Insights from Björn Lorentzon on AI Usage in HR
Introduction:
Artificial Intelligence (AI) is increasingly becoming a game-changer in various fields, and human resources (HR) is no exception. Recently, I had the opportunity to view a conversation with Björn Lorentzon, a specialist in HR, in which explored how AI is reshaping HR practices, particularly in recruitment and employee management. Here are my key takeaways from the insightful conversation.
AI in Recruitment: A New Era
One of the most impactful areas where AI is making strides in HR is recruitment. Lorentzon points out that AI’s ability to automate administrative tasks is beneficial, though often overlooked. While automation is frequently discussed in the context of financial or accounting tasks, its potential in HR is significant, particularly in reducing the administrative burden.
However, the real buzz around AI in HR centers on recruitment. Many leading companies are leveraging AI to minimize bias and enhance competence-based hiring. AI tools can help streamline the selection process, making it more objective and efficient. This shift not only promises to improve hiring outcomes but also aims to create a more diverse and inclusive workforce.
Enhancing Employee Management with AI
AI’s impact is not limited to recruitment. It extends to the ongoing management of current employees. Automation, again, plays a critical role here, enabling HR departments to handle routine tasks more efficiently. Additionally, AI can assist in evaluating employee performance by analyzing productivity and efficiency metrics.
However, as Lorentzon cautions, measuring productivity and efficiency, especially in creative environments, is complex. AI can offer new perspectives and insights, but the data it processes must be accurate and relevant. Combining qualitative and quantitative methods is essential to getting a holistic view of employee performance.
Herein lies a fundamental question: What is quality? To one manager quality means completing a task quickly even if the error count is relatively high. To another, it may mean strictly limiting the number of errors even if it takes twice as long as estimated. We have difficulty defining what we mean by quality within an organization. – Dave Ranck
The Pitfalls of AI in HR
Despite its potential, the implementation of AI in HR comes with challenges. One major issue is the quality of the data and the questions being asked. Employees are often inundated with surveys and questionnaires, leading to survey fatigue and inaccurate responses. To mitigate this, Lorentzon suggests asking fewer, more targeted questions. This approach ensures higher quality responses and more meaningful data.
Moreover, AI solutions are only as good as the definition of the problems they are designed to solve. If the problem is not correctly defined, AI might not provide the desired solutions. Therefore, it is crucial to identify the right problems and ask the right questions before implementing AI tools.
Here are some other pitfalls that I see:
- Lack of transparency, If an employee’s review includes a negative score, there may be no way for them to challenge the evaluation. Managers may either depend too heavily on the system’s evaluation or tend to ignore it because they don’t trust the results.
- AI Bias: AI has the promise of assisting in the limitation of bias in hiring decisions, but AI systems themselves can contain inherent biases because they were created by and trained by humans.
- Reductionism: Employers have begun to track employee micro-behavior such as how many times and for how long they leave their desk, and other empirical data points. Focusing on micro data like this is not helpful or fair, in my opinion. If employee A consistently creates quality work within a reasonable amount of time, why is it important to know how many times they took a bathroom break?
That said, AI is very useful in HR today. But some common sense must be used when implementing it. – Dave Ranck
Customer Service and AI: A Delicate Balance
Customer service is another area where AI has shown promise. By predicting customer needs and preferences, AI can help improve the customer experience. However, understanding the limitations of AI in predicting human behavior is vital. AI can assist but not replace the human touch required in customer interactions. Ensuring that the data fed into AI systems is accurate and relevant is essential for effective implementation.
Risks of AI in Customer Experience
The biggest risk in designing AI-powered customer experiences is solving the wrong problem. AI can be highly effective, but only if it’s aimed at the correct target. For instance, measuring the wrong metrics can lead to ineffective solutions. In HR, starting with structured, measurable tasks like document handling or payroll automation can provide clear benefits and manageable implementation of AI.
Advice for HR Units Implementing AI
Lorentzon advises HR units to begin with a clear business case. Define what you want to achieve with AI. Whether it’s measuring employee happiness or productivity, having realistic goals is crucial. AI will not eliminate biases or solve deeply ingrained issues overnight. Start with clear, measurable targets and be transparent about the limitations and potential biases in the data. This approach will set you up for success and help you make more informed decisions about implementing AI.
Conclusion:
AI holds tremendous potential to transform HR practices, from recruitment to employee management and customer service. However, its success depends on clearly defined goals, accurate data, and an understanding of its limitations. By taking a structured and realistic approach, HR units can harness the power of AI to enhance their operations and create a more efficient and inclusive workplace.
Author: David Ranck from Envision AI Consulting
Website: Envision AI Consulting
