How tech will change the role of the line manager, according to Gartner

Over the next four years, the role of the line manager is likely to change beyond all recognition as a result of technology, according to Gartner.

In fact, a vast 69% of managers' current tasks, which range from expense to project management, will be automated by 2024, Gaston Gomez Armesto, the market research firm's Director of Advisory in London, told attendees at its online ReimagineHR conference last week — where attendees also heard from Brian Kropp, Chief of HR Research at Gartner, that the relationship between employers and employees is radically changing. Armesto said:

It's great news as managers are expensive and spend a large percentage of their time in meetings. Some 64% of employees also say they trust robots more than their managers. So does this mean the end of the era of human management? Not necessarily, but there'll be a big change in their role and in how managers and technology relate to each other because today, the relationship is sub-optimal.

The reason why this relationship is so poor, believes Armesto, is that technology all too often does not align with how managers work and so fails to provide them with the support they need at the right time or in the right place. As a result, they have little faith in it — and trust it even less if they fear losing their job to it.

Another issue that also does not help is that most people look at technology, such as artificial intelligence (AI), in a binary way, believing it to be either effective or ineffective at certain tasks. For instance, AI is generally considered good at activities that involve finding specific answers in large amounts of data, such as CV-screening, but poor if the focus is on socialising, strategizing or being creative, areas in which humans fare much better.

In reality though, there is actually a space in the middle in which humans and machines complement each other and work well together, Armesto pointed out. Here the emphasis is on "extrapolating patterns in data", which entails humans interpreting the raw findings that AI has unearthed. He explained:

Technology and humans have different skills that are both essential to get the job done, such as speed, accuracy and pattern recognition in the case of technology, and creativity, judgement and empathetic decision-making in the case of humans. The trick is to make them perform together like an orchestra so everyone can do what they do best. But there are also activities where humans and technology work better together, so it's about ‘technology augmentation' in areas like predicting future learning needs.

Technology augmentation in action

Armesto cites the example of multinational pharmaceutical company Sanofi, which piloted a natural language processing-based job candidate-screening tool using its WeChat channel. The aim was to evaluate the tool's efficacy compared with a purely human assessment.

Candidates were asked five questions in order to evaluate their cultural fit and learning agility, and if the algorithm deemed them unsuitable, they received an instant response to let them know. The assessment reports for successful job-seekers, on the other hand, were passed onto the relevant hiring manager to use as input in face-to-face interviews.

The tool was considered to hit the spot in that it enhanced the quality of manager evaluations and also overcame their concerns over the use of such technology.

Another area in which this ‘technology augmentation' effect is expected to play out, meanwhile, is in the making and communicating of pay decisions. According to Gartner's 2020 Employee Pay Perception survey, a mere two out of five people around the world today believe the amount they are paid is fair, while three out of five have little faith in their managers' ability to make pay decisions equitably.

A key challenge here, pointed out Carolina Valencia, a research director at the company's HR practice, is that the quality of such decisions relies on each individual manager's experience and competency levels. But the situation is also not helped by the tension in a manager's role between being a performance adjudicator and a performance coach. Another issue is that they spend inordinate amounts of time working out what amount to small pay variations.

Despite this difficult situation, Gartner's 2020 Pay Communication Benchmarking poll indicates that only 27% of the employers questioned had so far chosen to use algorithms when deciding pay, while a mere 7% were considering doing so in future. Of those that had gone down this route, 79% had managed to standardize pay decisions, 72% found managers had more time to focus on other things, and 57% had enhanced their performance-related pay activities.

Myth-busting

Of the two thirds that had not automated the decision-making process, two key "myths" predominated, Valencia said. Research showed that three out of five total reward leaders feared resistance from both staff and managers, while 68% were concerned that technology would be unable to reward unique employee contributions.

According to the findings of a pilot project assessing which factors most influence staff perceptions of pay fairness though, the single biggest consideration was the actual decision as to whether a pay rise or bonus was received or not. Other elements, such as who made the decision or how much information was provided about the decision-making process, were seen as secondary.

Moreover, a further survey revealed that a mere 19% of the 900 managers questioned around the world were keen to actually make pay decisions anyway. Some 36% said they would prefer not to and the remaining 45% were neutral.

This revelation was borne out by the experiences of a European engineering company, which found it had low levels of inconsistency in how pay was assigned internally. To address the issue, it created a software tool that enabled managers to make three choices: they could offer a 4% annual increase, a pay rise of either 2% higher or lower than the pre-set 4%, or they could take the decision themselves.

The upshot, said Valencia, was that 70% went with the 4% rate proposed by the tool, while those who went their own way were, without exception, experienced personnel who had worked at the company for a long time.

So, on the one hand, what this demonstrates, she believes, is that employees and managers are more comfortable with AI software making decisions than employers give them credit for. On the other, it seems that taking a ‘technology augmentation' approach is beneficial in improving outcomes, particularly in the case of inexperienced managers.

My take

According to a report by the UK's Institute for Employment Studies entitled The squeezed middle: Why HR should be hugging and not squeezing line managers by research fellow Dr Zofia Bajorek, all too many managers have been experiencing rising levels of work-related stress and pressure for some time — and it is difficult to imagine that the situation created by the COVID

-19 pandemic will have made their lives any easier.

This means that, in theory at least, managers should benefit from the automation of mundane tasks and the support provided by ‘technology augmentation' even if it means significant changes to their job spec — as long as such ideas are thought-through; presented in a non-threatening way they can buy into, and finally that they are provided with appropriate levels of training to help them make the most of it.