebase, and development workflows to identify where AI-assisted coding could deliver real value. Based on the gathered insights, the consultant developed a hands-on, targeted enablement program centered on Claude Code. Claude was selected over OpenAI Codex due to Anthropic’s strong record of setting industry standards and a significant leap in code quality introduced with Claude Opus 4.5. The enablement program included 10 one-hour live sessions. Each session combined demos, interactive exercises, homework assignments, and feedback discussions, allowing participants to experiment safely with Claude while receiving guidance and support. To maximize the practical value, the consultant deliberately kept all sessions focused on real-world challenges, proven workflows, key productivity techniques, and use cases relevant to both developers and non-technical roles, such as product owners.

The program gradually moved trainees from foundational concepts to basic AI use and ad hoc experimentation, and ultimately to the structured, reliable, and effective use of AI in real development work. The consultant began by explaining common failure patterns in AI-generated code and how to overcome them effectively.

Dedicated sessions focused on verification strategies (e.g., testing, code review, and behavioral checks) and debugging approaches to ensure AI-generated code would meet production standards. These sessions directly addressed the most common reasons AI-generated code fails in real projects: missing critical context, code that technically compiles but doesn’t actually work, and incorrect architectural assumptions.

To support adoption at scale, the training covered how to adapt AI tools to complex codebases, teaching Claude Code through architecture maps, reusable context guides, and pattern-based inputs. Advanced topics included multi-step workflows (e.g., defining safe code boundaries and using logging and observability when working with AI-generated code), sub-agent orchestration, automation with Claude hooks, and integrations with existing engineering tools (e.g., Jira, repositories), enabling teams to embed AI into their development lifecycle.

The program concluded with a live end-to-end feature engineering session and a review of team progress over three months. A post-training survey showed strong adoption of Claude Code, measurable productivity improvements, and high employee satisfaction with the training, leaving the Client with a clear path to integrating AI into its workflows.

Developers Report Up to 50% Time Savings Following Claude Code Workshops

ScienceSoft helped the Client move AI-assisted coding from experimentation to everyday engineering use, with 61% of trainees adopting Claude Code daily. Confidence and effectiveness grew in parallel, as 45% reported high confidence in using the tool, and 42% confirmed that the generated code was often or almost always usable with minimal edits. The most commonly reported use cases — feature implementation (68%) and debugging (55%) — highlighted that Claude Code was applied to core development tasks, not just boilerplate generation.

This shift in working patterns was also reflected in perceived business impact. A significant majority of training participants (87%) supported continuing or expanding the use of Claude Code, demonstrating strong acceptance and perceived value across teams. As a measure of productivity, more than half of trainees (52%) reported saving between 26% and 50+% of their time on daily activities such as development, research, and documentation, while 19% observed a tangible increase in overall team throughput.

Technologies and Tools

Claude Code.