Why Your Business Needs AI to Monitor Tail Spend

How many businesses today can effectively manage their tail spend? The answer is not difficult to guess, considering that this portion of spend is often not actively monitored.

Comprising thousands of small purchases scattered across many suppliers and product types, tail spend often remains under the radar.

The question now is: Why do internal business teams make several purchases outside their contracts? How can they lower their maverick spend?

Insignificant as it may seem, ignoring tail spend can prove costly, especially when multiple internal teams make varied purchases under this category.

In the past, tail spend never got the attention that it deserved. But the general opinion to managing tail spend is now changing, thanks to advancements in the latest AI models, says Michael Seitz, vice president of consulting at GEP in a webcast.

Let’s take a deep dive into the world of technology-driven tail spend management.

What is Tail Spend? 

Tail spend comprises several one-time, non-contracted purchases made by different internal teams. Traditionally defined as high-volume, low-value transactions, tail spend can refer to ad hoc spending and uncategorized purchases that are low in value or frequency. 

The large number of non-monitored transactions means that tail spend can account for more than 80% of the total transactions. Still, tail spend may account for 20% of the overall spend. This data shows that businesses have an opportunity to significantly increase savings by effectively managing tail spend.

In the absence of a streamlined approach to managing this spend, companies lose 15-20% of potential savings and add to their administrative costs due to inefficient ad hoc processes.

Because of the low-value and infrequent nature of transactions, tail spend can also be a source of non-compliance. It can even provide cover for fraudulent purchasing. 

Why Managing Tail Spend is Complex  

Businesses face many challenges in managing tail spend. Firstly, lack of data visibility into the vast number of transactions makes it difficult to monitor this spend. A high volume of purchase orders and invoices adds to the confusion.

Another challenge of managing tail spend is the lack of clear ownership. This means that no individual function or internal team is solely responsible for managing tail spend. System and technology limitations and lack of compliance and controls make it difficult for businesses to effectively monitor tail spend. 

Further, many businesses do not have a cost baseline for tail spend management, says Seitz. This means that a buyer looking to optimize tail spend to show potential value needs to first spend a lot of time to even get a cost baseline and show its impact. With a considerable amount of time spent on this endeavor, the business case is no longer beneficial, he says. In other words, because of the low value of these transactions, it is difficult to justify the time and expense involved in attempting to control them.

Because of all these challenges, it is often difficult to build the right business case for investing in tail spend management.

How AI Transforms Tail Spend Management

Technology advancements have enabled procurement to bring tail spend under their control. 

Jonathan Kinghan, senior director at GEP, says in this white paper: “We know tail spend takes a lot of time, but the technology is there now to address it and the orthodox thinking that it’s not worth the hassle is not valid anymore.” 

Creating a simple, e-commerce-like user experience is key to managing tail spend. This means that internal buying steps and processes should be easy to understand, with the right catalogues and buying channels in place. 

AI tools are known to enhance end-user experience, with semantic search detecting user intent and guiding end-users via an intuitive conversational interface. They also boost operational efficiency by replacing manual validation and exception handling with automated routing and validation and OCR-enabled invoice matching process.

Further, AI tools can analyze tail spend patterns and guide employees toward centralized sourcing agreements. Recommendation engines showcase preferred suppliers when employees browse catalogs or e-procurement platforms. By matching search queries or basket contents to supplier contracts and pricing data, AI tools can provide recommendations ranked by savings potential. 

Guided workflows are designed to allow requisitions through authorized suppliers and channels only. They can redirect a buyer from a non-approved supplier to a preferred supplier, ensuring compliant purchasing in this process.

AI enables autonomous execution of low value, high volume bids and negotiations based on defined criteria. With certain rules and boundaries, the autonomous sourcing tool, for example, takes specs and demand from the orchestration layer, automatically suggests suppliers and initiates the sourcing process in a nearly fully automated way, says Seitz. 

Additionally, with continuous review and monitoring of transaction and performance data, AI tools can identify potential risks or irregularities. They can embed policies into workflows to better ensure compliance.

Finally, effective tail spend management requires end-to-end process integration and controls. By integrating the buying activity with accounts payable in an end-to-end process, you can eliminate the possibility of users engaging in maverick spending at any stage.