• Written by Sonali Subhadarsini, Product Manager

On a typical Monday morning, the human resources manager checks the latest headcount and skills data to inform the week's recruitment planning, while the shift supervisor reviews the maintenance log to ensure compliance with company policies, and the category manager compares two suppliers to determine the best value for the organisation. As they work, they may need to query the organisation's data to answer specific questions, and this is where enterprise AI governance for human resources comes in.

Enforcing permission at retrieval

Most people get wrong the idea that instructing the model to only return certain data is sufficient to enforce permission, but this approach is flawed because it relies on the model being correctly configured and updated. Instead, enforcing permission at retrieval, when the user actually requests the data, is a more effective way to ensure that sensitive information is only accessed by authorised personnel. This approach allows for more fine-grained control over who can see what data, and it reduces the risk of unauthorised access.

The shift supervisor, for example, may need to access employee records to verify attendance, but they should not be able to view salary information. By enforcing permission at retrieval, the organisation can ensure that the shift supervisor only sees the data they are authorised to access.

Auditability

Auditability is critical in enterprise AI governance for human resources, as it allows the organisation to track what questions are being asked, what data is being returned, and on what data the queries are being run. This information can be used to identify potential security risks, such as unauthorised access to sensitive data, and to monitor compliance with company policies. The category manager, for example, may need to query the organisation's data to compare the prices of different suppliers, and the audit trail can help to ensure that they are only accessing data that is relevant to their role.

By maintaining a record of all queries and data accesses, the organisation can demonstrate compliance with regulatory requirements and internal policies, and it can also use this information to refine its governance policies and procedures.

Human resources data

The effectiveness of enterprise AI governance for human resources depends on the quality and availability of human resources data, including the policy library, headcount and skills, attrition history, and hiring plan. The policy library, for example, should contain up-to-date information on company policies and procedures, while the headcount and skills data should reflect the current organisational structure and workforce. The attrition history and hiring plan data can help to inform recruitment planning and talent management decisions.

For more information on human resources data and how it can be used to support enterprise AI governance, see our human resources page.

What it changes

Enterprise AI governance for human resources can change the way the organisation approaches policy questions and planning, by providing a more data-driven and transparent approach. The same policy questions that are asked every week, such as "what is the current headcount?" or "what are the latest salary scales?", can be answered quickly and easily using the organisation's data, without the need for manual searches or requests to the HR department. Planning that runs on impressions, such as recruitment planning or talent management, can also be informed by data and analytics, rather than relying on intuition or anecdotal evidence.

The category manager, for example, may use data and analytics to identify the most effective recruitment channels, or to develop targeted training programs to address skills gaps in the organisation.

Measuring effectiveness

The effectiveness of enterprise AI governance for human resources can be measured using a range of metrics, including repeat question volume, time to answer, and attrition by tenure. Repeat question volume, for example, can indicate whether the organisation's data is being effectively used to answer common questions, while time to answer can reflect the efficiency of the query process. Attrition by tenure can help to identify whether the organisation's talent management strategies are effective in retaining key employees.

By tracking these metrics, the organisation can refine its governance policies and procedures, and it can also use this information to demonstrate the value of enterprise AI governance for human resources to stakeholders.

Limitations

While enterprise AI governance for human resources can be a powerful tool for supporting decision-making and compliance, it is not a solution for all organisational challenges. It does not, for example, replace the need for human judgment and expertise in areas such as recruitment planning or talent management. It also requires significant investment in data quality and governance, as well as ongoing monitoring and maintenance to ensure that the system is operating effectively.

If you have questions about how to implement enterprise AI governance for human resources in your organisation, or if you would like to learn more about our solutions, please contact us.