While critics might argue that we haven’t achieved as much ever since the advent of artificial intelligence in the 1950s, we have, nonetheless, come a long way.

Who would have thought that one day, robots would help us with tasks and answer our questions at a speed faster than humans could ever reach? 

AI in customer service is one of the many industries that has seen significant adoption of AI technology.

This article talks about a brief history of AI in customer support that started with ELIZA, the first chatbot created in history, to today’s NLP and ML systems where instant, personalized support across multiple platforms is no longer a dream. 

Let’s get started.

The Early Days of AI in Customer Support

The history of customer service influenced by AI models dates back to the 1950s and it has seen profound achievements in almost every decade. 

Read along to see how it originated in the first place. 

The Birth of AI and Its Initial Applications

The concept of machines simulating human intelligence began to take shape in the early 1950s. 

In 1950, British mathematician Alan Turing published his famous paper, “Computer Machinery and Intelligence.” 

The paper introduced The Imitation Game (what we now know as the Turing Test), which was a benchmark to assess if artificial intelligence could “think” like a human. 

Cut to 1952, when Arthur Samuel, a computer scientist, developed a program to play checkers that actually learned how to get better at the game on its own. 

Then in 1955, AI officially got its name. John McCarthy organized a workshop at Dartmouth College, where the term “artificial intelligence” was used for the first time. 

Then, for a couple of decades, things came to a halt until the invention of rule-based systems.

Rule-Based Systems and Their Role in Customer Support

In the late 1980s, rule-based systems came into being. They were programs that relied on predefined sets of rules to make decisions. 

The 1987 launch of Alacrity by Alactrious Inc. was one of the earliest expert systems made for strategy and management advice. 

Alacrity operated with over 3,000 rules and could follow logic trees to answer questions. 

In the next section, let’s understand the limitations of early rule-based models and solutions that overcome those limitations.

Evolution of AI Technologies in Customer Support

The “if-then” rules used by early AI models made them rigid and unable to handle the complexities of real-world customer interactions. 

The need for AI systems capable of learning and improving led to the development of more sophisticated systems like:

• Machine learning (ML) algorithms
• Natural language processing (NLP)
• Modern chatbots
• Virtual assistants

1. Emergence of Natural Language Processing (NLP)

Research on Natural Language Processing (NLP) kicked off in the 1940s after WWII. 

The linguist Noam Chomsky recognized that the early NLP models couldn’t differentiate between grammatically correct nonsense and completely garbled sentences. 

From the late ‘50s to 1970, NLP researchers split into two divisions: symbolic (rule-based) approaches and stochastic (statistical) methods. 

As tech advanced, new logic-based NLP paradigms (leading to Prolog), natural language understanding (inspired by Terry Winograd’s SHRDLU system), and discourse modeling, which studied how computers handle conversations, came into being.

2. The Rise of Machine Learning in Customer Support

The foundation of machine learning traces back to Donald Hebb’s 1949 brain cell interaction model. 

The term ML was officially coined by Arthur Samuel in 1959 while teaching machines to play chess.

In the 1990s, the concept of boosting algorithms refined machine learning by turning weak models into strong ones.

Jürgen Schmidhuber and Sepp Hochreiter introduced LSTM (long short-term memory) in 1997. LSTM is currently the key driver of speech recognition in customer service.

By 2012, Google’s X Lab made waves with an algorithm that autonomously identified cats in YouTube videos. 

Two years later, Facebook launched DeepFace, a face recognition model that matched human-level accuracy and laid the foundation for smart machine learning models.

3. Modern Applications of Artificial Intelligence Customer Support

Twitter, Facebook, and Netflix started using AI to fine-tune ads and personalize user experiences starting from 2006 that led to today’s customer engagement tools. 

In 2011, IBM’s Artificial Intelligence model Watson made headlines by defeating Jeopardy champions. 

That same year, Apple introduced Siri, the first mainstream virtual assistant, that became the foundation for AI in customer support interactions. 

4. Conversational AI and Chatbots

AI took a giant leap with the rise of conversational models in the late 2010s and early 2020s. Unlike earlier rule-based systems, these models could generate human-like responses. 

In 2017, Facebook programmed two AI chatbots to learn negotiation. 

However, instead of sticking to English, they began communicating in a language of their own creation, which did not make it into public usage. 

Just a year later, Alibaba’s language-processing AI outperformed humans on a Stanford reading comprehension test.

Then, finally, in 2021, OpenAI launched DALL-E (an advanced text-to-image AI model), which could process and generate visual content from just a simple text command.

Just a year later, in 2022, OpenAI launched GPT-3, a groundbreaking deep learning model capable of writing human-like text and computer code.

5. Predictive Analytics and AI in Customer Insights

The roots of predictive AI also date back to the 1940s when governments started using early computers. 

However, it wasn’t until the 1990s that businesses truly started to use data mining for massive datasets to improve their customer service.

With data mining and advancing database and data warehouse technologies, companies would store and analyze information faster than ever before.

It helped businesses predict what customers would need next based on their past purchasing habits rather than reacting to customer behavior. 

What is the Future of AI in Customer Service?

As we reflect on how far artificial intelligence in customer service has come, it’s only natural to wonder, what’s next? 

The future of AI in customer support promises even more exciting developments and, of course, new challenges.

Emerging AI Trends in Customer Support

Traditional customer support has largely been reactive, with customers reaching out to businesses only after encountering an issue. 

The future of AI in customer service will see a shift to proactive engagement.

We expect AI to move from being a support tool to a collaborative partner for human agents in the near future. 

Multimodal AI in customer support is another rising trend, where tools like Amazon Connect’s contact lens are being deployed in the market. 

Such tools use AI that combines voice, text, and facial recognition to improve communication. 

Think about AI that can understand both the words you say and the emotions behind them through the highs and lows of your tone and your body language.

Challenges and Ethical Considerations

While AI definitely delivers incredible efficiency, just like every technology, it’s not without hurdles. 

One major challenge of AI in customer support is balancing automation and human empathy. 

No one wants to feel like they’re talking to a robot when they need emotional support, for example, a customer who reaches out to report a lost item of sentimental value. 

Even advanced AI models struggle to deliver empathy the human way, at least for now. 

There’s also the risk of bias in AI algorithms. If customer service models are trained on biased data, certain groups are risked facing unfair treatment. 

For instance, in 2023, an AI-powered recruitment software used by the company iTutorGroup was reported to reject job applicants due to age.

FAQs

How is AI used in customer support?

AI in customer support automates tasks through chatbots for:

• Instant response
• Sentiment analysis to gauge customer emotions
• Real-time agent assistance
• Personalized experiences through data analysis 

What is the history of chatbots in customer service?

Chatbots in customer service began with ELIZA in the 1960s, which was by MIT’s Joseph Weizenbaum. 

WeChat popularized more advanced bots in China in 2009. 

Later, Facebook Messenger enabled businesses to use AI-powered chatbots before OpenAI’s GPT models took over the chatbot industry.

What is the AI system customer service?

An AI customer service system uses artificial intelligence to interact with customers and assist human agents. 

It reduces wait times for customers by providing responses to simple queries. This takes the burden off human agents so they can dedicate their focus to uncommon customer service situations. 

Conclusion

What began as an experimental concept in the 1950s is now an integral part of thousands of companies that use AI-generated customer support today. 

AI was once considered science fiction, but now, it operates in nearly every major company, automates tasks, improves user experiences, and provides 24/7 scalability. 

If your business hasn’t introduced AI in customer support, now is the time to be a part of the revolution.

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• Operate 24/7 globally
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• Improve customer satisfaction and retention
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