Category Archives: Technology

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) …”

A Good Negotiation is Key in Technology Acquisition

But whatever you do, please don’t mistake cost savings with value generation. But, as usual, let’s backup.

A recent article over on The Financial Express on the importance of a technology procurement negotiator noted that the art of negotiation has taken on a whole new level of complexity, especially in technology procurement and that discovering the most equitable prices … is a strategic imperative at a time when maximizing returns on investments is paramount.

And this is certainly true, as are most of the other messages in their article. Specifically, such a negotiator must:

  • understand the digital disruption
  • have high intelligence, which must go beyond technical expertise
  • understand the high stakes of technology investments
  • have the personality, worldview, and knowledge to navigate the negotiation beyond the technical aspects
  • be able to reflect on the bigger picture
  • be able to sync with the project

… but the criticality of ensuring that the technology procured provides exceptional value for the money spent cannot be over-emphasized. One cannot understate the importance of understanding the product’s role, functionality, and how it aligns with organisational goals. It doesn’t matter how much you save if the product isn’t the right fit. It’s critically important to not only have the technology experts identify the products that could serve your needs, but the right configurations, the associated services that will be required, and the right partners for the organization.

Additional savings is worthless if it comes at the expense of the vendor removing a key module from the reduced offer, not including necessary implementation or integration services, limiting computing or storage, and so on. If you end up paying significantly more after implementation as a result of change-orders, you not only haven’t saved, but you’ve cost the organization more. This is what often gets missed when negotiators lead. While the eventual owners shouldn’t lead, as they’ll always go with their top ranked provider (even if three systems can do the job equally well, and it’s just a preference as to which system is easiest to use), if they’re not kept in lock stop, it’s easy to miss key details or requirements or stray away from what is truly needed for value generation and ROI in the search for the ultimate deal. This is especially true if the negotiator brings a new vendor in at the last minute for price pressure, believing the new vendor, if not perfect, meets all the key requirements, when in reality the vendor’s platform doesn’t.

This is especially important to remember in SaaS negotiations, where it’s common knowledge that most organizations that buy without using a skilled negotiator are overpaying by an average of 30% or more. This is because an average negotiator’s inclination is to drive for massive discounts to prevent overspend, which might result in not only choosing the less optimal vendor, but the less optimal agreement. At the end of the day, price matters, but ROI matters more, especially in Procurement where the right solution will generate a 5X ROI or more and the wrong solution will barely pay for itself.

9% of Companies Claim To Be Ready to Managed Risks Posed by AI? Bull Crap.

the doctor could not believe the recent headline in Forbes that said Only 9% of surveyed companies are ready to manage risks posed by AI. Because there is no way that 9% of companies are ready to manage the risks posed by AI. There’s no way even 0.9% of companies are ready to manage the risks posed by AI.

Why? Because of the rampant introduction of massive LLMs and DNNs that no one understands, for which I’m sure we’ve yet to seen the last of the abysmal failures, hallucinations, and suicide coaxing. There’s simply no way we can even begin to predict all of the potential errors they are going to make, the risks they are putting us under, the repercussions if those errors are made and risks materialize, and how the risks can be minimized, if not mitigated. No way whatsoever.

Not only is it theoretically impossible to be fully prepared, but when you consider that the average organization is not even equipped to handle regular software failures, how can the average organization expect to handle a software-based AI failure it can’t even predict?

The article, which quoted a recent study by RisKonnect (who are obviously able to detect and protect against most types of risk by using RisKonnect, and maybe that’s why they are so confident they can protect and defend against AI risks, but RisKonnect is for traditional enterprise and third-party risk, not cyber risk, and definitely not AI risk — no one can protect against a risk when they don’t even know what the risk is), did quote some very useful statistics on areas of concern. Specifically, of the companies surveyed

  • 65% are concerned about data and cyber,
  • 60% are worried about employees making decisions on erroneous information,
  • 55% are worried about employee misuse and ethical risk,
  • 34% are worried about copyright and intellectual property, and
  • 17% are worried about discrimination risk.

The risks are the right risks, and the order of priority is about the right order, but the percentage of companies concerned is much too low.

1. 100% of companies should be concerned about data and cyber. Not only are we in the age of state-sponsored hacking, which makes any company with useful confidential designs and information a target, but with almost all significant commerce being conducted online, all companies are a target for financial fraud.

2. 100% of companies that need to make decisions based on data analysis should be concerned about erroneous information, as all companies have bad data, and the bigger the company, the worse the data.

But none of these match the risks of AI. As per the quote in the article from Caitlin Begg, an over-reliance on AI can risk robotic, insensitive, spammy, or off-topic messaging, and that’s just the beginning. As noted, most companies haven’t simulated their worst case scenario, and since one can’t even predict what that is with AI, they aren’t even close to ready. It’s not just another article in the organization’s tech stack, even though the article seemed to indicate it is. One can prioritize transparency, accountability, threat and vulnerability monitoring, and risk mitigation, but when most AI applications can’t explain their actions, aren’t accountable humans, have no realistic threat and risk assessments, and there is no way to mitigate risk except not to use the technology in the first place for any decision that should be made by a HUMAN, it’s just not enough.

The precautionary steps are not to identify where AI can be most effective and incorporate it, the steps should be to

  1. identify where partners and third parties are using AI and putting your organization at risk
  2. identify where employees might be using unapproved web-based AI applications and put a stop to it
  3. identify where your SaaS providers are not only using, but introducing, AI into their applications after purchase and delivery and ensure that any utilization is bounded, tested, and properly constrained to prevent risk

Then, instead of unbounded AI, identify appropriate automation technologies that can be properly configured, integrated, and managed as part of an enterprise stack. And reap the rewards while your competitors deal with risks.

Do you want to get analytics and AI right? Don’t hire a F6ckW@d from a Big X!

Note the Sourcing Innovation Editorial Disclaimers and note this is a very opinionated rant!  Your mileage will vary!  (And not about any firm in particular.)

Now, I’m going to upset a lot of people with this, but I don’t care because the linked article below is literally the best article I ever read on why you should NOT hire F6ckW@ds from Big X (or any other) Consulting Firms who claim to be analytics and AI experts when they don’t actually know

  • the difference between a mathematical formula to calculate the center of gravity of a falling object and to calculate the median spend in a category
  • proper software architecture
  • proper compute resource allocation
  • your business
  • the difference between real ML technology, RPA and a few formulas, and the current Gen-“AI” where the “AI” stands for artificial idiocy

because

  • you’ll spend 3 years and millions of dollars to implement something that should take 3 to 6 months
  • you’ll spend hundreds of thousands on big vendor software licenses you don’t need
  • you’ll spend hundreds of thousands on compute power you don’t need

After all, these guys and gals get paid by the hour and the commission on the resell license is a percentage of the total price they convince you to pay for it. So, the longer the project takes and the more licenses and compute power they sell …

Read the linked article. Twice. And then tape it up to your fridge. The situation described in the article is NOT the exception. As a former CTO and 25 year consultant/analyst, I know this is the norm!


I Accidentally Saved Half A Million Dollars
 

Now, if you’re wondering how to tell who is a F6ckW@d and who’s not when it comes to analytics and AI at the Big X, I’m sorry to say that it’s not so easy (especially when it only takes a few bad apples to spoil the bunch, and while the good firms will do mandatory pruning of the consulting tree annually to weed those bad apples out, you don’t want to be the unlucky client who gets one on your project) .

It used to be if they were there for more than a year or two, their was a possibility that they were, or at least not as good as they claimed to be,  that especially if they were junior, right out off school, no real experience. This was because, first of all, tech talent wants to go either to the big glorious tech firms (Alphabet, Meta, etc.) or the wild-west startup frontier, and big consultancies were the backup until they got enough talent to move on.

Thus, the real talent in tech and analytics, who didn’t get promoted quickly in the Big X, usually didn’t stay long before they moved on to specialist firms where they felt they were more respected, higher up, could control the projects, and, more importantly, being higher up, were higher paid.

(Tech/Analytics people take pride in their work [and not their title], and seek the job that gives them the most pride.  Also, even though good tech/analytics people won’t contradict managers because they want to be important, and will only contradict managers because they want the job done right, the reality is that junior people or new hires in big firms often have the impression that this is discouraged in a larger firm [even if it’s not] where you are supposed to learn from and follow your manager’s lead because you don’t see the big picture and may not speak up on the way a project is being approached when they are unsure.  They might be wrong, and should stay quiet, but they don’t learn if they don’t ask.)

However, now that all the big firms are acquiring mid-market experts, with some of the Big X acquiring 3 or 4 specialist plays in analytics and AI over the past couple of years, it’s much harder to differentiate if you are getting the best talent or not.  You have to vet every candidate.  Not the Big X.  YOU!

And you need to remember that some of this AI and analytics stuff is literally so complicated that you need degrees in mathematics and computer science and sometimes a decade of experience to get it right! (It took the doctor two advanced degrees and building advanced analytics and optimization systems for multiple leading companies in the 2000s before he really understood the art of the possible and, more importantly, what was relevant for an industry and what was not.)

In other words, it’s okay if you don’t really get it as a manager. Just find those one or two people who do who you can trust, pay them well, and let them do what they need to make your department look good (be it hire internally, choose a consulting firm you never heard of, hire former colleagues on short-term contracts, use their contacts to get the right person at the Big X, etc.).

They’ll get the job done right and be quite happy to let you take all the credit IF you give them regular raises and a bonus any time they do particularly well. Just put your ego aside and let the people who get it make the tech/analytics decisions, and everyone will win!

But, whatever you do, don’t throw a poorly formed project description over the wall in advanced analytics and AI to a Big X (or any other vendor) and expect good results.

If you don’t know what you need, why, and how you expect to get it, instead focus on what you understand and Use the Big X firm for all of the things you know it is good at, understands implicitly, and has the history and experience to figure out simply based on the type of company you are.   Used appropriately, like any service provider, a Big X can deliver amazing value.   See the linked article on when you should use Big X in our opinion.

Fail Fast And Forward? How About Not Failing At All?

A recent article over on The Sourcing Journal indicated that one should Fail Fast and Fail Forward When Implementing AI into Workflows. WTF? Why fail at all? Especially since if you’re using AI where you are expecting a high risk of failure, there’s no reason to expect that you’ll only fail once, or that you can actually fail forward.

Now, if we were talking traditional ML, where it’s just a matter of continually expanding and refining the model and training data, tweaking the parameters, and starting small, then fail fast, fail forward, get it working, use the spice weasel, knock it up another notch, and continue until you have automation across the platform in appropriate places, it would be good advice.

But when we are talking full fledged Gen-AI (which is the article’s focus) based on massively large and entirely unpredictable LLMs or super-sized DNNs, you can fail fast, but, with absolutely no way to control the models, you can’t fail forward. So while fail fast and fail forward is a good motto in general for technology, process digitization, and automation, as long as you take things step by step and control the risk, it’s not appropriate at all when we are talking about AI!