The contact center AI story so far is a trilogy. Yet, unlike most, these plotlines get better and better.
First came the speech recognition models that enterprise tech firms, like BT, built back in the mid-90s. Yet, implementations proved few and far between, as the cost-benefit ratio didn’t quite stack up.
Next, in the early 10s, came the natural language revolution, as contact center vendors built bulky natural language processing (NLP) engines to spot trends in customer conversations.
Natural language understanding (NLU) also played a key role in early chatbots, uncovering customer intent and presenting scripted responses.
Yet, the third edition of AI in the contact center is where Star Wars' Return of the Jedi meets Lord of the Rings: Return of the King.
Of course, it's the generative AI (GenAI) tale. It has proven massive in enabling contact centers to not only extract insight much faster but act autonomously on that insight.
As such, contact center AI is not only cheaper and more accessible than before, it's much more powerful.
Cue a spike in contact center AI use. Indeed, a recent study found that 42 percent of businesses have fully integrated AI into customer interactions. Meanwhile, a further 29 percent are testing chatbots and AI support.
But, before stepping forward, it’s best practice to look back and consider those lessons learned from the AI implementations of yesterday, which can inform the AI strategies of tomorrow.
What We’ve Learned Along the Way
Still, many associate AI with chatbots, fixating on the opportunity to automate customer communications. However, as the natural language boom taught us, there’s much more to consider.
Two excellent, often-overlooked examples are automating quality assurance (QA) and mining unstructured data to identify more points of frustration within the service experience.
By becoming more familiar with such use cases – and the many others – contact centers can uncover various other impactful applications they might miss by hyper-focusing on automation.
Yet, no matter the use case, contact centers should start small, learn quickly, and scale intelligently. That lesson still rings true.
One helpful best practice is to deploy AI amongst the agent population first. By doing so, CX leaders gain insight into where it works well and fails. That enables optimization before rollout.
While many may completely trust AI and its "guardrails", it's best to make mistakes where no one sees them.
But remember, AI is not something a contact center can install and leave. Continue testing, learning, optimizing, and embedding workflow to ensure long-term success.
To ensure this happens, the contact center needs to assign resources. Whether it includes new hires or uplevelling supervisors, the team must continually refine these tools.
Then, there's the agent piece of the AI pie, especially when applying conversation automation. That's critical as handling times climb higher and agents get fewer easy calls to take a breath.
In recent years, tools have helped, with virtual assistants providing instant access to pertinent information, offering real-time coaching, and automating tedious tasks like post-contact processing.
