Today we will be discussing the lifecycle of AI projects and how they are undertaken, both internally and externally. The focus will be on why certain internal AI projects fail to meet the expected outcomes, and how organizations can avoid falling into these traps.

One of the biggest factors affecting the success of internal AI projects is the quality of data. Gathering complete data, including metadata, is crucial to developing accurate AI models. If an organization is trying to solve a customer retention problem, for example, they will need a lot of signals and hard facts about their customers to provide context for the AI. It is important to gather data in all the sequences and times that the environment and customers were observed to avoid missing data.

Furthermore, bad data quality can negatively affect the success of AI projects. To ensure that data is usable down the line, organizations must ensure that it is clean and labeled well. Knowing what a good outcome looks like and showing it to the AI model is essential, especially in the world of inference and classification in AI.

Another issue that can arise in internal AI projects is siloed data. Different departments within the organization may have gathered or created data about the same problem or context. However, due to security concerns, ego, or other factors, the data may be siloed, and departments may not communicate with each other. This can lead to incomplete and missing data, as well as duplicate data sets and even duplicated AI models.

Organizations should prioritize breaking down data silos and collaborating on data gathering and modeling efforts to avoid these issues. It is essential to have a complete picture of the customer journey and transaction to inform the AI model accurately. By breaking down data silos, organizations can save time, money, and resources and build more accurate AI models that meet their desired outcomes.

In conclusion, the success of an internal AI project depends on the quality of data and the level of collaboration between different departments within the organization. Organizations can build more accurate AI models that meet their desired outcomes by prioritizing clean, labeled, and complete data and breaking down data silos.