Core Insights - Companies are investing heavily in data platforms, analytics, and artificial intelligence, aiming for better insights, faster decisions, and measurable growth, yet many AI programs underperform and productivity gains stall [1] - The technology itself is not the primary issue; rather, it is the existing systems and organizational structures that hinder effective implementation [1][2] Group 1: Operating Models and AI - Many organizations still operate under outdated models that centralize authority and slow down decision-making, which undermines the potential benefits of AI [3][4] - AI requires clarity in decision-making, ownership, and trust in data; without these, performance can deteriorate rapidly [3] Group 2: Execution Challenges - The operating model dictates how work is done, who makes decisions, and how success is measured, yet these models often change the least despite evolving strategies and technologies [4] - AI tools can provide real-time insights, but ambiguous decision authority and risk-averse incentives can hinder effective execution [5][6] Group 3: Breakdown of Execution - Execution failures are not due to a lack of ambition or investment but stem from operating models that do not support the necessary behaviors for sustained performance [7] - A common issue is the reliance on centralized approvals, which creates delays as insights outpace leaders' ability to process them [8]