Navigating the AI maturity curve in customer support
Whether you’re ready to embrace generative AI for customer support or not, it’s here to stay. Your competitors know this, which is why 83% of support leaders say their company has plans to invest in generative AI technology this year. Wherever you’re at in your AI for customer support journey, you can’t afford to stay in the past — or you will get left behind.
For customer support teams that are entirely new to (and maybe even a little skeptical about) AI, we recommend a controlled AI deployment. This phased approach starts with using AI to pull answers from your knowledge base, identifying places where AI excels at giving answers, and working your way to automating those answers without agent intervention.
But what about the teams that have already dabbled in some form of AI, before the generative AI revolution took the support world by storm? How can they get the most out of the latest breakthrough in AI technology? It all starts with understanding where your customer support operations fall on the AI maturity curve.
In this article, we outline different starting points along the AI maturity curve and how an AI-powered agent copilot and issue resolution engine like Assembled Assist can meet your business where it’s at today.
The bottom of the curve
If you’re just dipping your toes into the AI waters, chatbot deflection is a popular first step. Chatbots are great at handling the simplest, most repetitive questions — those “what are your business hours?” and “how do I reset my password?” type queries.
When done well, deflection can significantly lighten the load on your human agents by directing customers to self-service resources. Just remember, the questions that do make it through to your agents will likely be the tough nuts to crack. Make sure your team is ready to handle these trickier queries with finesse.
Here’s the catch: your chatbot experience needs to be top-notch. A poorly deployed chatbot can make your customers feel stuck in a never-ending loop, desperate to speak to a living, breathing human. We call that chatbot purgatory.
It’s also worth mentioning that chatbots only provide coverage for chat, one of the many channels your customers expect to connect with your company through. For this and other reasons, you may find that chatbots are limited in their ability to bolster a comprehensive omnichannel support strategy.
The middle of the curve
If your chatbot deflection strategy is dialed in and you’re ready to see what else AI has to offer, an agent copilot is a great next step.
Remember those tough nuts that chatbot deflection can’t seem to crack? These are the more complicated customer interactions that can zap a lot of your human agents’ time. Your agents need quick access to information and ways to streamline their workflow to keep things moving smoothly.
That’s where an agent copilot comes in handy. These internal AI tools slash the time agents spend hunting for answers by instantly pulling suggestions from your company’s knowledge base. Leveraging the power of generative AI, they can whip up a pre-drafted response in your company’s brand voice for the agent to review and tweak as needed.
These time-savers let agents stay focused in the help desk, reducing the number of clicks and tab-switching they usually have to do to find and prepare an answer. The result? Swift, consistent responses for your customers every time.
A word of caution: If the agent copilot doesn’t come with robust reporting capabilities, steer clear. The ability to understand how the AI is behaving is paramount to keeping things on the rails. Bonus points if the copilot can be grounded in your brand voice, processes, and specific use cases (this is where Assembled Assist really shines).
The top of the curve
If your team is on cruise control with chatbot deflection and an agent copilot, you’re probably ready to accelerate toward some custom automation workflows. We call these targeted automations — automations that are backed by data and built with intention.
Targeted automation exists at the top of the curve for a reason. To uncover truly meaningful opportunities for automation, you need data from your chatbot deflection and agent copilot activities. Trying to create automations without those insights is like navigating a maze with a blindfold on.
Targeted automations can range from the simple to the sophisticated. Take the "Thank You Solver" in Assembled Assist, for example — it automatically follows up on “thank you” emails from customers at the end of an interaction and closes out the ticket, while ensuring the agent gets credit for the solve. This straightforward yet time-saving automation reduces manual, repetitive work for agents and tidies up your backlog like nobody’s business.
Then there are the more complex automations. Think about returns — a multi-step process that typically involves lots of tab-switching and manual verification. When a customer wants to initiate a return, the agent has to check the order number, customer details, and purchase date, then match all that against the company’s return policy to ensure the item is eligible for return.
If everything checks out, the agent has to initiate a return authorization in the system, generate a return shipping label, and provide the customer with return instructions. If the item isn’t eligible for return, the agent has to explain why, which can lead to some tough conversations. Some customers accept it and move on, while others might push back. Either way, it’s not a fun chat.
With well-mapped steps and procedures, a targeted automation can take over this entire process without needing your team to step in. This ensures all returns comply with your policies and lightens the emotional load on agents when they have to tell customers their refund can’t be processed.
Stay ahead of the curve
Assembled Assist is helping companies like Thrasio and Honeylove stay ahead of the AI maturity curve with targeted automations that support faster, smoother customer experiences.
Curious to learn more? Take a quick, self-guided product tour of Assembled Assist now.
