Long tail spend inherently represents a large number of diverse purchases. In any industry, this translates to a certain complexity in management. It raises significant challenges, particularly in terms of operational efficiency and competitiveness. It's in this context that Artificial Intelligence (AI) - especially generative AI - can make a real difference by boosting procurement teams' productivity.
Long tail spend: Definition and challenges
According to Pareto principle, also known as the 80-20 rule, long tail spend represents a tiny portion of procurement budgets (5%), but accounts for the majority of product categories (50%). It generally involves infrequent purchases covering an extremely wide range of items. These purchases, however diverse, are essential to the smooth running of any company and its competitiveness. Once procurement departments have brought their strategic purchases under control, they need to pay closer attention to long tail spend. The latter has long suffered from poorly structured management, characterised by two main phenomena.
Flawed procurement processes
Long tail spend, due to the volume and variety of products it represents, entails a significant administrative burden. However, procurement departments struggle to manage and optimise these purchases due to lack of resources. In fact, procurement and/or sourcing processes can sometimes prove complex, time-consuming, or even deficient.
Multiple flows
These purchases represent a phenomenal amount of product categories and references. Without any rationalisation work, these flows are multiplied. Companies end up with an extremely large supplier portfolio as well as a multitude of transactions, deliveries, and stocks to manage. Not to mention the volume of data this generates, without always being properly exploited.
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75% of the number of suppliers;
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70% of the number of deliveries;
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60% of the volume of orders.
Artificial Intelligence, supporting long tail spend
In the procurement world, Artificial Intelligence (AI) has established itself as a prominent tool, particularly generative AI. This type of AI focuses on independently creating data and can interact with users. This is what helps procurement teams with operational procurement management, optimisation levers identification, and strategic decision-making.
Alex Saric, Chief Marketing Officer at Ivalua, explains: "AI can be the catalyst for procurement transformation, with clear use cases for data processing, automation, creating actionable insights, and guiding strategies to strengthen procurement and supply chain operations."
This is especially true in the context of long tail spend, which represents a significant workload, generates vast amounts of data, and involves a wide range of stakeholders, whether suppliers or users. It's precisely on these purchases that we need to focus the automation of the most time-consuming manual tasks such as product search, document production and analysis, or identification of potential cost savings.
(Inset) According to the latest study conducted by Sapio Research on behalf of Ivalua, 63% of procurement leaders have already implemented AI or machine learning technologies, or plan to do so.
Long tail spend: 3 main AI use cases
Today, the AI applications within procurement departments are numerous and varied. Integrated at the heart of Source-to-Pay processes, some AI solutions even take the form of virtual assistants, combining generative AI and Large Language Models (LLMs) like ChatGPT, Google Gemini, or Copilot, to offer a user-friendly interface between users and complex data management and spend analysis systems. All this with the aim of helping buyers in their daily operations, thus multiplying their productivity.
Product search
Buyers, as well as internal customers, can use AI to benefit from advanced product and/or service search in their e-catalogue. They simply need to type a keyword (even misspelled) or describe the desired functionality to obtain relevant results. This can even go as far as considering their purchasing habits or other parameters specific to each individual. Lastly, AI can also take the form of a conversational agent, or chatbot, which accompanies all these interlocutors on product search and information.
Document production and analysis
Procurement departments can be assisted by effective AI tools in content production, namely the design of requests for proposals or the drafting and sending of communications to their suppliers. AI can also help them analyse these documents, summarise them, or even identify relevant information (obligations of both parties, supplier risk...). With conversational agents, buyers can even ask specific questions about documents and receive instant answers. This digital solution is valuable support for spotting compliance issues or preparing supplier negotiations.
Identification of potential savings
Thanks to AI, procurement departments can automate their process and extract real time and granular information from their spending data, with a view to cost savings. This includes automatically aggregating, cleaning, classifying, and reporting data across the enterprise. Buyers can thus access consumption reports, identify price increases... All this with the aim of making informed decisions and achieving savings.
Faced with the major challenges that long tail spend represents within companies, by automating manual tasks, Artificial Intelligence (AI) proves to be a major asset in optimising processes, operational efficiency, and above all, the procurement teams' overall performance.
Ultimately, integrating AI in procurement undoubtedly allows for improving overall efficiency and reducing costs throughout the procurement processes: From category analysis to supplier management, including risk management. In addition to this tremendous time saving, it allows them to strengthen their relationships with internal customers and suppliers, guide their procurement strategies accurately, and thus create strong added value.
