Most people—about eight out of ten—regularly have bad experiences with customer service. But blaming customer service teams doesn’t solve the problem. The reality is that 93% of customer service reps feel that customer expectations are higher than ever, and 71% of customer service leaders are seeing an increase in the number of support requests. (1)
There just aren’t enough people to handle all these requests, at least not in a way that’s affordable for businesses.
Customer support has always been seen as a cost to the business, and it’s easy to think this is just how things are in 2024. But does it have to be?
We believe it’s time to switch to AI-driven first-line support. Recent advancements in retrieval augmented generation (RAG) and Cognitive Architectures allow us to create chatbots that can handle first-line support with quality responses.
This shift can also transform the customer support center from a cost center into a revenue generator by providing valuable data and insights for sales, marketing, and product teams.
Support services are often seen as a necessary cost of doing business, something companies provide to keep customers from leaving. Because of this, there’s little incentive to invest more than the bare minimum to keep customers satisfied.
As a customer, this often means dealing with support that’s just good enough to solve your issues before you decide to walk away. We’ve all had those frustrating moments with customer support that pushed us to the edge.
But blaming support services isn’t the solution. To really address the issue, we need to shift the focus from viewing support as a cost center to seeing it as a potential revenue driver. After all, money talks.
AI support agents can make this shift possible. Every interaction can be tracked at the customer level, allowing us to know exactly what customers are searching for and whether their problems are resolved. Unlike current tracking systems that depend on human input, AI can track data flawlessly, even adding insights like customer sentiment and needs, all without extra effort.
Imagine you’re running a SaaS platform—how valuable would it be for your sales team to know the exact problems your customers are facing and the challenges they need to overcome? This level of detail is what AI support agents can offer.
Customer interactions can be tracked even before they sign up and during trial periods, giving your sales team the insights they need to close deals.
While this example focuses on a SaaS platform, the possibilities are endless. For instance, think about the personalization or retargeting opportunities for marketing teams at airlines if they know where a user is traveling.
Not sure how this could apply to your business? Reach out, and we’d be happy to set up a free 30-minute brainstorming session to help you turn your support into a revenue driver.
This probably isn’t the first time you’ve heard that AI can improve support, and it likely won’t be the last. So why is now the right time?
Recent advancements in AI have addressed many of the issues that plagued early chatbots. With state-of-the-art retrieval augmented generation and cognitive architectures, we can now create AI that thinks and acts like a support employee, making independent decisions to provide better support. We’ve even written a blog series on the topic; you can check out the first article here (though be warned, it’s a bit technical).
In simple terms, we can equip AI with all the tools and data access that a regular support employee has—and more. Take the SaaS business example from earlier: we can feed the AI all support documentation, tutorials, and anonymized past interactions, creating a retrieval algorithm that finds the right answers quickly. Even your best support employee doesn’t have that much experience!
Beyond that, by building the right cognitive architecture, we can give AI access to account-level information, like billing details and more.
In addition cognitive architecture can be extended to take action. Let’s revisit the airline example, a cognitive architecture can be designed in such a way that it can assist users in rebooking or canceling their flights. The API endpoints to do so likely already exists and can be transferred to the cognitive architecture. This helps the AI solve problems rather than just reiterating information.
There’s no such thing as a one-size-fits-all solution. Your situation and data are unique, and trying to use an off-the-shelf AI chatbot often leads to disappointment. These generic solutions usually skip over the critical data needed to create a truly effective chatbot, resulting in poor performance and, ultimately, a failed project.
To avoid these pitfalls, we tailor our algorithms to fit your specific data needs. We begin by mapping out all the available information and consulting with your support teams to understand the types of requests they handle.
Next, we index all the necessary information and build custom functionalities for the chatbot, allowing it to take action (within set guardrails) based on user requests.
The result is a chatbot designed specifically to help your customers, driving real results. It’s not just a tool to keep people busy while avoiding real support; it’s a solution that solves customer problems before they need human assistance.
We know you likely have more questions than answers right now, and we’re here to help. Let’s set up an exploratory chat to discuss the possibilities for your support department.