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.

AI Doesn’t Change the Fundamentals of Spend Analysis

Twenty years ago, we wrote a post that said, so you want to do spend analysis?

And in that post, we noted that you want to get on with it, but you’re not sure where, or how, to start. So we provided you with a step-by-step process you can use to get your spend analysis effort under way and keep more of those corporate dollars in the corporate coffers, where they belong. And, after 20 years, the steps remain, more or less, the same. Because the song hasn’t changed. Only the noise by those trying to sell you fancy tools (that don’t work) has gotten louder in their attempts to drown the song out.

1. Locate Your Data

You need to identify where all of the source data is. Not the data warehouse or data lake you’re pointed at or someone’s random, monthly data dump, but the source data. That’s because it’s not likely that all of the data you need will be in the warehouse, lake, or dump because that likely consists just of the ERP or AP system, and we know that no single system contains all the data you need. Spend will be split between the ERP, AP, and T&E. Supplier information in the ERP, SXM, and Risk Management Systems. Contracts, and key data, in the CLM and Legal Systems. And so on.

Make sure you know where all the source data you need is, what warehouses and lakes it is pushed to, on what schedule, and which of these you can use. If you can work with system owners to get the missing data pushed to central locations, or if you will need to retrieve it directly.

2. Create a starting taxonomy

You can’t integrate the data otherwise. As long as the taxonomy integrates and federates all of the data, it will be sufficient. Remember that a modern spend analysis system supports multiple cubes, with inheritance, that each cube can have its own hierarchy, and that cubes and sub-cubes can be created on the fly.

3. Centralized the Data

Centralize the data into a single (virtual) starting cube that everything can work off of. Do this by defining the master transaction records, absorb all of the related information, and build the initial (virtual) cube(s) that every (derived) cube will work off of. (By virtual, we mean that the system can pull data on demand as needed with predefined definitions, it doesn’t have to be stored in a single cube in a single warehouse or lake.)

4. Family the Data

In just about any ERP or enterprise system, there are multiple entries for every supplier, product, or other entity. Make sure these are familied into one instance in the master data set.

5. Map the Data

As the data gets pulled in, map to the starting taxonomy so that the data is ready for use.

6. Pick the Low Hanging Fruit

Always start with the easy opportunities — today those might be the highest ranked opportunities spit out by the AI, the ones from the “start here” guide (supplier rationalization, bypass spend identification, growing, off-contract, categories, etc.). A few quick wins will build up support for a real, manual, spend analysis effort — which is where the real results will come from.

7. Make sure you get the right tool!

Remember, the tool, which should have all of the features defined in our previous entry in this series, must be one that gives you the agility, speed, and power you need to apply your intelligence to the problem at hand.

8. Get a good consulting mentor

You want someone to teach you not only how to do spend analysis, but how to approach and even think about spend analysis so that you can translate your instincts into analysis with quick, pinpoint, accuracy and answer your queries faster and better than any AI ever could. That’s not your run of the mill consultant who just executes the standard slate of analysis and applies the AI tools in their stables, that’s a real analyst who understands what analysis truly is and is willing to teach you. They are few and far between, and may not come cheap due to high demand, but once you learn, you won’t need them anymore. And what you spend up front will be insignificant with respect to the opportunities real analysis can uncover.