To choose the right approach to generative AI, enterprises should consider factors such as model quality, serving cost, serving latency, and customizability. Instead of adhering strictly to one path or another, it is essential to carefully evaluate the use case and various factors that will ultimately determine the most suitable approach. For instance, in a legal firm where data and information are highly sensitive, it is advisable to ensure that the data never leaves the company’s environment. In such cases, opting for a self-hosted open-source model would be a preferable choice. As AI innovation continues to progress rapidly, more options will emerge, providing enterprises with even greater possibilities.
Smartbridge recommends using Azure Open AI or similar, with the ability to access the most updated model, customized to the industry domain and the company. This recommendation was based on the life cycle cost of the model, data privacy and security, efficiency, and long-term maintainability.
By now, you should have a better understanding of whether their business use cases require a generative AI solution, the type of solution to consider, and the optimal approach for adoption. However, it is equally crucial to establish a framework and ensure all necessary resources are in place for the successful implementation of a generative AI solution. In an upcoming article of this generative AI series, we will explore the potential risks associated with generative AI and present an enterprise checklist for assessing its readiness in terms of people, processes, technology, and data.