Alex Nazha is head of Gatestone Digital.
When you call a company’s help line and hear an automated voice on the other end, what’s your first reaction? Chances are it’s searching for the option to speak with a real person. That predictable response is evidence that the technology has moved faster than customers’ appetite for it. AI is powerful—but do customers want to interact with it in the first place?
We hear constantly from clients and companies who have invested in expensive AI customer service systems and are not seeing the returns they expected. While CFOs push for the financial outcomes, customer service and sales teams are asking for something different: more personalization and happier clients. The tension between those two sets of expectations is a big part of why so many AI rollouts fail.
Where Companies Go Wrong
The biggest challenge I see companies underestimate is the sheer amount of work required to get an AI system ready for client-facing use. Most people’s first experience with AI was typing prompts into Claude or ChatGPT, which felt simple and immediate. Enterprise AI is a different beast. It requires a lot of unglamorous work behind the scenes, including getting data organized and making systems talk to each other. Processes that were built around humans need to be adapted so they work for bots and agents.
There’s a tendency to overestimate how willing customers are to deal with a system that can’t fully meet their needs. Many companies are able to install AI at a basic level; it can give a customer their account balance, say, but not resolve the actual problem they called about without talking to a person. That half-measure creates more frustration than if AI hadn’t been there at all. And when companies pour money into client-facing AI only to discover the appetite simply isn’t there, they often end up scaling back and blaming their customers for their own miscalculation.
Rolling Out AI Responsibly
To avoid that kind of expensive lesson, the most important step is building the process around AI rather than layering AI on top of an existing one. Companies often try to bolt AI onto a workflow that was designed for people, and that rarely works. The process itself has to change.
Part of that means consolidating systems. Most companies run a contact center platform, a CRM, a ticketing system and internal messaging tools that don’t naturally talk to each other. When the human agent is the connective tissue, those systems need another way to communicate if you remove that person. Consolidating usually means choosing one system to serve as the source of truth and carefully migrating data into it, so the transfer doesn’t compromise the data itself.
It’s just as critical to define a clear business case before any of that work begins. What is the actual outcome you’re after? Automating simple requests or a better customer experience? Entry into new language markets? Too often, companies implement AI because a board is asking for it or because they want to tell shareholders it’s happening. What they need is a real reason for why it matters for their customers.
Take travel and hospitality, an industry built on human judgment and emotional connection. A lot of what makes that experience valuable is an actual, experienced person’s knowledge of where you should eat or what you should see. An AI agent can generate a recommendation, but it can’t replicate that human read on a situation, and customers see that immediately.
Where I’ve seen the strongest returns on AI is in supporting agents instead of replacing them. We run AI tools in the backend that our human agents use directly, but customers never see. They just notice the call was fast and the person on the other end seemed to know exactly what to do. It’s the AI working in the background that turns our agents into “super agents.”
How can companies differentiate themselves when everyone has access to the same AI tools? The answer is using the technology selectively and intelligently. Customer interaction is one of the most sensitive touchpoints a company has, and if a customer is going to have a negative experience with AI, it’s most likely going to be there. Approach AI as a way to make your people better at their jobs, not just a cost-cutting measure, and watch the real returns show up.
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