Every organization claims that they want to be more customer-focused by offering personalized support. But you know what doesn’t exactly scream ‘personalized experience’? Having to listen to 15 different phone menus to reach the right department.
What customers actually want is simple: simplicity. One clear entry point that gets them to the right answer or solution as quickly and efficiently as possible. Sounds like a job for AI, right? Well… not really, if you look at it like a quick fix. To get the most out of it, you’ll have to make AI work for you. Let’s take a closer look.
Customers have high expectations of your service, and that leads to high amounts of pressure from leadership. AI sounds like the perfect way to deal with that pressure. It can handle high contact volumes and keep costs under control, so why not invest in setting up a chatbot?
It’s not a bad idea, in essence. We’re also excited about AI and how it can transform customer service! But at the same time, it’s good to stay realistic. Plugging a modern AI tool into your existing systems is not a shortcut. You’ll need to put in the effort to get it working like you want. If you skip the basic groundwork, it will backfire.
To understand why setting things up right matters, let's look at what happens when you rush it. Take a recent example from a parcel delivery service in the Netherlands.
Delivery service customers are used to a high level of service, so this company wanted to go the (figurative) extra mile without spending lots of resources on it. Their team quickly built an AI agent, connected it to an existing knowledge base, and put it live on their website. Not bad, right?
Since they were looking for a quick win, they didn’t check in with the customer service department. The bot hadn’t been trained on real customer behavior, so it started hallucinating and giving out wrong information. Instead of reducing workload, calls spiked. Angry customers flooded the contact center, and agents had to spend extra time fixing the bot's mistakes.
The business impact was immediate and costly. The company had to pull the bot offline, operational expenses went up instead of down, and management was left dealing with frustrated customers and lost trust.
This brings us to our core point. AI doesn’t fix bad processes, it just retrieves information from them.
If your search function doesn't work well today, or your data isn’t structured (and in most cases, these two are connected), an AI bot won’t magically find the right answers. It makes a strong foundation even stronger, but if that foundation has issues or missing information, AI will amplify those flaws.
As an enterprise architect from the financial sector told us at our recent roundtable, the goal isn't to use AI for everything all the time. Instead, we recommend finding the specific situations where it can bring the most value by taking over repetitive manual tasks.
But to get to that point, you need to put in the work first: structure your data, clean up your knowledge base, and organize your channels. It’s not immediately shiny and customer-facing, but it will help you with those more exciting initiatives later on. And as they say, good things come to those who wait.
Our advice is simple: before you start shopping for (or developing) the latest AI tools, take a step back and look at your current setup. How do you actually know if your foundation is strong enough to support a digital workforce? Where are the cracks in your processes that an AI bot might accidentally reveal?
In the end, it boils down to taking an in-depth look at your operations. In one of our other blogs, we walk you through one of those health checks, based on the five pillars of a mature customer service organization. This will help you figure out where your foundation needs work, and where you are ready to safely accelerate. Let’s get to work!
