Leadership by Algorithm. David De Cremer

the aim of promoting our understanding of what the data is actually saying. Algorithms are learned scripts for mathematical calculations that are applied to data to arrive at new insights and conclusions that we may not directly see. Specifically, they allow us to arrive at insights that can help us to develop more comprehensive and more accurate predictions and models. Algorithms act in autonomous ways to identify patterns in data that signal underlying principles and rules.

      As you can easily see, algorithms are not only useful but powerful tools in a society interested in continuously improving and enhancing knowledge. Indeed, algorithms are en route to serve such an important function to how we act and live in society that they will be as much part of our social and work lives as other human beings. In other words, the ability of algorithms to analyze, work with and learn from external data, means that algorithms today have reached a level where they can interact and partner with the outside (human) world.

The rise of algorithms in organizations

When you look around today and see what excites people about the future, it quickly becomes clear that the influence of our new hero (the algorithm in action) is rapidly growing, especially in domains where the potential for realizing significant cost savings is high. One such domain concerns our work life, where algorithms are increasingly becoming part of how organizations are managed.1 Although it may be a scary development for some of us, there are good reasons why algorithms are applied to a wide variety of problem-solving operations.2

Let us first look at the economic benefits. Current estimates show that the application of AI in business will add at least $13trn to the global economy in the next ten years. In a recent report by PwC, it was predicted that using AI at a larger scale – across industries and society – could boost the global economy by $15.7trn by 2030.3,4

Why do we expect AI to contribute in such enormous ways to the global economy? Mainly because algorithms are expected to have an impact on how businesses will be managed and controlled (as indicated by 56% of interviewed managers by Accenture) and therefore will facilitate the creation of a more interesting and effective work context (as indicated by 84% of managers interviewed by Accenture).5,6 This enhancement in effectiveness will ensure economic growth. Indeed, surveys worldwide indicate that the adoption of algorithms in the work context will help businesses to promote the fulfilment of their potential and create larger market shares.7,8

      For some, these numbers have been used to suggest that algorithms represent steroids for companies wanting to perform better and faster. It is nevertheless a reality that companies today are developing new partnerships between machines and AI on one hand, and humans on the other hand. Developing and promoting this kind of partnership also has an important implication for humankind. It is likely that the new technology, available to push companies’ productivity and performance to a higher level, is bound to steadily take more autonomous forms that will enable humans to offload parts of their jobs. Importantly, this development is not something that is likely to happen tomorrow. In fact, it has arrived already. AI is developing so fast that an increasing number of machines are already capable of autonomous learning. In reality, AI has achieved a level of development that makes it capable of taking actions and making decisions that previously were only considered possible under the discretion of humans.

If this is the case, then it is no surprise that the availability and possibility of implementing intelligent machines and their learning algorithms will have a significant impact on how work will be executed and experienced. This reality is hard to deny because the facts seem to be there. As mentioned earlier, Google’s DeepMind autonomous AI beat the world’s best Go-player, and recently Alibaba’s algorithms have been shown to be superior to humans in the basic skills of reading and comprehension.9

If such basic human skills can be left to machines and those machines possess the ability to learn, what then will the future look like? This predicted (and feared?) change in the nature of work will be seen across a broad range of jobs and professions. It is already widely accepted that automation of jobs in the business world is happening. For example, algorithms are being employed to recruit new staff, decide which employees to promote, and manage a wide range of administrative tasks.10,11,12

But companies are not just investing in complex algorithms for passive administrative tasks that can lead to hiring the best employees. They are also being used already for more active approaches. For example, the bank JPMorgan Chase uses algorithms to track employees and assess whether or not they act in line with the company’s compliance regulations.13 Organizations thus see the benefit of algorithms in the daily activities of their employees.

As another case in point, companies have set out to enable algorithms to track how satisfied employees feel, in order to predict the probability of them resigning. For any organization this type of data is important and useful in promoting effective management. After all, once the right kind of people are working in the organization, you want to do all you can to keep them. In that respect, an interesting study from the US National Bureau of Economic Research demonstrated that low-skill service-sector workers (where retention rates are low) stayed in the job 15% longer when an algorithm was used to judge their employability.14

      Automation and innovation

      Automation and the corresponding use of algorithms with deep learning abilities are also penetrating other industries. The legal sector is another area where many discussions are taking place about how and whether to automate services. Legal counsellors have started to use automated advisors to contest relatively small fines such as parking tickets.

      The legal sector is also considering the use of AI to help judges go through evidence collected to reach a verdict in court cases. Here, algorithms are expected to help present evidence needed to make decisions where the interests of different stakeholders are involved. The fact that decisions, including the interests of different stakeholders, may become automated should make us aware that automation in the legal sector introduces risks and challenges. Indeed, such use of algorithms may put autonomous learning machines well on the way to influencing fair decisions within the framework of the law. Needless to say, if questions about human rights and duties gradually become automated, we will enter a potentially risky era where human values and priorities could become challenged.

Another important industry where technology and the use of automated learning machines are quickly becoming part of the ecosystem is financial services. Traders and those running financial and risk management are working in an environment where digital adoption and machine learning are no longer the exception.15 Rather, in today’s financial industry, they seem to have become the default. In fact, the use and application of algorithms to, for example, manage risk analysis or provide personalized products based on the profile of the customer is unparalleled. It has reached the level where we can confidently say that banks today are technology companies first, and financial institutes second. It’s no surprise that the financial industry is forecast to spend nearly $300bn in 2021 on IT, up from about $260bn just three years earlier.16

      It is not only that banks have embraced technology so much that it has transformed the workings of their industry significantly. No, it is also the other way around. Technology companies are now moving into the financial industry. Indeed, tech companies are becoming banks. Take recent examples such as Alibaba (BABA), Facebook (FB), and Amazon (AMZN); all are moving into providing financial services and products.

A final important area where we see that the use of autonomous learning algorithms will make a big difference is healthcare.17 The keeping and administration of medical files is increasingly being automated to provide an interconnected and fast delivery of information to doctors.18 Transforming the healthcare industry will also impact medical research, hence better results can be achieved in saving human lives.19 Doctors making use of technology to detect disease and subsequently propose treatment will become more accurate and truly evidence-based. For example, examining how to increase cancer detection in the images of lymph node cells research showed that an AI-exclusive approach had a 7.5% error rate and a human one a 3.5% error rate. The combined approach, however, revealed an error rate of only 0.5% (85% reduction in error).20

      Us versus them?

      Putting