According to Amazon CEO Jeff Bezos: “AI is in a golden age and solving problems that were once in the realm of sci-fi.”
However, confused bots, cumbersome IVRs, and crazily long wait times are still a staple of many contact centers.
For consumers looking from the outside in, the golden age of AI-led contact centers still feels a long way away.
Nevertheless, the potential for AI in customer service is massive, and the three following industry experts are moving the needle:
- Wayne Butterfield, Partner for AI, Automation & Contact Center Transformation at ISG
- Neil Smith, VP of Technical Support at Iterable
- David Hwang, Chief Customer Officer at Grammarly
Recently, they took part in a webinar, sharing advice to contact centers on leveraging AI for better, faster customer service.
Here are just five bite-sized takeaways from the informative discussion.
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Start with the Problems, Not the Solutions
It’s tempting to see a demo of the latest, shiny AI, get carried away, and jump at the opportunity to implement it.
However, contact centers should start by looking internally and identifying their greatest challenges and pain points.
For example, if agents still handle basic transactional queries like "What's my balance?" or "Where's my order?", those are great candidates for AI or self-service channels.
Alternatively, if the issue is agent performance - maybe training is slow, or the job is complex - consider tools like agent assist that streamline their work.
Reaffirming this point, Butterfield stated:
“Too often, we’ve used technology as a hammer looking for a nail.”
“We’ve spent years chasing marginal gains while customer experience metrics have sometimes declined.”
As such, Butterfield urged contact center leaders to take stock, identify their problems, and then match those to the appropriate AI solutions.
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Consider the Full Spread of AI Applications
At Iterable, Smith's team considers contact center AI through three lenses: automation, assistance, and analytics.
Conversation automation is exciting, as large language models (LLM) have accelerated the building process of virtual agents and ensured they can pivot with changing contexts.
Yet, as agents take on more of the complex contacts, they need more support. As such, agent-assist solutions are building momentum.
These automate replies, summarize cases, and present relevant insights in real time, eliminating the need to chase down information in 20 places.
Building on this point, Smith noted:
“One use case is bullet-to-response conversion. Agents can type out bullet points, and AI turns them into a fully formed answer. That’s a workflow that feels more natural for agents who work on complex products like ours.”
Then, there’s analytics to help monitor team performance, spot issues, and pinpoint opportunities to improve the service experience.
Moreover, vendors are increasingly attaching analytics tools to their AI applications. This is a positive trend that helps prove ROI.
According to Hwang, Grammarly has been very deliberate in its approach. "Our Analytics Hub helps leaders see how Grammarly is impacting metrics - like CSAT and productivity – and we now include a "Effective Communication Score" to track progress over time," he said.
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Run Pilots to Prove a Business Case
First, try and build a multifaceted business case. Consider critical agent and customer outcomes alongside cost-cutting.