• Written by Sonali Subhadarsini, Product Manager

On a typical Monday morning, a category manager checks the latest purchase orders to identify areas where procurement processes can be improved, often finding that manual handoffs and data inconsistencies are causing delays and inefficiencies, which is where AI process automation for procurement can help by automating the steps around a decision rather than the decision itself.

Mapping the current process

Before automating any part of the procurement process, it is essential to map out the current workflow, including all the steps involved in making a procurement decision, from identifying the need for a product or service to finalising the purchase order, and understanding how data flows through each stage, which can be a time-consuming task, but it is crucial in identifying areas where automation can add the most value.

This mapping exercise typically involves the category manager working closely with the procurement team, including the shift supervisor, to document every step of the process, including the systems used, such as enterprise resource planning (ERP) software, and the data required at each stage, which can help to identify bottlenecks and inefficiencies in the process.

Handoffs that create delay

The handoffs between different teams and systems are often where the procurement process stalls, causing delays and inefficiencies, and it is here that AI process automation for procurement can have the most significant impact, by automating the transfer of data and tasks between teams and systems, such as from the procurement team to the finance team, or from the ERP system to the contract management system.

For example, when a purchase order is raised, it may need to be approved by the finance team, which can cause a delay if the approval process is manual, but with AI process automation, this approval process can be automated, allowing the procurement process to move forward more quickly, and reducing the risk of errors or lost documents.

Procurement data

The success of AI process automation for procurement depends on access to high-quality procurement data, including purchase orders, supplier contracts and terms, invoice history, and category structure, which provides the context and information needed to automate the procurement process effectively.

This data can be used to identify trends and patterns in procurement activity, such as spend fragmented across multiple suppliers, or contract terms that are not being adhered to, and to inform the automation of the procurement process, ensuring that it is aligned with the organisation's procurement strategy and goals, and that it is using the most up-to-date and accurate data available, which can be accessed through operations systems.

What it changes

AI process automation for procurement can help to address two common issues in procurement: spend fragmented across multiple suppliers, and contract terms that are not being adhered to, by automating the analysis of procurement data and identifying areas where consolidation or renegotiation of contracts can help to reduce costs and improve efficiency.

For example, a category manager may use AI process automation to analyse purchase orders and identify opportunities to consolidate spend with a single supplier, or to renegotiate contracts to ensure that the organisation is getting the best possible price, and to ensure that contract terms are being adhered to, which can help to reduce the risk of errors or disputes.

This can also help to improve the organisation's relationship with its suppliers, by ensuring that contracts are being managed effectively, and that payments are being made on time, which can help to build trust and improve communication, and to ensure that the organisation is getting the best possible value from its suppliers.

Measuring success

The success of AI process automation for procurement can be measured using a range of metrics, including spend under management, price variance to contract, and supplier concentration, which provide a clear picture of the impact of automation on the procurement process.

For example, a category manager may use these metrics to track the effectiveness of automation in reducing spend fragmentation, or in improving contract compliance, and to identify areas where further automation or process improvement is needed, which can help to ensure that the organisation is getting the most value from its procurement processes, and that it is using its resources effectively.

What this does not do

While AI process automation for procurement can help to improve the efficiency and effectiveness of the procurement process, it is not a replacement for human judgment and decision-making, and it is not suitable for all types of procurement decisions, such as those that require complex negotiations or strategic thinking.

In these cases, the category manager and other procurement professionals will still need to be involved in the decision-making process, using their expertise and experience to make informed decisions, and to ensure that the organisation is getting the best possible value from its procurement activities, and for more information on how to get started with AI process automation for procurement, please contact us.