Engineering teams can't measure whether AI-assisted development actually improves productivity or profitability

Development teams adopted LLMs expecting faster code generation and higher output, but lack concrete metrics to determine if the speed gains translate to meaningful business value or actually hurt code quality and maintenance costs. Companies are stuck between continuing expensive LLM usage without ROI clarity or reverting to traditional development without knowing if they're leaving productivity gains on the table. Current solutions don't provide visibility into the true cost-benefit tradeoff of AI-assisted vs. hand-written code at the organizational level.