• Written by Muhammed Abdulla NC, CEO

A procurement manager sits at her desk, staring at a dashboard that is supposed to help her make sense of the organisation's spending, but she rarely uses it, and when she does, it's only to confirm what she already knows. This is a common problem in enterprise AI adoption for procurement, where capable systems go unused because they are not properly integrated into existing routines.

Attaching new capability to a routine that already exists

The key to successful adoption is to attach the new capability to a routine that already exists, so that it becomes a natural part of the workflow. For example, a procurement manager might already check the organisation's purchase orders on a weekly basis, so it makes sense to integrate the AI system into this routine, allowing her to ask questions about the data as she reviews it. This way, the new capability is not seen as an extra task, but rather as a way to enhance an existing process.

By doing so, the procurement manager can start to see the value of the AI system in her daily work, and begin to rely on it to inform her decisions. This is particularly important in procurement, where decisions are often made quickly and with limited information, so having access to accurate and up-to-date data can make a big difference.

Who needs to be able to ask, not just who needs the answer

Most people get wrong the idea that only certain individuals need to be able to ask questions of the AI system, when in fact, it's often the case that many different people need to be able to ask questions, depending on their role and responsibilities. For instance, a category manager might need to ask questions about supplier contracts and terms, while a financial analyst might need to ask questions about invoice history and spend patterns.

This is why it's so important to consider who needs to be able to ask questions, not just who needs the answer. By doing so, organisations can ensure that the right people have access to the information they need, when they need it, and can make informed decisions as a result. This is particularly important in procurement, where decisions are often made by a team of people, rather than a single individual.

The procurement data this depends on

The AI system relies on a range of procurement data, including purchase orders, supplier contracts and terms, invoice history, and category structure. This data is used to build a detailed picture of the organisation's spending, and to identify areas where costs can be reduced or optimised. For example, a shift supervisor might check the maintenance log to see which suppliers have been used in the past, and what the terms of the contracts were.

By analysing this data, the AI system can provide insights that would be difficult or impossible to gather manually, such as identifying spend that is fragmented across multiple suppliers, or contract terms that are diverging from what is actually being paid. This information can be used to inform procurement decisions, and to identify opportunities for cost savings and process improvements.

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

One of the main benefits of using an AI system in procurement is that it can help to identify and address issues such as spend that is fragmented across multiple suppliers, or contract terms that are diverging from what is actually being paid. By analysing the organisation's spending data, the AI system can identify areas where costs can be reduced or optimised, and provide recommendations for how to do so.

For example, the AI system might identify that a particular category of spend is fragmented across multiple suppliers, and recommend consolidating this spend with a single supplier in order to negotiate better prices. Alternatively, it might identify that certain contract terms are not being adhered to, and recommend renegotiating the contracts to bring them back in line with what is actually being paid.

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

The success of the AI system can be measured in a range of ways, including spend under management, price variance to contract, and supplier concentration. These metrics provide a clear picture of whether the organisation is getting the best possible value from its suppliers, and whether the AI system is having a positive impact on procurement decisions.

By tracking these metrics over time, organisations can see whether the AI system is having the desired effect, and make adjustments as needed. For instance, if the price variance to contract is increasing, it may indicate that the organisation needs to renegotiate its contracts with suppliers. The operations team can use this information to inform their decisions and make adjustments to the procurement process.

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

While the AI system can be a powerful tool for procurement, it is not a replacement for human judgement and expertise. There will be times when the AI system is not the right choice, such as when dealing with highly complex or nuanced procurement decisions. In these cases, it's often better to rely on the expertise of a experienced procurement professional, rather than relying solely on the AI system.

If you're interested in learning more about how AsscherAi can support your organisation's procurement decisions, please get in touch to discuss your needs and find out whether our solution is right for you.