Category Archives: SaaS

SaaS is everywhere. Are you SaaSy?

Back in our 39 Part Series to Help You Figure Out Where to Start with Source-to-Pay in part 13 we gave you some vendors to shop around to the rest of your organization if you thought you can’t touch the sacred cows of Legal, Marketing, and, new-to-the-sacred-cow-list, the SaaS used in other organizational departments.

While the management of SaaS spend was not that important in the early days, and even only moderately important near the end of the last decade, it’s become critical since COVID (when everyone had to go on-line) as software spending has now become the third largest expense for many organizations after employees and office costs (that many organizations, who realized that employees don’t have to be in an office everyday to do office tasks and who don’t feel the need to force people to go back to buff the egos of the micromanagers who have no useful skillset and feel they need to micromanage to add value, are now trying to minimize, even to the tune of paying huge penalties to reduce office space).

A recent article in the FinTech Times really puts this into perspective. Summarizing the EagleEye SaaS Spend Report (2023), which analyzed over 400M worth of SaaS transactions, recently released by CloudEagle, the article noted that companies spend an average of $1,000 to $3,500 per employee on SaaS, while smaller companies, with less than 100 employees, spending (up to) 1M annually (on 50 to 70 apps) and mid-size organizations, of up to 5,000 employees, spending up to 100M annually on 300 to 400 apps! OUCH!

The article also noted that the highest departmental spenders were Engineering (45%), Marketing (19%), Sales (17%), Finance (7%), Customer Success (7%), and HR (5%). (Note there is no Procurement in this list, and that any apps are obviously classified as finance or Engineering [which includes cloud providers], which is sad.) Engineering/IT makes sense, it supports the entire organization, but that’s a pretty high percentage for Marketing and Sales. However, it makes more sense when it notes that, in terms of the number of applications used, marketing leads with 76 and sales is third with 42. Why? (The answer: because there is no central management or strategy, there are multiple tools doing almost the same thing, and it’s just total chaos in those departments.)

Obviously, it is becoming vital to scrutinise how their software budgets are allocated and ensure every dollar spent returns a significant value, and the article gets it right when it notes this, and while it should be on the radar of every CFO and CIO to get this spending under control, the article really misses the mark when it doesn’t mention the CPO — who is probably best positioned to help the organization come up with a sound spending strategy, as it not only puts every purchase it makes under the microscope, but gets put under the microscope for every purchase it makes (as most organizations still see it as a cost center despite the enormous value it brings by containing costs under chaotic cadences of the markets it has to buy in).

Furthermore, the first step is to get a true understanding of SaaS spend across the organization, which is likely buried on P-Cards to hide just how much rampant, off-contract, off-protocol spend there is. To this end, we do recommend engaging an expert SaaS Analytics firm which has pricing benchmarks on the most commonly used SaaS applications across the major areas (IT/Engineering, Marketing, Sales, Finance, and HR) to help identify all the SaaS spending and the best opportunities for cost reduction through termination of under/un-utilized licenses, consolidation to one provider for a specific function, and re-negotiation. Most mid-size or larger organizations that do this the first time will identify almost 30% of cost savings opportunity, which can typically be fully materialized within two years (given typical contract lengths and how long it takes to make all the migrations).

And while the doctor can’t say which firm is likely the best for you without a consultation, he can say that many of the firms on that list can do a do a good job and you should quickly be able to zoom in on the top two or three for you with an RFP and a few phone calls. Basically, you’re looking for a company that’s in your region, has analyzed the SaaS spend of a number of companies in your industry, has good spend analytics technology, and benchmarks on the major player that you feel comfortable working with. (And has really good spend analysis. Yes, we said it twice. Because it is important.) Since you don’t have to enter into a subscription for an initial project, you can easily get started because if the company is not the best for you, you’ll still get value and can redo the project with a different company in a year or two. There’s no reason not to do it and you’re guaranteed to identify savings. So why not Get SaaSy, now, get SaaSy!

“Ooh, the way that you spend it
Makes me go crazy, show me you can end it
You could be saving more
Ooh, the way that you buy
Makes me go crazy, show you I can end it
You could be saving more

Much more
Much more
Much more

Get SaaSy, now, get SaaSy
Get SaaSy, now, get SaaSy
Get SaaSy, now, get SaaSy

Savings
Now (much more) …”

Procurement Automation: Good. Automated Procurement: Bad.

We shouldn’t have to say this. It should be very clear by now. But given that a number of vendors are using the terminology interchangeably, possibly to convince you they have the right solution, maybe it’s not clear. But it needs to be. Because procurement automation is NOT the same as automated procurement and while procurement automation, properly done, is the best investment an average over-burdened and under-resourced Procurement department can make, on the flip side, AI-driven automated procurement is the absolute worst. To put things in perspective, downgrading Excel to Lotus 1-2-3 would be a better move. But let’s back up, and start with some definitions.

Procurement Automation is the process of automating certain procurement tasks that can be best accomplished by machines and procurement automation technology is the technology that automates the tasks that can be best done by machines. In simpler terms, it automates the “thunking” by doing all of the tactical, almost mindless, work that is a waste of a senior Procurement professional’s time.

The Source-to-Pay cycle is full of tasks that are best done by machines when appropriate rules and boundaries are defined. For each major area, we’ll outline some of these tasks as an example.

Intake/Orchestration

Procurement Automation will analyze the request, identify similar requests made in the past, identify the actions used to resolve those requests, identify the suppliers considered and selected, the products and services used, and other information. It will present that information to the buyer, including the suggested actions, and allow the buyer to one-click initiate any of the suggested actions, which might include a sourcing event, contract renegotiation, catalog purchase, etc.

Sourcing

Procurement Automation will, when a user kicks off a sourcing event for one or more products, automatically bring up the suggested suppliers, automatically suggest the appropriate questionaries and forms, automatically suggest the appropriate Ts and Cs to insist on up front, automatically send the RFP to suppliers, automatically analyze the responses to make sure they are complete, in the correct format, and in an expected range; automatically compare the responses to find deviations from the norm; automatically highly the lowest and highest costs, CO2 factors, etc. and present all that information to the buyer.

Supplier Management

Procurement Automation will, when a supplier is selected, automatically handle the onboarding; monitor the data for changes; monitor the performance metrics; monitor the OTD; monitor third party financial and risk metrics; and alert the buyer to any issues and performance changes that are detrimental or may indicate forthcoming problems.

Contract Management

Procurement Automation will, when an award is selected, push the award into the Contract Management system, automatically generate the draft contract, send it to the supplier, highlight any redlines the supplier makes when it comes back and automatically inform the supplier if any non-negotiable terms and conditions (including those they agreed to when they responded to the RFP), and automate the generation of the response email when the buyer does their redlines.

e-Procurement

For catalog buys, it will automatically generate the POs, route them for necessary approvals, distribute them to the suppliers when approved, automatically match the ASNs when they come back, alert the buyers if ASNs are not received in a timely basis, and match the invoices when they come in.

Invoice-to-Pay

When the invoice comes in, it’s automatically matched to the purchase order, it’s checked for price accuracy, identified as partial or full, verified to be non-duplicate, and if any checks fail, it’s bounced back to the supplier with a description of the issues and a request for correction and resubmission. If the resubmission deals with the problems, it’s queued waiting for goods receipt/confirmation if not present, or matched if present. If the match is made, then it’s automatically sent down the approval chain, and if it’s not made within a certain time period, an alert is raised.

In all cases, it’s automating the tactical tasks that don’t require any decision making and only involving the human when necessary.

In contrast, Automated Procurement is the process by where entire procurement processes are handed over to the machine to fulfill instead of the human. In other words, when an intake request comes in and the buyer marks it for sourcing, an Automated Procurement solution will handle the entire event up to and including the award and auto-generate and distribute the Purchase Order(s). The buyer is completely bypassed and the right inventory showing up at the right time at the right price is left entirely up to the machine. Sounds good in theory. Looks good in practice when it actually works, which it will some of the time. But grinds the company to a halt when it fails.

A machine that pursues lowest cost will select an unproven non-incumbent supplier for a critical part when the suppler, who has not supplied that particular part to the company before, outbids the incumbent. It will not detect that the bid was made in an desperate attempt to help the financially struggling supplier stay in business, that the bid is not sustainable, and that the supplier is not capable of producing the part at the indicated level of quality. Then, when the first shipment is mostly defective, and the promised rush replacement order never arrives because the supplier goes out of business, the production line for the 75K luxury car folds all for lack of a single control chip. (A similar situation has occurred in the past. Recently, chip shortages stopped Cherokee production in 2021, and that wasn’t the first occurrence. Or even the second, or third.)

Machines are not intelligent. Not even close. And expecting them to make a good decision every time with no logic whatsoever (as modern Artificial Idiocy algorithms just stack probabilistic equations on top of probabilistic equations almost ad infinitum) is lunacy. So while you should invest in the best Procurement Automation tech you can get your hands on, you should steer clear of any and all Automated Procurement Solutions those fancy new startups try to sell you. While those solutions may work 90% of the time, that last 10% of the time, they won’t work that great. And, in particular, that last 1% of the time they will fail so miserable that the disruptions and losses that result will more than cancel out any and all savings and efficiencies you might get from the 90% of the time the tech worked in the beginning.

Dear (Software) Vendor: If you Missed the Ten (+ 2 Bonus) Best Practices for Success, Time to Catch Up Now!

  • Part 1 Best Practices #1 to #3
  • Part 2 Best Practices #4 to #7
  • Part 3 Best Practices 8 to 10
  • Part 4 Bonus Best Practice #1
  • Part 5 Bonus Best Practice #2

In twenty years as an independent analyst and consultant, the doctor has never encountered a small/mid-size vendor who wasn’t doing at least one of these, usually there were a couple they weren’t doing, and the lack of these practices (and knowledge) was (and sometimes still is) holding these vendors back. In other words, you definitely should read these. We are only posting these articles once.

DO NOT CONFUSE THE ILLUSION OF UNDERSTANDING WITH ACTUAL UNDERSTANDING!

Because if you do, you will believe AI is Actually Intelligent when, in fact, as we have pointed out again and again and again, it is Artificial Idiocy, and the best modern technology only uses AI for thunking, not thinking, as thinking needs to remain the domain of us humans (before X robs us of our ability to use actual words).

Not only is there no AI, but when you type a command, there isn’t even any understanding by the algorithm of what you are asking for when you type a query into an AI tool. NONE. It’s all based on a statistical algorithm that uses pre-computed similarity probabilities to infer what you are asking. That’s not understanding. Not even close.

The Guardian recently published a long read article on Weizenbaum’s nightmares: how the inventor of the first chatbot turned against AI that anyone who is even mildly contemplating an AI tool needs to read. Slowly and carefully. Three times.

Weizenbaum, who was a mathematician, computer scientist, and a student of psychoanalysis, was one of the founders of modern artificial intelligence who not only invented the first chatbot (Eliza), but also built early (mainframe) computers (back when they used vacuum tubes and took up entire rooms) for the University he was studying at, General Electric, and the Navy. In the 1960s, he was part of Project MAC at MIT, a Pentagon program for “machine aided cognition” that perfected time-sharing, created in-system messaging (like instant messaging or early email), and created new tools for word processing.

He was also one of the first to think about the implications of Artificial Intelligence years, if not decades, before anyone else and one of the founders of computer ethics. He was a genius, and when he said that Artificial Intelligence is an “index of the insanity of our world“, he was totally right — and he was right five decades before AI became the buzz-acronym-du-jour. Few people effectively saw that far ahead in technology, so maybe we should sit back and listen. Carefully.

So please take the time to read Weizenbaum’s nightmares: how the inventor of the first chatbot turned against AI and realize that AI is not the answer. Deterministic algorithms developed by smart people that have studied the problem, tested their assumptions, and been consistently proven reliable are the answer. They may be based on machine learning, but machine learning that is expertly selected, tuned, and monitored by validation code that detects when the algorithm is not performing to expectation and interjects a human into the process. Not a multi-layered pseudo-random statistical algorithm that randomly predicts the next seven days worth of orders, starting on Monday, are 210, 198, 307, 250, 185, 250, and 3095 and thinks everything is A-OK even though the store is closed on Sunday.

Ten Best Practices for (Software) Vendors, Part 5 (Yet Another Bonus Tip)

In this series we went over the ten best practices that you as a startup or small vendor should be aware of and address appropriately if you want any hope of growing and scaling a successful vision beyond blind luck. We did this because the majority of analysts and experts don’t give you this insight in the clear cut fashion to help you understand what you need, why you need it, and who you need it from (in the form of an expert) to get you where you need to be.

While buyers need a lot of help, and the primary purpose of Sourcing Innovation is to give them the insight into the market, the vendors, the best practices, and the knowledge they need to be successful, Sourcing Innovation realizes it also needs to help vendors because buyers need better solutions as well as better education, and they won’t get those better solutions without successful vendors to deliver them.

And while the challenges might be too numerous to ever fully cover on any publication, as the list of best practices would get very long indeed, many are very niche and would only help a few vendors and can be overlooked with the goal of addressing, and solving, all the common issues first and if the niche ones are significant, then a vendor can engage an expert for a short period of time to address them.

To date, we have covered the following 11 best practices in this series:

  1. Identify the Market Sector You Are Competing In
    … and the Niche Your Solution is Targeting
  2. Do Your Market Research
  3. Define Your Target Industries
  4. Identify the Core Pain Points Your Solution Will Address in the First Release
  5. Understand the Data Needs and Design the Full Data Model
  6. Understand the Current Customer Processes and Typical Restrictions
  7. Don’t Overlook the UX (User Experience)
  8. Get the Messaging Right
  9. Price It Right
  10. Get Advice AND Listen to It
  11. Get The Help You Need! (And Get It Sooner Than Later!)

They are all important, but they don’t cover everything. And while we shouldn’t have to cover this 12th bonus practice, because it should be covered by Best Practice #8 and #10, given the state of the the technology space today, we have to bring it into the limelight.

#12 Don’t Mention AI. Not Even Once. Not Even If You Are Using Proper AI!

Customers are looking for vendors who are offering solutions, not buzzwords. Who are offering solutions that provide repeatable, explainable, provable answers, not random, black-box, suspect answers that could be based in fact or fiction, especially if trained off of random internet data with no fact checking or supervised learning.

Maybe AI gets you analyst attention (and it might be required to get ranked high in some analyst reports, but as we’ve already explained, that’s complete bullshit and we would not expect those analyst firms to stick around very long, or stick to this if they want to stick around and be taken seriously), but as more and more buyers experience the false promises of “AI” first hand (and push back against analyst firms that only push AI vendors on them), we expect customers to start blacklisting vendors that only sell “AI” and not actual solutions or services (and analyst firms that only push “AI” vendors on them).

Maybe AI gets some potential customer attention because you must be a technology advanced vendor to be using AI, if your claims are true, but all it’s going to do at a smart company (and you don’t want dumb customers in tech, they always cost more than they pay you) is get you in the door, and if you can’t deliver a good demo, and convince the C-Suite (who, seeing all these failures, are, or soon will be, becoming suspicious of AI for AI’s sake) you have a valuable solution that is guaranteed to deliver a significant ROI, you’re not going to get the sale.

Furthermore, as we’ve said over and over again, there is no true AI (at least Level 4) and anyone with a working brain who uses that brain knows this to be true. Your target customers are beginning to realize that most solutions are Augmented Intelligence (Level 2) at best, and often only Assisted Intelligence (Level 1), and then only for specific functions or insights, which are often a very small subset of everything they are expecting the solution to do.

Plus, any advanced capability that is reliable is not based on some random, black box, untrained mystic technology, it’s based on specific algorithms, trained on known data sets, and tweaked with a well defined set of parameters in a well defined range that have known, predictable, responses to specific data sets and situations. More specifically, we’re talking parametric curve fitting, (MILP) optimization, clustering, pattern matching, neural networks, deep learning networks, etc.

Thus, instead of just claiming “AI”, you should name the technologies, describe how you’ve applied the technology to solve the problem, be prepared to overview the validations you applied, and summarize the results you achieved and how much better they consistently are compared to more traditional algorithms and solutions the buyer is likely using at the present time, if they are using any solutions at all. This will get the buyer’s attention and prove that you know what you’re doing and your technology is an actual solution, not buzzword vapourware.

At the end of the day, customers want success, and AI, on its own, does not guarantee success. (In fact, unhindered AI guarantees failure if utilized long enough. That’s the beauty of probability and statistics. Eventually anything built purely on black box statistics WILL FAIL!) Plus, many buyers are old school, barely trust tech as it is, and are very worried that AI will take their jobs (and even if it can’t, they believe that management is looking for every opportunity to use AI and replace them, even if the technology is inferior, so the last thing they want to do is bring in technology that management could try to use against them). So not only can focussing on AI undermine the power of your solution, but AI can turn off potential customers who want to feel safe in their jobs.

We’re not saying to lie about using AI, or to avoid the discussion when you get in front of the customer, we’re just saying don’t follow the crowd and the hype and don’t focus your marketing on AI. Focus on the solution. “AI” is just another tool in the technology development tool belt. It’s not a solution on its own. And customers need solutions, not Automated Idiocy. Finally, here’s another bonus best practice.

#12B … And Don’t Use AI to Write Your User Manuals, Thought Leadership, Blog Articles, or Sales Materials Either!