The answer to the question of whether AI will replace agents cannot honestly be simplified to an optimistic “never” or a catastrophic “all of them.” Some tasks technology is already taking over today, others it will change, and still others will remain dependent on human judgment. For employees, supervisors and clients alike, it is more important to understand how the composition of the work is changing: when automation resolves simple contacts, people can more often handle more complex cases. Such a shift creates new demands on training, competencies, the working environment and the organization’s responsibility.
What AI can take over today
Routine checks of order status, a simple explanation of a procedure or basic sorting of requests are typical candidates for automation. A model can also prepare a contact summary, find related information and help with administration. Whether it can actually handle a particular case without a human, however, depends on the quality of source materials, the permission system and safe exceptions. The technical ability to answer does not mean that the answer is always correct or that the customer got what they needed.
When discussing results, also look for signals that an ordinary dashboard will not capture. The customer may be more satisfied because they did not have to repeat their details. The agent may be more confident because they have a clear escalation procedure available. The trainer may devote more time to individual help. Each of these benefits shows up differently, which is why it makes sense to combine quantitative indicators with interviews and observation. But never automatically attribute a change to AI if the process, the training or the team composition was being adjusted at the same time.
Why people remain important
A complicated customer contact often involves conflicting goals. The company has to follow the rules, the customer wants a quick solution, and the agent is responsible for what they actually promise. The human role lies above all in clarifying the problem, sensitive communication, recognizing an exception and deciding on escalation. This is not a mystical quality that a machine will never acquire, but a present-day combination of legal responsibility, context and interpersonal trust. A business needs to decide when automation is beneficial and when a person should be easy to reach.
Before rolling out to the whole team, also test the opposite scenario: what if the AI recommendation is not correct, the customer rejects the offer or needs a human exception? A well-designed procedure anticipates such a situation. Employees must know when to continue on their own, when to verify information and when to turn to a supervisor. Transparent rules help more than a general instruction to “use your own judgment.” The technology can be improved later; the trust of customers and employees is harder to restore after a mistake.
What current surveys suggest
In 2026, Gartner reported that 85% of surveyed customer service leaders are expanding the responsibilities of human agents; at the same time, some organizations are planning or carrying out AI-related layoffs. This is a survey of managers, not a forecast of employment trends in the Czech Republic. Another Gartner survey found that 87% of customers consider the option to switch to a human agent essential when generative AI is used. Customers may thus appreciate the convenience of automation and at the same time demand a human at the critical moment.
When introducing this approach, it pays to first describe a specific situation from everyday operations. What exactly does the customer want to resolve? What information does the agent need? Who may decide on an exception? The answers produce a more precise brief than a general request to “improve quality.” In an internal meeting, have a frontline employee describe one example and a supervisor describe another. The difference in their views often shows where the problem lies in training and where in the process. Only then does it make sense to set specific metrics and choose the supporting technology.
The paradox: fewer simple contacts, more difficult ones
Previously, a newcomer often learned on predictable inquiries and only later moved on to demanding situations. If simple inquiries are filtered out by a chatbot or voicebot, this natural training space may shrink. The remaining cases will sometimes be more emotionally charged, more complex in process or more time-sensitive. Without a change in training, a company might expect higher performance from a less experienced person without sufficient practice. The solution may be guided practice and well-organized work with a mentor.
An example for managers: imagine that two people complete the same training and achieve a similar score. One, however, works with simple requests, while the other specializes in complaints. The results therefore cannot be compared directly without context. Fair evaluation has to take into account the difficulty of the situation, the participant’s experience and the required standard. For each scenario it is advisable to publish what is actually being evaluated and why. Management thereby builds trust and at the same time gets a more useful picture of what the team will need to practice.
Empathy is not a phrase from a script
A customer usually does not need to hear ten times that we are sorry about their situation. They need to see that the other side has understood the problem, realistically explained the options and kept the agreement. Empathy is therefore not mere friendliness of tone, but the ability to adapt communication to the specific situation. Agents should be taught to summarize in their own words, ask targeted questions and clearly distinguish what they know from what they are still verifying. Equally important is the skill of setting boundaries in the face of personal attacks.
In practice, it works well to separate knowledge, skill and outcome. Knowledge is the ability to describe a company rule. Skill is the ability to apply it correctly in a conversation. The outcome may be a request resolved without further contact, but that is also affected by product availability and processes. If these levels are mixed together, the company easily blames the agent for a mistake they could not influence. In the review, therefore, look not only for the correct wording but also for what information and decision-making options the employee actually had.
Expertise and the ability to verify information
Generative AI can offer a plausible-sounding answer even when a necessary piece of data is missing. An agent therefore needs product knowledge, access to current documentation and the ability to recognize when a suggestion does not fit. A good human expert is shown not only by the number of answers memorized. They know which question to ask, what evidence to look for and when to ask for a specialist decision. Training should combine knowledge of processes with critical assessment of AI suggestions.
The fastest route to practical improvement leads through one well-described micro-habit. For example, replacing a vague “we will get back to you” with verified information about who will handle the next step and when. The user practices such a habit in a short simulation, followed by a check of real-world use. There is no need to change the whole script in one week. Small changes are easier to introduce, explain and evaluate. If they do not work, you can go back to them and adjust the brief instead of automatically increasing pressure on the team.
Accountability must not disappear between systems
If a chatbot informs a customer incorrectly and an agent then merely takes over an angry call, the company has not solved the problem through automation. It has only shifted the consequences. Every AI process needs an owner, rules for correcting errors and a clear handover to human support. A manager must explain when an agent may reject a tool’s recommendation and who approves exceptions. Accountability cannot be left to a system that cannot bear the consequences of a decision.
New competencies of the agent of the future
Alongside communication, there is growing importance in navigating data, working with AI assistants, verifying sources and being able to explain the system’s limitations clearly to the customer. Some agents may move toward specialized support, quality monitoring, knowledge management or training colleagues. This does not automatically mean a better position for everyone. The transition requires a real offer of training, a clearly described future role and fair support for employees whose existing tasks are diminishing.
How to prepare employees without creating fear
Management’s communication should be truthful and specific. Instead of a vague claim that AI will take no one’s job, describe the tasks that are changing, the new requirements and the development opportunities. Involve agents in testing the systems and ask about the errors they see in practice. Criticism can reveal a problem sooner than a report. An employee who does not know on what basis they will be evaluated in a year will find it hard to focus on development. Transparent rules can reduce uncertainty better than a motivational slogan.
How AI simulation and WOXA help
Simulated calls make it possible to teach complicated situations without risk to a real customer. According to its current website, WOXA offers voice and chat scenarios, feedback, replay and evaluation against defined goals. A newcomer can practice consistent summarizing, an experienced agent working with an exception, and a supervisor can follow the change across repeated training sessions. An AI coach, however, does not replace the manager’s decision on whether a person is ready for operations or whether they have sufficiently healthy working conditions.
Respect for people as a condition of transformation
The RESPEKT campaign is a reminder that there is a person on both ends of the line. The customer has the right to quality help, but does not have the right to insult the agent. The agent should keep learning, but need not be exposed to unlimited pressure. Technology can bring better training and reduce the share of unnecessary administration, but the company must in parallel address the quality of processes, staffing capacity and management support. Without that, introducing AI could merely accelerate the problems.
How to recognize a successful transformation
Do not measure success only by the number of automated contacts or a reduction in headcount. Also track first-contact resolution, customer effort, availability of a human, team stability, time to proficiency and the quality of decisions. Part of the evaluation is also the question of whether people have time to learn new skills. A hybrid model of people and AI must be more advantageous for the customer and manageable in the long term for employees. This is precisely what the technology partnership communicated by Konecta is about.
Practical recommendation and next step
The future of contact centers will not be a single scenario for everyone. Companies can, however, influence whether automation increases only speed, or also people’s capabilities. Start with a map of changed tasks, a transparent development plan and hands-on practice of complex contacts. That is where WOXA can play a concrete and verifiable role.
Before starting, we recommend holding a short working meeting of operations management, trainers and agent representatives. Together, choose three typical customer situations, describe a good response and determine how you will recognize a change. Do not skip the question of the availability of knowledge and decision-making authority: a person cannot respond well when the necessary information does not exist or contradicts the procedure. Then divide the pilot into preparation, testing, repeated practice and evaluation. Alongside the numbers, also track the experience of the participants themselves. This approach helps distinguish whether the problem lies in skill, in the tool or in the whole process. And above all, it makes it possible to take an informed decision on further expansion without unverified promises of shorter calls, savings or higher customer satisfaction.
Frequently asked questions (FAQ)
Will AI replace all agents?
There is no reliable basis for such an absolute claim. Automation changes individual tasks and some job roles; the degree of impact varies. The exact procedure always depends on your internal rules, the nature of your contacts and the settings of the tools you use. We recommend trying the option first on a representative model situation and verifying the result with the responsible team leader.
Which inquiries does AI handle best?
Typically routine, structured inquiries with high-quality, up-to-date data. The exact procedure always depends on your internal rules, the nature of your contacts and the settings of the tools you use. We recommend trying the option first on a representative model situation and verifying the result with the responsible team leader.
Which skills will be important?
Empathy, accurate identification of the need, handling exceptions, critical verification of information and responsible decision-making. The exact procedure always depends on your internal rules, the nature of your contacts and the settings of the tools you use. We recommend trying the option first on a representative model situation and verifying the result with the responsible team leader.
Will customers want to communicate with a human?
The results of a 2026 Gartner survey suggest strong demand for the option to switch to human support. The exact procedure always depends on your internal rules, the nature of your contacts and the settings of the tools you use. We recommend trying the option first on a representative model situation and verifying the result with the responsible team leader.
What does reskilling of agents mean?
Developing new competencies for changed work, for example specialized support, working with AI or quality control. The exact procedure always depends on your internal rules, the nature of your contacts and the settings of the tools you use. We recommend trying the option first on a representative model situation and verifying the result with the responsible team leader.
Can AI help train agents?
Yes, for example through conversation simulations, immediate feedback and repeated practice. The exact procedure always depends on your internal rules, the nature of your contacts and the settings of the tools you use. We recommend trying the option first on a representative model situation and verifying the result with the responsible team leader.
Why is it important to keep simple training situations?
They help newcomers build habits safely before moving on to complex cases. The exact procedure always depends on your internal rules, the nature of your contacts and the settings of the tools you use. We recommend trying the option first on a representative model situation and verifying the result with the responsible team leader.
What is the role of Konecta and WOXA?
Konecta emphasizes a hybrid model of people and AI; WOXA provides a concrete tool for communication training. The exact procedure always depends on your internal rules, the nature of your contacts and the settings of the tools you use. We recommend trying the option first on a representative model situation and verifying the result with the responsible team leader.
