Large enterprises can experience various challenges when implementing Generative AI (Gen AI) solutions across their functional lifecycles. The primary challenges are often more than the technical aspects of deploying a vendor-provided or in-house-developed Gen AI solution. The primary complexity culprits are usually one or more of the following: (a) lack of explicit business models to drive new revenue streams for the company; (b) incomplete company North Star vision that defines their Gen AI success strategy; (c) poor executive alignment and incentive structures created to ensure successful and timely Gen AI implementations; (d) unrealistic delivery timelines imposed on Gen AI project implementations; (e) lack of well thought out business cases to measure Gen AI project impact, including cost efficiencies and margin improvements for the company (f) insufficient focus on end-to-end business process design; and, (g) inadequate internal team expertise in delivering large-scale digital business transformations for the company. This case study will identify, generalize, and prioritize challenges with Gen AI and similar enterprise technology implementations. These insights are based on the authors' experience, over the past 25+ years, in designing, enabling, and scaling digital business transformations at Fortune 500 and other high-tech companies.
Tom Wala (Mon,) studied this question.