Software Acquisition Insider Tips 2026 Part II

It’s been 17 years since SI published its first major series on generic insider tips back in 2009 where we gave you a lot of advice that more-or-less still stands today if you want to safely acquire software. Yesterday we over-viewed what those 11 pieces of advice were then, and summarized the 6 major difference that affect how you apply that advice today so you can continue to make the right decisions when acquiring software in the age of AI Hype and exaggerated I2O claims. Starting today, we address the major points and what you look for.

Don’t Get Blind-Sided

There are two primary ways you’re going to get blind-sided with tech acquisitions in a modern Procurement organization. The first is still as it was 17 years ago — IT. In many organizations, IT still has too much power over software acquisition. Where the software is cross-department and enterprise core, it should have a considerable say, but for department/function specific apps or apps that sit on top of the core platforms, IT’s influence should be limited. But in organizations where IT still has too much sway, when IT decides it doesn’t like one of your choices, it can step in and, in a very public way say “we already have a product that can do that with extra licenses (and we just need to add one module)” or “it won’t work with our infrastructure, but this other product we’ve been looking at will” and, even if both of their suggestions definitely won’t do what you need them to do from a functionality requirement, it doesn’t matter, the damage is done, your selection (which might have undergone months of research and negotiation) is dead and it will be all you can do to prevent the bad choice from being mandated by the COO or CFO.

As we said before, this does happen but can often be easily prevented simply by taking the time to get IT on board before you present your suggestion and, preferably, before you even make the final selection. Find out day one any absolute and desired requirements they have, incorporate those that are truly absolute and relevant into your RFP, and be sure to convince IT up front that your process addresses your needs and theirs. Then get IT on board with the selection before going to the C-Suite.

Speaking of the C-Suite, this is the second primary way you’re likely to get blindsided. In the age of AI Hype, when every CXO is being convinced that, if they don’t have AI they’re going to fall behind, chances are they’re going to have their favourite overpriced Big X consultancy make a provider recommendation for whatever tech they think you need and then tell you after they’ve signed the deal that provider X with BS “AI” product Y is your new solution, and you better make it work because they just blew the software budget for the next 3 years.

This will generally be last generation junk with a bit of automation being sold as next gen AI or a hallucinatory Gen-AI LLM in a shiny wrapper with no real, solid, functionality, and neither will solve your problem.

The only way you can prevent this is to ensure you’re on top of all the major C-level consulting engagements and their purpose. That Procurement is seen as central in all services and consulting as a knowledgeable provider that can not only select the right Big X partner (event though the CXO’s favourite provider often isn’t the right one, you will never convince a CXO with a predetermined mindset otherwise) but ensure the organization gets the best deal possible. That Procurement should be kept apprised to ensure the invoices are accurate and the services delivered. That way you can understand what they’re looking for and monitor how the engagements are evolving, and once you see that the goal is to identify a product or service that will impact, or, even worse, be forced upon, Procurement, you can start educating the C suite as to what a true solution is, what the organization really needs, and how to weed out the charlatan solution providers from the real ones. You may still get stuck with a sub-optimal solution, because the C-Suite will insist on a big-name vendor with “AI” inside, but at least you’ll get one that at least partially solves the problem you have.

Watch Out for the Big Lies

Traditionally, the big lie was that many software vendor sales reps would lie and say “yes, we have that capability” when asked if their software could do something specific even if it couldn’t because, if asked to demonstrate it, they could say “it’s in beta and we can demo it next time ” (and assume their team could get it done, or at least enough fakery done, to convince you, by the next demo).

But now we have a new lie — and it’s the biggest lie of all. AI (or AGI) exists, it can do whatever you need it to, and its your new employee. And CEOs, supposed to be brilliant leaders, are falling for this BS left, right, and center. There’s no AI, Gen-AI hallucinates unpredictably on a regular basis, and it’s just as likely to bankrupt your business on a single buy than save you $1. As Joël Collin-Demers stated in his AI post (linked in this post), vendors with real AI (where AI stands for Augmented Intelligence, as that’s the best you can get)
tell you exactly what they do, in which sequence [to employ it], and [help you] understand how it solves your exact problem. They don’t sell just on AI hype, they show actual solutions.

There’s a reason I’ve advised you repeatedly to ban “AI” from your RFP responses and kick out any vendor that leads with AI, and that’s because those vendors are mostly, if not only, selling BS.

Software Acquisition Insider Tips 2026 – The Terms Have Changed But the Game Remains The Same

SI has been giving you best practice advice on acquiring software to solve your extended sourcing and procurement (related) needs since the beginning, with deep dives into every major technology you might need, and deep exposes on (fake) tech that didn’t work.

However, it’s first major series on generic insider tips on software acquisition was back in 2009 when it published a seven-part series on the seven-shards of software acquisition that gave you a lot of advice that more-or-less still stands today. In a nutshell that advice was:

  • don’t get blindsided by IT
  • watch out for the big lie
  • chuck the checklist
  • wait for the blush to leave the rose
  • read the contract
  • forego the escrow
  • draft a real unbiased RFP
  • … streamlined for performance, not wokeness
  • separate software from service
  • monitor the market
  • skip the mind games

And it more or less stands today. The main differences are where the sideswipes come from, what the big lie is, what you have to look for in the contract, what you really need in place of the escrow, how software and services are blurring in new ways, and what to look for in the market. The rest remains the same. So to ensure you know what to look for and that you continue to get the best deals on your software (because, regardless of what new fangled terms are used to describe it, or the delivery mechanisms, it’s all still software), we’re going to revisit these series and make the necessary updates.

AI Hasn’t Changed The Fundamentals Of Analysis — It’s Reinforced The Needs For It

AI is Not AI … And AI-Related Tech is Not Created Equal

Joël Collin-Demers recently made a post that correctly stated that real “AI solutions” tell you exactly what they do, in which sequence, and [help you] understand how it solves your exact problem. the doctor totally agrees. Otherwise, they are using buzzwords and trying to cash in on the hype to sell you old-school automation at best, or third party (Gen-AI LLM) wrappers at worst, and don’t have any real AI.

He covered ten different types of technology and attempted to capture the positives, the negatives, and the best uses therefore in source-to-pay. For a relative non-techie (compared to the doctor with a PhD, degrees in CS and Mathematics, and actual experience implementing everything but BS LLMs from scratch), he got a lot right. But he got a few things wrong. As a result, there was a need to correct him (in this post) and ensure the corrections have as much permanence as the original post.

We do recommend you read his original post, but for each tech addressed, integrate the correction below.

RPA – does not “break” when processes change; it breaks when you feed it bad data; when you change processes, it simply loses its usefulness until you change it to match the process — with a good RPA system, that shouldn’t be hard

Machine Learning – requiring clean data is NOT a bad thing; it ensures the algorithm “learns” the patterns you need it to learn to use it effectively

Natural Language Processing – doesn’t struggle with jargon, just context — you define the dictionaries, the grammar, the language — it’s accuracy boils down to that; if you are feeding in documents that use the same words/phrases in multiple contexts, it will always struggle with that to a point

Predictive Analytics – in lay terms, classical predictive analytics is essentially just multi-dimensional curve fitting based on the data available — when something has not been modelled, there is nothing the algorithm can learn to fit against — but to be fair, nothing you’ve listed will succeed with unprecedented events/data

Anomaly Detection – this is based on outlier detection, and well trained outlier detection does NOT have high false positive rates (which are no higher than false negatives), and any “wrong” classifications from a business perspective simply means that the definition of a valid transactions needs to be amended to reduce the “outliers”

Computer Vision – lighting is not as much of a problem as you think as most good algorithmic interpretations will always mathematically adjust the brightness and contrast to a consistent range in pre-processing, and sometimes even greyscale; angles are a problem, because if they can’t be determined, the right transform can’t be applied to appropriately orient the image to maximize identification likelihood

Optimization Algorithms – “requires precise problem definition” is not a bad thing, it’s a good thing — if you don’t have a precise problem definition, you cannot get a precise answer with any technique; one of the best uses is product mix, not just supplier portfolio

LLMs – not “can” hallucinate false info; “will” hallucinate false into — every single time, just a question of the degree; it’s “generative” AI which literally means it makes stuff up, and how accurate what it makes up is with respect to your problem depends on how it was trained, what was asked of it, and how you ask it … very unreliable all around

Pattern Based Recommendations – the whole point is to “filter” to what you would normally buy so that you don’t have to sort through everything, that’s not a problem

Reinforcement Learning – it does not require extensive time, it requires extensive data — computers process mathematical calculations billions of time faster than we do — it’s never time anymore!

AI Doesn’t Help You Get The Most From Your Spend Analysis System

Twenty years ago we wrote a post on how to get the most from your spend analysis system, and the reality is that not much has changed. In fact, AI has only cemented the need for the foundations.

Twenty years ago, we essentially told you the keys were to:

  • streamline the tactical
  • focus on strategic analysis that always delivers

And those haven’t changed. Because:

1. With AI overloading you, you need to verify, or dismiss as fast as possible, which means tactical should be quick, easy, and effective.
2. With AI vendors claiming they can replace you, it’s critical to start with analyses that deliver results to prove the power of manual analysis.

We’ll review the tactical capabilities that are still super relevant and the strategic capabilities you should always keep in mind.

Your tactical skills with respect to the following should be best-in-class with leading peers:

  • (sub) cube creation off of the primary cube (using inheritance)
  • re-classification, derived dimension creation, and view modification
  • baseline and compound view-based report creation
  • direct integration with the contract repository, supplier management system, and external data feeds
  • automated maverick spend and override approvals

Your strategic skills with respect to the following should be second nature:

  • overspend calculation on “best price” agreements against market prices offered by the vendor through other channels
  • purchase order vs invoice vs payment analysis for overpayment recapture
  • outlier analysis tuned to (potential) fraud identification (both internal and external)
  • loss prevention (repair vs. replace analysis, keep vs. return analysis, price reduction vs. charity donation for write off analysis, etc.)
  • compliance analysis (right products/services being used, payment terms being adhered to, etc.)

But, most importantly, your perception skills need to be top notch. When the AI spits out an opportunity, you need to:

  • identify precisely not only what the AI thinks the opportunity is, but how you would verify it, and if the opportunity is real, capture it; but if not, disprove it quickly
  • when the opportunity turns out to be false, identify the direction the AI was heading, what the AI got wrong, how to identify such errors instantaneously in the future, and what direction to give to the AI handlers to stop wasting your time with obvious garbage
  • how to identify the gaps in what the AI analyzes to find opportunities even faster than the cognitive atrophied team driving the AI

While the AI can kick off the tactical, and support some of the strategic, it should be clear that it falls short in all three categories, and doesn’t even scratch the surface where real perception is required.