Getting Compensation Planning Right for the Year Ahead

Compensation Planning Strategies for a Changing Workforce

Employers planning compensation for 2027 face steady salary budgets, evolving labor market conditions, and growing questions about how AI will change jobs and pay. In this episode, learn how compensation teams can allocate limited salary increases more strategically, address pay equity and salary compression, identify critical roles at risk of turnover, and use AI to support compensation decisions without compromising fairness or employee trust.

Key Takeaways

Salary increase budgets for 2027 are projected at 3.2% for merit increases and 3.5% for total salary increases, making targeted compensation decisions more important than across-the-board raises.

Employers can make limited compensation budgets go further by prioritizing critical roles, retention risks, market competitiveness, and pay equity gaps before distributing remaining funds through general merit increases.

Salary compression between new hires and experienced employees can create fairness and retention challenges that compensation teams should identify and address.

Off-cycle salary increases should be tracked alongside annual merit increases to provide a complete view of compensation spending, pay equity, and potential budget risks.

Compensation planning should begin with a clear business problem, whether that involves market competitiveness, pay equity, salary compression, or acquiring critical skills.

Benefits, workplace flexibility, career development, and targeted retention strategies can help employers attract and retain talent when salary budgets are constrained.

AI is changing job responsibilities faster than job titles and pay structures can adapt, making it important to evaluate how work is evolving rather than relying solely on existing job descriptions.

AI can help automate repetitive compensation tasks such as job matching and job leveling, but salary recommendations and pay equity decisions require greater human oversight.

Compensation teams should consider the potential consequences of AI-driven errors, particularly when decisions affect employee income, fairness, compliance, and trust.