Contact Center Pipeline February 2026 | Page 39

Turnover was high, given the lack of advancement and the stressful working environment. More customers expected 24 / 7 services, an expectation that was often cost prohibitive and difficult to staff, especially for overnight shifts.

Many customer inquiries involved basic, routine questions: checking order status, account balances, seeing if prescriptions were ready, or the locations of stores. But paying human agents to handle these inquiries was inefficient and costly, while customers endured unnecessary wait times.

And, of course, humans are... human. They get sick, take vacations, and aren’ t always in the best mood.

THE EARLY ERA OF SPEECH RECOGNITION IVR

In the 1970s, early IVR systems were launched, bringing a new tool to automate voice interactions and reduce their handling by humans.

These first phone trees let you“ press 1 for sales, press 2 for service,” and so on. They didn’ t need sick days or PTO: and mood swings weren’ t an issue. Delivery of information could become standardized across the brand.

While initial systems were expensive and difficult to implement, hardware advancements brought down costs and simplified deployment.

These developments led to an explosion of IVR use in the 1990s, especially across financial services, telecommunications, utilities, and the airline and travel industries. If your business wasn’ t using IVRs, you were behind the eight ball.

However, IVR’ s limitations soon became apparent. Early systems required customers to listen to long lists of menu options, and there was no guarantee their specific issue would be on that list.

These speech-enabled systems often struggled with accents and background noise, leading to frustration and the infamous“ press zero " option to reach a human operator: defeating the purpose of the IVR entirely.

For businesses, the worst part was that instead of solving problems, these systems became emblematic of bad customer service, eroding trust in the brand.

NLP AND IVAS

Advances in natural language processing( NLP) enabled major steps forward. NLP was an early method for understanding and processing human language and human intents. It allowed IVRs to ask more open-ended questions, such as“ How can I help you?”

With NLP, IVRs became IVAs. The customer was now able to engage with the system more verbally instead of incessantly pressing buttons or screaming the ask to speak to a human. NLP enabled intent detection, which is the ability for the model to identify what the customer was asking about.

But while IVAs were heralded as a huge step forward, from a customer perspective they were still pretty lackluster.

Issues with the system misunderstanding the customer’ s request were still common. The IVA would take what was said by the customer and match it to the closest result based on their interruption. It didn’ t seem to matter if the IVA’ s response was irrelevant and had nothing to do with the request.

So, if you said,“ I ' m really struggling with opening the window,” the IVA might respond,“ Great. I’ ll help you find a new window.” It didn ' t understand the nuance of what you were saying and tried to match its settings to the closest thing it knew.

VOICE AI

Customers realized the IVA couldn’ t help with complex or nonstandard requests, and often immediately resorted to mashing“ 0” or saying“ representative,” to get out of them. And it was actually hugely time consuming for companies to build and manage these systems as they were so rigid.

THE RISE OF AI VOICE AGENTS

Through it all, customers still seek out that voice engagement. Up to 70 % of customer interactions still happen by phone despite companies’ best efforts to hide their customer service numbers.

Large language models( LLMs) enable the understanding and generation of language as well as reasoning. This massive technological advance is powering a new generation of automation: voice AI agents.

Whereas traditional NLP-based IVAs excelled at structured, rule-based customer interactions, LLMs enable more natural, nuanced, and contextually fluent engagement.

BOX

When you come across the term“ temperature control” in discussing AI it doesn’ t refer to that in the user’ s office, the conference room, or the data center.

Instead, temperature control refers to tuning how deterministic or creative an AI model’ s responses are.

Lower temperatures make the model more predictable, consistent, and on-brand, while higher temperatures allow more variation but increase the risk of going off-script.

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