Category Archives: SaaS

Data is Too Darn Expensive Today … But It Won’t Be For Long

THE PROPHET, who has recently discovered ranting is his new favourite thing to do (on LinkedIn), recently complained that Procurement, Commodity, and Supplier Data is Too Darn Expensive.

And while he’s right in that data is often too expensive for what it is, it’s not going to stay that way. Next generation providers are going to commoditize quality data and lower anonymized community data subscriptions to win (and keep) clients, because they know that there’s no value in advanced technology alone (and especially in analytics, optimization, and AI wihtout quality data to feed it) but there are three key points he missed in his rant where he complained about data prices and advocated the use of LLMs and Gen-AI as a substitute (which they are not, and considering how much they hallucinate, we wouldn’t even trust them to be directionally accurate — just feed the historical data you can get your hands on into Excel and do some basic trend plotting if directionality is enough).

1) As Lisa Reisman noted in the comments, sometimes you need highly granular accurate data by geography, volume, and production methodology. When pennies make a difference, because you are buying tens or hundreds of millions worth of the material for a global operation, it matters.

2) Most firms are still ignoring their own data, which, when run through something like Covalyze (which THE PROPHET should love as it was founded and designed by economists), gives very accurate target cost models on any category the firm has enough historical data on, allowing them to pinpoint where they need more data and why for cost breakdowns (and should cost models to refine the target cost models), and which suppliers they actually need those expensive profiles on. Then they can go to pay by the sip providers like Veridion for basic supplier data or other emerging commodity and supplier data portals.

3) The amount of data most firms need is much less than they think. In the tail, most of the spend is not significant enough for any market data to provide insight on a significant savings potential beyond what you will get from analyzing your own historical data and market quotes. When pennies won’t make a difference, you don’t do detailed cost breakdowns by raw material. When the product is a commodity that can be supplied by multiple suppliers at similar price points and equal quality levels, you don’t do deep risk profiles because you can just go to the next supplier in the queue if the first one fails you. And so on. You only do detailed analysis where there is statistical likelihood of a real opportunity or a real risk. Otherwise it’s a waste of time, money, and resources as no organization today even comes close to fully analyzing the significant categories and risks they have in any given year. Thinking you will do more is delusional and not worth it if you don’t have the basics covered.

By the time firms actually need more data, you can bet a next generation of data providers will have it readily available and cheap by today’s standards.

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.

With Suites, What you are Sold Vs. What You Get Vs. What You Need are Three VERY Different Things!

A while back, Dan Gianfreda published a piece on LinkedIn on how what you need is not what you are sold when you buy a a shiny, “all-in-one” procurement platform that is 10X bigger than what you will actually use (on a multi-year contract with a massive implementation that takes months longer than promised and ensures you don’t have the majority of the functionality you need until the contract is almost up), and he was right. But it missed the full picture. The reality is that not only are you sold 10 times more than you will use, but what you will use doesn’t cover what you need, and with a poor selection, might only be one 10th of what you actually need!

In other words, you need to see the full picture:

As outlined in the response post, just because a suite has a module, there’s no guarantee that module is anywhere close to what the organization really needs, especially when the capabilities can vary greatly (and the definitions even more so). Sourcing can be a simple RFX or a multi-staged integrated RFX/Auction platform with embedded strategic sourcing decision optimization. We still see canned reporting modules sold as “modern spend analysis” when they are anything but. And most AI claims are pure BS (or an indication that you should probably run for the hills if that’s the only selling point).

Even if the suite theoretically has the core/must have functionality the organization needs, that’s only meaningful if that functionality is implemented in a way that supports the organizational processes and policies. If approval chains are required, tamper-proof audit logs need to be in place, validated process steps are needed for public sector compliance, and so on — and the suite has none of those, it don’t matter how user friendly, integrated, or “powerful” it is because the organization will NOT be able to use it.

Moreover, the core functionality differs by organizational type and since most platforms only do one of indirect, direct, services, capex projects, or tailspend well, selecting the wrong suite will render it totally useless for the majority of sourcing/procurement projects, which will add insult to injury of the huge cash outlay you agreed to (for an ROI that will never, ever, materialize).

Moreover, as previously indicated, you can NEVER assume that all (or sometimes, even any) of the solution providers will:

  • ask the right questions to understand the challenges
  • do the right due diligence to ensure their solution will solve those challenges
  • be honest about their capabilities (or, outside of the dev team, even understand those capabilities)

because, chances are, as I have indicated many times, everyone in the ecosystem exists to make money off of YOU, but not necessarily to help you. (Especially when too many vendors took too much money and are now under extreme pressures to fulfill ridiculous growth requirements in just a few years or risk massive layoffs, being folded into a bigger player, or getting dropped from the portfolio entirely before going bankrupt.) There’s no time to do it right, just to sell, sell, sell. (Which is why we keep advocating employing an independent consultant to help you with selection, project planning, and project assurance — since their remuneration depends on helping you, not someone else.)

So remember this before you start looking at big suites as there is a good chance you’ll likely be paying 10 times what you should be (based on what you are using) while still only getting 25% of what you actually need in the best case. (And there’s nothing wrong with building your own Best-of-Breed ecosystem, even if you need to add an orchestration player to that mix, if that is what maximizes the return on every dollar spent.)

How AI Enhances 10 Common Procurement Challenges Part II

A recent CIO article drew my ire because it claimed that AI Overcomes 10 Common Procurement Challenges as it oversimplified the problems and overstated the benefits of AI. Let’s finish them one-by-one.

Legacy Systems Complicate the Adoption of New Technology: The article claims AI streamlines integration by assessing system compatibility, automating migration, and reducing downtime. While two out of three ain’t bad, it ain’t good when the critical requirement of assessing system compatibility cannot be met by AI — since simple text matching isn’t helpful if the interface of a legacy system isn’t specified in a standard format (as otherwise it’s essentially field-name matching, which is no different than human guesswork). The reality is that humans still have to define/verify the mappings before the AI can take over.

Letting AI do the mappings is fraught with errors. And its even worse when you let it automatically connect systems, pull and push data, replicate incorrectly mapped and bad data across systems, and “fix” data that was actually correct on system integration because the “bad” data in one system is used to overwrite the good data in another system just because it appeared to be more recent. Because it’s automated, AI can propagate and exacerbate errors at an unprecedented rate and in a matter of seconds make a mess that can take months to repair.

Managing Supplier Risks is a Growing Concern: AI can continuously monitor supplier performance, predict risks, and ensure compliance. This is one situation where they were almost perfectly correct, but, when they say vendor evaluation can be time intensive and imply that AI can speed it up, they overlook the fact that evaluations still have to be done by humans and tech can’t speed that up.

Moreover, if you think you can augment your data with third party data to speed up the evaluations, you’re just fooling yourself. You just make bad decisions faster.

Manual Procurement Process Drain Resources: AI can definitely automate repetitive tasks, reduce human error, increase efficiency, and free your team to focus on strategic initiatives, but only for tasks that are well defined, typically free from exception, and capable of being processed by standard rules. However, this can’t be done until the repetitive tasks are identified, processing rules defined, standard exceptions identified, and additional rules defined. Only then can the AI automate enough to be useful.

Moreover, using a next-gen LLM with chain-of-compute to try to break the requirements of a task down into subtasks, execute those subtasks automatically, and automate a process without any human intervention is just as likely to go wrong as it is to go right.

Demand Forecasting is Often Inaccurate : AI can improve demand forecasting, but only if you have the right data — it’s not a magic box, just a black box that you need to understand.

It’s not just demand trend based on utilization / point of sale data, its also market conditions which can sharply change a demand curve overnight … traditional curve fitting / machine learning that most “AI” is based on cannot detect a change in market conditions or a political situation that can cause a rapid change in demand.

Procurement Remains Transactional Rather Than Strategic: AI DOES NOT transform procurement into a strategic function that optimizes spend, improves supplier collaboration, and aligns purchasing decisions with your business! Only people-powered Human Intelligence (HI!) can do that. Remember — transforming Procurement requires defining a strategy, defining appropriate processes, identifying the right people to transform it, and then, and only then, identifying the right technologies.

Assuming that you can slap in AI and transform a tactical function into a strategic one is worse than a pipe dream, it’s a recipe for disaster. Running fast and hard doesn’t get you any closer to the finish line if it’s not in the right direction. For more details, see the dozens of posts about AI in the archives.

Again, we’re not saying that AI is bad. Technology is neither good nor bad. But, like any technology, it has to be ready for prime time, correctly identified, correctly implemented, and correctly used — and that requires a lot of Human Intelligence (HI!) and planning, and the right processes put in place. Shoving it in and expecting a miracle is dangerous. And this is yet another article that implies you can just shove it in and get results. And you can’t. Especially if it’s the wrong technology, which can enhance your problem instead of shrinking it. That’s the problem. This article, like many others, doesn’t tell you about the dangers and downfalls and what you have to do to avoid them.

How AI Enhances 10 Common Procurement Challenges Part I

A recent CIO article drew my ire because it claimed that AI Overcomes 10 Common Procurement Challenges as it oversimplified the problems and overstated the benefits of AI. Let’s take them one-by-one.

Procurement Takes Too Long, Slowing Innovation: According to the article, AI-driven platforms can generate RFPs, accelerate sourcing, automate approvals, and reduce cycle times … which is mostly true. Properly applied, AI can accelerate sourcing, reduce cycle times, and automate approvals … but not all approvals. As for RFP generation, that’s very limited — LLMs can generate RFPs with a simple prompt, but not necessarily a good RFP. The best RFPs are designed by humans (and then automation, which may or may not use AI, can pull in data from supporting documents as needed), and as for acceleration, it depends on the project — it can’t speed up supplier qualification where humans need to inspect the products and verify the requirements.

Moreover, a rush to AI can make things worse, and not better. Letting AI generate an RFP that misses a key requirement in terms of required certifications, performance criteria, production capacity, etc. can entirely invalidate an RFP process and lead to months of wasted effort if no human realizes that this key requirement was missed until an award is offered and a request for the certification, capacity, etc. is delivered and a “sorry, we don’t have / can’t do that” is returned.

Legal and Budget Complexities Create Bottlenecks: Budget tracking systems and rules-based automation allows for instantaneous budgetary approvals. Contract negotiation software can automate redlining, compliance checks, etc., but cannot handle a complex negotiation for a complex project where each side has a lot of requirements and multiple parties to satisfy. AI speeds up the technical drudgery, but not the human interaction.

Moreover, if you turn over negotiations to software, you have no idea what the end result will be. If you let it negotiate based on market data, and the cost data is off, you could be committing to a bad deal. If you let it predict timeframes based on how it expects prices to rise/stay high, but it’s off by two years, it could lock you into a three year deal when you only need a one year deal. And so on.

CIOs Need to Upskill Their Teams in AI and Cybersecurity: Just because “AI” can simplify processes with guided intelligence, that doesn’t mean the team is upskilled in the process. The reality is, there is no incentive for users to learn anything if they think the system will guide them in everything they need to do.

Thus, if you over invest in AI, especially the kind that guides users in every task they have to do, and works quite well on the basic tasks they have to do daily, and doesn’t screw up the first half dozen or so moderately complex tasks, the user will believe the system is almost flawless, start to trust it implicitly, stop questioning it as time goes on, start believing there is no need to learn anything else because the system knows it, and, over time, stop thinking. And then, instead of performance improving, it will decline … and that decline might be accompanied by a major financial loss if a bad contract is signed or major risk ignored.

Data Inaccuracy Leads to Poor Procurement Decisions: While it’s true that over three quarters of organizations struggle with unreliable data, AI doesn’t magically fix the problem. It can help with cleansing, validation, and procurement trend analysis, but ask any spend analysis vendor who has tried to apply an LLM to unclassified vendors about the classification accuracy (which tends to top out around 70%) — good data still requires manual cleansing and classification, especially where the system reports good confidence. It can definitely help, but it doesn’t take the onus off of the human.

In other words, if you believe that you can plug in a magic AI black box ad that it will fix your data, you are gravely mistaken. Sure it will tell you that it has cleansed, classified, and validated all of your data, but if it’s only 70% accurate, it’s only made matters worse if you trust the data 100% and don’t know what 30% is inaccurate. When you base your decisions on data, and the data is bad, you are bound to make a bad decision. The question is, how bad. You don’t know. And that’s a big problem!

B2B Software Selection is Increasingly Complex: Moreover, despite the claims, AI-powered vendor analysis doesn’t really help that much — see Pierre Mitchell’s crazy conversations with DeepSeek-Rq. Note how it not only recommends inappropriate vendors, but also recommends vendors that don’t even exist anymore … it can help you discover potential vendors, but you still need human reviews and deep pricing intelligence (from expert SaaS optimizers).

Trusting AI to select your software is worse than trusting an analyst firm map! And we know all of the problems those maps contain. (First of all, they only mention the same 10 to 20 vendors year after year, ignoring the dozens of other vendors that might be more appropriate for you.) AI cannot understand your needs, cannot truly map needs to requirements, cannot truly map requirements to features, and cannot truly assess how relevant a solution is, and definitely can’t assess how well a provider’s culture will match yours.

Come back Thursday for Part II!