Most AI budgets focus on software and infrastructure while ignoring the massive human time investment. RAND research confirms more than 80% of AI projects never reach production, and 85% of organizations misestimate project costs because they do not count employee hours, integration work, productivity losses, and opportunity costs. Here is a framework for calculating the true total cost of AI implementation.
Open source AI models look free until you add infrastructure, staffing, and maintenance. With RAND Corporation research showing over 80 percent of AI projects fail, most mid-size companies find proprietary solutions cost less overall.
For mid-size development teams, Claude Code costs significantly more than Cursor Teams. But the real cost difference extends far beyond license fees - GitClear found AI code duplication grew 4x across 211 million changed lines. Factor in integration setup complexity, training cycles, ongoing support, and productivity losses during adoption and tool migration.