Artificial Intelligence In HR: Knowing What The Future Holds

Artificial intelligence and machine learning are becoming increasingly important for companies. Because such technologies can predict developments with a certain probability based on various parameters and influencing factors, they are already actively used in the insurance industry or sales.

Still, they also offer great potential for forward-looking planning and market-driven action in human resources. Digitization generates a large amount of data that has to be analyzed and evaluated. Simple list reporting is no longer up-to-date and does not bring any significant added value to companies.

With increasing digitization, new instruments come into play that do justice to the new VUCA world (Volatility, Uncertainty, Complexity, and Ambiguity). Looking into the crystal ball is a thing of the past: Thanks to artificial intelligence (AI) and machine learning, precise forecasts can now be made for developments on the market or within a company.

In sales, forecasts that are created with intelligent digital tools are now standard. They are also used in the insurance industry to calculate risk ratios for policies. In the human resources (HR) area, however, they are pretty new. The knowledge and creativity of using them are still missing here, as not many examples and templates are available yet. As a rule, at the current stage, the instruments are mainly used by corporations with the financial resources to operate their business intelligence department.

Recognize Trends At An Early Stage And React To Them

Machine learning and AI perform calls on existing data sets and can answer very different questions. For human resources, too, where there are numerous constellations of data, the instruments enable statements about possible changes in the workforce: for example, redundancies can be predicted with precise probabilities. This allows HR managers to identify trends early and react to them to retain skilled workers. In this way, they protect the company from loss of knowledge and prevent termination costs.

Benchmark comparisons are also possible with AI and machine learning and compared with the competition based on the existing salary data. In addition, personnel measures can be counted concerning their frequency to determine process costing, for example, for the application process. Potential analyzes and career development paths can also be analyzed. In this way, it can be found out which requirements and characteristics are required to apply them to the junior management team.

The New Generation Of Machine Learning

A new generation of machine learning in BI applications is represented by unique cloud-based analysis and planning functions. Special forecasting tools make it possible to make forecasts for the future based on the development of data points over time. Connections between individual objects of investigation can also be identified, and statements about the degree of correlation can be made. In addition, it is possible to predict the probability that certain events will occur.

The first step in any analysis is to identify the specific problem. HR managers then design the investigation case based on this. This checks whether the necessary data is available and whether the appropriate method has been selected concerning the expected results and interpretation. It is crucial to choose the question precisely; standardized templates cannot cover many. An evidence-based approach can also be helpful if not all those affected are asked. Still, samples are selected from a reasonable statistical group and extrapolated from these results with probability and accuracy.

A summary of the results of the queries is then available in the cloud environment, and influencing factors are also mapped. In addition, further information is made public – for example, a mix-up matrix for assessing the model performance or a profit simulation. Performance curves that compare the object of investigation with a random, hypothetically perfect model are also available from time to time. The item evaluated in this way is applied to the current target data. A result is finally available that can be used for personnel management measures.

Records Need To Be Prepared

Such applications can be used on any data basis; individual or investigation data sets are used depending on the subject of the analysis. But before machine learning scans the data, it must be processed: The data must be reliable and available in a structured manner. This preparation of the basic data set is the main task that is often underestimated – with the result that the chosen question cannot be answered correctly.

The analysis tools are often self-explanatory in their use, but skills from statistics are necessary to put the results in the proper context and interpret them correctly.  The instruments make it possible to identify effects. The end-user must check this for causality. The challenge lies in interpreting causes correctly since a discovered correlation does not necessarily result in a basis. In addition, technical expertise is required to derive suitable measures.

The Use Of The Tools Will Increase

At the moment, companies are still set up differently: Some have the digital maturity level to get fully into tools with machine learning; others are still at the very beginning. In the future, however, using these tools will increase exponentially since the essential requirement of computing speed is met in the technology. The applications themselves will require less knowledge – even today, specific tools are already able to select the correct procedure for answering a question independently. In the future, humans and machines will collaborate more closely: The device analyzes the data, the human evaluates the results, and makes the decisions.

Machine learning and AI also promote a rethink in human resource management, focusing on headcounts, full-time equivalents (FTE), and personnel costs towards promoting talent and showing development paths. Ideas have to be found and innovative products implemented – that requires good staff. Obtaining this will not be more accessible in the future.

Conclusion

In digitization, the amount of data is steadily increasing – but so are the possibilities of using them for forecasts. Modern technologies such as AI and machine learning make it possible to provide precise answers to relevant questions based on the recorded data. They also determine the probabilities of future events occurring. This also opens up completely new opportunities for human resources to meet market trends and bind skilled workers.

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