Another day, another AI headline designed to spike your blood pressure. The Service Management industry is no stranger to dramatic proclamations about AI, and neither are you. But what’s the real story behind CNBC’s latest take, “The tech support desk at work is one of the first jobs AI is rapidly replacing?”
Does the Palo Alto Networks company need media coverage? Does CNBC? CompTIA? All of them? Perhaps there is a hidden agenda behind the article being published. CompTIA has been sharing a lot of press about AI recently – maybe there is a new cert or training in the works? We can only speculate about their motives.
But what we can do is examine the substance of their claims and ask the critical question: What’s really happening to IT support as AI evolves?
Let’s break it down.
What CNBC got right
Let’s give credit where credit is due. The article raises some valid points about AI’s role in reshaping tech support and highlights trends that are hard to argue with – ones that many of us in the industry already see unfolding every day.
“It’s boring work.”
I agree. Repetitive, low-value tasks are exactly the kind of thing AI was made for, and honestly, good riddance. Password resets, ticket triage, and basic troubleshooting are the IT world’s equivalent of washing dishes – necessary but hardly inspiring.
Automating these tasks not only makes IT teams more efficient, but it also spares human agents from the burnout that comes with grinding through a mountain of mundane tickets.
Let’s not forget: these “boring” tasks are also often the ones most prone to human error. How many times have you clicked the wrong button because you were doing something mind-numbing for the fiftieth time that day?
AI doesn’t get bored. It doesn’t sigh audibly when it has to reset yet another account. And that’s a good thing for everyone involved.
But the real magic isn’t just in offloading these tasks – it’s in what it frees humans to do instead. With AI handling the grunt work, IT professionals can focus on higher-order tasks that demand creative thinking, strategic planning, and actual human interaction. In other words, they can do the work that makes them indispensable, not interchangeable.
AI is already writing – a lot.
Generative AI and large language models (LLMs) are already shaping how modern service teams operate, and CNBC was right to point this out. Whether it’s drafting knowledge base articles, populating ticket templates, or making auto-suggestions in Service Management tools, AI has become a quiet but transformative presence in the background of IT operations.
Open up any cutting-edge ITSM platform, and you’re likely to see AI hard at work. LLMs aren’t just spitting out boilerplate text; they’re contextualizing responses based on historical data and real-time inputs, giving teams a head start on tackling requests.
This isn’t a hands-off process. AI might be able to write 90% of a knowledge article, but that last 10% is where humans shine. Adding nuance, tailoring advice to the quirks of a particular organization, and ensuring the tone hits the right balance of professionalism and approachability – these are tasks that machines just aren’t built for (yet). And honestly, would we even want them to be?
Another angle worth noting: AI’s prolific writing capabilities have the potential to democratize knowledge. Instead of relying on a handful of overworked SMEs (subject matter experts) to document every process and workaround, organizations can now scale their documentation efforts with ease.
By automating routine tasks and generating content at scale, AI is helping IT support shift from a reactive to a proactive function. Instead of putting out fires all day, service teams can focus on preventing them in the first place.
This is exactly what technology is supposed to do: take the tedious, repetitive parts of our jobs and handle them so we can focus on what matters.
What CNBC got wrong
AI isn’t replacing IT tech support anytime soon. That’s not just wishful thinking – it’s a reality grounded in both the limits of technology and the complexity of human systems.
Why full AI replacement is a fantasy
Let’s talk about chaotic systems. IT environments are a patchwork of legacy tools, new platforms, human workflows, and (often) a little bit of duct tape and luck. Sure, AI can excel in controlled scenarios – ones where it knows exactly what to expect and how to respond. But throw it into the swirling chaos of an actual IT support desk, where no two problems are ever quite the same, and cracks start to show.
Then there’s the matter of human psychology. It might sound a little corny to say that tech support is about “shared human experiences,” but think about it: these interactions shape how employees view their workplace.
A helpful, compassionate IT agent can turn a bad day into a tolerable one. I truly believe that a company’s culture is highly impacted by the internal shared service teams and their own team culture.
The fear factory
I keep coming back to that headline. CNBC’s framing seems designed to create fear, uncertainty, and doubt. Headlines like this get clicks, but they also distort reality.
Yes, Palo Alto Networks is reducing its IT support staff by leveraging AI. But let’s put that in perspective: the company’s 300-person team represents a small fraction of its workforce. Even the reported “80% reduction” doesn’t mean 240 people are getting pink slips tomorrow. It means the nature of their work is changing, likely shifting from repetitive tasks to roles that require higher-level skills and strategic thinking.
And the article doesn’t tell you that those “boring jobs” are the ones most ripe for automation precisely because they don’t engage people’s full potential.
If those tasks can be automated, it frees up human workers to focus on work that actually matters. And what happens to the people displaced by AI? Are they all laid off? Reassigned? Retrained? The piece is frustratingly silent on this, leaving readers to fill in the blanks with worst-case scenarios.
When organizations use AI responsibly, they don’t just eliminate jobs – they reimagine them. Companies don’t invest in training and onboarding employees just to toss them aside. It’s bad business, bad PR, and bad for morale. Instead, they shift workers into roles that leverage their unique skills – roles that make them feel like contributors, not cogs.
The investment problem
Finally, let’s talk money. The article suggests AI is some silver bullet for tech support inefficiencies, but it glosses over a harsh reality: many companies don’t even spend enough on IT services to begin with.
How many organizations have you seen scraping by with outdated ticketing systems, bare-bones staffing, and a patchwork of manual processes? Those same organizations aren’t magically going to start throwing money at cutting-edge AI just because it’s trendy.
If anything, AI’s promise will remain just that, a promise, until companies are willing to invest in the tools, infrastructure, and training necessary to make it work.
And if they do invest, it’s unlikely to result in a wholesale replacement of IT support staff. Instead, they’ll end up with hybrid systems where AI handles the repetitive stuff and humans step in for the tricky, sensitive, or high-stakes tasks.
Conclusion
When it comes to AI coverage, it’s often hard to tell the wheat from the chaff, truth from half-truth, and genuine promise from empty hype. This article is a hype piece.
A carefully crafted celebration of AI’s potential designed to position its featured companies as leaders in innovation. It’s a documentation of success stories, yes, but also a reinforcement of a narrative that garners attention, clicks, awe, and anxiety.
Articles like this are part of a larger trend, one that often weaponizes fear and uncertainty about the future of work. There will be hundreds more articles just like this one, spinning tales of wholesale job replacement and robot overlords at the service desk. But we know the truth: AI won’t erase the human element, but augment it, empowering teams to do their best work, and turning the tedious into the transformative.
Or maybe this article was written to train a model. If that’s the case, I can’t help but wonder how much of the “boring work” was left for the humans.
