3 key areas you need to prepare while introducing AI in customer support

This is the second of three posts in a series called Knowledge is Power, and today I’m talking about introducing AI in your customer support. It can be overwhelming when you don’t have time to research and figure out what you need to know about implementing AI into your customer-facing tools. 

I’ll cover three key areas:

• Your first 30 days with an AI chatbot
• developing a company-wide knowledge base
• the changing landscape of customer support roles

At the end, I share a couple of new roles you may not have heard about and you might consider this year.

Your first 30 days with an AI chatbot

A solid first step to start is implementing an AI chatbot or an AI agent. Think of it as your first step into automation. Chatbots are a great place to start because they’re already familiar to most support teams and many customers. Communication softwares like Intercom will guide you through the onboarding process and you’ll reference the knowledge libraries the chatbot can use, but you don’t turn it on and see what it does. You will need to train the bot on its responses. During the first 30 days, I suggest dedicating at least 20% of your time training the bot, just like you would spend time training a new employee. After 30 days and you see some results, scale it back to 10%, but never 0%. A company that wants to provide incredible support should never set it and forget about it.

When launching an AI bot, it’s best to test with a smaller segment of your customer base that is more likely to embrace new technology. Consider your early adopters or those who are tech-savvy. This allows you to test in a lower risk environment and gather valuable data to perfect your process.

In your first month with the AI bot, be prepared to be surprised, but in a good way! The power of AI agents to synthesize information and provide creative solutions can be impressive. You’ll likely see high resolution rates right away. The more advanced bots will highlight areas where your knowledge base needs improvement, so expect to do weekly content reviews to fill in gaps.

Developing a company-wide knowledge base

Building a complete knowledge base is key. It’s not just a customer support task, but a company-wide effort. Your product teams need to be key stakeholders in this process to help expand or clarify information. By doing so, you’re embedding this initiative into the fabric of your business. It becomes a company-wide priority and you get different perspectives that will improve performance of the AI agent.

What about the challenges beyond those first 30 days? It’s important to continuously monitor and refine your process, which includes updating your knowledge base.
If your communication tool is able to report on the performance of your AI agent, use it. Otherwise, you can start manually by sampling conversations, looking for content gaps and areas where the experience wasn’t great. Look at metrics to gauge whether the agent resolved a question, or if the user simply stopped engaging.
And, be prepared to pivot. You might have spent hours or days creating what you might have thought was the perfect setup, however, triage flows can sometimes add too much friction if they’re too long. You may have to pull back layers to reduce customer effort.

The changing landscape of customer support roles

Finally, let’s talk about the changing landscape of customer support roles. AI is not here to replace human agents but to elevate them. AI will handle the routine tasks while the human support team is free to focus on more complex and nuanced cases as well as support operations.
You need to adjust your expectations for handling times and caseloads. You’ll also need to evaluate the skills, compensation, and career paths of your team. This might mean adding roles at the top of the ladder for career growth of your valued employees. 

One suggestion around a new role is a Knowledge Specialist. Someone that will cross-collaborate with Product to ensure all documentation is up to date with the latest releases and product adjustments. I have another podcast about revamping your support articles that speaks specifically to this role.

Another suggestion is an AI Architect. Someone that works with the Knowledge Specialist to develop and train the AI chatbot, ensuring the company’s tone and empathy are used in each AI response. They may also develop AI responses to assist human agents with faster responses. Another opportunity for the AI Architect would be to create upselling opportunities, identifying usage and proposing to the human agent how they could suggest jumping into the next pricing tier or bundling more seats. There are always opportunities to transition Support from a cost center to a profit center, and choosing to leverage AI could be a profitable transition for your company this year.

Rush to Resolution

My goal with this podcast is to give you a starting point for improving your customer experience. Subscribe with your favorite platform so new ideas can be cultivated in just a few minutes.

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Rush To Resolution is a CX implementation and consulting company.
Have a great day and a productive week.