Hired or fired? How data is helping to define the future of work

How will AI impact the enterprise -- who will actually lose their job and why and what will legislation do if anything?

Speaking at The Economist Innovation Summit in London last month, Morag Watson, vice president and chief digital innovation officer at BP said that "AI will revolutionize the future employment market, creating jobs we won't recognize and can't even conceive of today." This new breed of job, she continued, "risks creating its own form of skills shortage, with employees requiring retraining and upskilling."

It's the sort of view we have become accustomed to, ever since Oxford University researchers Carl Benedikt Frey and Michael Osborne published their paper in 2013, on the most likely jobs to be lost to automation. There have been countless studies since, not least a recent OECD study, which diluted Frey and Osborne's predictions that 47 percent of US jobs and 35 percent of UK jobs were "high risk". The OECD put the US figure at 10 percent and the UK at 12 percent.

Clearly there is concern. This was exacerbated in the US recently when the Department of Labor released data showing that for the first time ever, US job openings exceeded US job seekers. It suggests something is very wrong with the skillset of the unemployed, despite US Secretary of Labor Alexander Acosta saying that "this is a great time be a job seeker in the United States."

If this really is an indicator of a disconnect in skills, then it has come a little early. We don't really have AI and robots yet, so what is going on? Is this the impact of automation or is this a shortage in the necessary skills to build the foundations, the infrastructures on which automation is expected to flourish?

What is interesting is how technology companies, especially those concerned with infrastructure, are being drawn to the sector. Human Capital Management (HCM), not a particularly likeable term, is an example of this. On the surface it looks as though it just sanitizes individual workers into performance numbers. The ability to track employees through a wide range of data points, treating them as, well, assets, sums up the technology approach to the world of work issue. Technology, widely touted as the reaper of gainful employment also plans to be its savior.

So, we have a solution before the problem has even become a problem. It suggests that technology businesses are seeing an opportunity here, to help customers identify workforce issues, improve efficiencies and increase bang for buck.

It's interesting because a Gallup poll came out recently saying that for all the technology innovation we have experienced over the years, productivity has not improved for decades. It was a stat cited by Charles Phillips, the charismatic CEO of Infor, during his keynote at a recent Inforum event. His point was that the digital revolution has transformed consumers' lives but hasn't quite transferred across into the enterprise yet.

"Can we take all the data from our applications and converge it around individuals?" he asks an audience sheltering from the September rain in a Washington DC convention center. His idea, which incidentally was a bit of a set-up to plug the Infor CloudSuite products, centers on collecting data from areas such as sales, HR, finance and production. Traditionally silos, Infor claims to have joined the dots, enabling industry-specific analytics across these diverse departments.

So, what is Infor doing here? Phillips says that the aim is to give businesses insight into working processes and practices, to identify bottlenecks and give businesses the ammunition to manage staff more efficiently. Infor is also pushing its Talent Science tool, a data analytics engine that aggregates key characteristics of successful employees in each job function and matches them against potential candidates. It seems to be working. Anne Benedict, Senior Vice President of Human Resources, Infor says, "we've found that those candidates that fit the profile for the role are 40 percent less likely to fall away."

Talent Science only makes a recommendation. It doesn't make the final decision (yet!) but interestingly it can also identify employee flight risk, as employees are assessed on an on-going basis. It's starting to sound a little draconian and wouldn't this sort of tech ironically render Sir Alan Sugar's The Apprentice redundant? Watching a data tool decide who he should hire would be marginally less interesting.


You're fired

So are we any nearer to really understanding which jobs are going to go? The mistake of course is to assume it's going to be a big bang moment. The reality is that it's gradual and, in many cases, it will make so much sense. Mundane tasks that require no creativity and management ability are sure to be automated. In theory this should do two things - reduce errors and increase job satisfaction for the people who no longer have to do those mundane tasks. They can, say the experts, be re-trained.

Infor's Benedict says that its tools help organizations understand where training needs to be applied to ensure increased skills and job satisfaction. This is also the point when it comes to the US Department of Labor's figures. Re-training the unemployed to meet specific roles makes sense but who is doing the training? Is this the role of governments? Is it a case of catching employees before they fall out of employment?

Of course, we are making huge assumptions here, that corporates actually care. Some do but many don't and therein lies a problem that data cannot solve. Leadership and managers will be driven by what they need and skills shortages are part and parcel of the digital age. If automation means greater efficiency then so what?

The future of work, regardless of what anyone says, is not mapped out. Businesses and governments for that matter, have yet to truly consider the longer-term impact of automation on their workforce. It's not that they are burying their heads, it's that, as always, there are more pressing issues today, such as skills shortages and staff retention. Where companies such as Infor do well, is establishing the rules for now with one eye on the future, by creating a platform which challenges the HR and talent acquisition norm by using data to help drive decision making. The more businesses understand the skills, and gaps in skills of each individual, the more likely they are to manage training better, keeping employees relevant (and getting rid of those that aren't). That's the theory anyway.

Understandably this is a burgeoning area. HR and talent acquisition technology is big business - a $17.57bn business by 2023 according to a WiseGuyReports.com study. As Future Market Insights suggests, machine learning is transforming the talent acquisition process, as much as it's transforming other areas of business.

What this means to the future of work is that data will increasingly ‘decide' on what work people will actually get and do. Yes, there will be managers but data is going to be increasingly used to pigeon hole people into working roles. Is it more right than wrong? Are there variables? What about privacy? Not all regions will take kindly to the idea that workers can be tracked and have their working lives cross-examined. This of course is the future of corporations, and not necessarily the future of work for everyone but you get the idea.

The inference here is that if you want to understand the future of work, you have to live it now and that means diving into data and ML analytics. Not everyone will like it but many will. Essentially, we are creating the framework on which we will be hired and probably fired in the future as automation continues to creep into working lives. Should governments get involved here?

Jo Swinson, MP for East Dunbartonshire and Deputy Leader of the Liberal Democrats, suggests we do need some ethical standards, especially around the development of AI.

"We should consider a form of Hippocratic Oath for those working in AI - a ‘Lovelace Oath' outlining a set of ethics to abide by, which will become an integral part of being a programmer or data scientist," she said, speaking at The Economist Innovation Summit. "Although we subcontract our judgement to AI, it's still just lines of code, and we need to maintain a level of human accountability for the decisions a machine might make."

It's a noble idea but you feel that by the time everyone gets around to it, the industry will have already steamed ahead. Businesses have to get smart, use the technology but apply empathy and human intelligence. There is no easy answer.