IBM is running its own AI transformation as a live case study, and Joanne Wright, SVP of Transformation and Operations, is the operational architect behind it. Rather than centralizing AI ownership in a single function, IBM has distributed accountability across every business unit while Wright’s team removes friction and accelerates adoption. Her remit spans Quote to Cash, Procurement, an AI-First Transformation organization, the Chief Data and Analytics offices, Global Real Estate, and the CIO function, giving her a cross-domain view of where enterprise AI deployment<\/a> actually stalls or scales.<\/p>\n The structural choice IBM made, embedding AI accountability into each business unit rather than housing it in a central team, is the decision most large enterprises are still circling. If your AI program lives primarily in IT or in a standalone “Center of Excellence,” you’re probably measuring adoption by deployment counts while the business units quietly route around tools they didn’t choose. The org design question is not who owns the technology; it’s who owns the outcome when the technology underperforms.<\/p>\n Wright’s role is worth reading carefully because it describes a specific failure mode most transformations hit: AI strategy and business strategy running on separate tracks until someone at the top forces a merge. Her answer is to make that merge structural from the start, with each operational leader accountable for AI results on their own team. Based on limited public information from IBM Think, the interview doesn’t quantify what that accountability looks like in practice, whether it’s tied to performance reviews, budget authority, or operational metrics, which is exactly where this model tends to break down at companies without IBM’s internal leverage over its own workforce.<\/p>\n The honest framing here is that IBM, a company whose consulting and software businesses depend on selling AI transformation to others, has an obvious incentive to present its internal model as mature and replicable, which nudges the story toward the clean version of the journey. Even so, the distributed-accountability structure it describes matches what actually works in enterprises that have moved past pilot purgatory. If your renewal conversations with AI vendors are happening in IT while your CFO and COOs are asking different questions about ROI, that gap is the problem Wright’s model is designed to close, and it’s the right place to pressure-test your own operating model before the next budget cycle.<\/p>\nWhat this means for your business<\/h2>\n