Core Insights - General AI tools often fail to assist users in completing specific tasks required in unique workflows due to their overly generic nature [1][3] - Even when AI tools are customized for specific fields like finance or HR, they still do not add sufficient value as they lack the necessary specialization [3][4] Group 1: AI Tool Limitations - A case study from a Fortune 500 retail company revealed that an AI tool designed to streamline vendor negotiation contracts did not improve team output despite high expectations [3][4] - The AI tool could generate generic text, but the team still had to manually customize each contract with specific vendor information and terms, rendering the tool's impact minimal [3][7] Group 2: Understanding Workflow Context - The gap between general AI capabilities and specific team needs highlights a deeper challenge: current tools are not designed to understand how work is actually done [4][8] - The article introduces two key concepts to bridge this gap: Work Mapping (a digital map of team workflows) and Reverse Contextualization (customizing AI models based on team needs) [4][10] Group 3: Work Mapping and Reverse Contextualization - Work Mapping involves creating a real-time, dynamic view of how teams execute workflows across systems, capturing both explicit actions and implicit decision-making patterns [9][13] - By inputting detailed insights from Work Mapping into AI tools, organizations can transform generic models into highly specialized tools that understand local "language" [13][14] Group 4: Continuous Optimization - Organizations must continuously update Work Maps and provide feedback to AI models to ensure they adapt to evolving team needs and maintain high accuracy [15][17] - The process of Reverse Contextualization allows for the integration of local context into AI tools, enhancing their ability to serve teams effectively [11][16] Group 5: Strategic Implications for Leadership - CXOs must recognize that AI is not a "set it and forget it" technology; its value is realized when systems align with specific organizational workflows [17][18] - Investing in tailored approaches can significantly reduce error rates, cut operational costs, and yield higher returns on AI investments [17][18]