Let’s start by outlining some fundamental points about managing people. Managing people or human resources (HR) management as we commonly know it, is a complex and demanding task and data science initiatives are equally destined to be challenged if you approach this solely from that perspective.
Although intelligent data metrics undoubtedly provide employers with useful information, it is important to ensure that these tools should also contribute toward building human value and that these solutions preserve and improve the human element of managing people. There are potentially many repercussions for how you lead and manage people, many of which differ significantly from data science optimisation principles that emerged from analysing machines and systems. However, the good news is that there is also a huge opportunity to introduce data science because many of the people™s management decisions have simply not been consistently applied up to this point.
Challenges in People Management
When we approach people management naturally, one of the unique aspects is the importance placed on values like fairness. For example: when the government regulates employment matters such as minimum wages, working hours, vacation time, skills development, affirmative action and discrimination, keeping pay records and termination of employment (notice and severance pay), we start experiencing arising fairness issues in employment decisions. This is different from other data science fields.
Likewise, with data science, another major problem is change management. Organisations that have been operating in a certain way face difficulty in transitioning if an algorithm™s decision-making process differs significantly from a conventional decision-making process.
Similarly, people management is a personal, human-centred function. Ambition and passion are two examples of emotional and psychological traits that are significant, and AI cannot replace the role of a manager, for example, to read or react to their subordinates™ real-life experiences and emotions. Currently, AI is unable to observe or assess human emotion or take into consideration how emotion influences behaviour. Moreover, personality and the impact of emotion on teams are not necessarily taken into consideration by AI.
Big data programs also require human resource practices to gather, clean, combine, and analyse data from numerous departments and business functions, such as finance. performance management, wellness, talent management, labour relations and training. It may also be challenging for HR to gather and use sensitive information such as private health information. For HR departments seeking data outside of their organisations, the challenges can be worse due to the confidential nature of most people-related information. These data sources are also key in initiatives involving data science that aim to address problems affecting employees, such as:
- How do we progressively measure and improve employee and team performance?
- How does an employee™s pay compare to others, both inside and outside the organisation?
- How can we better engage and motivate groups of employees in an organisation?
- How do people metrics unlock or relate to key business performance matrixes?
People management has significant financial costs as well. To demonstrate, it is estimated that up to two-thirds of all the costs in a typical organisation would come from outsourcing, managing payroll and performance, as well as staffing and hiring.
The Potential of organisations to manage people using AI
According to IBM™s executive interviews, AI is useful in HR since it helps:
1. Attract and develop new skills
2. Improve employee experiences
3. Provide analytical decision support
4. Make more efficient use of HR budgets
Similar to human minds, AI algorithms are excellent at seeing patterns, planning, and adjusting. However, human decision-making can be subject to fatigue or biased decisions. With AI, HR departments can deliver novel insights and services at scale without incurring astronomical costs or personnel increases.Applications such as Google’s Hire enable people to work more quickly and concentrate on developing their networks and organisation.
The typical workplace experience is no longer acceptable to employees, given that they now demand a unique one. Moreover, from the beginning to the completion of a task, they expect their needs to be satisfied in a way that is personalised and works for them. Also, the ability of AI to: alter workflows, recommend career progression opportunities, and adjust remuneration to fit budgets that are in line with the company’s clear business strategy, is the most advantageous aspect of this technology for businesses.
Most of the time, these processes require human skill, which can be costly, especially when a highly specialised expert is involved. However, in the case of machine learning, we don’t necessarily need an expert, rather, we need the data that the experts have to produce, and through AI, the data that HR departments require is available when they need it to complete tasks in a more efficient and transparent way.
As illustrated, AI in HR has the potential to provide considerable organisational advantages opening the door to previously unimaginable possibilities. Above all, join me as we examine how AI can be applied to decision-making in HR management, in a series of four articles:
A. AI for Employee Engagement
B. AI for Employee Attrition
C. AI for Employee Career Progression
D. AI for Employee Hiring
