AI Driven Performance Management of Future – Asrar Qureshi’s Blog Post 975

AI Driven Performance Management of Future – Asrar Qureshi’s Blog Post 975

Dear Colleagues! This is Asrar Qureshi’s Blog Post 975 for Pharma Veterans. Pharma Veterans Blogs are published by Asrar Qureshi on its dedicated site https://pharmaveterans.com. Please email to aq.pharmaveterans@gmail.com for publishing your contributions here.

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Talent Management has three top metrics: Engagement, Performance, and Retention. Most organizations in Pakistan, however, separate these three and apply different tools and tactics to each of these. Performance management is easier because systems in vogue such as KPIs can be copied and installed. Engagement is more complex as it relates to employees’ emotions. Retention is usually maneuvered through offering extra money, perks, and benefits. Performance management, if done holistically, can tackle all three issues. Our focus in this post is on how new technology of AI will impact the process of performance management.

Power of AI

An HBR article says, “If one universal law regarding the adoption of new technologies existed, it would be this: People will use digital tools in ways you can’t fully anticipate or control”.

It goes on to say that the ChatGPT is the most widely diffused and fastest-adopted product in history. Just two months after launch, it had 100 million users. In comparison, Instagram took two and a half years, and Facebook took four and a half years; even TikTok took nine months to reach the milestone of 100 million subscribers. 

However, the faster the technology spreads, the less time users get to learn from one another and maybe follow patterns of greater use. Secondly, unlike any other technology, AI-enabled tools are designed to change by themselves, continuously. Harnessing the power of AI is therefore, a complex thing but it can still be put to good use. 

AI – Powered Performance Management

Performance management, a cornerstone of organizational efficiency and employee development, has continuously evolved to incorporate new knowledge and technological advancements. Traditional performance management focuses on previous performance and past behavior with the overall aim to increase efficiency. Primary data is provided by the appraisee and is endorsed or challenged by the appraiser. The scope of data is too small to make proper judgment. More importantly, there is no trend analysis to predict future and make plans to improve performance.

Performance reviews have also been the Achille’s Heel for both appraisers and appraisees. The appraisers shun it and the appraisees dread it. Performance management was initially linked to increment, or bonus, but it was abandoned later due to undue stress on both appraisers and appraisees.

With the advent of Artificial Intelligence (AI), the landscape of performance management is poised for significant transformation. An HBR article says that 

Here is how AI may be shaping the future of performance management.

Data-Driven Insights

Traditional performance management relies on periodic reviews, often influenced by subjective judgments and limited data points.

AI can analyze vast amounts of data from multiple sources, such as project management tools, communication platforms, and productivity software, to provide real-time insights into employee performance. AI-powered analytics can identify patterns and trends that may not be evident to human managers, enabling more accurate and objective performance evaluations.

Continuous Feedback and Development

Presently, performance reviews are typically conducted annually or biannually or even quarterly, which can result in delayed feedback and missed opportunities for improvement.

AI can facilitate continuous feedback loops by monitoring employee activities and providing instant feedback on their performance. Machine learning algorithms can recommend personalized development plans and training programs based on individual performance data, helping employees to address skill gaps and enhance their capabilities in real-time.

Predictive Analytics for Talent Management

Succession planning and talent management are often based on historical performance data and managerial intuition.

AI can use predictive analytics to forecast future performance and potential, helping organizations to identify high-potential employees and plan for succession more effectively. By analyzing factors such as employee engagement, learning agility, and career trajectory, AI can provide a more comprehensive view of an employee's future potential.

Bias Reduction in Performance Reviews

Human biases can affect performance evaluations, leading to unfair assessments and impacting employee morale and diversity.

AI algorithms can be designed to reduce bias by focusing on objective performance metrics and eliminating subjective judgments from the review process. However, it's crucial to ensure that these algorithms are trained on diverse and unbiased data sets to prevent the reinforcement of existing biases.

Enhanced Employee Engagement and Retention

Employee engagement and retention strategies often rely on generic surveys and lagging indicators at present.

AI can analyze real-time data from various touchpoints (e.g., employee surveys, social media, internal communication) to gauge employee sentiment and engagement levels. By identifying early warning signs of disengagement or dissatisfaction, AI can help managers take proactive steps to address issues, thereby improving employee retention rates.

Personalized Employee Experiences

Current, one-size-fits-all approaches to performance management may not address the unique needs and preferences of individual employees.

AI can tailor performance management practices to individual employees by considering their career goals, learning preferences, and work styles. Personalized plans and recommendations can enhance employee experiences and drive better performance outcomes. AI can also keep track of the progress of these plans.

Challenges and Considerations

Ethical Concerns – Ensuring the ethical use of AI in performance management is critical. Issues related to data privacy, transparency, and the potential for algorithmic bias need to be addressed. Despite its cognitive ability, AI is still dependent on the input it receives.

Change Management – Organizations must manage the transition to AI-driven performance management carefully, providing training and support to both employees and managers to adapt to new systems and processes. Given the factor of autonomous learning in the most-advanced AI-based tools, the employees are not learning to use a technology once, rather, they are learning to use it nearly every time they engage with it.

Integration with Human Judgment – While AI can enhance performance management, it should complement, not replace, human judgment. Managers still play a crucial role in interpreting AI insights and making educated decisions. In fact, no one should even think that AI will be replacing the human appraisers.

Sum Up

The integration of AI into performance management holds the promise of more accurate, efficient, and personalized approaches to employee development and organizational success. By leveraging AI's capabilities, organizations can create a more dynamic and responsive performance management system that benefits both employees and employers. However, it is essential to navigate the challenges thoughtfully to harness the full potential of AI in this domain. Even more importantly, performance management should become part of an overarching talent management program.

Concluded.

Disclaimers: Pictures in these blogs are taken from free resources at Pexels, Pixabay, Unsplash, and Google. Credit is given where available. If a copyright claim is lodged, we shall remove the picture with appropriate regrets.

For most blogs, I research from several sources which are open to public. Their links are mentioned under references. There is no intention to infringe upon anyone’s copyrights. If, however, it happens unintentionally, I offer my sincere regrets.

Reference:

 https://www.forbes.com/sites/brentgleeson/2024/06/11/revolutionizing-talent-management-and-employee-development-with-ai/

https://www.forbes.com/sites/forbeshumanresourcescouncil/2023/12/22/revolutionizing-performance-reviews-with-generative-ai/

https://hbr.org/sponsored/2024/01/artificial-intelligence-at-work-enhancing-employee-engagement-and-business-success

https://hbr.org/2023/11/helping-employees-succeed-with-generative-ai

https://essay.utwente.nl/91198/1/Riecken_BA_BMS.pdf