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March 29, 2026

Back to the Future - A Historical Perspective on AI

Submitted by Ulysses Learning

Dina Vance, Senior Vice President from Ulysses Learning, takes on our reader’s question this month. She offers several observations and best practices that are commonly overlooked in contact centers across the US. According to Dina, these are among the top of those best practices that can transform your contact center into a customer experience-focused operation that gets high marks. 

NOTE: We’re looking for more of your challenges. Email your contact center-related questions to: ChallengeSolved@ulysseslearning.com

Q:  We know Ulysses Learning was a pioneer in AI. What has changed since you were founded in 1995, and do you have any suggestions for where AI is going to take us based on 30 years of experience in the space?

A: While the scale has certainly changed, at its core AI has stayed exactly the same: helping people think better, learn faster, and perform at their best. 

When people hear the term “AI” today, they often think of dazzling chatbots and futuristic systems capable of generating ideas, analyzing sentiment, or completing tasks at astonishing speed. Artificial intelligence has become a shorthand for efficiency especially in customer service environments where the promise of faster handling times and better customer experiences dominates much of the conversation. 

But nearly three decades ago, when Ulysses Learning first embedded artificial intelligence into our learning programs, the word “AI” carried an entirely different meaning. It was not a buzzword, nor a trend line on a conference slide. It was a science based on rigorous, research-based study, deeply human in its origins and it was the foundation on which we built our mission to help call centers elevate conversations to drive better results.  

As we look toward the next evolution of contact-center excellence, we believe it’s worth revisiting what “AI” has meant throughout our history, how it transformed our methodology, and why the human-centered approach we embraced in 1995 remains more critical than ever. 

1995: When AI Meant Cognitive Science, Not Code

Ulysses Learning was founded during a time when artificial intelligence was still largely the domain of academic research. Companies were not talking about AI-powered coaching or automated quality monitoring. Instead, our team of industrial psychologists were focused on understanding how people think: how experts reason through complex problems, how decisions are formed, and how learning can be accelerated. 

It was in this environment that Ulysses Learning began as a joint venture with the world-renowned Learning Sciences faculty at Northwestern University Learning Sciences Department. Our founding question was not, how can we automate call-center tasks? but rather:
How do top performers reason and could a system help others learn to do the same? 

Artificial intelligence, for us, was synonymous with expert-modeling. It meant capturing the decision patterns of high performers while breaking down the implicit knowledge that makes someone exceptional.  Taking this a step further we coupled our learnings with intelligent tutoring systems that could replicate the reasoning process of a skilled coach. 

In other words, AI was not about replacing people. It was about revealing what people do best and scaling that excellence.

Expert Modeling: The First AI Engine Behind Customer Service Excellence

Our earliest AI systems focused on identifying the cognitive patterns that distinguished the best customer service representatives. Through interviews and observation our industrial psychologist researchers mapped out how top performers:

• Diagnosed a customer’s needs
• Evaluated competing priorities
• Chose the next best action
• Balanced empathy with problem-solving
• Used structure to maintain control while remaining conversational 

This body of research became the backbone of what we now call Framework with Freedom™—a framework that captures the mental processes behind effective customer conversations. Long before machine learning models were trained on vast datasets, our expert models were trained on something much richer: human expertise in its purest form. 

The AI that powered our early simulations guided learners through branching scenarios, offering feedback that was both context-sensitive and pedagogically sound. The goal was to help learners internalize expert reasoning, not just memorize scripts. 

From the beginning, we believed the heart of customer service performance lies not in automation, but in thinking well. And our early AI was designed to teach exactly that. 

2000s: When “AI” Meant Intelligent Tutoring and Human Coaching at Scale

As we entered the 2000s, the term “AI” slowly began shifting. Businesses started to see the potential of computer-based instruction and early analytics. But for Ulysses Learning, the definition stayed consistent: AI was an engine to coach learners at scale, using simulations powered by expert modeling. 

Our systems offered personalized feedback and adaptive paths which are capabilities that many organizations today still label “AI-driven.” Yet back then, they were simply the natural outcome of applying learning science to real-world performance.

During these years, the contact-center world was wrestling with rapid technological change: customer relationship management tools, knowledge bases, digital communication channels were all transforming the agent’s workflow. But one truth remained: the core of customer experience was still the conversation between a customer and a representative.

This era cemented our belief that the best use of AI in learning is to amplify human capability, not overshadow it. 

2010s: When AI Became Synonymous with Automation—But We Stayed Focused on People

The 2010s ushered in a new wave of interest in artificial intelligence—driven by big data, machine learning, and increasing desire for operational efficiency. In many industries, including customer service, this decade sparked conversations about automation: chatbots, self-service tools, algorithmic routing, and predictive analytics. 

While these tools were valuable, they often overshadowed the deeper question:
What about the human at the center of the customer experience?

At Ulysses Learning, we doubled down on our founding principles. We continued refining our expert-modeling methods and expanded our simulation engines to reflect modern customer expectations. Rather than using AI to replace human interaction, we used it to help agents adapt to the rapidly changing nature of conversation focusing on remaining deeply human. 

We recognized that when automation handles routine tasks, the conversations that do reach a live agent are more emotionally charged and more complicated. And for those moments, agents need stronger cognitive and conversational skills.

2020s: When Generative AI Reignited the Conversation About What Intelligence Really Means

Now, in the 2020s, “AI” has acquired yet another meaning. Generative models can produce responses, analyze sentiment, summarize calls, and even coach agents in real time. These tools are powerful when implemented responsibly and can be a gamechanger for call centers.

Yet the rise of generative AI has also reminded the industry of an essential truth we understood back in 1995: technology is only as effective as the human guiding it.

Today’s generative AI can assist agents brilliantly, but it cannot replace the judgment, empathy, adaptability, and nuanced thinking that define outstanding customer service. Nor can it independently create the deep behavioral change needed for long-term performance transformation.

What it can do is enhance learning experiences and provide great insights. And in that sense, we have come full circle: AI is once again a tool for scaling expertise.

What Hasn’t Changed: Our Commitment to Human-Centered AI

Across nearly 30 years of technological evolution, Ulysses Learning’s view of AI has remained remarkably consistent:

• AI should model and amplify human expertise, not overshadow it.
• AI should support better conversations, not replace them.
• AI should accelerate learning, not short-cut it.
• AI should elevate human judgment, not undermine it.

We are proud that the principles established through our early partnership with Northwestern University still guide our approach today. As AI becomes more capable, we believe these human-centered principles matter more than ever. 

The call center of the future will undoubtedly incorporate AI and automation at every level. But the organizations that will thrive are those that invest equally in the people at the heart of their customer experience. When technology is paired with skilled, confident, and well-trained agents, the outcome is unmatched: strong customer relationships, operational excellence, and conversations that truly matter.

In Closing: AI Has Evolved, But Our Purpose Hasn’t

Over nearly three decades, the meaning of “AI” has shifted from expert modeling to automation to generative intelligence. But for Ulysses Learning, AI has always been about the same thing: helping people think better, learn faster, and perform at their best.

As the call-center industry enters a new era of possibility, we’re excited to continue leading with the same philosophy that launched our work in 1995—an approach that sees AI not as the star of the show, but as the tool that elevates the brilliance of the people who serve customers every day.

About Dina Vance