“You are doing great this week, Sophie. Your sales are up 70 %, which puts you in the first position on your team’s leaderboard. But you still have a lot of open opportunities, so keep up the good work!” Receiving performance feedback helps employees to grow. It encourages learning and rewards good performance. But what if an algorithm provides the feedback instead of a human manager? That’s the fundamental of algorithmic management in organizations.
What is algorithmic management?
Algorithmic management is the strategic tracking, evaluating, and managing of workers through algorithms. Such algorithms take over tasks that used to be performed by human managers (Duggan et al., 2020). This innovation in management is especially common in the gig economy. For example, platforms like Uber, Deliveroo & UpWork manage and closely monitor their global workforce with algorithms. The algorithms assign tasks and rate performance. They also give feedback and provide recommendations on how to improve performance (Kellogg et al., 2019).
However, managing workers with algorithms is no longer limited to the gig economy. Traditional organizations are also discovering the benefits of increased efficiency and data-driven decision-making.
Big Data and automation are high on the agenda of most departments with HR directing its focus towards data-driven decision-making, too. Algorithms used in HR can increase efficiency and even out-perform human decision-making (e.g., Cowgill, 2019). In fact, 40% of HR departments in international companies use AI-based tools.
For example, the use of algorithms is quite common in employee selection. The algorithms screen CVs and match applicants to positions. They are analyzing facial expressions in video interviews or applicants’ written motivation through Natural Language Processing. In addition, algorithmic systems provide performance feedback to employees and managers.
With algorithms gaining influence in organizational decision-making, the question emerges: Does algorithmic decision-making live up to its promise of objectivity and accuracy?
Like any technology that offers such substantial benefits, algorithmic decision-making is a double-edged sword and comes with several challenges. How do applicants perceive a company that automates parts of the recruitment and selection process? What are reactions to automated performance feedback? How much do employees rely on this feedback? How much do managers rely on decisions that algorithms make?
The fact is: implementing algorithms changes organizational and interpersonal dynamics. That’s why it is essential to take a closer look at the benefits and challenges of algorithmic management and discuss best practices for implementing it.
The 3 key benefits of algorithmic management
1. Kick-starting organizational performance
Increasing productivity and efficiency is one of the most significant advantages of algorithmic management. For example, imagine how many CVs an algorithm vs. one recruiter can scan in an hour? The difference is considerable and can help companies to stay on top of the talent game.
Automating manual tasks will also give managers more time and resources to focus on strategic undertakings. Together, this can increase organizational performance. Firms that fail to integrate analytics and AI into their strategy run the risk of falling behind.
L’Oréal Group, an international personal care company with 80,000 employees worldwide receives over 130 applications per job opening, on average. They implemented an AI solution based on computational linguistics to make their recruitment process more efficient. As a result, the company was able to start hiring 10x faster and interview 25% more applicants.
2. Improving managerial decision-making & remote management
Evidence-based and data-driven decision-making has become more common in recent years. Algorithms can help deal with the increasing complexity of issues that managers deal with every day. Data processing capabilities of algorithmic systems go far beyond the human possible. They can take into account all relevant data and exclude irrelevant factors. This enables objective and fair, data-driven decision-making. What’s more, it can reduce bias in decision-making.
For instance, cognitive bias might lead retailers to believe in the need for unstable scheduling of their staff. Unstable scheduling in retail is when work schedules vary on a day-to-day basis to decrease labor costs. Many retailers consider this type of scheduling effective, because they see the immediate, short-term benefits like cutting payroll, while overlooking the long-term negative effects, such as the impact on customer service. This is where algorithms that forecast staffing needs based on customer traffic and other data come into play. Studies have shown that “combining algorithms with manager intuition can lead to better staffing decisions”.
Algorithms can also be beneficial for remote work. Workforces will become more and more distributed, and remote and hybrid working will, to some degree, become the norm. This may come with great benefits for employees. Yet, managers may struggle to stay up to date with their employees’ progress and performance. Algorithms for performance monitoring can be an essential tool to enable successful remote management.
3. Receiving personalized insights & feedback
Algorithmic management does not only offer benefits for managers but also for employees. Algorithms can provide personalized performance feedback.
Deliveroo sends their couriers personalized monthly reports on their performance. They get information on their average ‘time to accept orders’, ‘travel time to restaurant’, ‘travel time to customer’, and other metrics that algorithms track.
Algorithms can create insights into employee’s progress, to-do’s, and projects. They are also used to improve employee wellbeing. Such algorithms analyze employee needs and goals and recommend training and developmental programs. Algorithms can also track and evaluate what matters most to employees’ wellbeing and motivation. Based on that, they can give recommendations to managers on how to increase employee wellbeing (Buck & Marrow, 2018).
The 3 most important challenges of algorithmic management
1. How fair is algorithmic management?
Besides the benefits of algorithmic management, there are several important ethical issues.
The main goal of algorithms is to improve decision-making and make it more objective and fair. However, just the opposite may be the case. Algorithms remove or reduce human involvement in decision-making. As a result, people might see algorithms as unfair (Dietvorst & Bharti, 2020; Lee, 2018; Newman, 2020).
The main concern is the data that algorithms base their decisions on. Algorithms are trained on sample data to predict events and make decisions. Thus, the quality of the data is an important factor. For example, an organization could train an algorithm on historical talent data where very few women hold management positions. Then, the algorithm may predict women to be less likely to succeed in management positions. As a result, women might be excluded from talent management initiatives.
Algorithms also often have a “black-box” character. They are not transparent and how precisely the algorithm works often remains unclear. This can challenge trust in the algorithm and present accountability issues for the algorithm’s decisions.
Some states in the US are already looking into the use of algorithms and AI in recruitment and how to ensure their fairness and transparency. The state of New York is working on legislation that would oblige recruitment technology vendors to conduct anti-bias audits and ensure compliance with employment discrimination laws. Illinois has enacted the Artificial Intelligence Video Interview Act, imposing limitations on companies that use AI to analyze candidate video interviews.
Utilizing algorithmic management is not a question of yes or no. Often, only parts of decision-making are automated. As such, fairness and accountability issues depend on how much companies rely on algorithmic decisions. The real question here is: augmentation vs. automation? It makes a difference whether you use algorithms for consultation or to replace human decision-making.
2. Algorithmic management challenges the roles of management & HR
Algorithmic management reduces or replaces human involvement and interaction in different processes. This poses challenges to managers and HR. How does people management change when the personal and empathetic side is removed?
Both managers and HR practitioners need to adapt to the new dynamics that come with algorithmic management. They need new skills and competencies that prepare them for the responsible use of algorithms (Angrave et al., 2016).
Managers and HR also need to adopt the perspective of (potential) employees. For example, algorithms used in recruiting can become problematic when candidates do not believe that the algorithms see how unique they are (Narayanan et al., 2018).
So how do managers and HR compensate for increased automation and reduced human contact? And how do they successfully create change towards a data-driven culture? All these are questions that HR and managers will have to find an answer to.
3. The risk of algorithmic management for employee wellbeing
Algorithmic management can also pose a risk to employee wellbeing. Some even compare the real-time behavioral tracking, feedback, and evaluation to Taylorist surveillance. Algorithmic management can be seen as an invasive form of control over employees (Kellogg et al., 2020).
It also appears to conflict with the trend to give employees more autonomy, enable flexible work and schedules. Companies have to closely monitor how employees react to the introduction of algorithmic management. Some employees may see it as a threat to their psychological safety and autonomy. Then, wellbeing could decrease as a result of algorithmic management.
For instance, an international hotel chain was using a software tool to manage housekeepers. They were continuously updated on which rooms to clean next and the company was also able to track how long it took them to clean a room. However, the workers pointed out that the algorithm failed to take into consideration the nuances of their job and made it harder. They became unable to organize their day and the work got more physically demanding, as the algorithm sent them “zigzagging across the hotel floors.”
Recommendations for implementing algorithmic management
The good news is: It is possible to reap the benefits of algorithmic management while mitigating its challenges. The following strategies can help managers responsibly implement algorithms into organizations.
First, it is crucial to determine the degree of algorithmic management. Gig platforms rely entirely on algorithmic management, but this might not be the right solution for more traditional companies. Consequently, it is not a question of either/or but rather where and to what degree.
Companies could identify processes that are costly and relatively standardized and start there. There, you can expect the biggest gains of algorithmic management. In any case, integrating algorithms into business and decision processes requires a clear strategy: determining whether they augment or automate human decision-making.
2. Change management
In implementing algorithmic management, it is also important to consider employee wellbeing. Introducing algorithms in organizations is a substantial transformation. A change management perspective can be helpful. Proactive change management is a deciding success factor in the introduction of algorithms. You need to make sure that there is readiness for change. By helping your employees and managers understand the value added by the algorithm, you can prepare them to welcome the change.
People may also feel threatened by the introduction of algorithmic management. This can come from a lack of communication about the algorithm. Similarly, employees might fear that machines are replacing them. To overcome this, it is important to include employees and managers early on in the change process. Setting up open lines of communication helps tackle the concerns people have. This includes active communication about what the data is used for and who is accountable for algorithmic decisions. It prevents employees or managers from feeling left behind.
Communication and change management should go hand in hand with training. Training allows people to feel comfortable working with algorithms and hand over decision-making. If people don’t understand how the algorithm works, they may not want to use it. It is vital to train employees and managers on the skills and competencies necessary to work with algorithms.
3. Constant evaluation
Finally, companies need to adopt a culture of constant evaluation. It is necessary to track how algorithms perform. Only when the decisions are accurate and of high quality will people accept the value added by the algorithm.
Not every algorithm increases efficiency, so it is important to monitor its quality. The effect of the change on employees also needs to be tracked. Organizations could provide opportunities for employees to voice concerns and provide feedback. In particular, those employees who are (partly) managed by algorithms. This gives organizations valuable information for adjusting and improving algorithmic management.
A final word
The benefits of algorithmic management and how it can help companies stay ahead of the competition are clear. Algorithms cannot only improve efficiency but also enhance decision-making. Then again, this should go at the expense of employee wellbeing. Automating tasks that humans performed (like providing feedback) represents an enormous change. To transform this change into something positive and sustainable lies in the hands of everyone involved. But managers and HR will play a significant role in creating readiness for change.
There is no one size fits all approach. Every organization needs to go through a careful assessment of the benefits and challenges that the introduction of algorithms brings for them. The presented strategies can help guide organizations through this transformation. The promise of algorithmic management is big. Yet, it is necessary to always keep the focus on the most valuable asset that organizations have: their people.
Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1–11. https://doi.org/10.1111/1748-8583.12090
Buck, B. and Morrow, J. (2018), “AI, performance management and engagement: keeping your best their best”. Strategic HR Review, 17(5), 261-262. https://doi.org/10.1108/SHR-10-2018-145
Cowgill, B. (2019). Bias and Productivity in Humans and Machines (SSRN Scholarly Paper ID 3584916). Social Science Research Network. https://doi.org/10.2139/ssrn.3584916
Dietvorst, B. J., & Bharti, S. (2020). People Reject Algorithms in Uncertain Decision Domains Because They Have Diminishing Sensitivity to Forecasting Error. Psychological Science, 0956797620948841. https://doi.org/10.1177/0956797620948841
Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020). Algorithmic management and app-work in the gig economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114–132. https://doi.org/10.1111/1748-8583.12258
Kellogg, K. C., Valentine, M. A., & Christin, A. (2019). Algorithms at Work: The New Contested Terrain of Control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society,5(1). https://doi.org/10.1177/2053951718756684
Narayanan, J., Lavanchy, M., & Savani, K. (2018). Impact of Hiring Algorithms on Candidate Perceptions. Academy of Management Global Proceedings, Surrey(2018), 182. https://doi.org/10.5465/amgblproc.surrey.2018.0182.abs
Newman, D. T. (2020). When eliminating bias isn’t fair_ Algorithmic reductionism and procedural justice in human resource decisions. Organizational Behavior and Human Decision Processes, 19.
About the co-author
Corine Boon is an associate professor of HRM at the University of Amsterdam Business School, where she does research on strategic HRM and HR analytics. She is the director of the Amsterdam People Analytics Centre (APAC), a center which helps facilitate industry-academia partnerships in the area of people analytics.