Today, many organizations have to grapple with a range of architecture decisions while they are undergoing the digital transformation. While most companies already are in the middle of this transformation, they are now faced with yet another transformation, called the AI transformation that is fundamentally going to disrupt the technology architecture framework.
As new tools, models and AI ecosystem is evolving rapidly, Organizations need a reference framework that can be easily explained to the C-Suite and the boards. In order for the organizations to become smarter, we propose an Architecture framework that will help understand and navigate the AI transformation along with Digital transformation. We believe this is a typical architecture framework that most CTO's/CIO's in organizations are going to deal with.
There are forces that are rapidly evolving to make hard decisions at every layer. At the core of the modern architecture of Smarter Organizations, the first layer is the hardware that support the Infrastructure is fundamentally changing and getting disrupted with more computing power, GPU's and modern chips from NVIDIA, AMD, Intel and others.
The next layer is the Infrastructure layer, where Organizations range from maintaining their own Data Centers to managing Private Cloud, Hybrid Cloud and Multi-Cloud strategy. While there are pros and cons for each of these strategies, we will limit our discussion to the fact that many Organizations are still going through their own digital transformation.
The next layer above Infrastructure is Security. This is a very complex area, and Organizations at scale have to deal with reliability, access management to applications, governance and permissible purpose. The area of Security is very complex, typically requiring CISO's to recommend the right tools that need to be deployed, maintained and updated to keep the threat matrix under constant vigil. We will limit the discussion of this layer for now and move on to the next one.
The next layer is the databases or the data layer or the data fabric. This is where Organizations are maintaining their data warehouses, hosting BI tools, performing ETL's and running their analytics. There is a general need to understand the maturity of the organization in this layer. One needs to understand, where Organizations are in their data evolution and journey towards Insights and using advanced analytics like predictive modeling and Machine Learning. The hope here is that a modern organization that is undergoing digital transformation is sufficiently capable of leveraging the digital transformation tools to become a data, insights driven organization.
So far we discussed the layers in which the digital transformation takes the main role. The next few layers will discuss about the evolution of AI or AI fabric and how applications are built on the top of it.
The first layer in the AI fabric, is the AI models. In this layer there are rapid developments that are going on today in the name of generative models or LLM's, Foundational models which are low grade version of LLM's, Custom models where the LLM's or Foundation models are fine tuned with an Organizations data. Finally, we also have 3rd party models that are offered by multi-AI players.
The next layer is the architecture is the human inputs layer. In one of our earlier insights, we discussed the importance and the role that humans play in smarter organizations. This layer is important to consider while architectural, product, use case evaluation decisions are being made by the organizations. One should note that a system that is well designed consists of humans and machines with humans doing what they do best ( emotional and social intelligence), and computers being assigned tasks that they do best(automating tasks, translation, Organizational knowledge management etc).
We finally reach to the point, where our applications reside that interacts with the Organizational customers. As we think about this layer, Organizations need to consider that there are many possible use cases one can build a product, however not all use cases are worth the Investment, ROI, customer benefit, managing customer concerns, ethical considerations, regulatory framework and Privacy( to name a few). Organizations need a roadmap to list the use cases that will be a prime candidate for AI product development.