Automation as a key to quality and efficiency
Traditional job evaluation processes often require a lot of time and human resources. A possible solution lies in the use of artificial intelligence (AI) in job evaluation.
Job descriptions and evaluation workshops are typically required to accurately evaluate jobs. This standard process ties up scarce resources in compensation and benefits departments, which have become very lean.
And in our disruptive world of work, strategies, business models and jobs continue to change rapidly. Accordingly, job evaluation has become an ongoing task that must be carried out quickly, efficiently and expertly.
Finally, in traditional job evaluation, there is the problem of the subjectivity of different people involved in the process – both in the description and in the evaluation of the jobs. Some managers describe their roles very ambitiously; others more conservatively. The same picture emerges when different people in a company evaluate jobs. Again, some apply the methodology rather liberally, while others do so more strictly, which can ultimately lead to a bias in the evaluation results.
In projects, AI accelerates the actual job evaluation phase. You can upload available job profiles in any number via bulk upload and evaluate them within minutes at the push of a button. In addition to the evaluation results, AI tools also can provide information on confidence levels (i.e. how likely the respective evaluation is to be accurate).
Extensive testing has shown that such algorithms provide a high level of consistency and objectivity, with significantly reduced bias and errors. AI tools can also validate the quality and comparability of job descriptions as input to the assessment.
Thus, an AI-based solution helps to compensate for the weaknesses of the traditional approach and quickly leads to an initial objective markup of the assessment results.
The process is not completely handed over to the machine. With the use of an AI tool, the HR team’s focus shifts from conducting the assessment to calibrating and interpreting the results and identifying organizational anomalies and misalignments.
The human-in-the-loop approach includes critical evaluation, calibration and classification of the results. With the help of an AI solution, it is no longer necessary to illuminate and evaluate every single point in detail.
Instead of discussing individual factors , the focus is now primarily on questions such as:
Overall, this means greater added value for your HR team. They not only receive consistently evaluated jobs, but also recommendations for an optimized organizational structure and for the consistent design of job requirements.
The regulatory or legal requirements for companies regarding fair compensation are increasing across the globe. The topic plays an increasingly important role for employers and other stakeholders, including employees, the broader public, and investors and shareholders. The latter will give greater weight to the issue of compensation fairness in their investment decisions in order to avoid compliance and reputational risks.
The central basis for compensation fairness and transparency is a structured job evaluation: Jobs are assigned to a specific level (pay grade, level, grade) according to objective criteria customary in the market and, based on this, systematically linked to compensation - company-wide, across all functions and hierarchy levels.
Compensation fairness and transparency regulations will likely continue to increase. Beyond regulations, your employees and possible job applicants are demanding greater transparency. Getting ahead of the shift toward greater transparency by automating job evaluation will help address this trend.
