Amid the staggering pace of innovation in enterprise technology, it’s all too easy to overlook the role of employees whose job is to process, interpret and action the data emanating from these systems. Dr Anne Hsu is a behavioural psychologist and computer science lecturer at Queen Mary University of London. In this article, she shares her perspectives on how people are interacting with, and being impacted by, the volume and velocity of data in the modern workplace.
There is a common misconception surrounding the use of AI that remains pervasive in many organisations. It’s that AI exists purely in the domain of data scientists and technical teams, with the endgame of ‘dehumanising’ the workplace – making the future of work appear calculated and mechanical. This pessimistic vision needs to be put to rest and replaced with one more aligned to the reality; that the future of AI will embrace, not displace human skills and emotions in the quest for better business outcomes.
When decision makers within a business are able to envision and craft the role AI can have in their organisation, it opens up many possible opportunities for growth and innovation that would previously have been unimaginable. Through combining the positive business impact of AI with a deep understanding of human behaviours, executives can enhance the way their organisation operates and help their employees to be happier in their roles and more productive in their work. And for the employees themselves, the effective analysis of data via AI promises to augment their innate human skills, freeing them to be more strategic, and innovative in their daily work.
The (evolving) role of data in the workplace
It’s for this reason that leaders are becoming more attuned to the risk of (unintentional) bias, both in the data itself and in those working with it. Take, for example, the use of AI in hiring processes – if modelling for new hires is based on data from past hires, then the design of the selection frameworks should account for that. If previous hires have typically reflected a limited social grouping, taking into consideration factors such as age, race or educational attainment, then without checks and overrides in place the model is going to overvalue these factors in new candidates and sway judgements on their suitability for the role.
The advantage of using AI and data to make such decisions is that the bias can also be systematically removed. As long as the data modellers are aware of the biases in the data, which can be measured, then they have the opportunity to systematically remove these biases much more reliably than diversity training ever could. However, the bias must be accurately recognised for this to happen.
Embracing human qualities
Furthermore, in the quest for greater data maturity, it’s worth remembering the adage that not everything that can be counted counts and not everything that counts can be counted. Leaders should be aware that if everything starts to be observed with a data-centric lens, then all aspects of their business can become abstracted to quantifiable measures and metrics, bringing the risk that emotional connections can easily become devalued.
As data science aggregates individual data points, the nuances of individual emotions can quickly get lost, removing the unique elements of human interactions. For example, how can you put an accurate numerical value on qualities like loyalty, creativity, empathy and humour, all of which can contribute to a happy and productive workplace? Fundamentally, the goal of AI is not to reduce everything down to different sets of data; rather it should look to enhance the humanity of a workplace, and with it employee wellbeing.
At its core, AI is a human technology, and it needs to be approached as such. The most desirable employers of the future will be those who are able to operate human-centric workplaces in an increasingly data-centric world, so it’s imperative for leadership to articulate how data will be used throughout the organisation to support employees to achieve more in a better, faster and smarter way.
Taking such an approach requires appreciation not only of the opportunities that exist in data, but also in the psychological and behavioural limitations inherent in those working with it. By having AI applications and the data it collects work in synchronisation with people, leaders can direct their AI initiatives to be implemented in a way that augments the uniqueness of their organisation. That uniqueness comes from its people.