AI agents to the rescue!?

What do AI agents, data science, and big data have in common? Everything. They are different incarnations of the same goal: turning data into insights that enhance business decisions using artificial intelligence (AI).

Some of these and similar terminologies have been hyped over the years. While big data got hyped about ten years ago and data scientist was declared the "sexiest job of the 21st century" by the Harvard Business Review around that time, AI agents shot to fame only five months ago.

So, what are AI agents (or agentic AI, which is another term they go by)?

AI agents are AI solutions created to address a specific need. They analyze data and triggers to take actions, sometimes independently of a human. 

Enterprise AI agents

In complex business settings, successful implementation of AI requires understanding of business processes and systems. Since the gist of AI agents is to perform processes as autonomously as possible, these processes and the corresponding business problems need to be first clearly understood. Often the AI component in the solution is relatively small in comparison to the complex process automation. 

Under the hood, AI agents are basically software systems talking to each other. They often do that through APIs, Application Programming Interfaces, which have been used to exchange information across computer systems since the 1990s. 

The current hype behind AI agents seems to imply that they became possible only with Large Language Models (LLMs), such as ChatGPT and other AI chatbots. This couldn’t be further from the truth. The core of AI agents lies in intelligent process automation. They don’t need LLMs for that. 

It’s true, however, that AI chatbots improve access to AI solutions and process automation by allowing communication with these systems in natural language. In the technology space, this is called on- and off-ramping. Here, instructions to execute processes can be provided in natural language through AI chatbots and outcomes can be explained to humans by AI chatbots. LLMs may add some value for non-technical employees in engaging with AI agents.

The new hype

If you feel like “AI agents” is a new term replacing “GenAI” in the hype cycle, you are not alone. It surfaced only in 2023 and joined the hype five months ago. 

Many of the AI solutions that are now labeled as AI agents, however, have been used by companies for decades. This includes AI systems such as predictive maintenance in manufacturing, fraud detection in finance, and personalized product recommendations in e-commerce. So far, this was simply called AI.

The AI agent terminology is currently occupied mainly by social media “influencers” and AI hypemongers. Experts usually shy away from the latest buzzwords since the new labels don’t change the underlying concepts. However, experts could benefit from adapting their communication to reclaim the AI space.

I was in a similar situation six years ago. 

Story from the trenches 

Back then, I built and led a data science team at a pharma company. My team has been developing machine learning solutions to segment customers, optimize marketing investments, and predict revenues. At that time, cloud, big data, and AI were the buzzwords. Consultants started selling AI to executives while we were developing “only” machine learning solutions. 

My team and I have hesitated calling our work AI, even though machine learning is a form of AI. For us, the term AI was reserved for something closer to Artificial General Intelligence (AGI). 

But then we changed our approach. Before consultants label the things we have been doing for years as AI and sell it as the new greatest invention, we started labeling our work as AI, rightfully so. 

Four years ago we developed a personalized recommendation system. These recommendations were for sales reps: which customers to contact this week, through which channels, and with which content. This AI system learned from the user behavior to refine its recommendations, creating a cycle of continuous process improvement. Back then this was simply called AI. Today this would be called an AI agent. 

Back to AI

It’s easy to get distracted in a space that you just only learned about and that is being continuously hyped in (social) media. This is especially challenging if the terminology changes constantly. It’s hard to distinguish between true innovations and new labels. 

So, AI agents to the rescue? Yes, for tech vendors who are seeing the hype around AI chatbots fading and need to replace it with another hype.

But there is one benefit that AI agents bring for all companies: they require defining upfront what business needs the AI is supposed to address. Instead of simply taking AI as a technology and searching for a problem that it could solve, agentic AI forces companies to start with understanding and documenting the business problem. 

Companies who are successfully using AI have a clearly defined data and AI strategy in place, created based on their business goals. They understand what AI really is, allowing them to recognize and ignore the latest hype. And they adjust the strategy only if there is a business reason to do so, not when a tech vendor promotes its latest shiny object.

With this knowledge about AI agents, it comes down to one new insight for growing profit through AI: 

Define and follow your AI strategy. 🚀

Until next Wednesday,

Jack

For a deeper dive into Agentic AI, including more technical aspects and examples, I recommend the book “Agentic Artificial Intelligence” written by Pascal BORNET and other AI experts.