Mike Matchett from Small World Big Data conversed with Nick King, CEO of Data Kinetic, about the potential of AI and large language models in enterprise settings. Nick shared his experience of addressing enterprise applications, emphasizing the need for solutions that drive specific business outcomes rather than diving deep into the underlying technology. He learned from CIOs that business leaders often know their problems but need assistance to derive outcomes. Nick's approach is distinctive in that it starts with defining the value or problem first before developing the models, flipping the traditional data science approach. Data Kinetic's objective is to create repeatable models that can be easily implemented across different sectors, especially in complex industries like oil and gas, insurance, and healthcare. He highlighted three crucial layers in building a repeatable model: blueprint definition, functions or declarations, and the utilization of Transformers for data handling. Nick suggests that instead of companies getting overwhelmed by the technical intricacies of AI and machine learning, they should focus on their business goals and adopt ready-to-use models.