The story begins with a dishwasher. That’s already suspicious, because a dishwasher is probably the most boring appliance in the house. And this isn’t just any dishwasher. It’s my new one – the replacement for the old machine that the service technician finally gave its last rites to after nearly 25 years of loyal service. The new dishwasher had one simple job: wash dishes. It was not supposed to put my nervous system through a spin cycle. But things turned out very differently.
In this particular case, a completely reasonable customer request, one that could just as easily happen in almost any industry – somehow turns into a spectacular multi-act tragicomedy: Order placed. Appointment scheduled. Installation completed. Job botched. Rework promised. Wrong parts identified. Multiple emails left unanswered. A team of technicians arrives, has no idea what the problem is, has brought nothing useful, and leaves again. The hotline menu barely recognises the request. The chat asks for information that has already been provided. The store’s response is essentially: “Yes, it’s complicated because the processes are running in parallel.” And in the end, all I want is to speak to a manager who actually has the authority to solve the problem. Hardly an unreasonable request.
Not surprisingly, over the course of several weeks, customer service turns into an escape room with no exit. And again and again, the same sentence appears: “Please be patient. We’re taking care of your request.”
The interesting part is not that mistakes were made. Mistakes happen. The interesting part is that the system is unable to absorb and recover from those mistakes effectively. There is no common thread. No shared memory. No one with real ownership of the problem. Instead, the customer is forced into the role that a well-designed service operating model should be performing. The customer becomes the middleware: connecting information silos, explaining the same issue over and over again, reconciling contradictions, and manually compensating for both human and organisational packet loss.
That is not customer experience. It is unpaid systems integration work – carried out by the customer.
The Magic Wand Called AI
This is exactly where Agentic AI becomes interesting. Because, if we are honest, many companies currently talk about AI as though technology has the magical ability to transform poor process design into operational excellence. Spoiler: it does not.
Agentic AI can plan, retrieve information from systems, invoke tools, orchestrate multi-step workflows, and pursue goals with limited supervision. Modern contact center platforms can preserve context across channels, unify data, route interactions intelligently and equip agents with deep interaction context. That is considerably more than a chatbot that is simply a little less stupid.
In a dishwasher case like this, a well-designed agentic service setup could do very practical things.
It could manage the issue as a single case across every channel instead of treating it as a collection of disconnected notes. It could detect contradictions when two departments send messages on the same day, saying both that the work has already been assigned and that it still needs to be assigned. It could automatically verify which parts are actually required, identify missing photos or diagnostic information, and ensure that the right service provider arrives at the next appointment fully informed. It could monitor callback commitments, actively escalate when promised responses fail to happen, and hand the case over to a manager with real decision-making authority before the customer mentally reaches for the flamethrower. It could also immediately show the hotline agent everything that has happened so far instead of forcing the conversation to start from scratch.
These are precisely the kinds of multi-step, tool-enabled, context-dependent workflows where agentic patterns become valuable.
The Dilemma of Accelerating the Drama
But here comes the big caveat.
Agentic AI does not solve the problem of nobody being willing or empowered to take responsibility. It does not solve inaccurate, incomplete, or outdated data. Nor does it fix an organisational mindset in which the customer is treated primarily as a disruption in the ticket queue. If the underlying data is not correct, complete, current and integrated, AI simply makes bad decisions faster. If no escalation logic exists, the system simply fails to escalate in a more elegant way. If online channels, stores, hotlines, and service providers all operate in separate worlds, then what gets automated is not the customer experience – it is bureaucracy with a turbocharger.
Platforms such as NICE and Cognigy demonstrate where the industry is heading: away from isolated interactions and toward orchestrated customer journeys. But the same principle applies here: orchestration does not replace process design. It amplifies it.
Put differently: if you connect three broken processes with Agentic AI, you do not end up with a good process. You end up with faster drama. That is why thinking about Agentic AI cannot stop at technology. It requires thinking about operating models.
About accountability. About data ownership. About service design. About escalation rights. About knowledge management. About whether employees can act when it matters or merely enter polite phrases into a system. From the customer’s perspective, “I’ve forwarded your request to the next level” is not a solution. It is simply a more poetic version of: “Nobody is really in control here.” A modern contact center should therefore be designed less like a collection of individual channels and more like a problem-solving operating system. Voice, chat, email, retail stores, field service, logistics, spare parts and complaint management should never feel like parallel universes from the customer’s perspective.
They all need to work on the same case. Context must persist. Responsibility must be transferable without losing knowledge. Clean handovers are essential. Case prioritisation must be clear. There must be a single source of truth for case status, along with clearly defined rules for when a human takes over. Human-in-the-loop models are particularly important because they enable transparency, auditability, and genuine quality control. Customers do not want to be blindly handed over to automation. They want progress, fast results, clarity and, when necessary, a competent human being. Trust is not created by making automation invisible. Trust is created when customers understand what is happening, who is making decisions and when someone takes responsibility.
Why Every Stage Needs a Play
The business case is actually simpler than many people assume.
Poor service destroys margins. It generates repeat contacts, unnecessary service visits, escalations, avoidable operational costs, cancellations and, in the worst case, permanently damages customer relationships.
Anyone investing in Agentic AI should therefore not begin with the question, “What can the technology do?” They should begin with the harder one: “Where are we currently creating friction, repetition, and accountability gaps, and how do we eliminate them first – or at least address them alongside the technology?”
Only then does AI become a genuine lever for better service. Until then, it is little more than stage technology without a play.
Perhaps that is the most important takeaway in this entire debate: customers are not buying an omnichannel architecture. They are not buying a ticket number. They are not buying an escalation email dressed up with polite wording and holiday greetings. They are buying an outcome. A working appliance. A resolved case. The confidence that their concern has been taken seriously.
And that is exactly how Agentic AI in the contact center will ultimately be judged. Not by how impressive the demo looked, but by whether “We’re taking care of your request” finally becomes “Your problem has been resolved.” Everything else is, to put it politely, digital stage fog.