It’s already learned to manage your taxes, automate your home, and even raise alerts about your baby’s soiled nappies — it was only a matter of time before artificial intelligence (AI) made a foray into your professional life. One of the most crucial aspects of work that AI is out to reform is performance management, and this isn’t of course, without controversy.
We’ve seen AI prove its competence in speeding up mechanical tasks like data entry, customer service requests, and order processing. That’s cool, but what really caught our eye was its entry into a field like performance evaluation that has historically been known to be dependent on a (human) manager’s best judgment.
While the newest generation in the workforce is all for AI and its promised bounties, an older, more cautious community of HR managers have raised legitimate concerns around its application in fields that require heavy human involvement.
Who's to say that an AI system won't unfairly penalize employees who don't fit the algorithm's definition of a "good performer"? What if an AI system's reliance on quantitative data causes it to overlook important qualitative aspects of an employee's performance? Who's to say that an AI system won't inadvertently encourage unethical behavior among employees who prioritize meeting performance metrics over doing what's best for the company or its customers?
The growing divide between those that vouch for and those that stand against its implementation in performance management is stronger than ever. Effectively incorporating AI into your organization requires bridging this gap and this means taking into account and answering the concerns of both sides. Let’s start with the Gen Z viewpoint.
Rethinking performance management and enablement with AI
Performance management is the Yoda to your Jedi. It’s the guiding force helping your employees navigate challenges, overcome obstacles, and become more proficient in their roles.
But traditionally, performance management has been a top-down process that relies heavily on subjective assessments and limited data points. Only 2% of respondents in a recent study by Mercer believe that their existing performance management systems are highly effective.
However, artificial intelligence is changing the game by providing organizations with data-driven insights and predictive analytics that enable a more holistic and objective evaluation of employee performance.
By leveraging AI algorithms, organizations can collect, analyze, and interpret vast amounts of data, ranging from employee performance metrics, feedback from peers and managers, customer interactions, and even data from wearable devices. This wealth of data provides a more comprehensive view of an employee's strengths, areas for improvement, and potential for growth, enabling organizations to make more informed decisions about performance, career development, and succession planning.
"At Mesh, we use AI in performance management to simplify data sharing, coach managers to improve conversations, and enable data-driven decision-making. From summarizing text to identifying key gaps in performance, it’s helping individuals and organizations achieve better outcomes with ease and efficiency."
— Gaurav Chaubey, People Science Leader, Mesh
Balancing technology with ethics
While AI is a promising gamechanger in performance management, a sizeable portion of the HR community is turned off by the potential risks it poses. There are several circumstances where its application may prove to be a hazard—causing it to retain its perception as more of a threat and less of an aid in performance management.
But is there any truth to these claims?
The question of fairness and bias
While we human beings aren’t exactly known for our honest, unbiased feedback, AI could have a profoundly more devious impact on your organization if it internalizes bias and discrimination into its algorithms. We’ve seen AI chatbots in recent times spew violently racist, sexist, and discriminatory messages to users and the idea that something as malevolent driving performance management in your organization is terrifying, to say the least.
While this is undoubtedly a very real concern, the base of the problem doesn’t lie in AI as a system of management but in the data that feeds its algorithms. There are techniques to mitigate bias in AI, such as using diverse and representative data for training, conducting regular audits to identify and rectify biases, and involving diverse teams in the development and validation of AI models.
Active human intervention is a requisite to not only monitor the working and influences of artifical intelligence in your organization but also to complement it. We still need qualitative assessments from our peers, we still need 360-degree reviews supported by continuous feedback. And AI in performance management is not yet in any form, a complete substitute for human judgment.
Meeting short-term metrics over long-term results
When an AI system is designed to primarily measure and reward employees based on performance metrics, such as meeting sales targets or increasing production numbers, it may create a narrow focus on those metrics alone. This can lead employees to prioritize meeting those metrics, sometimes at the expense of ethical considerations or long-term consequences.
For example, an employee may engage in aggressive sales tactics, misrepresent product information, or cut corners in order to meet their targets and receive incentives, even if it means disregarding ethical practices or the best interests of the company and its customers.
However, this is nothing that can’t be addressed through responsible implementation, training, balanced performance metrics, accountability mechanisms, and human oversight. Organizations can establish clear guidelines and policies for the ethical use of AI in performance management, prioritizing company values, mission, and customer-centricity over meeting performance metrics.
Why leaders should prioritize transparency in AI processes
Transparent AI processes are those that provide insight into the workings of AI algorithms, their decision-making processes, and the data they use, enabling stakeholders to understand and trust the outcomes. Leaders should be concerned about developing transparent and explainable AI processes for several reasons.
First and foremost, transparency promotes accountability and trust. When leaders adopt transparent AI processes, they can demonstrate to stakeholders, including employees, customers, and regulatory bodies, that their AI technologies are operating ethically, responsibly, and in compliance with relevant laws and regulations. This enhances trust and credibility, mitigates potential risks of bias or unfairness, and helps prevent potential negative impacts on stakeholders.
More importantly, the more your AI processes are explained to your employees, the more the rate of understanding and adoption. When AI technologies are not perceived as black boxes but rather as tools that are comprehensible and explainable, it promotes its acceptance by the workforce as they can better understand the decision-making processes and outcomes that it generates.
“By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.”
— Eliezer Yudkowsky, Decision Theorist and Computer Scientist
AI is a tool to assist, not replace us in managing performance
While AI is transforming the landscape of performance management and enablement, it's important to acknowledge that the human element remains crucial. AI should not be looked at as a means to replace human judgment or intuition but rather to augment and enhance it. It's the synergy between human expertise and AI-powered insights that creates a powerful combination for driving organizational success.
Organizations need to ensure that their AI-powered performance management and enablement initiatives are designed with a human-centric approach. This means involving employees in the design and implementation of AI-driven programs, providing training and support to enhance their digital literacy, and creating a culture that values continuous feedback and improvement. Human judgment, intuition, and ethical considerations continue to remain indispensable in managing performance.
