WHITEPAPER
Don’t Fear the Reaper: How AI Empowers Procurement
On a seemingly random Wednesday in 2022, a new piece of technology roared onto the scene and the tech world went ballistic. In the span of just a few months, it had become a conversation mainstay in places where cutting-edge computer science never gets attention. Of course, everyone had anticipated this technology for several decades. Movies and books about how it would change life and even possibly destroy it have dominated Western culture for years. But outside of research circles, it was anything but an imminent reality. In fact, rumor had it that technological progress had slowed and our sci-fi future was still hundreds of years away. But that perception changed on November 30, 2022 when OpenAI released their large language model ChatGPT to the public. Most people had never seen anything like it. ChatGPT could answer nearly any question and even hold conversations that felt distinctly human. But what caught the world’s attention the most was how well it could write, code, and come up with new ideas when given enough information. For a world that had been limited to Apple’s finicky voice assistant Siri and cheap online chat bots, ChatGPT
was the future, and now it was available for free to anyone who had a connection to the internet. Before long, YouTube channels, LinkedIn posts, and blog articles were everywhere practically prophesying how AI would reshape society within 6 months. New tech companies took advantage of the buzz and launched products that harnessed AI to create art, music, and even voice-cloned podcasts. Within a few months, every product hitting the market had artificial intelligence at its core. At one point, a pair of electronic roller skates were even being advertised as “AI-powered.” The world seemed like it was on the brink of the next Industrial Revolution and people were scared. Yet less than a year later, life is relatively unchanged, and professionals everywhere are left unified by a single question: what does AI mean for me? Procurement is no exception. One of the leading digital transformation questions of 2023 has been how AI will redefine the industry. Are we all going to lose our jobs to the greatest strategic mind that never was? It’s a sobering question with an answer that’s more hopeful than you might think.
UNDERSTANDING AI’S PLACE IN PROCUREMENT To understand how AI will affect Procurement, we first need to understand what AI is. Contrary to the images that Westworld, Terminator, and Blade Runner have conjured, AI isn’t a nearly-human computer intelligence with dangerous motives, super-power strength, and omniscience. Instead, artificial intelligence is an umbrella term that represents many different computing concepts. These include large language models (LLMs) like ChatGPT, machine learning (ML), robotics, natural language processing (NLP), and neural networks.
initiatives by taking data work off their plates, organizing data faster than ever, and solving the poor categorization issues that have limited Procurement to guesswork in years past. To prove it, let’s compare the inner workings and results of our AI-based implementation process to the inner workings and results of other spend analysis platforms who are leaning away from AI and everything its responsible use can accomplish.
FROM CHAOS, ORDER
At the most basic level, AIs are mathematical models that are thoroughly trained on a set of data, which they then reference to recognize patterns, synthesize information, and suggest answers to questions. So unlike the robots in sci-fi movies, they’re usually designed to do a few specific tasks, like data entry and analysis, very well. In other words, AIs are great at doing the work that humans don’t want to do.
If you have 100% of your organization’s spend under management and can confidently list out how much your organization spent last quarter, all the categories and subcategories you spent it in, what vendors you did business with, how you’re faring against supplier diversity targets, and what your scope 3 emissions picture looks like, you’ve mastered spend management. In fact, you’re ahead of even the best-in-class organizations, who claim to have 91.5% of their spend under management according to Ardent Partners. For everyone else though, spend data is notoriously messy.
So what does that mean for procurement? Well, it has a few implications. First, the “scary good” writing and coding abilities that defined ChatGPT aren’t AI’s main business cases. But more importantly, it means that AI doesn’t pose a threat to procurement professionals. Instead, it presents an opportunity to break away from tedious, overwhelming data work and turn their focus back toward strategic impact.
To conduct a spend analysis, you need a holistic view of your spend. However, getting to this point is notoriously difficult. Even if you ignore the reality of data siloes and pretend that all your transactions flow through a single suite or ERP system, you’re still left with one massive problem: naming conventions. Hundreds of internal buyers filling out POs always lead to a variety of naming conventions for the same company.
Nowhere is that truer than in the world of spend optimization. For companies that have significant sourcing and procurement spend, conducting spend analysis has always been an all-consuming task simply because there’s so much data to work through. AI offers them a new path forward—not one that will disrupt how Procurement operates, but one that will empower the function to focus on strategic
Now add in P-Cards and different systems that all format data differently and you have a whirlwind of information that would take multiple people weeks of painstaking work to format, clean, and normalize. And that’s for a single month’s worth of data. Imagine slogging through this data four to twelve times a year to achieve spend visibility. Not only is it inefficient, it also regularly distracts your team from pursuing strategic projects. ©2023 Rights Reserved
that trained our artificial intelligence. So while every business has a unique spend profile, our AI has seen more combinations of vendor names, categories, transaction details, etc. than a single human could fathom.
But this is the kind of task that artificial intelligence excels at. Give it your collection of data, ask it to find patterns, and then set it loose while you focus on other strategic, important, or urgent tasks. In this case, AI can use phonetic comparison, a form of natural language processing, to identify which vendor records correspond with each other without requiring a human to sift through the data and make the selection. Some spend analytics platforms use AI for this process, but they take a very light-touch approach, only using AI to make initial, high-level suggestions for obvious vendor matches. These solution providers warn that AI isn’t well-equipped for this process because every company’s data set is unique. From their perspective, AI introduces the risk of mis-organizing data or grouping the wrong vendors under a normalized name. This approach handles a lot of data, but it leaves the end user with an expensive, costly, manual implementation process. However, misapplication of data is only a risk with AIs that haven’t been trained on adequate data sets. While it’s true that every business has a unique spend profile, this is only a problem because many spend analytics platforms don’t come from a Procurement background. In contrast, SpendHQ has been analyzing spend for more than 20 years. In that time, we’ve analyzed over $8 trillion of enterprise spend, which has formed the data lake
When we receive a company’s messy spend data, our system is well-equipped to make sense of it down to a granular level. Once it formats data, it locates and suggests normalized vendor name groupings and assigns confidence scores to its assignments. Then we bring humans back into the mix. Our internal team checks the results and presents them to the end user’s team to make sure that everything is correct. All of this happens in a fraction of the time and with a fraction of the effort that it would take to normalize spend data any other way. That means our clients spend less time working with data or sitting on implementation calls and more time pursuing spend optimization strategies. Using AI to normalize and categorize spend data the way we do is like studying for a test with a friend who’s already taken the class. Not only do they know what you should study, but they have a pretty solid idea of what questions the test will ask and what the answers are.
EVERYTHING HAS A PLACE; AI IS GREAT AT CLEANING Normalizing spend data quickly and accurately is a fantastic start to implementing a spend analysis solution. But alone, it won’t accomplish anything. In order to find action items in your data, you need a way to organize your entire spend profile into categories and subcategories. This is another exercise that AI can streamline if it’s used the right way. Unfortunately, it’s also another area where many spend visibility and analytics platforms do their users a disservice.
Procurement’s perspective on spend is focused on purchasing and sourcing, making it unique from a practical standpoint. However, many spend analytics platforms are built off Data and Finance bestpractices. This means that when they break spend into categories and subcategories, they do so with tools and an underlying philosophy that aren’t built for Procurement or on a Procurement background. For example, one popular option is to build a taxonomy with the organization’s internal financial coding, like its GL account. This is a fantastic tool when used in the right context, like financial analysis, but Procurement isn’t the right context. Because the way Procurement works is so unique, financial-based taxonomies are almost always simultaneously too broad and too granular for Procurement. The way they organize data doesn’t allow Procurement to effectively analyze and manage spend across the enterprise.
employ another light use of machine learning in this step, but the combination of incorrect taxonomy and untrained AI often leaves teams with about 40% of their spend analyzed. This is certainly an improvement over starting from scratch, but it still means that Procurement has to devote time to identifying where over half of its spend belongs. Even if this approach started with the right taxonomy, it would still rely on a massive amount of manual work. In many cases, the process gets even more labor-intensive after the initial classification work. Customers then collaborate with implementation teams to review the results, which surfaces even more manual editing. This process can continue back and forth for weeks and even months until everything seems to be correct. And because it requires the implementation team to be involved the entire time, you can expect it to come with a hefty price tag.
Of course, the shortcomings of these taxonomies aren’t a secret, so many teams opt to design their own categories and subcategories with the help of implementation specialists. At this point, the solution deploys a collaborative approach to classification, which relies on heavy involvement from the implementation team. Some solutions providers may ©2023 Rights Reserved
Classifying spend manually creates several issues. First, investing in a tool that you can’t use for months severely hampers the time to value and pushes the ROI timetable back indefinitely. When financial stakeholders don’t see results, they’ll start to question how valuable Procurement’s investments really are. Secondly, a manual approach creates implementation fatigue. After working with enterprise-level data for months, even the most detail-oriented person in the world is likely to miss critical errors. And the needs of the organization never stop either. After several implementation workshops, it’s easy to overlook incorrect classifications when contract renewals and conversations with stakeholder are also on your todo list. For example, one organization going through this process accidentally classified $1 million of FedEx spend as an IT expense. So we believe in using AI to simplify matters. Instead of manually classifying spend, we return to our data lake and our procurement background to make implementation as short as possible while still delivering results. We start by approaching categorization from a sourcing perspective. Over the last two decades, we’ve developed a taxonomy that organizes spend the way Procurement looks at it—by supplier capabilities. Of course, we stay flexible; no one has to use our taxonomy. If they want to build one of their own, our veteran Delivery Organization will work with the team to build one. Then our AI uses Natural Language Processing, String Matching, Keyword Sweeping, and Transaction Details to generate categorization suggestions based on associations between free text and spend categories. Of course, AI can never replace a human, so we don’t give our AI the final say. Instead, it simply
makes suggestions with different degrees of confidence. Then our implementation specialists confirm or redo the suggestions. Finally, they work directly with clients to validate the results and correct any mistakes. If the client agrees with the classification, we turn our attention to the spend that the AI didn’t recognize. As a result, our clients have 97% of their spend data classified within a short period of time.
It’s important to note that the difference between 97% and 100% categorization isn’t as large as it seems. The extra 3% usually comes from a combination of P-cards, T&E data, and one-off expenses and rounding them up quickly becomes an exercise in diminishing returns. For most companies, categorizing 97% of spend positions them to kick off strategic projects within a few weeks. That’s why we believe in bringing AI into Procurement. It never removes procurement professionals from the process—it just keeps them
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from doing it manually. That means less time looking at raw data and more time executing on exceptions, strategic initiatives, and projects that affect the company’s bottom line. And that’s our superpower. As a software company, we know the power of technology. But we’re also procurement veterans with deep relationships in the industry, so we know first-hand the power that inquisitive, dedicated people bring to the table. We’ve built our platform to leverage their strengths, and we believe the results speak for themselves.
“I’LL BE BACK” Despite the seeds of anxiety that Arnold Schwarzenegger’s iconic line evoked, it’s not the relentless nature of computing that procurement professionals should fear. Instead, it’s the constant flood of spend data that they should be worried about. A decade ago, changes in Procurement developed more slowly. Leaders had time to watch trends take shape over quarters. They could observe their
effects and then pivot to get ahead of them. This meant that refreshing spend data, while always tedious and resource-intensive, was something teams could plan over the course of a quarter. But now supply shortages, global conflicts, inflation, and the interconnectedness of modern business have sped up how quickly trends develop. Companies that manually refresh and analyze spend data every quarter are in for a nasty surprise when they see how quickly it can get out of hand. The need for updated spend analytics never ends, yet manually sorting through enterprise volumes of data on a monthly basis is impossible. Even if companies are hesitant to leverage machine learning to wrangle their historical data, they can no longer handle their monthly data refreshes without it. We know because many platforms have tried, and the results speak for themselves. Not only do they start with a Finance-based taxonomy, but they also use rules to organize expenses based on GL codes for ongoing classification and refreshes. On the surface, this seems like a great approach, but it comes with a few serious flaws.
WHAT ARE RULES? Rules are instructions that tell a computing system what to do in specific circumstances. First, these rules are rigid. Once they’re set, the system will do exactly what it’s been told to do. In most cases, this is a good thing. But it also means that you’ll need to think of a rule for every possible situation. If you forget to plan for something important, the system won’t know the difference. It will execute based on the rules you set. And if you don’t have a rule to account for new vendors or purchase types, the system may just leave the new data unclassified. Rules aren’t perfect either. In fact, they’re prone to breaking. But you’re not going to hear alarm bells when that happens, meaning you could go months before you notice a problem. Unless, of course, you’re manually observing your data down to the line item, which defeats the purpose of rules and technology in the first place. Procurement needs some degree of control as well. In addition to all the other problems of relying on a rules-only approach, Procurement usually doesn’t have any control over how the rules are set up. Teams assume they can trust this approach, but because the taxonomy and the automated processes all have fatal flaws, the result is almost always messy, inaccurate categorization. Finally, this approach takes a lot of rules, think hundreds of thousands. You’re going to have to think up all of these scenarios, making sure you don’t overlook something or create rules that counteract each other. And to be clear, this is a step you’ll take after implementation, just to ensure that your data refreshes work correctly.
Compare that to an approach that combines rules and machine learning. It simply refreshes data the same way that it normalizes and classifies it, except it’s constantly learning and improving so each refresh is faster than the one before. Instead of relying solely on the “If X, then Y” logic of rules, the machine learning process we’ve described uses a wealth of training information to interpret data, even when egregious typos or new scenarios show up. You also never lose control. AI is only here to speed up the data process. Not only will it make suggestions instead of decisions, but you’ll always be aware of what it’s doing because the process is relatively simple. Bottom line: Procurement’s data is becoming too complicated and too big for rules or manual processes. Unless you want your refreshes to terminate your productivity, you need a process that will handle data and reveal better opportunities so you can think about strategy.
QUITE AN EXPERIENCE TO LIVE IN FEAR, ISN’T IT? When ChatGPT burst onto the scene at the end of 2022, the business world was stunned. Most people didn’t even know that there were AI models that could code, answer questions, and write like humans. ChatGPT felt like the intro to a science fiction novel and the fear that came with it was on-brand. So now that the world has had time to accept AI, should Procurement professionals be worried? We don’t think so. In February 2023, Ardent Partners predicted that AI would be the Person of the Year and that it would bring nothing but good to Procurement by empowering the function to find hidden opportunities, risks, and challenges. So far, that’s turned out to be true. We won’t claim to have a crystal ball, but if we had to make our own Big Trends prediction, we’d bet that AI is going to become a procurement mainstay in the best possible way.
At SpendHQ, we believe that humans have unique capacities for creativity, dedication, strategy, and experience. We also know that AI is uniquely talented at recognizing patterns, sorting data, checking for mistakes, and doing it all really, really quickly. So why shouldn’t we each do what we’re best at? That’s why we don’t fear the reaper. Our first-hand experience has convinced us that AI isn’t going to disrupt Procurement; it’s going to allow the function to accomplish more than ever before. Excel allowed professionals to focus on what numbers meant instead of entering them. We believe that AI is just another step in that direction. When you combine that with Procurement’s rising influence, there’s a lot to look forward to for teams who choose to combine AI’s strengths with their own.
Discover how SpendHQ’s Procurement AI can enhance your procurement function. info@SpendHQ.com
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