What\u2019s that coming over the hill \u2013 is it a monster? No, it\u2019s just the 21st century. And whether Sarah Connor likes it or not Artificial Intelligence (AI) and Chat GPT, also known as Generative AI (GenAI) is here to stay. But despite Skynet\u2019s best efforts, it won\u2019t take over, we are certain of that. We can live with it, in fact, even cohabit with it (almost) harmoniously in the spend data classification world, but not before it has been classified and checked by a human.<\/p>\n
You may have been told by your suppliers or a potential supplier that they\u2019re using AI, machine learning or more recently, Chat GPT\/GenAI, and this is what they\u2019ll be using to classify your spend data. You may even have heard phrases like \u201cneural networks\u201d, \u201calgorithms\u201d and \u201cLLMs\u201d, lots of fancy words being thrown around. It all sounds very technical and a bit scary, especially if they\u2019ve not given you any more information or explained what it actually does, and there can be an implied trust that it works, because it sounds like it does. But it doesn\u2019t always.<\/p>\n
In reality, AI and GenAI in spend data classification is not yet there. It may sound like a wonderful tool where you can just press a button and your data is magically classified, however the reality is much different. AI has to learn from an immense number of training data sets, and these data sets need to be clean and accurate. How do we do this? We use people, preferably subject matter experts (SMEs) to check and verify those training data sets. And to add more complexity into the mix, context is hugely important in spend data classification, and something that is still an issue within AI, GenAI and Machine Learning. So, humans still need to be heavily involved.<\/p>\n
AI and GenAI\u00a0<\/em>will be here to help us, not to take over. Think of it more as a flower chasing Wall-E type situation than a gun-wielding Terminator and it becomes much less intimidating. The truth is, IT needs US. And to some extent, we need it too, but only when it\u2019s correct. And for those jobs we really don\u2019t want to do\u2026<\/p>\n Very simply what happens is the AI, in whichever form, will learn from training data sets that have been previously classified and it will keep learning as new data is added. This is particularly successful in areas such as MRO (Maintenance, Repair, Ops) where the items are hardware such as nuts, bolts and screws. Their classification is unlikely to change, and so large volumes of data can be classified accurately.<\/p>\n There are, however, other instances where it is not so clear cut. For example, DHL could be classified as a courier in the training sets, so the AI will learn from this and classify DHL as a courier whenever it shows up. But DHL is not always used as a courier. DHL can also be used for warehousing, distribution and logistics depending on who the data set belongs to, and so it\u2019s important to make this distinction, particularly for spend management.<\/p>\n And then there\u2019s free text descriptions, plus many suppliers will provide multiple products or services, so it\u2019s harder for the AI to learn. If you have a description \u201ctaxi from hotel to restaurant\u201d, how does the AI know which one to choose? With the prevalence of GenAI, we\u2019re getting better but you can\u2019t blindly trust the tech.<\/p>\n That\u2019s why it is absolutely crucial that the AI learns accurate data from a human from day one and is checked regularly for accuracy. What if there\u2019s a glitch in the Matrix and the AI learns something it shouldn\u2019t? (I know I\u2019m mixing my movie metaphors here, but can you imagine what a film that\u2019d be with Keanu Reeves AND Arnold Schwarzenegger?!). Imagine the implications of one tiny mistake in the early stages of the AI\u2019s learning \u2013 let alone hundreds, if not thousands? That\u2019s a lot of spend misclassified right there.<\/p>\n Picture being several processes down the line and realising a whole section of data has been wrongly classified over and over and\u2026 over again. And it wasn\u2019t spotted because the AI had been left to do its thang. Someone will then need to manually classify your data while the AI is retrained. Not to mention having to go back through, find and correct the earlier mistakes.<\/p>\n