• Written by Azhar Aliyar, Director

A human resources manager reviews the weekly budget report, looking at the allocation of funds for training programs, recruitment, and employee benefits, trying to identify areas where costs can be optimised without compromising the quality of services.

The difference between unit cost and total cost of an AI workflow

The cost of implementing AI cost optimisation for human resources is often misunderstood, with many focusing on the unit cost of individual AI workflows, rather than the total cost of the entire system. This narrow focus can lead to decisions that ultimately increase costs in the long run. The total cost of an AI workflow includes not just the cost of the AI model itself, but also the cost of data preparation, integration with existing systems, and maintenance.

A thorough understanding of the total cost is essential to making informed decisions about where to allocate resources. By considering the total cost, organisations can avoid making decisions that may seem cost-effective in the short term but ultimately drive up costs in the long term.

Caching, batching and model routing as the three real levers

When it comes to optimising the cost of AI workflows for human resources, there are three key levers that can be used: caching, batching, and model routing. Caching involves storing the results of frequent queries so that they can be quickly retrieved instead of having to be recalculated. Batching involves grouping multiple queries together and processing them as a single unit, reducing the overhead of individual queries. Model routing involves directing queries to the most appropriate AI model, reducing the computational resources required.

These three levers can have a significant impact on the cost of AI workflows, and organisations should carefully consider how to use them to optimise their systems. By applying these levers, organisations can reduce the computational resources required, lower their costs, and improve the overall efficiency of their AI workflows.

The human resources data this depends on

The effectiveness of AI cost optimisation for human resources depends on access to high-quality data, including policy library, headcount and skills, attrition history, and hiring plan. This data is used to inform the AI models and ensure that they are making decisions that are aligned with the organisation's goals and objectives. The policy library provides the rules and guidelines that the AI models must follow, while the headcount and skills data provides information about the organisation's workforce.

The attrition history and hiring plan data are also critical, as they provide insight into the organisation's workforce dynamics and help the AI models to make predictions about future staffing needs. By combining these different data sources, organisations can create a comprehensive understanding of their human resources and make informed decisions about where to allocate resources.

For more information on human resources data, visit our human resource page.

What it changes about

AI cost optimisation for human resources can have a significant impact on the way organisations approach planning and decision-making. By providing real-time insights and analysis, AI can help organisations to move away from planning that is based on impressions and intuition, and towards a more data-driven approach. This can lead to more effective use of resources, improved efficiency, and better outcomes.

One of the key benefits of AI cost optimisation is that it can help organisations to answer the same policy questions every week, without having to rely on manual processes or intuition. By automating these processes, organisations can free up staff to focus on more strategic and high-value tasks.

Measured with

The effectiveness of AI cost optimisation for human resources 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 impact of AI on the organisation's human resources processes, and can help to identify areas where further optimisation is needed.

By tracking these metrics over time, organisations can see the tangible benefits of AI cost optimisation and make adjustments as needed to ensure that they are getting the most out of their investment.

What this does not do

While AI cost optimisation for human resources can be a powerful tool for improving efficiency and reducing costs, it is not a silver bullet. It is not a replacement for human judgement and expertise, and it should not be relied upon as the sole decision-making authority. Organisations should carefully consider when to use AI cost optimisation, and ensure that it is used in conjunction with human oversight and review.

For organisations that are considering implementing AI cost optimisation for human resources, it is essential to carefully evaluate their needs and goals, and to ensure that they have the necessary data and infrastructure in place to support the use of AI. To learn more about how AsscherAi can help, visit our contact us page.