Section 1 – Agentic AI: The Next Evolution of Artificial Intelligence in Enterprise Software

Just a few years ago, many B2B SaaS firms didn’t want to highlight their use of Artificial Intelligence - AI sounded too scary, it was going to take jobs away, and no one was really sure what happened inside that “black-box.” However, the rollout of Large Language Models (LLMs) like ChatGPT changed the game. Business users started using AI in their personal lives to do anything from basic research on buying a car, to creating menus based on particular diets, to now creating their own agents to manage tasks like integrating personal and work calendars and planning holidays - AI is here, and it’s here to stay. With positive personal experiences, we’re now seeing enterprises shift to embrace the previous unknown, and now AI has become a common part of workflows across company departments. It didn’t happen right away, but once started, it’s rapidly picking up and driving incredible changes in the organization.

Rules Based Automation - Initially, artificial intelligence entered its rules-based automation phase. There, it was used to handle repetitive tasks with its predefined logic. This helps remove repetitive tasks from people’s days, freeing them for more creative tasks or those requiring a hands-on approach.

Predictive Analytics - But that phase quickly evolved into predictive analytics, which was where machine learning models analyzed historical data to begin forecasting outcomes.

Generative AI - Then, AI changed again and grew into generative AI, the one type of artificial intelligence that allows organizations to generate content, summarize information, analyze incoming data, and even take an almost unprecedented step of assisting in decision-making tasks.

It is true that artificial intelligence is powerful, and many now see it almost like another human worker. However, despite all the advances, most AI systems have limitations that hold them back. The primary one is an inability to act, even with all the knowledge needed to inform handlers of what needs to be done. That means that AI may know what to do and have the information to do it, but it still requires the “go” command or a specialized prompt from a programmer or other person at the company. That takes time, and it’s what has made way for agentic AI.

Agentic AI goes beyond providing you with the information you’ve been looking for or handling your repetitive tasks, like responding to common questions asked in website chat boxes or fielding phone calls to answer questions about account balances. Instead, agentic AI introduces systems that are completely able to make a plan, decide how that plan should be executed, execute those tasks, and take notes on the results to improve upon the next iteration.

Agentic AI works with goals set by humans, works with enterprise systems, and carries out required workflows, but the key difference between agentic AI and other types of AI is that agentic AI needs minimal human interaction. Rather than being reactive, it’s proactive, potentially saving time and money if designed and put into place properly.