The automation of workforce planning: 2026

The 21st century is proving to be fascinating and unpredictable.

Mike Ryan: BrightHR Resident Futurist

Against a backdrop of mass migration, where even the land beneath us is used for energy discovery, we are bombarded with the technology that was once the preserve of Hollywood science fiction.

2020, the century’s second act is approaching and work with technology will be firmly on the agenda for disruption and change. The new technology term ‘machine learning’ is the current flavour of artificial intelligence (AI), giving computers new abilities that mimic human learning. We could quickly descend into a panic of the impact of this technology however in reality, the gap between human intuition and machine’s abilities to think is still vast. Especially where reasoning and deduction is based on creativity rather than fact.

Many jobs will be at risk particularly those which are repetitive and ideal for machines to take over. This is nothing new. Go back before the personal computer revolution and many people were employed in huge typing pools. E-mail and self-service operation of technology has created massive changes to people’s roles in the workforce and differing job opportunities.

We are in a world where the richest companies (Google and Amazon amongst others) use algorithms and data-mining to stay on top. With ‘machine learning’ likely to replace business intelligence in many organisations, is it a huge leap forward to begin to trust computers to plan how the workforce of the future will look? Change won’t all be at the top, either. We could easily witness the extinction of most manual work in the next two decades.

Here are some at risk areas: Will warehouses become fully automated with robots (Amazon are well on the way with this) and could future driverless vehicles could bus drivers and haulage jobs disappear? As more devices are ‘connected’ to the internet - the so called internet of things - where will the repair and maintenance jobs go that could be done remotely and by machines. Will we know if we call a helpline whether the person helping is real - or a computer that sounds like a human? In 2014, an AI system called Eugene has already passed the Turin test, by tricking judges into to thinking it was human [1].

Many organisations are now collecting rich data about their structures and AI services such as IBM’s Watson could be unleashed in commercial situations to deliver both financial analysis and capacity planning by mixing externally gained information with internal. Certain industries, particularly those which have high volumes and low margin at the core of their business model, could only survive with machine automation of capacity and workforce planning. How many more companies will be a hybrid of intelligent machines and human workforce in the next decade?

Organisational complexity [2] is cited as one of the things that keeps CEOs up at night. Machines that learn this complexity can be used to scenario plan and provide insight to the outside factors providing a joined up view of the future. The actions around workforce planning will naturally fall from roadmaps designed by machines.

There are many who believe this rush to machine autonomy is a step to far. Even esteemed scientist Professor Stephen Hawking has declared AI will lead to the extinction of the human race. [3] Elon Musk, founder of Telsa Cars and Space X has similar fears.

What we are missing are the checks and balances that often need to exist to prevent exploitation of the capabilities of technology. We may be scared of the way that corporations could misuse the information stored about us to build domination in a marketplace. We need to be terrified of how a government - our own or a foreign one - could use AI to control us, a step which is not that far beyond the information gathering Edward Snowden revealed.

So where does this leave an average organization, not set on world domination or spying on its customers? For the majority of companies the introduction of machine learning will help them overcome internal complexity which can often work against the organisation or even become dysfunctional. Cloud-based services will provide value and allow individuals to have their assumptions challenged in a positive way. IBM Watson is already offering a cloud service where it becomes a virtual non-executive director who listens into senior meetings providing instant analysis of internal data and external information to provide advice or suggestions to leaders. One wonders if Kodak or Nokia had this service whether they would have ended the way they did…

In a world where machine learning drives business decisions, it is likely workforce and capacity planning will be partly designed by machines and in some cases remove all human involvement from the equation. The jury is out. However, by 2026 it should be apparent whether machines are better equipped than a human in many situations. Lower down the ladder in an organization, any repetitive jobs will be replaced by machines in different scenarios. Think this is far-fetched? A Japanese hotel Henn-na near Nagasaki is entirely staffed by robots already [4].

In fact, there are plenty of examples in our world already of machine learning AI systems. The power in Apple’s Siri to do simple (and complex) numerical solutions uses a computational knowledge engine known as Wolfram Alpha [5]. The browser based Cleverbot[6] is also fiendishly good at holding conversations with humans.

Wherever technology takes us, we will be swept away if our jobs can be replaced. However the entrepreneurial intuition and drive, that often defies logic and makes most organisations succeed, is unlikely to be synthesised into a machine based system and that is what makes us undeniably human.

About the author:

Mike Ryan is a futurist thinker and writer and his views are his own. He can be contacted via twitter using @mikemanchester.

[2] Harvard Business Review, March 2015, Boris Groysberg