What is typical AI architecture to follow for enterprise?  What are the architecture building blocks to consider for all kind of AI usecases?

The biggest challenge we see is how to connect AI tools to all of our various data sources. For example, CoPilot uses Graph, ChatGPT uses API or MCP, and AgentForce is integrated only with Salesforce. We are trying to avoid having to use multiple interfaces to our various app repositories but there is no overarching solution that enables connections to everything in our enterprise. The other challenge is our internal apps all use Identity IQ for controlling who has access to what data. Many of the current AI connection approaches struggle to enforce these kinds of data access protections.

The choice of AI architecture organizations adopt depends on their specific needs, use cases, and the nature of the problems they aim to solve. Commonly adopted architectures include machine learning for predictive analytics and decision automation; deep learning for handling large volumes of unstructured data in applications like image and speech recognition; rule-based systems for straightforward decision-making processes; reinforcement learning for environments requiring continuous learning and adaptation; and transfer learning for leveraging pre-trained models to reduce training time. 

Hybrid AI integrates multiple techniques for complex problem-solving, while federated learning focuses on data privacy and distributed sources. Edge AI is used for real-time processing at the edge, and explainable AI is prioritized in regulated industries requiring transparent decisions. The choice of architecture is influenced by data availability, business goals, regulatory requirements, and resource availability, with organizations often experimenting with multiple architectures to leverage their strengths.