AI in customer support has evolved dramatically over the years, with each new wave promising to revolutionize the way teams engage with customers. From early, scripted chatbots to today’s advanced AI copilots, support leaders have witnessed many phases of AI technology — some transformative, others fleeting.
But understanding the true potential of AI today requires a step back to see where it all began. The hype, the setbacks, and the gradual evolution have all shaped the AI we use in support today.
So, let’s take a look at how we got here — and why knowing the history of AI in support can help separate fact from fiction in today’s fast-moving landscape.
The idea of AI-powered support isn’t new. In fact, the first sparks of machine-human interaction go all the way back to the 1960s.
In 1966, MIT computer scientist Joseph Weizenbaum created ELIZA, a computer program designed to simulate human conversation. ELIZA’s script mimicked a psychotherapist by reflecting questions back to the user (“How does that make you feel?”). The interactions were surface-level, but they introduced a powerful concept: that machines could talk.
Around the same time, early interactive voice response (IVR) systems were being developed. These allowed customers to navigate phone menus using touch-tone keypads instead of waiting for human agents — laying the foundation for automating routine support tasks.
While primitive by today’s standards, these early systems showed the first glimmers of how AI could one day support human conversations at scale.
As computing power grew in the 1990s and early 2000s, so did ambitions for AI in customer support. But rather than focusing on open-ended conversations, most early tools zeroed in on specific, rules-based tasks.
Basic chatbots emerged that could follow simple decision trees — “If customer says X, reply with Y.” These bots could answer FAQs or help route tickets, but their rigidity meant they often hit frustrating dead ends.
At the same time, natural language processing (NLP) started gaining traction. Early NLP models aimed to understand customer input, but intent detection was still in its infancy. This marked a shift from pure automation to something slightly more intelligent — though still far from seamless.
Combined with the growing adoption of help desks and CRM systems, these early AI tools began carving out a place in the modern support workflow. They weren’t replacing agents, but they were starting to assist them.
In the 2010s, AI in customer support quietly leveled up.
Thanks to cloud computing, support platforms became more scalable and data-rich, giving teams access to the historical data AI tools need to learn. At the same time, advances in machine learning made it possible to move beyond fixed rules and train models based on real customer conversations.
Support orgs began using AI-powered features to:
These tools weren’t flashy, but they were effective. Instead of trying to replace agents, AI was now helping them work faster and smarter — spotting patterns, reducing repetitive work, and quietly improving workflows behind the scenes.
This era laid the groundwork for today’s more visible, conversational AI tools. It also helped shift the mindset around AI: from novelty to necessity.
The early 2020s marked a turning point. With the arrival of large language models (LLMs) like OpenAI’s GPT-3 and GPT-4, AI moved from behind-the-scenes tooling to center stage.
Suddenly, machines could generate human-like responses, summarize long threads, translate languages, and handle complex queries — all in real time. This shift from task-based automation to adaptive, conversational AI opened up entirely new possibilities for support teams.
Instead of just tagging tickets or suggesting help articles, generative AI could:
It wasn’t just a technology shift — it was a mindset shift. Support orgs began exploring how AI could not only increase efficiency, but also improve consistency, personalization, and agent satisfaction.
Of course, it also raised new questions around accuracy, brand voice, privacy, and oversight. But one thing became clear: this wave of AI wasn’t just an upgrade. It was a rewrite.
Today, AI is woven into nearly every part of the support experience — not as a replacement for human agents, but as a force multiplier for speed, consistency, and scale.
Across teams and tools, AI is helping support orgs:
It’s no longer about bots vs. humans — it’s about building smart systems where AI and agents work together. And while there’s still plenty of experimentation ahead, AI is already delivering tangible results: faster replies, happier agents, and better customer outcomes.
The next frontier of AI in support isn’t just faster replies or smarter summaries — it’s orchestration across every channel, team, and customer touchpoint.
Support leaders are moving beyond one-off automations toward AI agents that resolve issues, escalate with context, and stay on-brand from start to finish. These aren’t just bots — they’re connected systems that adapt in real time and scale with your team.
That’s what we’re building at Assembled: fast, high-quality AI agents for chat, email, and voice. Agents that plug into your workflows, follow your escalation rules, and speak in your voice — so every interaction feels consistent, personalized, and complete.
The goal isn’t to replace your team. It’s to free them up to do their best work — while AI handles the rest.
And the best part? It’s already happening.
See how Assembled’s AI agents are transforming support in the real world.
