Milena Traikovich, a seasoned expert in demand generation and performance optimization, understands that the bridge between a lead and a loyal customer is built on trust rather than just technology. With extensive experience guiding businesses through the complexities of analytics and lead nurturing, she has seen firsthand how digital initiatives can either accelerate growth or alienate the very audience they seek to serve. In an era where artificial intelligence is often marketed as a cure-all for operational bloat, Milena challenges the prevailing corporate narrative that efficiency for the business always translates to excellence for the user. She argues that the current disconnect in customer satisfaction stems from a fundamental misunderstanding of why AI should be implemented in the first place.
This discussion explores the psychological friction created when companies prioritize their own cost structures over the user experience. We examine the specific behaviors that cause customers to resent a brand—such as forced chatbot loops and repetitive data entry—and contrast these with “invisible AI” that simplifies the customer journey. Milena also breaks down why traditional operational metrics like handle time and call deflection often mask deep-seated service failures, ultimately offering a roadmap for leaders to align their AI investments with genuine human needs.
Many executives are finding that despite investing millions of dollars into AI, their customer satisfaction scores are actually trending downward. Why is there such a massive disconnect between the money spent on technology and the happiness of the end-user?
The core of the problem is that many companies are jumping into AI feet-first with the primary goal of saving money through increased efficiency, rather than solving actual problems for the people they serve. When a business invests millions in these tools, they often use the justification that it will provide faster support or more personalized content, but customers are incredibly savvy and can tell when a system was built to benefit the company’s bottom line rather than their own needs. It creates a sense of resentment when a customer realizes the AI is there to act as a gatekeeper or a cost-cutting barrier rather than a helpful assistant. To fix this, executives need to stop looking only at the technology and start asking what friction point or operational inefficiency is actually being solved for the human on the other side of the screen. If the AI doesn’t make the customer’s task easier or faster, the investment is essentially working against the brand’s reputation.
It seems that people don’t necessarily hate the technology itself, but rather how it is being utilized. How can a brand distinguish between an AI implementation that provides genuine value and one that feels “self-serving” to the customer?
The distinction lies in whether the AI is optimizing for the company’s efficiency or the customer’s effort. We use AI every single day in search engines, navigation apps, and streaming services, and we rarely complain about it because these tools genuinely help us accomplish a task more quickly or with less mental load. In those cases, the AI is almost invisible because it provides immediate value without forcing the user to jump through hoops. However, it becomes self-serving the moment it starts protecting the company’s cost structure at the expense of the customer’s time. If a user has to endure multiple chatbot conversations just to speak to a human, or if they are forced to repeat their issue three different times because the AI interaction restarts, they feel that their time is being undervalued. Genuine value is created when the technology removes friction; self-serving AI adds it in order to save the business a few dollars on support costs.
Could you walk us through some of the specific “red flag” interactions that signal to a customer that an AI system is working against them rather than for them?
One of the most frustrating red flags is when a customer is forced into a “chatbot prison” where they are stuck in a loop and cannot bypass the AI gatekeeper to reach a human expert. Another clear sign of a business-centric design is when a virtual assistant responds with massive paragraphs of text when a simple “yes” or “no” would have sufficed, which shows the system isn’t actually listening to the context of the request. We also see major friction when someone with an urgent, high-stress problem is presented with generic product recommendations before their actual issue is even acknowledged or resolved. There is also the sensory frustration of having to explain a complex situation multiple times because the AI doesn’t remember the previous interaction or the context of the user’s history. These interactions might help a company hit an internal metric, but they create an emotional distance that drives customers straight to the competition.
You’ve mentioned that traditional metrics can be misleading when evaluating the success of AI. Why are numbers like “chatbot containment” or “reduced handle time” often poor indicators of a healthy customer experience?
One of the biggest mistakes a business can make is assuming that operational metrics are the same thing as customer experience metrics. For example, a high “chatbot containment” rate might look great on a spreadsheet, but it doesn’t necessarily mean that problems were actually solved; it could simply mean that customers got so frustrated that they gave up entirely and logged off. Similarly, reducing the average handle time for a call might save money, but it often leads to rushed, poor-quality service that leaves the customer feeling unheard and unsatisfied. Companies often measure what AI prevents—like call volumes or support costs—while the customer is measuring what the AI enables, such as a quick resolution or a seamless transaction. When you lower support costs without increasing customer value, you aren’t improving the business; you are just damaging trust and making it less likely that those customers will ever return.
If the goal is to make AI “invisible” to the user, what do those successful, frictionless interactions actually look like in a real-world scenario?
The most successful AI experiences are the ones that work so well the customer doesn’t even realize they are interacting with an algorithm. This includes things like authenticating a customer automatically before they even ask a question, or a system that remembers every previous interaction so the user never has to repeat themselves. It’s about the AI understanding the context of a situation and routing the customer to the right expert immediately, or summarizing a complex conversation so the next person in the chain is already up to speed. When AI anticipates the next logical step in a journey and removes a hurdle before the customer even encounters it, that is when the technology is truly performing. The customer remembers these experiences not because of the “cool AI,” but because the interaction was easy and everything simply worked exactly the way it was supposed to.
There is often a fear that AI is meant to replace human staff, but you suggest that the best systems actually know when to step back. How should leaders determine the right balance between automation and human connection?
Leaders need to realize that the best AI doesn’t replace people; it empowers them and knows when a knowledgeable employee is the better answer for the customer’s specific needs. Customers generally don’t care whether their problem is solved by a person or a machine, as long as the resolution is delivered quickly and accurately. However, they can sense when a company is using AI as a shield to avoid human interaction because it’s cheaper, rather than using it as a tool to improve the service quality. The right balance is achieved when AI handles the repetitive, low-value tasks—like data entry or basic status checks—while remaining ready to hand off to a human the moment a situation becomes complex or emotionally charged. It is about using the technology to enhance the employee experience so they can be more productive and present for the customer, rather than using it to build a wall between the brand and its audience.
What is your forecast for the future of customer experience as AI continues to evolve?
I believe we are going to see a major shift where cost savings will no longer be the primary criterion for investing in these technologies. As the market becomes more saturated with automated tools, the brands that stand out will be those that use AI to reduce customer effort rather than just their own internal expenses. We will move away from clunky, visible chatbots and toward a future of “proactive service” where AI identifies and fixes issues before the customer even knows they exist. Businesses will finally stop measuring success by how many calls they “deflected” and start measuring by how much trust they’ve built through seamless, intuitive interactions. Ultimately, the future of AI in CX isn’t about the technology getting louder or more complex; it’s about the technology becoming a silent, supportive backbone that allows for deeper human connection and effortless brand loyalty.
