Understanding AI Economics and Assessing Your Readiness

This chapter critically examines the financial and organizational challenges of AI investments, highlighting the discrepancy between traditional ROI models and the actual economics of AI projects. It introduces the 70/20/10 budgeting rule, allocating 70% to people and processes, 20% to technology and data infrastructure, and 10% to algorithms, supported by Boston Consulting Group and McKinsey. The chapter analyzes three case studies—MegaRetail, RegionalTelco, and CommunityBank—to illustrate common misconceptions about organizational readiness and budget allocation, with 80% of AI projects failing due to human and organizational factors. It introduces the J-curve model, demonstrating that AI projects often incur losses before yielding returns, and proposes three financial models—value velocity, cost avoidance, and capability-building—to align investments with realistic timelines. The PRESS Readiness Assessment tool is presented to evaluate organizational preparedness across five dimensions: people-first, responsible, explainable, safe, and sustainable, with lower scores correlating to higher costs and lower success rates. The chapter concludes by urging organizations to complete this assessment honestly to avoid costly surprises and improve AI implementation outcomes.