AI's most successful enterprise use cases, customer service and coding, target opposite ends of the labor cost spectrum. It either replaces easily quantifiable, lower-cost roles or provides significant leverage to the most expensive employees like software engineers.

Once AI coding agents reach a high performance level, objective benchmarks become less important than a developer's subjective experience. Like a warrior choosing a sword, the best tool is often the one that has the right "feel," writes code in a preferred style, and integrates seamlessly into a human workflow.

Block's CTO quantifies the impact of their internal AI agent, Goose. AI-forward engineering teams save 8-10 hours weekly, a figure he considers the absolute baseline. He notes, "this is the worst it will ever be," suggesting exponential gains are coming.

Proving the ROI for developer productivity tools is challenging, as studies on their impact are often inconclusive. A more defensible business model focuses on outright automation of specific tasks (e.g., auto-updating documentation in CI). This provides a clear, outcome-oriented value proposition that is easier to sell.

Don't view AI through a cost-cutting lens. If AI makes a single software developer 10x more productive—generating $5M in value instead of $500k—the rational business decision is to hire more developers to scale that value creation, not fewer.

Human intuition is a poor gauge of AI's actual productivity benefits. A study found developers felt significantly sped up by AI coding tools even when objective measurements showed no speed increase. The real value may come from enabling tasks that otherwise wouldn't be attempted, rather than simply accelerating existing workflows.

Coastline Academy frames AI's value around productivity gains, not just expense reduction. Their small engineering team increased output by 80% in one year without new hires by using AI as an augmentation tool. This approach focuses on scaling capabilities rather than simply shrinking teams.

When offered a choice between an extra hire or expensive AI coding subscriptions for their team, line managers almost always choose the headcount for team growth. VPs, focused on broader business metrics, often prefer the AI tool for its potential productivity gains across multiple teams.

While AI coding assistants appear to boost output, they introduce a "rework tax." A Stanford study found AI-generated code leads to significant downstream refactoring. A team might ship 40% more code, but if half of that increase is just fixing last week's AI-generated "slop," the real productivity gain is much lower than headlines suggest.