Understanding AI chatbots to ensure meaningful and secure experiences

We’re back with part 2 of our AI series. In last month’s Introducing AI in code development and refinement we shared common AI terms and how our engineers are leveraging AI for innovation. Today we’re expanding our scope to chat about, well, chatbots. 

The first chatbot

Chatbots have existed as early as 1966. The first one was developed by a professor at the Massachusetts Institute of Technology (MIT) and referred to as ELIZA. Joseph Weizenbaum published a research article titled ELIZA–A Computer Program For the Study of Natural Language Communication Between Man and Machine about his work. 

He wrote, “It is precisely the prospect that such a program will converse with many people and learn something from each of them, which leads to the hope that will prove an interesting and even useful conversational partner.”

The AI chatbot evolution

When it comes to chatbots, it’s easy to picture a modal window in the corner of a website. The window mirrors a chatroom layout with automated text welcoming you to the site or asking you whether help is needed. A standard chatbot interacts based on an existing script. 

However, AI chatbots are more intuitive with ELIZA’s foundation. ELIZA was what made natural language possible with machines. We shared the definition for large language models in last month’s blog. Natural language is different in that it “helps machines to understand the meaning and intention of human words,” according to Elastic. 

In NLP vs LLMs: Understanding the Differences, the Elastic Platform Team details how a large language model (LLM) is based on existing text to develop answers. LLMs are the depth of knowledge, where natural language processing (NLP) is the translator with an ability to better interpret emotions and work toward accuracy. 

A few common AI chatbots or assistants include OpenAI ChatGPT, Microsoft Copilot, and Google Gemini. 

The security parameters needed

The recent surge of AI chatbots led to a quick adoption rate that made some experts pause to consider the security parameters in place. Users train AI by providing real-time corrections, increasing its intelligence. It’s using LLM to retain information while using NLP to keep users engaged in the experience.

However, we need to be mindful of the type of information we’re providing to these systems. Workers across industries realized the data they shared was reused with other AI enthusiasts. This included confidential and proprietary information they might have shared not recognizing the larger impact.

These AI chatbots are useful to start an idea or outline a schedule. There’s still a lot to learn and understand as they continue to evolve. It’s a paradoxical situation. There are ethical and security considerations at stake, as well as key benefits and speed to insights to leverage. 

The role AI has in user interface design

After attending the WorldatWork Total Rewards Conference and Exhibition 2025, our Chief Product Officer Tarah Lipperd shared with us a valuable moment. During a conversation, a fellow attendee suggested not to add AI for FOMO reasons. (That’s the fear of missing out.)

There are teams who are integrating AI to say they have AI. They want to be part of the conversation and seem on the cutting edge. However, does this integration improve the customer experience or simplify tasks? It depends. 

That conversation at Total Rewards emphasized having purpose and intention in design. Not to be a flashy gimmick. At RedCAT Systems, we’ll also continue to research and innovate on how best to ensure secure and purposeful processes. We’ll be intentional in how we incorporate our next generation of features and functionality.

Our RedCAT teams are focused on the customer experience and understanding what users need to succeed. Our priorities are aligned to refining the administrator and people leader experience in a meaningful way to create moments that matter.

Check back next month as we wrap up our current AI series. Look forward to further discussion about the meaningful use of AI in performance management and what ties that could have to compensation planning.