Artificial Intelligence-Driven Compensation 

Our rules-driven AI approach to Compensation Management and Sales Performance Management helps managers take the guesswork out of decision making.

Mathieu Prêtre
Mathieu Prêtre, Presales Team

Why AI for compensation and incentive management?

Artificial Intelligence in compensation management automates tasks that require manual actions to deliver an improved employee experience, combined with machine learning which recognizes patterns, predicts performance and explains drivers and influencers to optimize compensation models and incentives plans, and increase employee and sales performance and retention.


Talent retention and employee engagement through fair compensation

  • Machine Learning based AI can help companies use compensation analytics to identify their employee challenges and predict trends. This will give businesses invaluable opportunities to address employee needs and optimize compensation and incentives for targeted populations, resulting in a higher employee engagement.
  • By taking advantage of AI to make compensation fair—based on a variety of rules including education, experience, certifications and more—businesses move closer to closing pay gaps.
  • Natural-language processing can help with employees sentiment detection and react quickly to retain and engage employees. 

Read the article on Fast Company “5 ways AI will make your job easier”

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Better employee experience with optimized flexibility

In a rapidly changing work environment, organizations ask their employees to be more flexible in order to align with the company’s vision and growth strategy; going beyond their job description has become the norm. In this context, organizations also have to be flexible in defining and managing personalized compensation models and incentives plans for each employee. AI allows our Total Compensation Management and Sales Performance Management solutions to tailor your employees’ compensation and incentives plans and recognize their personal achievements. Flexibility is on both sides, the employees and the company, creating a fair place to work.

A tangible example of flexibility is a payees self-service chatbot, which can optimize employees request on compensation topics. This drives a real continuous compensation and sales performance management process. beqom is using natural-language processing and Azure chatbot engine technologies to understand questions being asked, interpret responses and generate follow-up questions. This is all linked with beqom’s compensation database.

Chatbots can also be used for sales and HR dispute management, where employees can talk to a machine to resolve issues instead of just submitting tickets and waiting for a resolution.

Key features for chatbot use:

  • Salary
  • Bonus
  • Long Term Incentives
  • Deferred Compensation
  • Benefits
  • Objectives, Targets,  Achievements, ytd Performance, etc.
  • Sales Incentives
  • Territories
  • Quotas
  • Dispute Management
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Cost reduction

Artificial Intelligence based on Machine Learning allows businesses to benchmark compensation, identify flight-risk employees and determine compensation and incentives boosts by deriving data that they would previously have had to ascertain through “gut instinct.” Use of AI-driven compensation technology can mitigate the risk of employee turnover, which costs businesses as much as 33 percent of a worker’s annual salary to replace them. Determining whether a company’s talents are rewarded accurately and providing management with AI, data-driven recommendations for optimized allocations is key in retaining top talents.

By automating compensation management communication, HR and sales teams will be able to redirect to self-service tools, providing compensation related answers in real time, improving HR responsiveness and reducing support costs.

Budgeting and forecasting

beqom’s Simulation Cockpit combined with Azure Machine Learning services simulates and analyzes different compensation model and incentives scenarios, to detect patterns and further optimize those models to increase performance and save costs. For compensation and rewards, this can result in optimized incentive plans with better outcomes at lower costs. For sales, this can result in optimized territory and quota plans to maximise sales performance.

Organizations can now leverage compensation and incentives experts’ knowledge, surfacing insights from experts to people holding operational roles in the organization and building a model that everyone can use easily. This is disrupting the way non compensation experts will be influenced and able to manage compensation through intelligent models.

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