AIOps Will Mean the End of Human Network Management

When early humans domesticated wheat, it changed the way they lived. The plentiful food produced by these pioneering farmers allowed populations to grow rapidly, and that meant there could be no going back to the nomadic hunter-gatherer lifestyle of the past. There were simply too many people for that.

Fast forward 10,000 years to today and network professionals are about to make a change to the way they work, which, once made, there can also be no turning back from. But this time it’s not about food production – it’s about artificial intelligence for IT operations, commonly known as AIOps.

In fact AIOps is a bit of a misnomer because it’s really about the use of machine learning (ML) rather than artificial intelligence (AI), but we’ll let that pass for now.

The point is that enterprise networks, the networks they are connected to, the applications which run on those networks and in the cloud, and all the supporting infrastructure that goes with that, have now become supremely complex when viewed as one gigantic entity. You can forget about understanding what’s going on in these systems: it’s at the very limit of human ability just to manage them and fix them when they go wrong.

That’s why networking teams around the world are looking at AIOps platforms to help them handle the vast volumes of data generated by these IT systems, networks and applications and to analyse events, metrics, network flow data, streaming telemetry data, and so on.

At the moment the trend to AIOps has only just started in earnest, although it’s been talked about for several years. But Gartner predicts that by 2023, 40% of DevOps teams will augment application and infrastructure monitoring tools with AIOps platform capabilities. There can be little doubt that a few years after this, AIOps will be the norm rather than the exception in virtually every large enterprise.

AIOps means humans can no longer manage networks

Once this has happened, though, there can be no going back. The main reason is that, freed from the constraints of what the human brain can cope with, network and systems complexity can go through the roof. It will no longer be possible for humans to manage such networks and systems, but as long as machine learning systems can watch over it all with loving grace, that won’t matter. These systems will analyse the entire system’s functioning, detect anomalies, fix problems before they occur, avoid outages and detect and prevent cybersecurity incidents.

That at least is the theory. They may prove not to be perfect, but that’s not the point. What’s important is that they will be able to perform these tasks better than humans possibly could, and that’s because we will have long passed the point where humans could perform these tasks at all. 

And there, in a nutshell, is the big potential problem with AIOps. If it doesn’t end up being as capable as we would like it to be, nothing can be done about that. Because, like becoming an agrarian society, once we go down the path of AIOps,  we will pass a point of no return.

Paul Rubens
Paul Rubens
Paul Rubens is a technology journalist specializing in enterprise networking, security, storage, and virtualization. He has worked for international publications including The Financial Times, BBC, and The Economist, and is now based near Oxford, U.K. When not writing about technology Paul can usually be found playing or restoring pinball machines.

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