• Written by Azhar Aliyar, Director

When a new employee joins a company, a series of steps must be taken to ensure they have the necessary access and equipment to perform their job. This can include setting up their user account, assigning them to the correct teams, and providing them with a laptop and other necessary equipment. If these steps are not taken, the new employee may be unable to work effectively, leading to delays and lost productivity. This is an example of a workflow that can be automated using agentic AI workflows for human resources.

What separates an agent from a chatbot

An agent is distinct from a chatbot in that it has the ability to use tools, maintain state, and work towards a goal. A chatbot, on the other hand, is limited to answering a single question or providing a simple response. In the context of human resources, an agent can use tools such as the company's HR system to automate tasks and workflows, while maintaining state allows it to keep track of the progress of these tasks and make decisions based on that progress.

For example, a shift supervisor checks the maintenance log and sees that a new employee is scheduled to start on the next day. The supervisor can then use an agentic AI workflow to automate the process of setting up the new employee's user account, assigning them to the correct teams, and providing them with the necessary equipment. This can be done by integrating the AI workflow with the company's HR system and other tools, such as the maintenance log and the equipment inventory system.

Where a human approval step belongs and why removing it is usually premature

While agentic AI workflows can automate many tasks and workflows, there are still certain situations where human approval is necessary. For example, when it comes to sensitive or high-risk decisions, such as hiring or firing an employee, human approval is still necessary to ensure that the decision is fair and reasonable. Removing the human approval step prematurely can lead to errors or unfair decisions, which can have serious consequences for the company and its employees.

In general, human approval steps should be retained in situations where the decision requires a high level of judgment or nuance, or where the consequences of an error are severe. By retaining these approval steps, companies can ensure that their agentic AI workflows are fair, reasonable, and effective.

The human resources data this depends on

Agentic AI workflows for human resources depend on a range of data, including the company's policy library, headcount and skills, attrition history, and hiring plan. This data is necessary to inform the decisions made by the AI workflow and to ensure that it is working towards the company's goals. For example, the policy library provides the rules and guidelines that the AI workflow must follow, while the headcount and skills data allows it to determine the best candidates for a particular role.

By integrating with the company's HR system and other data sources, agentic AI workflows can access this data and use it to make informed decisions. This can help to improve the efficiency and effectiveness of the company's HR processes, and can also help to reduce errors and improve compliance with company policies.

What it changes about

Agentic AI workflows can change the way that companies approach human resources by automating routine tasks and workflows, and by providing insights and recommendations to inform decision-making. For example, by automating the process of answering common policy questions, companies can free up their HR staff to focus on more strategic and high-value tasks. This can help to improve the overall efficiency and effectiveness of the company's HR function, and can also help to improve employee satisfaction and engagement.

Additionally, agentic AI workflows can help to improve planning and decision-making by providing insights and recommendations based on data and analytics. For example, by analyzing the company's attrition history and hiring plan, the AI workflow can identify trends and patterns that can inform decisions about recruitment and retention. This can help to reduce the risk of errors and improve the overall quality of decision-making.

Measured with

The effectiveness of agentic AI workflows for human resources can be measured using a range of metrics, including repeat question volume, time to answer, and attrition by tenure. By tracking these metrics, companies can determine whether their AI workflows are having the desired impact and can make adjustments as needed to improve their effectiveness. For example, if the repeat question volume is not decreasing, it may be necessary to refine the AI workflow or provide additional training to the HR staff.

For more information on how to implement agentic AI workflows for human resources, companies can visit our human resource page. Alternatively, they can contact us to speak with one of our experts and learn more about how our solutions can help to improve their HR processes.

What this does not do

While agentic AI workflows can automate many tasks and workflows, they are not a replacement for human judgment and decision-making. In situations where the decision requires a high level of nuance or judgment, or where the consequences of an error are severe, human approval and oversight are still necessary. Additionally, agentic AI workflows are not a substitute for good management practices, such as clear communication, effective leadership, and a positive company culture.