Move to the present day and it’s safe to say the term artificial intelligence (AI) has firmly made its way to the real world. This is no surprise considering that today data is widely touted as the new oil - especially when noting the power this data deluge presents.

Every part of an organisation’s infrastructure, no matter what sector, creates data – from metrics on machine performance, to software and customer interactions. However, while many focus on what the data can tell us, what is often forgotten is the lifespan of data and where it is kept. And, with more data potential, the data centre has now become a pivotal and strategic part of the mix.  

With this in mind, the questions we need to answer accumulate to: how do we navigate through the data, how will we use it and can we better understand where the potential for AI is in helping us utilise it.

Already, AI is being widely touted as the hot ticket for data centres in 2020 but with present fears of a Matrix-style apocalypse leading the job shortages ripe, is it too hot for staff to handle?

One small step for man, one giant leap for machine

Leveraging AI in the data center will become a necessity for every data-driven business. Gartner has already claimed that more than 30% of data centers that don’t deploy AI and machine learning won’t be operationally and economically feasible in the near future.

Historically, the depth of learning possible from data was limited by time and resource. The datasets available today are not only far larger, but require more resources to ensure the data is stored and monitored, and that uptime is optimised across the data center.

Now, couple this with how automation has continually prompted concerns that technology will make people redundant or alter society in unsettling ways with an industry-wide skill gap. And then add the rise of AI such as ‘social humanoid’ robot Sophia. Fears that ‘the robots will take over’ are not only logical, but more prominent than ever.

Those with roles in data and analytics in particular have growing concerns, especially when it is estimated that some five million jobs in the UK will be taken by robots by the end of 2025.

With this in mind, is there an opportunity for the hive mind? One where human and machine complement each other and are more valuable than the sum of their parts?

Cultivating human–machine harmony

Luckily for us, the answer to that question is yes. The machine-based dystopian apocalypse is still, for now, one of fiction. But that doesn’t mean we’re out of the woods. Deploy AI in the data center too early and staff confidence will be shaken and jilted adoption will become prevalent.

There is still a very strong need for hiring skilled professionals for maintaining and monitoring, in order to maximise uptime. With the skills gap increasing, upskilling and ensuring each member of the team is working on higher level critical tasks is crucial to success.

One of the reasons why it is unlikely that technology will completely replace humans within the decision-making process lies in its very purpose. Machine learning and AI are intended to create new, innovative solutions based on an analysis of the data it ‘reads’.

But technology cannot comprehend the emotional impact of these decisions or be trusted to act on them appropriately. Dispassion is not always a strength.

So, while the hive mind is achievable, the next question raised is, as usual, how? How will man and machine work together?

The future – more like Wall-e than HALL 9000

When people talk about how Tesla cars can now figure out which parts need to be replaced and order new ones, the hype isn’t around how the mechanics will be shut down and the profession lost to the dark ages. The hype is around innovation.

In fact, this will streamline the mechanics services, having already identified the cause and the part ordered. The mechanic is now able to spend most of their skilled time on more complex and unforeseen issues that they can advise their clients on, based on experience.

This should be no different in the data center. Advancements in the use of AI should be celebrated in the same way and the opportunity taken to start upskilling and better training staff.

Imagine, rather than simply reporting faults after they happen, the system also leverages machine learning algorithms to “get a sense” for what the environment looks like and when a fault is about to occur. Allowing AI to “learn” from historic data sets to recognise faults-in-the-making can alert us in real-time time to intervene proactively and prevent downtime.

However, the knowledge gained and shared from human experience is still crucial for business decisions to be made in suitable context. You wouldn’t perform a U-turn on a busy motorway into oncoming traffic just because your sat nav told you to.

To paint the picture within an IT environment, an AI tool might decide that CPU utilisation on a server is insufficient to maintain regular system performance. As a result, the AI may decide that a trigger must be set, as low CPU power may result in downtime, or could be an indicator of a virus or malware. But a person in the business might know that this peak is an anticipated occurrence and is short term. So, in fact, no trigger is necessary.

In essence, no matter what “thought processes” are being devolved, a person will need to decide whether action is appropriate.

The collective conscious

While machine learning and AI can deal with the heavy lifting of analysing vast amounts of data quickly, it is the human element that recognises nuances and sentiments that drives the value for the business.

Introducing AI into the data center should be a gradual process with training to support. This applies both for upskilling and so that staff understand the advantages of creating a hive mind, where both humans and machines learn, adapt and feed each other, will ensure that people will remain essential to every business process.

For AI to innovate and allow data centres to thrive, an active partnership of humans and machines must be established. Just as it has been for centuries.