Optimize your HR efforts with AI and your data

When skilled, experienced employees leave a company, they take both knowledge and revenue with them. With a new AI-based application for SAP, 2BM will help companies create an effective retention system that, by utilizing the company’s historical HR data, can estimate who is at risk of resigning – and why.

Many companies today hold large amounts of data that, used intelligently, will be able to prevent critical redundancies from skilled employees that are costly to replace. The potentially valuable data is already found naturally in the companies ‘historical administration systems and tells, among other things, about the employees’ age, seniority, absence history, satisfaction, performance, etc. By activating and analyzing this data using artificial intelligence (AI), companies can be able to to see new contexts and act proactively in relation to employees they are in danger of losing. And not least, companies can use their data to create a better workplace. “There is a growing need among companies to be able to diagnose and identify which employees who can be expected to resign within a certain period. AI can help with this, so that the company can meet an expected termination by taking care of the issues that motivate an employee to want to seek other options,” says Steen Bjørnskov, Head of HCM in the SAP house 2BM.

Finer patterns and contexts

Therefore, 2BM now invites its SAP customers to dialogue about how a new AI-based HCM application for their existing SAP system can help them create an effective retention policy by comparing data with previous terminations.

“Companies’ HR departments already have the opportunity today to look at their data manually or with data analytics as needed and investigate a matter further. The new thing is that the system, thanks to AI, can now find even finer patterns and contexts and draw attention to which employees you need to be aware of before the employee actually resigns,” says Steen Bjørnskov.

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Which parameters are important?

It differs from company to company which data it is relevant to let AI work with. But whether you have high or low staff turnover, an AI-based retention system can be beneficial.

“Companies in some industries have a very high staff turnover and may not find reason to give high priority to staff development because recruiting and onboarding new employees is easy and short-term. However, they may be able to discover new parameters that allow them to retain employees for longer than before. In more knowledge-intensive companies, other parameters may be more important. We help the customer to identify which data and parameters the system must work with, and then the system in principle takes care of itself before delivering a list of employees who can be expected to resign within, for example, six months and for what reasons.”

Loss of knowledge and revenue

Timely diligence in crew care and prevention of critical layoffs has great financial potential.

“Our expectation is that a knowledge-intensive company has earned its investment in an AI-based retention system back home when it has prevented two layoffs. The fact that an experienced employee resigns has a high cost for both new recruitment and onboarding of the replacement. A rule of thumb says that it takes half a year to land in a new job and perform optimally in knowledge-intensive companies. The loss of knowledge and turnover naturally depends on the employee’s position, but is often many times greater than the real, visible costs of the immediate recruitment of a new employee.”

HR as first mover

SAP is the world’s most widely used ERP system and runs both on premise, in the cloud and in different versions at companies in many industries and sizes.

2BM’s point is that although AI-based retention is a completely new application in the HCM part of SAP, the new system can be implemented and benefit all SAP customers.

“The crucial thing is that the company has its good, basic administration system with all the data that has been collected over the years, but which may not be used that much today. That data can form a goldmine of potential insight that can form the basis for wise timely decisions, just as data from sensors and machines form the basis for predictive maintenance when it comes to maintaining physical assets. Here, the yield is fewer critical redundancies among the human assets. And then it may be that the HR department becomes the first mover in the company’s intelligent use of data,” says Steen Bjørnskov.

Article is also published in Jyllandsposten, 28/05/2021