Category Archives: Procurement Innovation

In ProcureTech, Stop Caring What Gartner, Forrester, or IDC Thinks!

I shouldn’t have to address this again, but every year multiple vendors reach out and ask how to get on these vendors maps because they believe it’s the only way to get more market visibility and/or be selected by certain customers, including you. It’s not the only way to get visibility and if a vendor can’t convince a potential customer from thinking that only map companies are good, I’ll tell them this right now — that’s not a customer they want (because that vendor will be out on the renewal with whatever vendor overtakes them in the map when the CPO changes in 3 years, because companies without vision to look beyond a meaningless map don’t keep real talent, and only real talent will identify and select the best solution and ensure that solution is kept over time).

But I digress — this post is about you, the potential customer, and why you need to STOP caring what Gartner, Forrester, or IDC thinks.

First of all, we’ve said it before, and we’ll say it again: It’s NOT the Analyst Firm. It’s the Analyst!.

In addition to all of the skillsets and education that an analyst needs to have to get it right, which we covered in detail in that post, the analyst needs a lot of relevant experience, and history in the ProcureTech space, to make sense of the ProcureTech world today. Ask yourself: how many of the analysts with the right education have at least 10 years in our space? The answer is very few. How many have 20 years in (independent) analyst roles? You can count them on your fingers. I know of myself, Jon Hansen, Pierre Mitchell, and Chris Sawchuk with 20 years of (independent) analyst experience in our space and a deep technical (STEM) education. Everyone else who started covering this space day in, day out two decades ago has moved on or retired. Now, of these analysts, how many have also built actual solutions in the ProcureTech space, connecting the dots between the education, theory, and practice? Two of us — myself and Jon Hansen. (But we should note that Pierre and Chris spent part of their careers on solution advisory consulting and implementation guidance, and have deep knowledge about the implementation and integration requirements, which is also very unique and useful in technology selection.)

Now remember the second point: Vendors Have Lured Big Analyst Firms Astray and that you’re not getting a map of the best solutions, but the best solutions from the analyst firm’s pool of vendor sponsors and research subscribers, where the reality is that only the big, established, cash-rich companies can afford the high-priced subscriptions that keep them in front of the overworked analysts who have to spend over half of their time taking inquiries or keeping high paying subscription customers happy. (Whereas analysts at smaller firms or independents get to focus on studying and understanding the solutions, not general inquiries or whether or not the contract [or pricing model] is good.)

This means that these big firm analysts are not spending a lot of time, if any, looking at the up-and-coming mid-sized companies that have not only been around long enough to develop mature enterprise solutions, but solutions that are more modern, more powerful, more usable, and more intelligent (with embedded analytics, RPA, and the right AI for the task at hand), and possibly (much) better for you. Moreover, if the enterprise is a mid-market company, or able to go with a best-of-breed as a bolt-on to their enterprise ProcureTech platform, they’ll never know about the majority of these solutions (as only the overfunded startups will have the money to get the big analyst firm attention, and these vendors often have more financial stability problems than the smaller vendors who are bootstrapping or taking minimal funding and actually have stable, happy, paying customers keeping them afloat).

Third, and most important, it’s not the best rated solution, it’s the best solution for your organization. Not only is it the case that this solution is very likely not on a map of only 20 companies (when there might be 100 companies that offer that solution), but it might also be the case that it is the lowest ranked solution on those maps — especially when these maps tend to rate solutions on a lot of subjective factors that match what the analyst thinks are the most relevant for an average organization, whereas you are a specific organization which has a specific set of relevant factors that you care about, with specific requirements for those factors. The more divergence between your factors and the analysts’, and your scale and theirs, the worse the map is for your needs, and the worse the solution you select will be.

The only maps you should care about are those that rank solutions solely on the tech capabilities and/or the customer rankings. But only so far as potential solution identification, not selection. Maps that concentrate on pure tech (like Spend Matters Solution Map) allow you to identify vendors that have the tech foundations, giving you a starting pool, but don’t allow you to identify vendors that have a solution — because a solution is tech and appropriate process support and integration capability and support and culture and whatever else transforms another piece of potential shelfware into a solution that will be used daily by your employees.

Note that we used the word “potential” for a reason. No map (including Spend Matters) is complete, so you will need to look at multiple sources (like ProcurementSoftware.site and the upcoming Art of Procurement ProcureTech 100) to put together a complete list of vendors to consider. Then you will have to cross reference with real analyst vendor write-ups (which can include the hundreds of write-ups here on this site if one or more of your potential finalists are included) to whittle down that list to the best starting set for your best practice technology RFP (of which we have a lot of advice on how to write that on this site as well).

At the end of the day, it’s about what solution will work for you, not about which solution is on which map!

Despite Attempts to Simplify It, There Are MANY Categories of ProcureTech Solutions

When selecting a ProcureTech Solution, you have all the following buckets:

Function X Classic Type X SaaS Category X Integration
Sourcing
SXM
CLM
Analytics
e-Procurement Best-of-Breed Standalone App
(full function)		 Suite EcoSystem
Invoice-to-Pay Mini-Suite Lightweight App
(task specific)		 I2O Ecosystem(s)
ESG/Sustainability Suite Bolt-On
(extends a module)		 Open API
GRC
Category/Cost Intel
Niche (Legal, Marketing,
Hospitality, SaaS/Tech, etc.)
I2O

And if you do the multiplication, that’s 297 combinations … and that’s just the tip of the iceberg when there are 10 core areas of SXM, multiple niche areas being addressed (some classic solutions were just for print/telco), multiple buckets of risk management solution, generic and scope-3 specific sustainability solutions, different approaches to intake-to-orchestrate, and that’s just addressing the functional areas of Source-to-Pay+.

Then you have the situation where some vendors only offer a single best of breed (BoB) module, others offer a mini-suite, and others still offer a mega-suite with all of the core modules and often a half dozen more on top of that.

While most are SaaS apps these days, they vary from heavy standalone apps that implement full functions to lightweight apps designed for specific tasks (that are usually missing from larger standalone apps that purport to completely cover a function but don’t) to bolt-ons that offer advanced functionality, but require a core module to work on top of.

One also has to consider how you integrate them into a comprehensive workflow that supports Source-to-Pay+. Sometime modules integrate into one-or-more suite ecosystems out of the box (like the SAP Store or The Coupa Store), other times they just come with a (semi) open API, and now some, not built for integration, are integrating into one or more of the new orchestration ecosystems.

And while functionality should come first, you have to consider all of these other factors as well because if you select a suite for a module, you’re probably locking yourself into the other modules you need as those the suite offers due to cost and integration cost considerations, if you select light-weight or bolt-on apps, then you better have something to integrate them into, and you better be sure the ecosystem has all of the modules you will need to implement over the next five years or so before locking yourself into an ecosystem.

So even though THE REVELATOR believes that everything is going to be a bolt-on or an app and that’s all your going to have to worry about, unfortunately the ProcureTech world is NOT going to make it that simple. Overlooking traditional category and integration can completely destroy the value you require if you can’t easily integrate with complementary modules/apps (and especially if you are in a [primarily] direct industry and need to integrate with supply chain applications for the data you need to make good supply chain aware decisions).

However, it will be interesting to see the primary solution category, breadth, and integration of ProcureTech Solutions (by, and independent of, function) in the future.

Follow the Money to Find Future Opportunity — Which Will NOT Be Fully Found With Autonomous Sourcing!

Spend Matters has thrown caution to the wind and followed Gartner’s lead jumping onto the AI Hype Bus (with no steering and no brakes) that is still heading straight for the cliff and are wheeling out webinars on AI faster than a prairie fire with a tailwind. (Needless to say Sourcing Innovation does not think this is a good thing. There are valid uses for AI and automated processing, but fully handing over financial decisions is like wheeling in the Trojan Horse and leaving it unguarded in the server room with unrestricted access to your bank integration.)

Recently, The Maverick advertised yet another Spend Matters webinar on Autonomous and AI Sourcing where he said we should “follow the money”. Which we should, but there are a few things we need to clarify first.

1. No Money Changes Hands In Sourcing

It changes hands in Procurement … and it’s because most companies don’t follow the money after the contract is signed that 30 to 40 cents of negotiated savings never materialize in many companies, which The Maverick should remember from his AMR and Hackett days, as it was laid clear in Mickey North Rizza‘s famous 2009 “Reaching Sourcing Excellence” series, which we know is in his archives.

2. “Speed” is NOT a strategic edge if you don’t get it right!

If you don’t go out with the right strategy, don’t know the current market price, don’t know the reason for the current market price, and don’t have the knowledge to project if the trend is going to continue, stabilize or reverse, going to market is not a good decision … and it’s an even worse decision to automate the sourcing project and secure an award as fast as possible if you don’t know if it’s the best you could have done or the worst you could have done.

3. “Pecunia non olet”, but yet these vendors are asking you to treat it like it does!

They want you to automate spend analysis, sourcing, contracts, purchases, and everything else that involves money by turning over everything to their Agentric AI because, apparently, money stinks and you don’t want to touch it. (But they are quite happy to not only spend yours for you but takes as much of it as they can for their services.)

But here’s what they don’t tell you.

  • AI is NOT Intelligent.
    The level of intelligence in their “AI” is equivalent to the level of intelligence in a carpenter’s hammer. The level of effectiveness is entirely dependent on how skilled the person “training” the system and how skilled the person “using” the system is, just like the effectiveness of a hammer is dependent on how well the carpenter was trained and how experienced he is in it’s use.
  • AI Does Not Know What it Does Not Know.
    If the data is incomplete, the recommendation is very likely incorrect.
  • AI Cannot Do Better than the Best A Human Has Ever Done in Decision Making.
    So, if none of the situations it was trained on led to great results, neither will what it recommends for you.

You need to remember how Gen-AI does its work (or should we say does not work). It is large document search and summarization and chain of compute. Now, the more advanced players are trying to embed knowledge graphs into this, but these are not perfect either. With good training examples, and a very similar situation, the probability it will work well is very good, but it’s still only a probability. As a result, nothing should ever be fully automated where money is concerned. The tools should be used for their recommendations, and if the recommendations are good, and the risk is low, most of the tactical data processing and event management should be automated, but the decisions should ALWAYS be made by a human, who should be involved at every decision point. Even if that decision is verifying the system recommendation. It only takes one miscalculation due to an incomplete data source to project a wrong trend, rush an auction, lock in a price 3X what you are paying now, only for it to fall in a month later when a factory (which went offline temporarily due to a manmade or natural disaster) comes back online and the supply-demand balance returns to normal. And while you may have stocked out for two weeks, those losses will be orders of magnitude less than paying 3X at a contract you have to honour (unless you want to get dragged into court).

Now, if you really want to make money, forget all this Autonomous and Agentric AI BS, look for Augmented Intelligence solutions that make your staff two, three, five, and even ten times more efficient, purchase those, and, remembering that the US infrastructure is crumbling fast (and not going to get renewed under a Republican administration that is more interest in trickle-on economic tax cuts for its billionaires than ensuring you have running water), it’s time to remember how the smart made money in ancient Rome — public bathhouses and latrines. Time to invest in your own desalination facilities and be ready when the public wells run dry. After all, “Pecunia Non Olet“.

With Great Data Comes Great Opportunity!

In fact, it can quadruple your ROI from a major suite.

Not long ago, Stephany Lapierre posted that your team may only be realizing <50% of the ROI from your Ariba or Coupa investment, to which, of course, my response was:

50% of value on average? WOW!

Let’s break some things down.

A suite will typically cost 4X a leaner mid-market offering which is often enough even for an enterprise just starting it’s Best in Class journey (that will take at least 8 years, as per Hackett group research in the 2000s).

Moreover, even if the enterprise can make full use of the suite it buys for 4X, at least 80% of the “opportunity” comes from just having a good process, technology, baseline capability and automation behind it. That says you’re paying 4X to squeeze an additional 20% worth of opportunity in the best case.

On average, it takes 2 to 3 years to implement a suite (on a 3 to 5 year deal). So maybe you’re seeing an average of 66% functionality over the contract duration.

As Stephany pointed out, bad data leads to

  • increased supplier discovery and management times
  • invoice processing delays and errors
  • increased risk and decreased performance insight

As well as an

  • inability to take advantage of advanced (spend) analytics
  • inability to build detailed optimization models
  • decreased accuracy in cost modelling and market prediction

This is even more problematic! Why? These are the only technologies found to deliver year-over-year 10%+ savings! (This is where the extra value a suite can offer comes from, but only with good data. Otherwise, at most half of the opportunity will be realized.)

Thus, one can argue an average organization is only getting 66% of 25% of 80% of its investment against peers (based on 2/3rd functionality, the 4X suite cost, and the baseline savings available from a basic mid-market application that instills good process and cost intelligence) and 50% of 20% (as it is able to take advantage of at most half of the advanced functionality offered by the suite due to poor and incomplete data). In other words, at the end of the day, we’d argue an average company is only realizing 23% of the potential value from an opportunity perspective!

However, as one should rightly point out, the true value of a suite is not the value you get on the base, it’s the ROI on that extra spend that allows for 20% more opportunity than a customer can get from lesser peer ProcureTech solutions.

For example, let’s say you are a company with 1B of spend with a 100M opportunity.

If tackling 20M of that opportunity requires advanced analytics, optimization, and extensive end-to-end data, it’s likely that you’ll never see that with an average mid-market solution with limited analytics, no optimization, and only baseline transactional data. If the company paid an extra 1.5M over 3 years for this enhanced functionality, then the ROI on that is 13X, which is definitely worth it.

Moreover, if the suite supports the creation of enhanced automations, you could get more throughput per employee and realize the base 80M with half or one quarter of the workforce, which would lead to a lowering of the HR budget that more than covers the baseline cost.

However, ALL of this requires great data, advanced capability, and the in-house knowledge to use both. This is only the case in the market leaders. As a result, we’d argue that the majority of clients are only realizing about 25% of the suite’s potential — when sometimes the only thing standing in their way of realizing the rest is good data.

To Manage Innovation, Governments Must Fix Procurement … And Take Care Where AI is Concerned!

A recent article on Civil Service World noted two things that attracted my attention:

  1. To manage innovation, governments must fix procurement
  2. Too often, contracts in AI do not give governments powers to investigate algorithms or the data they are trained on. As a result, they risk taking the blame when things go wrong without the means to find out why.

Public Procurement is expensive. Very expensive. Given that it represents 12% of the annual GDP of an average developed economy, that is a huge amount of spend. Given that the overspend in most departments of most jurisdictions is likely as bad as in the private sector, which means, depending on the category, is likely in the 4% to 6% range at a minimum (based on the results high performing organizations see when implementing best-in-class processes and technology), that means a minimum of 1/2% of GDP is being wasted annually, but based on the fact that most public sector projects exceed initial budgets and timelines, we’d bet that the overspend is double that and at least 1% of the annual GDP. That’s a lot of waste — 770 Billion on the top 10 economies. Furthermore, that assumes that all of the spend is necessary and well planned. (There is likely considerably more savings with better demand planning, more operational efficiency, better project planning, etc. We’re just stating that the savings on committed spend alone is likely 10%.)

The article notes that despite the strategic importance of Procurement, it’s rarely seen as a priority and is more often treated as a standardized compliance function, rather than a tool for strategic investment and, in some cases, has become synonaomous with absurdity, due to an accumulation of rules so complex that even those administering them cannot interpret them creates the perverse incentive of doing the least risky thing to avoid individual liability. As a result, governments end up buying obsolete technologies that make them vulnerable, because innovation evolves so rapidly, and forces them to buy more. The cycle repeats, budgets balloon, and public capabilities diminish.

And, unfortunately, public procurement is a brick-and-mortar process, still more suited to bulk-buying precisely describable goods, accounting for them, and moving onto the next purchase. Innovation is different: you do not know today what is going to be possible tomorrow, even when you are the one inventing the tech. While governments work in one-off projects, innovation is made of ever-changing, always-fleeting products.

Furthermore, those in charge of procuring these technologies are not technologists. Public procurement is professionalized in only 38% of OECD countries, so even if officials had the incentive to experiment, they would not have the expertise.

To combat this, the authors of the article propose that Procurement systems should be like good software, fluid, flexible, and constantly evolving. However, as they note, this will take more than changing rules. As they note, it will take talent that are experts in what they are buying. It will take the treatment of Procurement as a strategic function, with clear lines for advancement for all personnel (as studies have shown that even a marginal improvement in skill can yield significant reductions in costs, times, and contracting complexity). Thirdly, they will need a federated data environment to make use of modern technology. (Especially if they want to use AI.)

This is just the start of what is necessary. There needs to be regular training. There needs to be specialization to different types of functions and purposes. There needs to be a rewrite of rules to focus on the right outcomes, not just a plethora of rules designed to prevent previously undesirable outcomes. There needs to be clear paths from buyer to public organization CPO to department head, not just paths of advancement within the Procurement function. There needs to be a focus on what’s best for the public being served, not best to minimize the risk to the buyer. And a willingness to accept that their may be a few mistakes made here and there as new buyers learn the ropes, while a willingness to weed out anyone that “makes a mistake” in order to give a contract to a supplier who is not the best fit (and do so in exchange for a kickback).

But most importantly, if they acquire AI technology, they also need to acquire the right to investigate the algorithms being used, the data it is trained on, the results of prior training, and the right to inspect any changes to the algorithms, data, and training. Otherwise, you can never trust any AI technology you might want to acquire.

Because governments need to apply the most appropriate AI-enhanced technology more than the private sector, but are the least likely to be able to use them properly.