There are several ways to leverage technology for categorizing procurement activity. Some employ top-down business rules, while others leverage Artificial Intelligence (AI). Historically, the rationale for taking a top-down, business rules-driven approach was the challenge of categorizing complex transactions quickly and at scale. Fortunately, neither of these are a problem for AI. Therefore, to achieve a high level of categorization accuracy, it is essential to understand how AI ‘thinks’ about taxonomy.
AI can assign standardized taxonomy information based on the line description for individual transactions in a Purchase Order (PO). It can work with an unstructured, user-defined description and derive enough information to categorize transactions accurately. AI can convert non-standard human descriptions into descriptions that align with a standard taxonomy because it can learn from multiple sources of information. Therefore, for AI detailed data is best.
Enterprises use several commonly applied standard taxonomies. It is important to remember, however, that taxonomies are not 'function agnostic.' For instance, a general ledger-driven taxonomy emphasizes a financial point of view, while bills of material (BOMs) categorize materials as used in the production process. Neither is wrong. They have different objectives, and for AI both are fine.
While standardized taxonomies offer a great deal of depth, most companies do not need the same degree of granularity in all categories. For AI, the information available is critical, and the differences in taxonomy are not an impediment.
The value of having well-categorized spend is to empower an enterprise to act appropriately and decisively because they can trust their data, from the most granular level to the aggregated top level. Hence, procurement’s best bet is to understand how AI regards a taxonomy and work with that, not hamper it by assigning human perspectives to it.
Brought to you by Scope e-Knowledge Center, an SPi Global Company, a trusted global partner for Digital Content Transformation Solutions, Knowledge Modeling (Taxonomies, Thesauri and Ontologies), Abstracting & Indexing (A&I), Metadata Enrichment and Entity Extraction.
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