Saas Business Transition to "Agentic" paradigm: Part 1

"Published as an independent thinker not affiliated with any commercial entity."

The Software-as-a-Service (SaaS) business model is transitioning from a focus on information aggregation and processing to an agent-centric approach, where AI-driven systems are empowered to act on behalf of the user. In the traditional SaaS model, the primary value proposition is the collection, analysis, and presentation of data, which users can then interpret and act upon. In some advanced system these Saas product also take limited set of actions.

However, as AI technology advances, there is a growing expectation for systems to not only provide insights but also autonomously execute tasks and make decisions based on user preferences. This shift towards actions on behalf of the user is a paradigm shift. Now we will allow this AI system to have agency. They will make decision and take action for us.

This shift enables greater efficiency and reduces the cognitive load on users, as the AI agent becomes a trusted intermediary capable of managing tasks across various domains, such as financial management, customer service, and operational logistics. Even when applied to a non-consumer entity such as government systems in Defense, Transportation and Social Security and Medicare, the large-scale impact is significant.

In an agent-centric business model, AI systems are designed with "agency," meaning they are capable of taking meaningful actions aligned with user objectives. These agents can interface with multiple systems, learn user behavior, and proactively execute decisions, whether it's adjusting resources in a cloud environment, handling customer queries, or optimizing workflows. This evolution is driven by a demand for real-time responsiveness and the ability to solve complex problems autonomously. As a result, businesses adopting this model are no longer simply aggregators of information but enablers of action, allowing customers to benefit from AI's ability to dynamically adapt, predict, and perform tasks with minimal human intervention. This approach transforms the SaaS landscape by making AI agents an integral part of decision-making processes, pushing the boundaries of automation and personalization.

Now for most part participant in AI/ML ecosystem have been aware of this transition for quite some-time (at least 4 - 5 years). The capability, accuracy and reliability of the AI based agents have beginning to reach an inflection point, where this paradigm will upend traditional business model for SaaS offerings. It also opens up technological challenges that must be addressed: Security, ability to audit the decision lineage and a control mechanism for consumer at large to have a transparent view of the ecosystem.

In part 2 of this article, I will explore the application and impact of this parading to an actual use case.