Data and Analytics

Proceed with Caution: When AI meets Pay

Lukas-blazek-mcSDtbWXUZU-unsplash (002)With great power, comes great responsibility.  The now infamous proverb popularized by the Spider-Man comic books is especially meaningful when leveraging Artificial Intelligence (“AI”) in our compensation management.

Admittedly, AI has an elegance to the way it simplifies, automates, and quickly synthesizes our analysis, pointing out all possible areas of contention. We should, however, not be fooled by its sophistication. To some extent, AI encapsulates a sense of ambiguity and therefore a forewarning to proceed with caution.

To use this technology responsibly means being educated; that is, educated in the system's functionality and its intended use.  Say, for example, you are trying to address issues around pay equity.  Are you familiar with the legislation itself and do you know what the expected analysis steps should involve? In the case of using AI’s suggestion for a possible survey job match, how well do you know the context of your internal position? is it a true manager or rather a specialist of a functional area?  With limited knowledge, you could inflate or devalue a position if matched incorrectly. 

Having users equipped with compensation and basic system fundamentals safeguards for correct data interpretation and effective use of AI’s outputs.

To best utilize the strengths of AI, we must feed it with accurate information and data around which we have the full context (consider red circling or other legacy pay scenarios).  As you import, be aware of how your information configures and aligns with the system components.  Have you matched data to the precise fields, have you interpreted the systems compensation elements correctly? Misguided inputs will only lead to “garbage in and garbage out!”  One way to avoid this is by adopting a consistent and standardized format for data uploads.  Doing so will give you the best return of AI’s analytical capabilities.

AI at the end of the day is a tool, meant to assist us to better navigate and handle the complexities of managing compensation.  Whatever it suggests - whether it be an alarming SOS, an invalid read of our data, or a more favorable acknowledgment of us meeting our salary budget – this is not the outcome.  We cannot be too quick to react nor adopt AI’s responses at face value; these outputs form the pillars of insights to guide our decision-making.  Most of us in our profession are analytical by nature, curious and inquisitive.  This is when we need to tap into our own AI tendencies and dig a bit deeper, validate, and investigate. 

Besides, you would not present a salary recommendation or re-structure to leaders, solely credited by the divine intervention of AI, would you?

Reena Paul (CCP, GRP) is a Senior Consultant with Morneau Shepell’s Compensation Consulting Team.  She is passionate for all things “total rewards” and has experience in dealing with all stakeholders of an organization and strategizing optimum client focused solutions.  A lover of data and the story it tells, Reena enjoys the exploration of presenting and discussing compensation with a fresh perspective.  Connect with Reena on LinkedIn, or contact her directly at [email protected].  

Unsplash Photos for everyone by Lukas Blazek