The Secrets of AI Value Creation

10Leading Successful Projects

You've got to start with the customer experience and work back toward the technology – not the other way around.

—Steve Jobs (former CEO of Apple)

Nearly half of IT projects exceed their budget, 7% exceed their time frame, and typically deliver 56% less value than initially expected (Bloch et al. 2012). However, it's important to note that an AI project is not just any IT project; software projects carry the highest risk of cost and schedule overruns, and AI projects tend to underdeliver even more frequently (Gartner 2018).

To mitigate the risk of project failure, classic project management techniques prove to be invaluable. In most cases, effective project management is based on mastering the fundamentals and ensuring that essential aspects are diligently executed. For instance, AI projects frequently fail because they have missing or wrong outcome expectations, miss stakeholder involvement at the initiation of a project, lack proper planning of resources and capabilities, or fail to understand the full business problem and therefore fail to anticipate how an AI solution will ultimately be integrated into business processes (Nunez 2021; Schmelzer 2022). For instance, we frequently see that AI projects miss an accurate value estimation and project scope, that a project is missing or not following a predefined life cycle, that committed and assured resources are missing when they are required, financial resources are not planned and allocated ...