How to Improve C&B with AI & Machine Learning

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Compensation & Benefits (C&B) is the key element of HRM value chain and more of a strategic necessity. It has touch points across the employee lifecycle which directly impacts the talent strategy. It further translates into wage costs which directly impacts organization’s corporate margins and bottom line.

However, looking at existing scenario does not paint a good picture as C&B teams are struggling with inefficient processes which are burdened to serve multiple objectives including performance and retention, workflows which are non integrated across enterprise, information which are more of scattered data points and hence it often comes more as adhoc, inconsistent and arbitrary decisions arising out of manual execution.

To achieve strategic objective of Talent Attraction and Retention, organisation are striving to improvise their broken compensation processes with usage of technology. Let us look how AI/ML can address our problem. 

  • To ensure fairness and equity, organisation needs to understand salary gaps at that time of hiring a candidate. These gaps should reflect to hiring managers in terms of internal parity and external competitiveness to avoid future imbalances. Here, systems can be trained to utilise artificial intelligence to generate real time deep level comparisons of salaries for a specific candidate. These gaps can be addressed either at beginning or with deferred approach. Over a period of time, success can be measured against offer-to-acceptance ratio or alignment-to-accepted range.
  • Another area where AI can immensely help line managers is on allocation of increments. C&B teams can generate multiple models and let machine to understand its effectiveness with strategic objective of talent strategy e.g. better engagement score, reduced attrition rate, insignificant critical talent resignations to name a few. These models can be adopted as algorithms which should suggest Line Managers in distributing rewards more objectively and guiding whom to give more money, where to give less, whom to promote now, whom to retain not.
  • Organisations can adopt to risk mitigation strategies in a better way by knowing optimum salary points with adopted machine learning not leading it to high allocation of money causing potential affordability issues for the organisation when it is done for many people, at the same time not letting it to so low causing potential cases of attrition. This can be better suited to arrive at employee level then at generic role level. Such optimum levels can be generated using data science which can further run as algorithm and improvise over a period of time with machines learning to enhance its capabilities.

As compensation processes are becoming more complex dealing in only cash to total rewards, tenure to performance to role basis, efforts to outcome basis, mixing short term incentives with long term incentives and stock based elements, premium skills to hot jobs and then serving to extreme demographics across countries. As a result, organizations need smarter compensation solutions powered with Artificial intelligence. Using the right compensation management solution will allow an organization to maximize value from their talent. 


Author: Anuraag Srivastav is the Founder and CEO with PayReview, HR Technology firm helps in managing and executing in compensation strategy. With 12+ years of experience of working in HR domain with focus on Compensation work stream and HR Strategy, Anuraag started PayReview based on his learning from start-ups to business conglomerates to MNCs. He has been associated in lead roles for Capgemini and Reliance. www.payreview.work

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