PWC Achieves 7x Improvement in AI Code Generation Through Agentic AI Technology

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🌏 COVER STORY: PWC Achieves 7x Improvement in AI Code Generation Through Agent Technology

PWC Achieves 7x Improvement in AI Code Generation Through AI Agent Technology

Professional services giant PricewaterhouseCoopers has transformed its software development process using artificial intelligence agents, boosting code generation accuracy from 10% to 70% - a development that could reshape how major enterprises approach AI adoption.

The initiative, spearheaded by PWC's Commercial Gen CTO Jacob, began two years ago when the firm faced challenges with traditional AI approaches to software development. "When we started our gen transformation, there weren't a lot of agent frameworks just kind of lying around," Jacob says. The firm initially developed its own solutions before transitioning to more sophisticated AI agent technology.

The breakthrough came in addressing a critical business challenge: generating code for proprietary enterprise systems. These systems use specialized programming languages like ABAP (used in SAP systems) and Apex (used in Salesforce), which typically require extensive manual coding. PWC's AI agents now handle this task with 70% accuracy, dramatically reducing development time and potential errors.

What sets this implementation apart is its focus on real-time validation. The AI agents don't just write code; they execute it in runtime environments and analyze the output, continuously learning from the results. This approach has proven particularly valuable for PWC's consulting teams, who previously struggled with long wait times for technical specifications and code generation.

The firm partnered with Crew AI, an artificial intelligence platform, to implement these changes. "Not everyone who's working on gen AI was born an agent expert," Jacob notes, explaining why they chose a platform that balanced ease of use with advanced capabilities. This decision proved crucial for widespread adoption across PWC's technical teams.

However, the transformation wasn't without challenges. Beyond technical hurdles like managing system loads and response times, PWC faced significant cultural adaptation needs. "The harder part is the change management, the human workforce element," Jacob explains. "Getting people used to working with agents in their day-to-day workflow and business process - that's where we've been spending a lot of our time lately."

One unexpected benefit has been unprecedented visibility into development processes. The AI agent system provides detailed metrics on task completion times and tool usage, allowing PWC to quantify efficiency gains and return on investment with precision previously impossible in software development.

For other enterprises considering similar transformations, Jacob emphasizes the importance of gradual implementation. "Start simple and then increase complexity as you go," he advises. "Make sure you start off with some of the basic prompt engineering use of RAG pipelines, things like that, and try to see what gaps you need to solve from there."

The success of PWC's initiative signals a broader shift in how large enterprises might approach AI integration. As companies struggle to efficiently develop and maintain complex software systems, PWC's agent-based approach offers a promising template for scaling AI capabilities while maintaining high accuracy and user trust.

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