By Joel Geddis
Decades after the passage of the Equal Pay Act, inequities persist across industries. Women make 82 cents for every dollar men earn. The gap widens further for women of color. With diverse talent in high demand, forward-thinking companies are leveraging technology and artificial intelligence (AI) to accelerate pay equity efforts. However, intentional design and diligent governance is required to maximize benefits while avoiding pitfalls.
This article answers the questions:
- How can technology and AI enable more equitable compensation practices?
- What are the business benefits of prioritizing pay equity?
- What are best practices for implementing tech-driven pay equity tools?
- How can AI be applied to compensation decisions responsibly?
- What risks and challenges come with using AI for pay decisions?
By examining case studies, expert insights, and emerging best practices, this article explores how analytics, software, and AI systems can enhance pay equity analyses. But these tools require thoughtful design, diligent governance, and human oversight to avoid perpetuating biases. With responsible implementation, technology can assist organizations in making measurable progress on fair compensation.
The Data-Driven Business Case for Pay Equity
Statistical analysis enabled by compensation management systems can precisely identify problematic pay gaps correlated to gender, race, or other factors while controlling for legitimate drivers of pay. For example, Salesforce conducted an adjusted gender pay analysis in 2015 finding women earned 99.7 cents on the dollar compared to men in the same job and location. While marginal, they still increased salaries for a small group of employees to fully close the gap.
In another case, after a UK bank implemented a new technology solution to analyze compensation, they discovered women were paid on average 80 pence for every £1 paid to men. This analysis enabled them to make appropriate adjustments.
Based on 2020 benchmarking data from Mercer on companies with mature pay equity practices, median pay gaps between men and women shrink to 98 cents on the dollar. Firms leveraging AI and skills-based compensation models have reduced bias by 15-30% in some cases according to Josh Bersin research.
Equitable pay also aids retention. Employees perceiving pay fairness are twice as likely to remain with an organization according to an ADP study. With diverse talent in high demand, pay equity strengthens recruitment and retention.
Moving Beyond the Status Quo with Comp Tech
Traditionally, compensation practices have heavily relied on job titles and levels. But with rapid digital transformation, specific niche skills can command a premium despite one’s formal role. For example, data engineers with machine learning expertise may warrant higher pay than other software developers. Dynamic tech tools allow organizations to continuously benchmark pay for in-demand skills rather than static roles.
“Certain new skills are scarce and high in value, while other skills have become commoditized,” explained Joanna Daly, VP of Compensation at IBM. The company has shifted to compensate based on AI-recommended skill market value rather than titles. This incentivizes continual development.
Forward-looking organizations are also leveraging AI to forecast scenarios of compensation needs based on projected talent pipelines and future skills. This allows more nimble responses to evolving markets. According to Mercer, companies deploying these capabilities report greater agility in meeting talent needs.
Implementing Equitable Tech: Best Practices
Specialized software and statistical packages purpose-built for fair pay analysis are recommended by experts like Josh Bersin. But the right technology alone is insufficient. Organizations must take care to:
Conduct comprehensive analysis: Audit across multiple demographics, including intersectionalities like women of color. Go beyond binary gender comparisons to identify gaps. Analyze external competitiveness and internal parity in one solution.
Incorporate inclusive policies: Update compensation philosophy practices in conjunction with deploying new tech. Build diversity and anti-bias requirements directly into AI systems.
Clean historical data: Remove existing biased data that could train algorithmic biases. Monitor AI systems for adverse fairness outcomes.
Apply human oversight: Require leaders to justify AI-recommended decisions that increase discrepancies. Do not fully automate determinations without accountability.
Practice pay transparency: Disclose compensation ranges in job posts and internally. Build understanding of pay equity goals.
Review continuously: Audit compensation regularly, not just during annual reviews. Monitor parity results at every stage from hiring to promotion.
Verify before deploying: Pilot AI and perform trial runs, reviewing outputs for parity before scaling usage. Be open to employee feedback.
Update as needed: Revisit algorithms frequently as skills emerge. Fine tune AI models to reflect evolving talent priorities.
Exploring AI Compensation Models
AI offers new potential to make compensation decisions based on market value of skills and reduce managerial bias. For example:
IBM utilizes AI in its compensation system to tie pay increases more closely to employee skills as demand fluctuates. Managers are provided data on the supply and demand for certain competencies to inform decisions. IBM reports increased retention for managers adopting the AI recommendations.
SAP SuccessFactors has embedded pay equity tools into its core HR system, generating data to identify gaps. While not providing market data, the company notes its solution gives HR visibility into potential areas of inequity.
PayScale leverages AI to analyze real-time job posting data and develop precise, localized pay benchmarks. The company updates its database biweekly to dynamically price skills according to market conditions.
ADP now offers pay equity analysis functionality as employers increasingly expect this from HCM solutions. ADP’s dashboard simplifies disparity reporting according to demographics and job groups.
However, AI is not a cure-all. Thoughtful design is required to avoid perpetuating biases and transparency is key. Continued human oversight remains essential for fair, explainable decisions.
Challenges of AI in Compensation
While promising, AI compensation tools have risks if not mitigated:
Perpetuating biases: If biased data trains algorithms, they will propagate those biases absent intent. Rigorously auditing AI is crucial.
Lack of transparency: The complex inner workings of AI can be opaque. Without explainability and visibility, distrust may grow.
Loss of human accountability: Over reliance on AI removes essential oversight and judgement. Leaders must be empowered, not replaced.
Unplanned impacts: AI can have unpredictable ripple effects like wage compression between roles. Continued monitoring is key.
Skill definitional errors: Misjudging the future value of skills can skew compensation recommendations if not flexibly updated.
With thoughtful governance, these risks can be managed. AI should complement nuanced human decisions, not attempt to replace them. Firms must listen and respond to employee feedback on AI-driven pay decisions.
A Responsible Path Forward
New technologies like AI and advanced analytics offer means to enhance organizations’ pay equity efforts – if applied judiciously. On their own, these tools cannot transform culture or guarantee fairness. But coupled with sound strategies, strong policies, and human governance, technology can accelerate organizations towards compensation equity goals in a responsible manner.
Leading companies will tap AI’s potential while heeding its limitations. With rigorous auditing, transparent implementation, and a priority on inclusion, the promise of unbiased pay powered by tech can inch closer to reality.