Category Archives: rants

Why Your ProcureTech Initiative Will Fail … Part 2

We’ve written many series on best practice tech identification, tech selection, and tech implementation in the hopes of inverting the odds from an 80%+ chance of failure to an 80%+ chance of success, but given that failures are literally still happening on a daily basis, it seems that most people can’t be bothered to read best practice advice so today we’re going to flip the script and tell you all the reasons you’re going to fail and then you hope you go back and read the best practice advice we’ve freely given you (in series such as Successful Vendor Selection – The Series).

5. You Don’t Understand How To Select Proper Systems

As per our previous instalment, the answer is never to select the systems recommended by your favourite Big X consultancy whose recommendations are always in the mutual back-scratching club. With all the finders fees and recommendation fees built into the relationships, you’re paying multiples of what you should be for the tech even if the tech is appropriate. Typically the tech in the big vendors isn’t the best or most modern tech, and often the tech you fall back on only if you’re such a large enterprise that smaller vendors can’t serve you.

It’s not vendor size, Big X recommendation, marketing, or hype. And it’s never (Gen-) AI unless there’s no other solution. AI accelerates what you give it. And when you give it bad process, bad data, and bad instructions, you get an even worse result than what you have now. Until you’re best in class with last generation tech, you’re not even ready to consider AI.

The answer, as we’ve said before, is to find an independent consultant or consultant from a niche consultancy with no vendor relationships and no implementation team. The consultant/consultancies only line of business must be advisory, solution advisory, and project assurance — not implementation and integration and definitely not vendor partnerships. While the consultancies with the partnerships will tout the benefits and how they get (their customers) top tier support, first response, and the newest releases; that’s hype, not results. Results come from the systems that are right for you, not the systems right for the Big X consultancy.

Unless, of course, you are skilled enough to implement the best practice selection process yourself.

6. You Don’t Understand How Long It Will Take To Implement New Systems

A technology system implementation, replacement, or upgrade is an organizational mega-project in a mid-sized or larger organization. Even though SaaS vendors can partition a new instance in minutes, that’s not implementation, integration into your systems, incorporation into your daily processes, or institutionalization into your team’s routine. That all takes time. Lots of time — and a lot more time than your vendors will tell you. They know that you’ll be drawn to the proposal with the shortest implementation time so they will lie about how long it will really take and base their proposal on the theoretical minimum if everyone worked 12 hours a day, 7 days a week, and nothing ever, ever went wrong.

But we know the truth. Everything that can go wrong, will, to some degree. No one can dedicate as much time as the project plan says. All of the integrations will take longer than estimated. The data will be 10 times worse and 10 times as incomplete as was assumed, so there will be delays to clean the data and integrate external sources to buff it up. So things will drag out well beyond the proposal. Typically multiples.

That’s because no one will admit it’s a mega-project, and that’s why the technology project failure rate keeps increasing year-over-year despite being at an all time high of 88%+ in general, and 94% if Gen-AI. The reality is that mega-project success rates are 0.5%, as chronicled by Bent Flyvbjerg & Dan Gardner in How Big Things Get Done, where a study of over 16,000 projects across 136 countries going back to 1910 revealed the success rate. This is where technology project success rates are headed until everyone wakes up to the reality that they are dealing with mega projects and all AI does is speed up the failure.

7. You Don’t Understand How To Do Change Management

It’s not just implement the software, integrate the data feeds, flip the switch, and go. That’s a surefire guarantee for system avoidance and bypass.

Change management involves understanding how the processes are changing, what training the users will need, how best to go about it, how fast the switchover can actually happen, planning for, and managing all the non-technology details and implementing proper project assurance to make sure it actually happens. That last part is key — project assurance that starts before the first step of system implementation and continues until the adoption and usage hits the required targets — which will typically be months, if not years, after initial system implementation depending on the system. A few months for a dedicated solution or small suite, to a few years for an organization wide ERP or SCP for a large multi-national organization in dozens of countries.

8. You Don’t Know How To Recognize and Correct Issues To Avoid System Bypass

Just like no implementation will go according to plan (which is why over-optimistic schedules will never happen), no design will be as perfect as believed. Something critical will always be missed and multiple significant issues will crop up over time. The ability to recognize these issues quickly and do something about them before your employees start bypassing the system — because once they start, they won’t stop — the harder it will be to get them back on the system.

Let’s say you buy a new e-Procurement system designed to curb P-Card tail spend and allow the vast majority of spend to follow procedure, be tracked against budgets, and properly managed over time. Sounds great in theory, but lets say that any purchase not in the catalog requires an RFQ with at least 3 vendors, or a 3-way price comparison between 3 online vendors with a manager’s sign-off, which adds time and hassle for purchases that used to be one and done because the amounts were under thresholds, all of the options were always within a few % of each other, and saving $2 on a $100 purchase is NOT worth 15 minutes of an employee’s time who’s fully burdened cost is $100 an hour.

If you don’t pick up on this quickly, and let them one-and-done purchases under the old P-card amount through easy-peasy punch-out, they’ll go back to P-carding everything and if you take the P-card away, they’ll go back to the personal credit card and monthly expense report. You need to quickly figure out who is bypassing, why, and how you make the system easier to use than bypass.

The Procurement Knowledge Devolution

The internet was supposed to kick off the procurement knowledge revolution, allowing Procurement pros to quickly:

  • find out about best practices
  • get commodity and market pricing for products and services
  • build reasonable (should) cost models
  • find out about new suppliers, carriers, and consulting partners
  • research new products and services
  • hold truly global sourcing and procurement events in real time
  • effectively communicate with, manage, and develop suppliers
  • etc.

For twenty years, that’s what it was. Procurement departments who learned how to use the internet properly for research, deployed the right SaaS to support their processes, and identified the right data for their processes were successful in their endeavours.

But then came Gen-AI LLMs, chatbots were replaced with chat, j’ai pété‘s and clod‘s, and knowledge was replaced with whatever content the LLM generated. Maybe it was correct, maybe it was mostly correct, and maybe it was a 100% fabrication — a hallucination if you please. The problem with LLMs is that they are NOT intelligent. They are essentially super sophisticated multi-level cross-connected deep neural nets that go beyond classification to generation of responses built up from sub-responses built up from deep training on incredibly large data sets.

Therein lies all of the problems. It generates built on random probabilities. Those are dependent on what’s in the training data set, what questions were asked, what results are reinforced, and how it’s used. If the training data is bad and full of bad data and falsehoods, the chances of incorrect, and even dangerous, responses being generated are quite high. If the training was biased, the output is likely to be very biased. And if it’s not “trained to please”, the models are fundamentally designed to “learn to please”, so if computations that are a complete lie will increase utilization of, and faith in the model, that’s what will happen.

Moreover, every vendor is now believing the hype from the big LLM players, treating the technology as real Artificial Intelligence (when it should be called Artificial Idiocy), and trying to plug it in everywhere … promising that it will provide their clients with true natural language interfaces, agentic tech, and even BS AI Employees. Those who are adopting it are literally getting dumber by the day. Not only has the cognitive impairment, atrophy, and potential long-term decline from regular use been well documented, but the failure rate has been well documented as well with MIT and McKinsey studies demonstrating success rates of 5% and 6% successfully. Most pilots are being abandoned, sometimes before they even begin, because the tech isn’t even good enough to put into employee’s hands for the tasks the overpriced Big X consultancies claimed the LLMs would be perfect for.

Furthermore, even when organizations are smart enough to ignore Gen-AI, over-automating using the most advanced last-gen (A)RPA and AI technologies will still give Procurement teams a false sense of security and, due to their very low error rate, as time goes on, the team’s skills will go rusty and their ability to deal with true exceptions quickly disappear, especially as the old Pros (get forced to early) retire and the younglings have never dealt with exceptional situations.

The age of AI hype has ushered in a knowledge devolution faster than any age that has come before.

I hope the profession can survive it!

Why it’s just easier to do your job in the Age of AI

Every year articles make the rounds on how to do nothing at work or look busy to either slack off or keep your annoying cow-orkers away so you don’t end up having to do their work too. Now, a few in 2006 really annoyed me and I decided to point out how stupid they all were by selecting one in particular and pointing out just how stupid it was because it was harder and more work to follow the advice then to just do your job for your 35 hours a week you had to work.

But I’m not here to tackle another inane article about how to slack off or avoid responsibility, because hopefully by now you’re not dumb enough not to fall for it, especially since smart managers (and workforce monitoring solutions) aren’t either.

I’m here to tell you why it’s just easier to do your job in the age of AI than to try to use AI to do it for you.

1. You’ll have to tell the AI what you want at least 3 times. Maybe 10. Or More.

Gen-AI LLMs should be called Artificial Idiocy. There’s nothing intelligent about them. They don’t understand anything. It’s all a Grand Illusion. Automated puppet-theatre … and we’re the suckers born every minute for using it.

As a result, they produce a lot of good sounding text that sounds close to what you need, but upon further review, always misses one or more key points of your request … until you repeat it and rephrase it ten times ten different ways and your strive to perfection slowly reduces to anything acceptable whatsoever.

2. You’ll still have to edit the final outputs and execute the work in the tools it can’t drive because they don’t have the APIs for the LLM to execute.

So, after spending hours trying to get it to output the perfect report and process, you have to go edit, finish, and execute that process. Spending as much time as if you just skipped the AI in the first place.

3. You have to deal with the consequences of the errors you miss.

If you’re using a modern Procurement system which is AI-first and allows external AI to drive it, a request to “restock the supply room at the lowest cost” can result in ten times the amount of printer ink, paper, pens, and cleaning solvent you go through in a year because it hit a volume break at the production plant and skipped the intermediate supplier, ignoring the fact that you have to rent a warehouse for the truckloads of cleaning solvent you just ordered for your office building (even though you only use one floor — it assumed you wanted enough for the twenty story office building).

You’ve spent too much, have no room for the inventory, and have to explain to your boss why ordering years worth of office supplies was a good idea.

4. You have to explain the AI bill that was five fold what you planned.

While also explaining that it was your idea, not the AI’s, to screw everything up.

It’s always easier, and less risky, to use your Human Intelligence, and not the AI, to get your job done.

Remember When The Worst We Had To Worry About Was The Patent Pirates?


Those were the good old days
Those were the good old days
The years go by, but the memory stays
And those were the good old days

Good Old Days, Weird Al Yankovic, 1988

Twenty years ago, echoing the great Dave Stephens of Procurement Central fame, we cried about the software patent pirates plundering away your hard earned revenue as they scooped up patents from failing enterprises (or enterprises not willing to enforce them) for pennies on the filing dollar, and then sued any decently sized company that was offering software that sounded like it was covered by one of the patents in their hold, threatening to bankrupt the company with an expensive lawsuit that would be dragged out endlessly if the threatened company didn’t pay a patent licensing fee for a totally bogus patent claim. It worked well, until they got greedy and went after bigger fish who fought back and made it costly for them to make bogus claims.

When that was the worst corporate theft we had to worry about, in hindsight, it really wasn’t that bad.

Considering that today the Big AI players are stealing all of your copyrighted and corporate data and using it to train their systems in the best case, then using those trained systems to output similar derivative works for their profit in the average case, and allowing shareholders and foreign governments to access it in the worst case, the patent pirates don’t sound so bad — you had to have similar software for them to even consider targeting you!

The posts on your private sites, the published articles in major publications, and the books that took you years to write are being sucked into these LLMs without a penny of royalty to you or your publisher. If you’re an artist, they’re stealing your entire catalog from Youtube, Spotify, etc. to train their music generator app to output music that sounds like you (just with worse lyrics, off tones, and no heart or soul), and if you’re a corporate enterprise — everything on your website, in your emails, and in your private meeting notes for meetings you send your AI assistants to for note-taking purposes. Once they speech-to-text those meetings, all of the output is fed into the LLM training archive for “future improvement” before it is summarized and fed back to you.

Now, if that AI platform is owned by a company based in the USA, it doesn’t matter if the instance you’re using in the EU is hosted in the EU — US law gives them the right to access all your data at any time. And if you’re an American accessing DeepSeek … all that data is passing though Chinese government servers!

So not only are they using your information without your consent and without compensation, since the majority of the big players are in the US, they are using it without any repercussions as the US Federal Government put a 10-year moratorium on AI legislation, basically allowing these companies to steal all your data without consequence for the next decade! Because, even if they say it’s “just for training”, we all know that training data leaks out of LLMs with the right prompts in the right circumstance. There is no safe “just for training” instance, so if the data is your copyright, they’re giving it away for free. And if the data is your trade secret, its a trade secret no more!

And there’s nothing we can do! Our only hope is for every major publishing house and media company that does business in every country outside of the USA and China with copyright and IP protection legislation to launch lawsuits in those countries against these global AI companies that are stealing their IP and copyright and serving it up outside the US. Force them to defend hundreds of suits across the global stage, while lobbying other governments to create stronger protections and mandate consent before using content, and penalties for violating the law equal to at least 10X what the fair market value of the content is. Since the market won’t bankrupt these companies that shouldn’t exist based on the fact they lose more every year than 99.9% of businesses generate in revenue, let the media industry and legal systems do it.

Today’s “Social” and “Professional” Sites are Bad Netizens! So What Do We Do?

Two decades ago, I asked: Are You Being a Good Netizen because odds were if you were reading Sourcing Innovation (or, at the time, e-Sourcing Form or Spend Matters or one of the other sites offering you best practice advice and free insights on a daily basis) you were a consultant, service, or software provider. And while that wasn’t a bad thing, it also wasn’t a good thing.

The reason: the people who needed to be reading these independent sites the most were the actual procurement and sourcing professionals that these sites were created to serve. People who didn’t have regular access to high-priced best practice consultants, training course, or local professional groups to learn from. People who were thrust into the Island of Misfit Toys with little or no experience. People who needed real help, especially when it came to understanding the root of their problem, what processes and knowledge were needed to address them, and how to identify the right technology, and then solution provider, to help them.

People who, once they understood what they needed, would happily invite the consultancy or solution provider for a briefing or demo once they understood what that provider had to offer — yet few, (and sometimes) if any, consultancies or solution providers would tell their customers about the great resources that would not only help the customer, but the consultancy or solution provider. They weren’t good netizens, even when it would cost them nothing and only help them in the end. (And, frankly, helping a customer understand you’re not the right provider for them and saving you months of sales cycle effort only for the customer to walk away when the light-bulb turned on is a good thing — you want to spend your time with potential customers whom your solution is right for, because, once those customers get a taste of that solution, they’ll never let it go. While it’s true that organizations never want to change tech because of the time, effort, and cost required, it’s ten times true for tech that actually works that users like — they will fight tooth-and-nail to keep it, and when it’s a department/low-enough cost solution, even if corporate mandates something new, that department or user will still renew a license on a P-Card to keep it.)

Back before the social and professional networks were a big thing, the average buyer had no source of information beyond the country’s professional organization, the highly redacted analyst and consultancy sites, and whatever Google would serve them. That was not nearly enough.

But then the social and professional sites came along, and, for a while, things started to get better. Peers could inform each other of third party and independent sites that had free knowledge for the taking. And for a while, that’s what happened.

But then things changed, especially with the two most popular and commonly used sites (and, more or less, the only two sites that remain), when their focus shifted from enabling people to connect and learn to extracting money from their users any way they could, tweaking the algorithms to only advertise paid content or content from their most popular posters and favouring the few that regurgitate the mainstream hype over the independent thinkers trying to move knowledge, or at least the conversation, forward.

They’re not good netizens, and it’s hard for people who need good content to find the content they want.

Used to be you could Google, but now that “AI summary” is injected by default, the sources, and real information gets buried.

So we’re back to the early 2000s, where the only solution is for providers to stop pushing the hype and start sharing the knowledge again. There aren’t many sites left from when SI started (with Jon W. Hansen‘s Procurement Insights now being the 2nd oldest blog), but others have arisen — but how many people know about them?

(Including those who might follow the authors on LinkedIn and maybe see every third or fifth post. We’re talking about

and others. How many people really know about these sites? The answer: Not enough.)

So, pretend it’s the early noughts and go back to sharing human to human what’s really useful and what really matters. Otherwise, you’ll just end up getting dragged down to the lowest common denominator as a result of all the derivative Gen-AI garbage posts that now clog your social and professional media feeds.