It’s a nearly universal opinion: Today’s customer service leaves much to be desired. But it’s not just customers who are frustrated. Service agents are, too. Many people have suggested that AI and chatbots are innovative, “easy” fixes to customer service challenges. They are wrong.
We’ve seen many examples of chatbots gone rogue. Just recently, one bot cursed at a customer and criticized its own company. Another promised a nonexistent discount to a customer, putting the company on the hook for damages.
When was the last time you walked away from a conversation with a chatbot feeling like your issue was actually resolved? Customer service bots have never actually been good at answering questions. Adding generative AI is not going to be a miracle fix. Companies must build an underlying action system first.
Why companies fail with chatbots
Effective customer service requires companies to deploy a system that interprets data, applies rules and takes specified actions to power automation, decisions, policies and system connectivity. The goal is to build a decision tree that automates as many customer inquiries as possible without requiring AI. This deterministic approach assists both customers using self-service tools and customer service agents fielding questions while also providing guardrails for AI to ensure it operates within company policies. Once companies establish a strong action framework, they can layer AI on top.
For optimal performance, chatbots require integration. Too often, companies use chatbots as a standalone solution. As a result, these tools don’t have access to the necessary knowledge and resources to provide personalized and relevant solutions. For example, automation scheduling requires connecting your self-service tool to a calendar platform and an SMS service. A chatbot won’t be able to execute this task until you build the underlying, connected action system that can accomplish it without AI.
Generic, out-of-the-box chatbot platforms don’t provide enough connectivity and flexibility for companies to implement specific business rules, policies and procedures. Without integration, standard chatbots struggle with even the simplest queries. Customers find chatbot interactions frustrating when their issue doesn’t fit neatly into the bot’s preprogrammed flows.
Many companies implement chatbots before building a system of action and automation, resulting in a steep learning curve and serious mistakes. A well-built, rule-based framework will prevent a chatbot from selling a truck for $1.
Keep humans involved
A company should not force every interaction to take place through a bot. Chatbots exist to manage routine tasks and queries, allowing agents to make a human impact rather than manage systems. By automating the right tasks and providing agents with the right information and tools, companies can free up agents to deliver more meaningful, personalized service.
While customers appreciate the speed and convenience of automated options, they still want the option to talk to a person for complex or sensitive matters. Users should always have a clear path to reach a live agent.
Going all in on generative AI may be trendy, but chatbots deployed without the proper systems are useless — and even counter-productive. Companies must thoughtfully build effective rules-based frameworks, integrate systems and invest in a customizable chatbot platform before they unveil customer-facing AI.
