Identifying AI Opportunities Through the PRESS Lens

This chapter transforms PRESS assessment scores into an opportunity filter, guiding organizations to identify suitable AI opportunities based on their readiness levels. It underscores the importance of observation over assumption in identifying genuine pain points, leading to higher adoption rates and sustainable ROI. The chapter compares AI opportunities with traditional IT projects, noting that AI systems require continuous evolution and success is measured on a spectrum of accuracy and adoption rates. Case studies, such as MegaRetail, RegionalTelco, and CommunityBank, illustrate how different readiness profiles influence AI strategy. The chapter presents ethnographic observation as a proven method to uncover AI opportunities, focusing on actual workflows and pain points to avoid costly failures. It explores the evolution of AI opportunities and human-AI collaboration modes, highlighting inefficiencies in inventory management and loan processing where AI could mitigate repetitive tasks. The chapter progresses through historical phases of AI adoption, from automation era failures to the trust frontier, where explainable and human-centric AI implementations are crucial. Key findings include the necessity of addressing organizational weaknesses before AI adoption and aligning AI technology types with appropriate collaboration modes.