Features Artificial Intelligence Compensation
Quick hit: Striking a balance between AI and managerial discretion in compensation

From automated benchmarking systems that provide real-time market information to predictive analytics for future salary trends and employee retention risks, artificial intelligence (AI) can go a long way in making data-driven compensation decisions in your organization.

As David Creelman, CEO of Creelman Research, noted in a recent Salary.com webinar, AI can also improve efficiency, scalability, objectivity, and consistency in compensation decisions. But even though we can offload a lot of the work to AI, humans continue to play a crucial role in compensation planning.

So, how do we balance AI with managerial discretion in compensation planning?

Creelman recommends the following:

Turn to AI when:

• High-volume, repetitive tasks such as initial salary benchmarking and compliance checks are required
• Fast analysis of data from multiple sources is needed
• Bias mitigation is crucial, and you must ensure objectivity in the initial stages of decision-making

Rely on human judgement when:

• Decisions involve significant changes to employee compensation, which may have an impact on employee morale
• Addressing a complex scenario that requires a deep understanding of your organizational culture or the nuances of individual performance
• Employee feedback or behaviour plays a crucial role in the decision you’re trying to make

Ready to let AI do some of the heavy lifting for you? Creelman suggests these next steps:

1. Assess current capabilities: Evaluate existing tools and processes and identify where AI can boost efficiency vs. where human oversight is crucial.
2. Implement AI tools where it makes sense: Choose tools that align with your organization’s needs and integrate with existing HR systems, then implement pilot projects where these tools can make an immediate impact.
3. Train and educate staff: Develop training programs that focus on using the tools effectively and ethically. Be sure this training covers understanding outputs and decision-making enhancements too!
4. Monitor and adjust: Set metrics to evaluate the performance of the tools you implement. Audit these tools regularly for accuracy, fairness and effectiveness.
5. Enhance managerial skills: Train managers in your organization on soft skills and discretionary decision-making, then encourage them to use AI-generated information as a tool rather than a directive.
6. Review and scale: After the pilot phase, review the results and refine your strategies. Scale the successful integrations across the organization while continuously fostering managerial discretion.