The fourth industrial revolution is upon us. While some organisations are welcoming the advances with open arms, others have their heads firmly in the sand, lodged firmly in a position of mistrust and suspicion.
Data analytics, machine learning and artificial intelligence are some of the biggest developments to hit HR in recent years. Used properly, they can drive consistency, efficiency, and objectivity, boosting the efficacy of the entire system.
The introduction of tech at scale also presents some significant risks. We need to be aware of these – and take measures to mitigate them – to make balanced decisions that work for the strategic initiatives of the organisation, not against them.
An organisation is a complex system composed of a number of processes that work together.
We need to start by having transparency around the nature of these processes, and then ask: which of them could be performed better by computers? Which will benefit from a human interface? How could the combination of processes – of tech and human ability – best collaborate to form a system that fulfils the strategic intent of the organisation? How can they be combined in a way that maximises the advantages of both to create exponential benefit?
No one tech-powered solution will be right for every company or every situation. The key is to use the right intervention in the right place, at the right time. Going in with your eyes open – not on the apron-strings of the latest shiny new thing in A.I. – is a must for far-reaching change that works across the board.
We need to work to build capability in both our technological processes and our human element. Even while we use technology to augment our results, we should be building sound in-the-moment judgement and equipping our talent teams with the technical skills and ability to perform.
In fact, as automation and A.I. continue to disrupt the workplace in unpredictable ways, human skills will become more important than ever. While tech will quickly become the common ground, having capable, well-developed people will give organisations the competitive edge. Employees with the developed capacity for qualities like collaboration, empathy, innovation, critical thinking and creativity will set businesses apart.
Understand the risks
Transparency and accountability are crucial when it comes to using algorithms to replace human judgement. The more opaque these systems become, the less control we have over them, and the more likely it is that problems will occur.
The tech companies’ marketing is compelling: “Objective assessment? Hiring decisions based on indisputable fact? Zero chance of human error?! Sign me up!”, you think, scrabbling to buy what they’re selling.
But algorithms, particularly those designed to learn and evolve, will embed human bias if left unchecked. They start as human decisions and are codified to become automatic – they are based on historic collected data, and can’t escape the limits of this. They simply propagate past practices and automate the status quo. Take this case at St. George’s Hospital Medical School where a trusted and unchecked algorithm led to around 60 students every year being refused interview because they were female or non-Caucasian.
So while human judgement is flawed, so are algorithms. Our task is to find a sensible integration of the two to minimise these flaws and maximise the systemic benefit.
We need to establish rigorous observation and control systems that review any learnt elements of the algorithm’s process. We need to have internal personnel who are technically capable of keeping tabs on the tech while holding the bigger picture in mind. We should also have regular external auditing of all systems by an independent, specialist team.
There’ll be times when we believe that a machine could technically match – or surpass – the ability of a human to perform a given task. But it’s important to be sensitive to harder-to-quantify but fundamental aspects of the organisational culture.
For example, what potential damage are we causing to the organisation by handing hiring power entirely to a set of algorithms? As a line manager, how can I be held accountable for the performance of my team if I’ve had no say in their selection? By creating a “computer says no” default, what damage are we doing to the wider culture?
What happens to our employer brand if candidate experience is reduced to an automated apology response? And even if we did want to give feedback, we’re not able to because we don’t understand how the algorithm has winnowed out the unfortunate losers.
Data-only approaches are at risk of systematically disempowering our people and turning them into a legion of ashen-faced button-pushers, right up the senior levels.
On the flip-side, use technology right and you have the opportunity to strengthen the lines of accountability: you introduce measurable, enforceable standards across the board, breaking down siloes and encouraging agility. You also elevate the nature of the work being done by the people you employ as you remove the repetitive tasks that encourage complacency and cultivate boredom and disengagement.
You free up resources to develop the people you have, and to continue to work on optimising the structures and processes at play. You also create a future-forward internal culture that offers your employees a service that matches their expectations as consumers in today’s tech-driven world of convenience: a flexible, user-centric experience that eliminates time-wasting and admin bottlenecks.
There’s no doubt that tech is a powerful ally for HR; but we need to be discriminating and inquiring about the nature of that alliance. We need to ensure that our people – who, after all, are often our most valuable asset – aren’t sacrificed on the altar of the PR drive towards a tech-forward, “advanced” enterprise. We also need to think systemically and follow the ramifications of a data-led approach to their most granular and fundamental conclusions.
If we do this, our human qualities in addition to the advances offered by the latest technology will create a combination greater than the sum of their parts.