• Written by Azhar Aliyar, Director

A payroll manager checks the employee database to answer a question about staff turnover, only to find the numbers do not match the report from the HR system. This discrepancy is a common problem when integrating AI systems with human resources, and it stems from a lack of care in designing the write paths to the systems of record.

Read paths, write paths and why the second needs more care

The read path is the process by which the AI system retrieves data from the systems of record, such as the employee database or the HR system. This path is typically well-defined and straightforward. However, the write path, which is the process by which the AI system updates the systems of record, requires more care. If the write path is not designed correctly, the AI system can become a second source of truth, leading to discrepancies and inconsistencies.

A well-designed write path ensures that updates to the systems of record are accurate and consistent, and that the AI system does not introduce errors or contradictions. This requires a thorough understanding of the systems of record and the data they contain, as well as the ability to design a write path that is robust and reliable.

Reconciling definitions that differ between systems

Different systems often have different definitions for the same data element, which can lead to inconsistencies and discrepancies. For example, the employee database may define "job title" as a specific field, while the HR system defines it as a combination of fields. Reconciling these definitions is crucial to ensuring that the AI system can accurately retrieve and update data from the systems of record.

This requires a thorough analysis of the data elements and their definitions in each system, as well as the ability to design a reconciliation process that can handle these differences. The goal is to create a unified view of the data that can be used by the AI system to answer questions and provide insights.

The human resources data this depends on

The AI system relies on a range of human resources data to answer questions and provide insights. This includes the policy library, which contains the organization's policies and procedures, as well as the headcount and skills data, which provides information on the organization's workforce. The attrition history and hiring plan are also critical, as they provide insight into the organization's staffing trends and needs.

For example, a category manager may use the AI system to compare the skills of different employees, or to identify trends in attrition. The AI system can provide valuable insights and recommendations, but only if it has access to accurate and up-to-date data from the systems of record. To learn more about how AsscherAi can help with human resource data, please visit our website.

What it changes about

The integration of AI systems with human resources changes the way organizations approach policy questions and planning. Instead of relying on impressions or anecdotal evidence, organizations can use the AI system to provide accurate and data-driven answers to policy questions. This can help to reduce the volume of repeat questions, as well as the time it takes to answer them.

For example, a shift supervisor may use the AI system to answer a question about staffing levels, or to identify trends in employee engagement. The AI system can provide valuable insights and recommendations, which can help to inform planning and decision-making. By using the AI system to answer policy questions, organizations can free up staff to focus on more strategic and high-value tasks.

Measured with

The effectiveness of the AI system can be measured using a range of metrics, including repeat question volume, time to answer, and attrition by tenure. These metrics provide insight into the AI system's ability to provide accurate and helpful answers, as well as its impact on the organization's staffing trends and needs.

For example, an organization may track the volume of repeat questions over time, to see if the AI system is reducing the number of questions that need to be answered manually. The organization may also track the time it takes to answer questions, to see if the AI system is providing faster and more efficient responses. To get in touch with our team and learn more about how AsscherAi can help with your organization's human resource data, please visit our contact page.

What this does not do

The integration of AI systems with human resources is not a replacement for the systems of record, but rather a way to provide additional insights and recommendations. It is also not a way to automate all decision-making, but rather a way to provide data-driven support for human decision-makers. The AI system is designed to augment and support human resources staff, not replace them.

There are also limitations to what the AI system can do, and it is not suitable for all organizations or use cases. For example, organizations with very complex or customized human resources systems may find it difficult to integrate the AI system, or may require significant customization and configuration. In these cases, the benefits of the AI system may not outweigh the costs and challenges of implementation.