While Artificial Intelligence and automation technologies is still nascent, the building blocks exist to suggest that Machine Learning could ease the burden of complex analysis, surface insights and trigger actions on behalf of managers.
Workforce management is essentially the art and science of managing people in order to have a productive workforce. While there is a lot of science and methodology around this domain, with many current modern methods evolving from the foundational scientific management techniques propounded by Taylor over a century ago, it still requires the fine art of understanding an individual’s capabilities and balancing human expectations to get the most out of people.
Traditionally, in high empathy countries like India, this has largely been the responsibility of the manager, who balances an organisation’s needs with individual wants and abilities. In short, the manager decides who needs to do what, when and where.
To illustrate this better, if a manager has to take any quick decision, he assesses his immediate environment (learns), utilises his personal understanding of the situation (infers), and takes a judgement call (decides). While the manager is extremely effective in smaller groups due to his understanding of the environment around him, as the group size increases, he can no longer maintain a personal understanding of the information around him purely because of the amount of data that is required to be gathered.
In other words, learning takes far too long and the manager is limited by the amount of information he can retain in his cognitive mind. At this point of time, the manager starts relying on other “trusted sources” who can supplement him with the additional information he needs, and even help him with the different options, and decisions that he needs to take.
Technology has been a great enabler over the last few decades. Earlier, transactional systems allowed automating processes in order to reduce the volume of work involved. They also helped organisations ensure compliance by consistent enforcement of rules. And, of course, while all this information was available easily in the system, the flip side is that it often resulted in a deluge of information, drowning managerial decision-making in the process.
It was to augment managerial decision making that decision support systems were introduced in the early 1970s. These systems gave managers access to more data points to help them come with the right kinds of decision. Over the years, these technologies grew more advanced with more colourful and intuitive reports, providing more facts in a much faster timeframe.
However, despite being presented with more information pieces, the human mind is still limited with the ability to process these diverse pieces of information. It is a known fact that the cognitive mind’s processing ability slows down when confronted with more than seven things at the same time. The more the information pieces thrown up by these systems, the less likely the manager will be able to process all of it. Hence, very often, managers take decisions keeping in mind what they know, and not what they have access to, quite often ignoring data and trends in systems leading to that age-old problem of technology adoption.
Also, these systems did not give the contextual understanding that is essential to make the right judgement. This contextual understanding is all the difference between identifying the “right decision” among a list of “possible decisions”.
The human cognitive process, while phenomenal in its creativity and versatility, lacks the consistency and neutrality to create sustainable and equitable decisions, often leading to biased situations that end up serving different self-interests. In other words, the trusted advisor sometimes provides “less than trustworthy advice”.
It is at this crucial juncture where Artificial Intelligence (AI) helps bridge the gap. AI helps balance the need for dispassionate and objective decision making, processing volumes of information simultaneously, and at the same time contextualising information leading to a greater probability of the outcome being favorable for its stakeholders.
AI systems analyse volumes of data in a logical fashion like any good technological solution and at the same time try to think like humans by choosing the right option out of a number of viable options. In order to do this, they look at data patterns and learn, infer and recommend the right option, all the while keeping the context of the business problem. The only thing is that the scale, speed, consistency and objectivity of the AI system far exceeds that of which can be achieved by an individual.
Workforce management can be a far easier and compliant task with the adoption of AI and automation. The automation of critical workforce processes such as timekeeping, labour scheduling and leave management is something most organisations are keen to adopt. This not only reduces the time consumed in the manual processes, but also delineates, increases accuracy and reliability. In the near future, AI assistance will be crucial in helping companies retain employees and in helping individuals grow and meet their career aspirations.
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)