The Year the Help Desk Becomes Invisible

In a few thousand working hours—well within the lifetime of the laptops already on our desks—most employees will solve computer problems by talking to a system that listens like a human, reads the screen in real time, and acts on both streams of data at once. This multimodal AI—equally fluent with voice, text, and pixels—will make calling the help desk, waiting on hold, and guessing at obscure knowledge-base articles feel as anachronistic as dial-up tones. 

That prediction is less heresy than simple extrapolation. More than half of IT workers already say they are overwhelmed by the tickets they handle each day; they can realistically address only about 85 % of the requests that reach them (CIO).  Mean-time-to-resolution for a “normal” support organization still hovers above thirty hours, but companies that lean on modern AI cut that number in half (moveworks.com). And this is happening in a world where 78 % of global businesses report using AI somewhere in their operations—up from essentially zero a decade ago (Exploding Topics).  

The Invisible Queue 

Every ticket is a silent tax on productivity. We pay it in lost focus: the designer who can’t load fonts, the salesperson locked out of CRM five minutes before a call. Multiply that by thousands of tickets, across thousands of companies, and you begin to sense the scale of human potential trapped in waiting rooms. 

We sometimes respond with brute force—hire more agents, outsource to follow-the-sun call centers, or bolt another self-service portal on top of the old one. These help for a quarter or two, and then the curve bends upward again. Complexity grows faster than headcount can follow; expectations grow faster still. 

Why the Old Fixes Break 

Adding people is expensive because expertise does not scale linearly. Outsourcing pushes the queue into another time zone but rarely shortens it. Static knowledge bases age the moment they are published; employees learn to distrust them. Meanwhile the underlying friction—the mismatch between what a human needs right now and what fragmented systems can deliver—remains untouched. 

The only durable remedy is to shrink the friction itself. That means systems that can watch, listen, read, and act in real time; systems that learn by encountering the problem, not by waiting for someone to document it. 

A Short History of the Future Help Desk 

Deep learning led to large language models. LLMs evolved into multimodal transformers, folding speech, vision and text into the same network. Now a single model can listen to a user, read the screen, and fire off a script—all within one forward pass. What once took a research lab now starts with a four-line cloud-API call. 

We have crossed an invisible line: for a whole class of repetitive IT problems—password resets, printer driver tantrums, VPN misconfigurations—the marginal cost of letting a model try first drops below the marginal cost of assigning a human. When that happens, adoption becomes an engineering detail, not a strategic debate. 

Leading analysts now expect that around half of all support organisations will run generative-AI agents by 2026, yet these agents will fully resolve only 10–20 % of interactions in the average contact centre. Inside homogeneous IT service desks, that share can climb to more than one third—high enough to change job design, low enough to keep humans in the loop (searchbox, moveworks, gartner). 

Four Steps to an AI-First Service Desk 

• Pick the first ten tickets you always wish never reached you. Password unlocks, Wi-Fi profiles, printer queues—the things every support analyst can answer in their sleep. Automate those and you clear the runway for everything harder. 

• Feed the machine what it needs to think. PDFs, Confluence pages, device inventories, the ticket archive. The dullest part of the project and the one with the highest ROI. Models starve without context. 

• Measure the right deltas. Forget vanity metrics like chatbot deflection rates. Track mean-time-to-resolution, ticket reopen rate, and agent burnout. MTTR dropping from 30 to 15 hours is not an anecdote; it is a productivity dividend large enough to show on a quarterly P&L. 

• Let humans teach the loop. Every resolved interaction is labeled training data. Good systems learn silently but they learn faster with feedback. The virtuous cycle is not automatic; it has to be wired. 

Why We Built nara—and Where She Fits 

Our own support backlog taught us that these four steps sound simple and live messy. We needed a system that joins the same channels users already trust, sees what they see, speaks in natural language, and acts only within guardrails we set. We couldn’t find one—so we built nara. 

nara is a multimodal, self-learning first-level agent. She plugs into Teams, Slack, or phone, files every interaction in your ticket system, and—when you allow it—executes scripts through a secure bridge you host yourself. The contract is straightforward: nara removes busywork, surfaces the interesting work, and proves every action with an audit trail that combines transcript, screenshot, and command. 

When nara unlocks an account at three in the morning, the evidence is already in the ticket. When she can’t solve a problem, she hands off the full context—not a sanitized summary—to a human colleague. The result is a queue that shrinks, a team that climbs the skill ladder, and an employee experience that feels more like having a backstage crew than waiting for a call number. 

The Road from Here 

The line between “IT support” and “workspace assistant” will blur quickly. If a model can reset a certificate, it can also provision the development environment that needed that certificate, schedule the follow-up training session, and capture the new knowledge in Markdown. The difference is scope, not kind. 

We are heading toward a world where every employee has, in effect, a tiny backstage crew—an invisible set of experts who handle the scaffolding of digital life so that the foreground is illuminated. Jobs will change, expectations will change, and the people now burned out by triage will find themselves designing better systems rather than firefighting broken ones. 

That transition will be messy. All real transitions are. But the upside—millions of hours of human attention reclaimed from the purgatory of password resets—seems worth the turbulence. 

If you want to see what an invisible queue looks like, let us know. We will share a screen—and then we will let the system watch yours. 

Ready when you are. 

Lukas Brückner 
CEO, nara GmbH