Over the past few years, artificial intelligence has become one of the most discussed topics in the world of technology. There is hardly any software product that doesn't add AI functionalities, and hardly any industry that isn't trying to understand what this will mean for its business. The world of self-service solutions is no exception.
The question, however, is not whether AI will change self-service. This is already happening. The more interesting question is what will actually change for customers and businesses in the next five years.
The history of automation shows that new technologies rarely change everything at once. They usually start as an addition to existing processes, then gradually change the way people interact with services. The same will likely happen with AI.
Today, most self-service solutions are built around a relatively clear logic. The customer selects an option from a menu, follows a predefined process, enters the necessary information, and reaches a result. This works well when the user knows what they are looking for and when the scenario is predictable. Problems begin when the person on the other end is not sure what they need, when they have a more specific issue, or when they simply don't understand the system's logic.
This is where AI has the potential to bring the biggest change.
Over the next few years, we will likely see a gradual shift from systems that require the customer to adapt to their logic, to systems that adapt to the customer. Instead of the user searching for the right menu or function, the system will increasingly try to understand what they want to achieve and guide them to the most appropriate path.
This does not mean that interfaces will disappear or that all interactions will become chats. Rather, we will see a combination of traditional interfaces and intelligent assistants that help the user navigate more quickly.
This trend is also observed by Gartner analysts. According to their predictions, over the next few years, more and more customer interactions will begin through conversational AI channels instead of traditional search or navigation. This does not mean the end of websites, mobile applications, or kiosks. Rather, it means that the first step will increasingly be asking a question, not searching for the right button.
The technology comes after that.
Sometimes the right solution is a self-service kiosk. Sometimes it's a vending system. Sometimes it's a software integration. Sometimes it's the automation of only part of the process. And sometimes the honest answer is that at this stage, automation will not solve the specific problem.
This is a significant change for companies developing self-service solutions. For years, the main task was to create good navigation. In the coming years, it will become increasingly important how the system understands the customer's intent.
Personalization will be equally important. Today, many systems show the same process to all users. A new customer sees the same steps as someone who has used the service ten times. AI makes it possible for this to gradually change.
A customer who has already used a service many times probably doesn't need the same explanations as someone encountering it for the first time. A user who usually performs certain actions can receive direct suggestions for them at the very beginning of the process. This is not a new concept, but the development of generative AI significantly expands the possibilities for this type of adaptation.
Another important effect will be the ability of systems to recognize user difficulties in real time. Today, most self-service solutions only understand that there is a problem when the customer gives up or seeks help. In the future, we will see more and more systems that can identify hesitation, repeated errors, or unusual behavior and offer assistance even before the person has reached the point of giving up.
What would this look like in practice?
Imagine a customer in front of a self-ordering kiosk in a restaurant. Today, they have to find the appropriate category themselves, choose products, and go through the payment process. In the future, the system could understand their intention from the start and offer a more direct path to the desired order.
For ticketing and payment systems, intelligent assistants could reduce the number of errors by detecting unusual actions and helping the customer before they reach a dead end in the process.
This is also where AI can be particularly valuable for self-service solutions. Not because it will replace the screen or interface, but because it will reduce moments of hesitation. Every second that a customer wonders what to do increases the risk of the process being interrupted. The more seamlessly the system helps the user move forward, the better the overall experience becomes.
From a customer's perspective, this often won't look like artificial intelligence. It will simply look like better service.
However, this is the moment when it's important to maintain critical thinking.
There are huge expectations and considerable exaggerations around AI. The history of technology shows that every major innovation goes through a period of hyper-enthusiasm before its real value becomes clear.
That is why not all predictions for AI are unconditionally optimistic. Gartner analysts warn that a significant portion of projects related to so-called agentic AI may not achieve the expected results due to unclear business value, high costs, or incorrect expectations. This is an important signal for companies that view AI as a solution in itself, rather than as a tool to solve a specific problem.
Because here lies a risk we've already seen with other technologies.
Businesses often start with the question:
"How can we use AI?"
A much more useful question is:
"What problem are we trying to solve?"
If customers struggle to find information, AI can help.
If the process is too complex, AI can help.
If employees are overwhelmed with routine inquiries, AI can help.
But if there is no clearly defined problem, technology rarely creates value on its own.
This view is shared by other leading analytical organizations. Research by Forrester and McKinsey & Company shows that the most successful AI initiatives are not necessarily the most technologically complex. They are those that solve a specific customer problem, reduce the time to complete a task, or improve service quality in a measurable way.
It is also unlikely that AI will lead to the disappearance of human service. A more likely scenario is a change in roles. Routine and repetitive interactions will become increasingly automated, while employees will focus on more complex cases requiring judgment, empathy, or unconventional solutions.
In fact, the biggest change may not be technological, but psychological. Customers will gradually get used to expecting systems to
understand them better. What seems like good service today may be perceived as a basic requirement in a few years. Expectations will grow, and companies will have to meet them.
Therefore, the most important question for businesses is not how to implement AI in their self-service solutions. The more important question is where AI can genuinely improve the customer experience.
Because ultimately, the customer does not buy artificial intelligence. They do not buy algorithms. They do not buy modern technologies.
The customer buys convenience.
Whether using a kiosk, a payment terminal, a registration system, or an online service, they expect the process to be clear, fast, and predictable. This is where AI has the potential to change self-service solutions most significantly – not by replacing the human, but by removing some of the complexity between the human and the service.
If AI succeeds in making services faster, more understandable, and easier to use, it will become one of the most important technologies in the history of self-service solutions.
If it doesn't succeed, it will remain just another buzzword that received more attention than real value.
That is why the next five years will not be a test of AI's capabilities.
They will be a test of companies' ability to use technology where it genuinely benefits the customer.
Sources used:
Gartner – analyses and forecasts for the development of AI in customer service and customer experience.
Forrester – research on customer experience, customer service, and self-service trends.
McKinsey & Company – analyses on AI implementation and business transformation.
Nielsen Norman Group – research in UX, usability, and user behavior.
