• Written by Sonali Subhadarsini, Product Manager

A category manager comparing two suppliers for a new contract often finds that the process takes longer than expected, with a significant amount of time spent searching for relevant information and verifying contract terms, which is where AI productivity in the enterprise for procurement can help return time to people.

The difference between time saved and time redeployed

When introducing AI-powered tools to procurement teams, the initial expectation is often that these tools will save time, allowing staff to focus on more strategic tasks. However, the reality is more nuanced, as the time saved by automating routine tasks may not necessarily translate to time redeployed to higher-value activities. The shift supervisor, for instance, may find that the time saved from automated reporting is absorbed by other pressing tasks, rather than being redirected to more strategic initiatives.

The distinction between time saved and time redeployed is crucial, as it highlights the need to consider how productivity gains are actually utilised within the organisation. Simply automating tasks without considering the broader workflow and organisational dynamics may not lead to the desired productivity improvements.

Why self-reported time savings overstate the effect

Self-reported time savings are often unreliable, as individuals may overestimate the impact of new tools or processes on their workload. This can be due to various factors, including the novelty of the new tool, the desire to please management, or a lack of understanding of how time is actually being utilised. The category manager, for example, may report significant time savings from a new procurement platform, but in reality, the time saved may be marginal or even negligible.

A more accurate assessment of productivity gains can be achieved by examining the actual workflows and processes within the organisation, rather than relying on self-reported data. This may involve observing how staff interact with new tools, analyzing workflow metrics, or conducting regular check-ins to assess progress.

The procurement data this depends on

Effective AI-powered procurement tools rely on high-quality data, including purchase orders, supplier contracts and terms, invoice history, and category structure. The accuracy and completeness of this data are critical, as they form the foundation for automated decision-making and analysis. The procurement team, for instance, needs access to up-to-date contract information to ensure compliance and optimize supplier relationships.

The quality of procurement data can vary significantly between organisations, with some having well-structured and easily accessible data, while others may struggle with data fragmentation, inconsistencies, or lack of standardization. Addressing these data quality issues is essential to realizing the full potential of AI-powered procurement tools.

What it changes about spend fragmented across suppliers and contract terms diverging from what is paid

One of the key benefits of AI-powered procurement tools is their ability to analyze and optimize spend across multiple suppliers and contracts. By examining purchase orders, invoices, and contract terms, these tools can identify areas where spend is fragmented or where contract terms are not being adhered to. The category manager, for example, can use this information to renegotiate contracts or consolidate spend with a smaller number of suppliers.

This can lead to significant cost savings and improved supplier relationships, as well as reduced complexity and administrative burden. Furthermore, by ensuring that contract terms are aligned with actual payments, organisations can minimize the risk of disputes or non-compliance.

Measured with spend under management, price variance to contract, supplier concentration

To evaluate the effectiveness of AI-powered procurement tools, organisations can use a range of metrics, including spend under management, price variance to contract, and supplier concentration. These metrics provide insights into the extent to which procurement activities are being optimized, and whether the desired productivity gains are being achieved. The procurement team, for instance, can use these metrics to identify areas for improvement and adjust their strategies accordingly.

By regularly monitoring and analyzing these metrics, organisations can refine their procurement processes and ensure that AI-powered tools are delivering the expected benefits. For more information on how to implement and measure the effectiveness of AI-powered procurement tools, visit our operations page.

What this does not do, or when it is the wrong choice

While AI-powered procurement tools can bring significant benefits, they are not a panacea for all procurement challenges. In some cases, the introduction of these tools may not be the right solution, such as when the underlying data is of poor quality or when the procurement processes are highly complex or nuanced. The shift supervisor, for instance, may need to exercise caution when introducing new tools to a team with limited technical expertise.

In such cases, it may be more effective to focus on addressing the underlying data quality issues or simplifying procurement processes before introducing AI-powered tools. For guidance on how to determine whether AI-powered procurement tools are the right fit for your organisation, contact our team at contact-us.