Category Archives: AI

Advanced Sourcing Today — No Gen-AI Needed!

Back in late 2018 and early 2019, before the GENizah Artificial Idiocy craze began, the doctor did a sequence of AI Series (totalling 22 articles) on Spend Matters on AI in X Today, Tomorrow, and The Day After Tomorrow for Procurement, Sourcing, Sourcing Optimization, Supplier Discovery, and Supplier Management. All of which was implemented, about to be implemented, capable of being implemented, and most definitely not doable with, Gen-AI.

To make it abundantly clear that you don’t need Gen-AI for any advanced enterprise back-office (fin)tech, and that, in fact, you should never even consider it for advanced tech in these categories (because it cannot reason, cannot guarantee consistency, and confidence on the quality of its outputs can’t even be measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence that were (about to be) in development five years ago and are now available in leading best of-breed systems. And we’re continuing with Sourcing.

Unlike prior series, we’re identifying the sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently found, or will soon be found, in Source to Pay technologies that are truly AI-enhanced. (Which, FYI, may not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we continue with AI-Enhanced Sourcing that was in development “yesterday” when we wrote our first series five years ago but is now available in mature best of breed platforms for your Sourcing success. (This article sort of corresponds with AI in Sourcing Tomorrow Part I and AI in Sourcing Tomorrow Part II that were published in January, 2019 on Spend Matters.)

TODAY

Event-Based Category Alighnment

As per our Procurement series, a good AI based platform continuously analyzes (i.e. re-runs an analysis on a monthly basis) every product or service for inclusion against every organizational category and comes up with the most logical mix for the procurement organization based on likeness, current supply-base, spend-mix, and other existing parameters.

However, when it comes time for sourcing, the category should be appropriate for a sourcing event. This depends on volume, available supply base, and the category strategy (see the next item).

When it comes to sourcing, the AI will look at not only the product specifications, but also ensure there is a sufficiently large supply-base, with supply availability, spend-mix, and price trends. It will do this based on key material analysis (to identify additional suppliers in the market not yet supplying the organization), identification of market offers and volume disclosures from third party distributors vs. organizational need and overall percentages, analysis of spend vs. typical sourcing event sizes using simple (k-means) analysis, and price trends using basic curve fitting/projection. Nothing fancy.

Based upon the demand (volume), available supply base, supply availability, spend mix, price trends, and defacto templated sourcing strategy, the platform will recommend the event proceed using the standard strategy and template, proceed with modifications, or not proceed (alerting the buyer it’s not a good time, not a good event, or a new strategy is needed). It’s all traditional analytics, a smattering of machine learning, a sprinkling of pattern matching, tolerances, and confidence calculations. Nothing super fancy. The recommendation(s) will depend on a number of factors that revolve around the market conditions at the time. Current prices. Available supply base. Category dynamics in the consumer marketplace. Etc.

Category-Based Sourcing Strategy Identification

In our prior series, we indicated we’d have market-based sourcing strategy identification, and while that is in development, we’re not quite there yet. Market-based strategy identification requires a lot of data — market, supplier, marketplace, (anonymized) community intelligence, past event data, and past data from similar situations … the global marketplace has been so dynamic in recent years that we haven’t seen anything like it since pre-2000 … which was before the introduction of mass-market sourcing / procurement / modern supply chain software and we just don’t have the data.

That being said, for the majority of commodity categories, a number of leading firms have developed one or more standard sourcing strategies for the category and categorized the market conditions under which the strategies work. Modern sourcing platforms will run all the analytics against the specified demand ranges, supply vs. demand imbalance, historical price variances (since the last event), current market prices, check the thresholds, compute the match percentage and confidence, and then recommend go, go with changes/caution, don’t go — all using straight-forward trend analysis and mathematical calculations — no Gen-AI needed!

Real-Time Market vs. Response Monitoring and Automatic Pauses/Updates

As the responses come in, the application will not only track bids vs open market prices (and current prices), but compute the averages and if the bids coming in are worse than expected, alert the buyer. In a multi-round scenario, or RFQ-powered auction, the trends will be analyzed and if they are not as expected, the buyer will be alerted. In both cases, if something is off beyond a tolerance, which will adjust over time as buyer feedback on go-no go is collected, the event will automatically be paused if necessary. This just requires simple calculations against means and expectations. Good old math, a few business rules, and some workflow automation is all that is required.

Suggested Award Scenarios

Even if the platform doesn’t contain (true) strategic sourcing decision optimization [SSDO] (and see this recently updated article on Questions to Ask Your Optimization vendor for the requirements for a true SSDO solution), most modern platforms will recommend one or more award scenarios that take into account cost, business constraints, risk and carbon. It’s just a lot of combinatorial mathematical calculations and basic analytic verifications.

Carbon Impact Analysis

Using standard models for carbon production based on available data by industry, country, and when available, factory, modern platforms will use standard models and formulas to compute the carbon footprint by item, based on the supplier, the source location, and the location it is going to (and even take into account logistics based carbon production). It will do this for every item you’ve purchased, every item you’re considering, and show you the carbon impact of different award decisions vs. the status quo. No Gen-AI required! (Just a lot of formulae and data!)

SUMMARY

Now, we realize some of these descriptions, like yesterday’s, are also quite brief, but again, that’s because this is not entirely new tech, as the beginnings have been around for a few years, have been in development and discussed as “the future of” Sourcing tech before Gen-AI hit the scene, and all of these capabilities are pretty straight-forward to understand (especially with many of the fake-take and Gen-AI providers marketing these, or similar, claims, even though they are not entirely realizable within their platforms). And, if you want to dive deeper, the baseline requirements for most of these capabilities were described in depth in the doctor‘s January 2019 articles on Spend Matters. The primary purpose of this article, as with the last, was to explain how more sophisticated versions of traditional ML methodologies could be implemented in unison with human intelligence (HI!) to create smarter Sourcing applications that buyers could rely on with confidence.

Advanced Sourcing Yesterday — No Gen-AI Needed!

Back in late 2018 and early 2019, before the GENizah Artificial Idiocy craze began, the doctor did a sequence of AI Series (totalling 22 articles) on Spend Matters on AI in X Today, Tomorrow, and The Day After Tomorrow for Procurement, Sourcing, Sourcing Optimization, Supplier Discovery, and Supplier Management. All of which was implemented, about to be implemented, capable of being implemented, and most definitely not doable with, Gen-AI.

To make it abundantly clear that you don’t need Gen-AI for any advanced enterprise back-office (fin)tech application, and that, in fact, you should never even consider it for advanced tech in these categories (because it cannot reason, cannot guarantee consistency, and confidence on the quality of its outputs can’t even be measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence (as we don’t really have true appercipient [cognitive] intelligence or autonomous intelligence, and we’d need at least autonomous intelligence to really call a system artificially intelligent — the doctor described the levels in a 2020 Spend Matters article on how Artificial intelligence levels show AI is not created equal. Do you know what the vendor is selling?) that have been available for years (if you looked for, and found, the right best-of-breed systems [many of which are the hidden gems in the Mega Map]). And we’re going to continue with Sourcing. (Find our series on Advanced Procurement — No Gen-AI Needed! Yesterday, Today, and Tomorrow at the following links.)

Unlike prior series, we’re going to mention some of the traditional, sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently found, or will soon be found, in Source-to-Pay technologies that are truly AI-enhanced. (Which, FYI, might not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we start with AI-Enhanced Sourcing that was available yesterday (and, in fact, for at least the past 5 years if you go back and read the doctor‘s original series, which will provide a lot more detail on each capability we’re discussing. (This article sort of corresponds with AI in Sourcing Today that was published in January, 2019 on Spend Matters.)

YESTERDAY

Workflow / Project Automation

Once a sourcing project is defined, which typically consists of identifying the required products and demand, the critical requirements of the supplier pool, the RFI, the RFP/Q, the evaluation criteria and weightings, the award rules, and the initial award offers, the entire project is easily automated using rules-based automation. Best-of-breed platforms will integrate fuzzy matching to identify additional suppliers who provide similar SKUs, RFI/P/Q templates which will automatically be pulled in and modified based upon the particular items in the category and organizational risk/compliance rules using semantic characteristic matching (traditional NLP will be fine), and built in “cherry-pick” algorithms that will compute standard award scenarios (lowest price, max 3 suppliers, geo-split, etc.) and create a default recommendation — which only requires math and traditional analytics.

Auto-Fill

For the better part of the past decade, the best platform auto-fills not just successive rounds, but auto-fills / pre-populates all of the supplier, item, and RFI data based on available information in all integrated systems — be it from past events, the supplier master, the forecasting platform, or market(place) data (for products).

This just requires rules-based automation and workflow with reg-ex pattern matching, and simple trend analysis and market data matching for price / demand population. Easy peasy on the tech ladder.

Outlier Identification

As we wrote years ago, it only takes one bad data element to make a good sourcing process go bad. Just one. One bid too low that takes a buyer down the wrong path. One risk rating too high that steers a buyer away from what would be their best supplier. One demand error that steers the best supplier away. But all of these “outliers” can be easily detected with traditional mathematical clustering algorithms used as the back-bone of machine learning — k-means, nearest neighbour, etc. — and identifying any values too far off the norm and then alerting the buyer to (have the supplier) correct them.

Rule-Based Auto-Award Identification

For simple scenarios where it’s always lowest cost, simple mathematical calculations can identify the supplier-item awards, and these can be limited to a max # of suppliers as then it’s just computing some combinations. No “AI” required.

SUMMARY

Now, we realize this was very brief, but again, that’s because this is not new tech, that was available long before Gen-AI, which should be native in the majority (if not the entirety) to any true best-of-breed Sourcing platform, that is easy to understand — and that was described in detail in the doctor‘s 2019 article for those who wish to dive deeper. The whole point was to explain how traditional ML methods enable all of this, with ease, it just takes human intelligence (HI!) to define and code it.

Advanced Procurement Tomorrow — No Gen-AI Needed!

Back in late 2018 and early 2019, before the GENizah Artificial Idiocy craze began, the doctor did a sequence of AI Series (totalling 22 articles) on Spend Matters on AI in X Today, Tomorrow, and The Day After Tomorrow for Procurement, Sourcing, Sourcing Optimization, Supplier Discovery, and Supplier Management. All of which was implemented, about to be implemented, capable of being implemented, and most definitely not doable with, Gen-AI.

To make it abundantly clear that you don’t need Gen-AI for any advanced back-office (fin)tech, and that, in fact, you should never even consider it for advanced tech in these categories (because it cannot reason, cannot guarantee consistency, and confidence on the quality of its outputs can’t even measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence that are (or soon will be) in development (now) and you will see in leading best of breed platforms over the next few years.

Unlike prior series, we’re identifying the sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently emerging, and will soon be found, in Source to Pay technologies that are truly AI-enhanced. (Which, FYI, may not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we continue with AI-Enhanced Procurement that is in development “today” (and expected to be in development by now when the first series was penned five years ago) and will soon be a staple in best of breed platforms. (This article sort of corresponds with AI in Procurement The Day After Tomorrow that was published in November, 2018 on Spend Matters.)

TOMORROW

AUTOMATIC CATEGORY IDENTIFICATION

Building on the above, there’s no reason it can’t look at common product / service characteristics from BOMs (bills of materials) and descriptions, find commonalities, and suggest new sourcing/procurement categories that would maximize opportunity and leverage. This is just building on last-gen tech with more encoded human intelligence (HI!), RPA, and (gasp!) math. This is especially useful for identifying when tail-spend should go to 3-bids-and-a-buy tactical sourcing and when mid-tier tactical categories are large enough for full blown strategic sourcing with strategy identification, in-depth market research, multi-round bids and negotiations, etc.

AUTOMATIC PROCUREMENT METHOD IDENTIFICATION

When we are talking about mid-tier tactical sourcing, when a category (currently in the tail) goes beyond a simple catalog / e-comm-like site buy, determining whether it should be a 3-bids-and-a-buy RFQ, auction, or negotiation with an incumbent (whom you have a relationship with in another category or who is currently getting most of the business off-contract) can be automated based on an assessment of current market conditions (supply vs. demand, price trends, category risk, etc.) and encoded Human Intelligence (HI!) on best-practice (and the conditions that tilt one method in the favour of another baed on past savings against similar market conditions). While it won’t be perfect, it will better than most buyers in most organizations will be able to do without deep category expertise and/or a lot of experience in strategy selection and implementation — and more than good enough for an average mid-market enterprise for the majority of their mid-tier spend.

ELIMINATION OF UNMANAGED TAIL SPEND

Tail Spend can be 30% to 40% of spend in some organizations, and overspend (as determined by a variance analysis, market prices across marketplaces, and/or average savings from a 3-bids-and-a-buy RFP or even just a bulk discount on standard catalog pricing) in the 15% to 30% range.

(That’s why so many laggards are getting bamboozled by the new generation of fake-take [better known as intake] procurement applications that make it easy to process requisitions and do one-time buys, because they often see a 10% savings on spend out of the gate and think they are doing fantastic, even when they aren’t. First of all, they are only getting market-price [because they aren’t doing real procurement, which requires a basic level of strategy, and definitely not doing strategic sourcing], which means they are leaving money on the table. Secondly, by not identifying items that should be bundled across requisitions from the week OR managed as MRO / commodity inventory [which can be managed automatically], they are wasting time (and thus money) processing essentially the same requisition over and over [and over]. And so on.)

However, given that we have made great advances in trend analysis, community intelligence, market price intelligence, demand management, market dynamics classification, etc., there’s no reason that, for any tail spend item, the system can’t, with high probability, identify the appropriate methodology for any requisition, which, for tail spend, should include:

  • fulfill from inventory (and auto manage / order the inventory)
  • fulfill from catalog (from contract / preferred suppliers)
  • combine requisitions and fulfill via RFQ
  • combine requisitions and fulfill via e-Auction
  • fulfill as standalone RFQ
  • fulfill as standalone e-Auction
  • promote to a tactical sourcing / strategic procurement category

PERFORMANCE IMPROVEMENT

Procurement is always overworked and under-resourced from a people, capital, and technological perspective, so performance is critical. A great system will increase performance not just along the “cost savings” dimension (as that’s a given with Procurement, whoever said “I have been tasked to spend more” in Procurement), but also along the time, risk, and sustainability measurements.

A great system will monitor utilization and not only allow itself to be configured to minimize steps and effort for everyday tasks through built in configuration capabilities in the dashboards, workflows, rules, etc., but will suggest to the admin changes to configuration, process, or policy over time as the metrics indicate that changes would reduce process time. Process analysis systems already exist, it’s just a matter of integrating them into procurement systems and integrating the analytics necessary to do the suggestions and linking them to the workflow.

But procurement systems aren’t limited to identifying savings opportunities across money and time, they can also identify opportunity across risk if appropriate risk metrics are incorporated (and suggest strategies, suppliers, or products with lower risk) using trend and comparison analytics.

Similarly, they can integrate carbon models and carbon data and identify the (expected) carbon cost of every product or service being considered (depending on whether the data comes from an industry data base, country database, supplier measurement, or third party auditor, will determine how accurate the carbon value is), and identify suppliers or products that would reduce carbon, as well as the cost decrease and/or risk increase of any carbon improvements.

SUMMARY

Now, we realize some of these descriptions are dense, but that’s because our primary goal is to demonstrate that one can use the more advanced ML technologies that already exist, harmonized with market and corporate data, to create even smarter Procurement applications than most people (and last generation suites) realize, without any need (or use) for Gen-AI, that the organization can rely upon to reduce time, tactical data processing, spend, and risk while increasing output and overall organizational performance. It just requires smart vendors who hire very smart people who use their human intelligence (HI!) to full potential to create brilliant Procurement applications that buyers can rely on with confidence no matter what category or organization size, always knowing that the application will know when a human has to be involved, and why!

Advanced Procurement Today — No Gen-AI Needed!

Back in late 2018 and early 2019, before the GENizah Artificial Idiocy craze began, the doctor did a sequence of AI Series (totalling 22 articles) on Spend Matters on AI in X Today, Tomorrow, and The Day After Tomorrow for Procurement, Sourcing, Sourcing Optimization, Supplier Discovery, and Supplier Management. All of which was implemented, about to be implemented, capable of being implemented, and most definitely not doable with, Gen-AI.

To make it abundantly clear that you don’t need Gen-AI for any advanced enterprise back-office (fin)tech, and that, in fact, you should never even consider it for advanced tech in these categories (because it cannot reason, cannot guarantee consistency, and confidence on the quality of its outputs can’t even be measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence that were (about to be) in development five years ago and are now available in leading best of-breed systems. And we’re continuing with Procurement.

Unlike prior series, we’re identifying the sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently found, or will soon be found, in Source to Pay technologies that are truly AI-enhanced. (Which, FYI, may not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we continue with AI-Enhanced Procurement that was in development “yesterday” when we wrote our first series five years ago but is now available in mature best of breed platforms for your Procurement success. (This article sort of corresponds with AI in Procurement Tomorrow Part I, AI in Procurement Tomorrow Part II, and AI in Procurement Tomorrow Part III that were published in November, 2018 on Spend Matters.)

TODAY

OVERSPEND PREVENTION

By integrating trend analysis on demand and price, the platform can easily predict the date the budget will be exhausted, and if that’s before the end of the year, it can proactively pause an order for budgetary review EVEN IF it would be automatically approved in a last-gen system because it was still within budget and from an approved supplier.

POLICY IDENTIFICATION and ENFORCEMENT

One reason fake-take (better known as intake) solutions are so popular, besides the fact they make tail spend procurement easy (which we’ll discuss in more detail in our next part), is that they make it easy to identify and follow organizational procurement policies, especially since they will even guide a user through the correct process once the product / service need is identified.

At the end of the day, this is just guided buying with integrated access rules (who can request / buy something), budget rules (what budgets do they have or have access to), approval rules (who needs to approve and when), as summarized in, and extracted from, policy handbooks (which can be done with traditional semantic processing and human verification).

AUTOMATIC INVISIBLE BUYING

In last-gen platforms you had to define items you wanted on auto-reorder, define specific rules for each, and manually maintain this list, and associated rules, on an ongoing basis. But, at the end of the day, for example, MRO is MRO is MRO and commodity stock is commodity stock is commodity stock and there’s no reason that you shouldn’t be able to turn over the entire category to the platform. After all, if you’re ordering the item regularly, as we described in Yesterday’s Smart Automatic Reordering, you have enough data to compute demand trends, price trends, delivery times, and EOQs (economic order quantities) and, as long as everything is within a threshold of predictability, the system should just re-order for you — and if something appears to be going off the rails, pause automatic re-order and alert a buyer to examine the situation and either do a manual re-order (which could include accepting the system suggestion), change the rules or thresholds for automatic reorders, or redefine the category / reassign the product or service.

AUTOMATIC OPPORTUNITY IDENTIFICATION

As noted in “AI In Procurement Tomorrow: Part II“, a high-performing organization tackles at most 1/3 of spend strategically on an annual basis, due to lack of manpower and time. The fact of the matter is that, unless you have a true best-of-breed spend analysis system and the experience to use it efficiently and effectively (as well as sufficiently cleansed and complete data to work on), it’s a significant effort just to do the spend analysis required to identify and fully qualify the market opportunity and shape it into an appropriate market event.

But there’s no reason that the platform couldn’t encode all of the standard analytic workflows used by best-practice consultants, identify the top product/services/categories with the most spend not under contract/management, look at the spend variability, look at current market prices and trends, look at average historical community savings data (from community, consultancy, and GPO intelligence), and evaluate and rank opportunities. And the best platforms do. (Are the rankings 100%? No — no platform has complete market data or complete knowledge of every variance to a market situation, but 90% is more than enough as that will free the buyers up to keep up with market dynamics and do real exploratory analysis that is not easily automated.)

SUMMARY

Now, we realize some of these descriptions, like yesterday’s, are also quite brief, but again, that’s because this is not entirely new tech, as the beginnings have been around for a few years, have been in developments and discussed as “the future of” Procurement tech before Gen-AI hit the scene, and all of these capabilities are pretty straight-forward to understand (especially with many of the fake-take and Gen-AI providers marketing these claims, even though they are not entirely realizable within their platforms). And, if you want to dive deeper, the baseline requirements for most of these capabilities were described in depth in the doctor‘s November 2018 articles on Spend Matters. The primary purpose of this article, as with the last, was to explain how more sophisticated versions of traditional ML methodologies could be implemented in unison with human intelligence to create smarter Procurement applications that buyers could rely on with confidence.

Advanced Procurement Yesterday — No Gen-AI Needed!

Back in late 2018 and early 2019, before the GENizah Artificial Idiocy craze began, the doctor did a sequence of AI Series (totalling 22 articles) on Spend Matters on AI in X Today, Tomorrow, and The Day After Tomorrow for Procurement, Sourcing, Sourcing Optimization, Supplier Discovery, and Supplier Management. All of which was implemented, about to be implemented, capable of being implemented, and most definitely not doable with, Gen-AI.

To make it abundantly clear that you don’t need Gen-AI for any advanced enterprise back-office (fin)tech, and that, in fact, you should never even consider it for advanced tech in these categories (because it cannot reason, cannot guarantee consistency, and confidence on the quality of its outputs can’t even be measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence (as we don’t really have true appercipient [cognitive] intelligence or autonomous intelligence, and we’d need at least autonomous intelligence to really call a system artificially intelligent — the doctor described the levels in a 2020 Spend Matters article on how Artificial intelligence levels show AI is not created equal. Do you know what the vendor is selling?) that have been available for years (if you looked for, and found, the right best-of-breed systems [many of which are the hidden gems in the Mega Map]). And we’re going to start with Procurement.

Unlike prior series, we’re going to mention some of the traditional, sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently found, or will soon be found, in Source-to-Pay technologies that are truly AI-enhanced. (Which, FYI, might not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we start with AI-Enhanced Procurement that was available yesterday (and, in fact, for at least the past 5 years if you go back and read the doctor‘s original series, which will provide a lot more detail on each capability we’re discussing. (This article sort of corresponds with AI in Procurement Today Part I and AI in Procurement Today Part II published in November, 2018 on Spend Matters.)

YESTERDAY

TRUE AUTOMATION

Not sorry to burst the Gen-AI believers’ bubble, but true automation has existed in leading Procurement technology for almost two decades, using tried-and-true rules-based RPA that supports advanced rule construction using the full breadth of boolean logic, mathematical formulae construction, and flexible (regex, clustering, etc.) pattern matching.

SMART AUTO RE-ORDER

Threshold re-order points, adaptive trend analysis (based on sales data for quantity, expected delivery time and economic order quantity for interval and volume determination), and contract/preferred suppliers can handle this better than most stock clerks for MRO / commodity stock items.

GUIDED BUYING

All you need to do this amazingly well is RPA, rules based on contract/preferred/budget, and semantically aware keyword/phrase matching, and, if you want a NLI (Natural Language Interface), traditional semantic processing to extract the key-words/phrases that are the appropriate nouns (and items of interest).

SMART (ADAPTIVE) AUTOMATIC APPROVALS

This is just RPA using a rules based workflow, thresholds, and exception-based decision pattern analysis to allow the thresholds to be adjusted within a range based on an approval and/or the platform to infer the thresholds/rules actually being applied by the approver using pattern identification (based on significant factor analysis or fingerprinting) across exceptions to suggest the necessary rule modifications.

ERROR PREVENTION

This just requires valid pattern definition, context-based range analysis, and outlier detection (using clustering, curve fitting, or trend analysis). Anything that can’t be done with the right mix of these methods can’t be done reliably.

M-WAY MATCH

Anything you can’t do with RPA using rules-based workflow, identifier matching, and confidence-based pattern matching and suggestion SHOULD NOT BE DONE. Moreover, anything that can’t be matched with certainty should be flipped back to the supplier for correction/completion (if key identifiers were missing), possibly with a suggestion/question (for e.g. does this invoice correspond to PO 123XYZ?).

SUMMARY

Now, we realize this was very brief, but again, that’s because this is not new tech, that was available long before Gen-AI, which should be native in the majority (if not the entirety) to any true best-of-breed Procurement platform, that is easy to understand — and that was described in detail in the doctor‘s 2019 articles for those who wish to dive deeper. The whole point was to explain how traditional ML methods enable all of this, with ease, it just takes human intelligence (HI!) to define and code it.