Category Archives: Technology

This Should Be Obvious But Expert in the Loop …

… is Human in the Loop. Not another (AI) system in the loop, no matter how specialized that system is or how well it is trained!

The future is Augmented Intelligence, NOT Artificial Intelligence (which doesn’t exist and won’t exist any time soon until brilliant researchers come up with a few more insights that get us closer to understanding

  1. what intelligence actually is and
  2. modelling it.)

The algorithms might be getting more accurate in average use cases, but the illusion of intelligence, no matter how grand, is still NOT intelligence. (And, even worse, The Wizard of Oz has been replaced by a very poor digital facsimile.)

Done right, Augmented Intelligence will still let your organization reduce its non-value-add tactical workforce by 80% to 90% because the right tools will enable the strategic experts to be 3, 5, 7, and even 10 times as productive and oversee all the tactical work that needs to be done using an exception based approach where every instruction that is given forms a rule that allows the system to automatically deal with the same, and similar, exceptions should they arise again in the future in a predictable and repeatable fashion.

Instead of having to oversee a team of tactical grunts that just take up space (because they don’t have the education, experience, or raw capability required to make good strategic decisions, manage projects, and identify value), a strategic expert can instead focus her time on value-centric activities and training a protege or two who will be one that posses the right mix of EQ and TQ to grow into, and take over, her expert role (when she moves on and up).

In the near future, there will be no more bodies in seats just to push bits around, because that’s what software does best. Number crunching and thunking. NOT analyzing strategically and thinking. (I admit most humans don’t do that well either, especially these days, because they are too attracted to the principle of least action and/or enjoying the cognitive decline from ChatGPT, but those willing to practice strategic thinking daily still do it way better than a machine ever will based on our current approaches to AI). [And while there might be fewer of us each year that are willing to think, there are still enough of us to get the job done if you let us select tools that work. Not necessarily AI. Tools that work.]

You CAN Afford to Wait for AI. But you can’t afford to wait to

  • get your data under control
  • build an infrastructure to allow for greater connectivity between apps within your enterprise and its greater ecosystem
  • update your processes
  • acquire and train the right talent with the knowledge they need to compete in the modern world
  • get digital and implement modern, current, generation technology based on best practices, proven (A)RPA ([Adaptive] Robotic Process Automation), and last-gen “AI” tech like optimization, predictive analytics (based on clustering and curve fitting), and point based neural networks with proven reliability and mathematically understood confidence where those apps are needed (and not a Gormless AI)

The reality is that you have to operate as lean and mean as possible. And

  • without good data, you can’t make good decisions
  • without good connectivity, you’re manually re-entering data across systems or missing critical external data you need to make good decisions
  • without good processes, you are inefficient and if not already, about to be circling the drain
  • without good talent, you are running on fumes at best, your ability to compete is at risk, and you can never improve
  • without modern tech, you are at a continual disadvantage and will continually fall behind

So you can’t wait to

  • institute Master Data Management (MDM)
  • enforce Open APIs in your solutions and acquire integration and orchestration solutions
  • review and modernize your processes where necessary
  • focus on acquiring, train, and retaining top talent
  • modernizing your tech to CURRENT generation proven tech, not experimental HYPE tech

BUT YOU CAN WAIT ON “GEN-AI“. It’s about getting the job done as efficiently and effectively as possible … with a low error rate and no significant risk! 99 times out of 100, you don’t need experimental “AI” to do that. Only the investors who spent millions/billions/trillionsw on unproven tech and the consultancies who need massive projects to employe bodies do … but that’s not to help you. That’s to recoup their wasted dollars. And that’s NOT your problem.

Building a Good Solution ABSOLUTELY Requires a Good RoadMap

A few weeks ago we tackled the subject of How Does a Vendor Build a GOOD Solution? and outlined seven key steps. SI received some feedback, and most of it revolved around the roadmap and how it should only look three months out!

So we have to address this insanity!

First of all, name ONE great or revolutionary technological invention that was invented with three months effort. You can’t, because there isn’t one.

Now name ONE great piece of software that solves a significant business problem that no other system that came before solved that was invented with three months effort. You can’t, because there isn’t one.

Now name ONE Billion dollar enterprise software platform that went to market with an MVP in 3 months that became a powerhouse that a large swath of businesses are using. You can’t, because there isn’t one.

All you can do in three months is a crap an app that is a piece of crap. Now, you might be able to make a big splash on the app store or in the consumer shareware market, but enterprise software is a complex piece of enterprise technology that requires years of development … and years of planning!

Secondly, remember what a roadmap actually is. It’s a graphical document that shows all of the roads you have available to you, how fast you can travel down them, and where they will take you. It’s not a detailed travel plan!

Similarly, in technology, a roadmap lists out all the things you would like to do, what it might take to get there, and what options could take you there. It is NOT a detailed functional specification or a development plan for the next three to five years (which should be the length of time you should be thinking through). (Also remember that, historically, great inventions came from research labs where the researchers were thinking three, five, and even ten years out and had years to develop groundbreaking developments!)

There are a number of reasons you need to be thinking three years out (even if your plans completely change nine months in), but the most critical reason is this:

If you plan for three months, or go for speed over quality (assuming you can always fix it later) your teams take shortcuts, build crap infrastructure, and add technical debt faster than you can ever eliminate it! (It’s almost as bad as vibe coding your way to an MVP, and then realizing you can never support an enterprise stack on it and have to go back and rebuild it from the bottom up after you’ve wasted months of effort and tens of thousands (or more) on AI credits. (Alex Turnbull gives a good summary in this LinkedIn post.)

When you start thinking about where your enterprise application might need to go, even if you choose not to go in that direction, you understand what processes you will eventually need to support and how you will need to build the foundational data model, workflow and orchestration engine, integration capabilities, internationalization support, and other core foundational features to either build that out or integrate that capability in the future. You’ll have a better idea of what you’ll need in the stack, what you’ll need for the platform, and what the best development environments for your team will be. (Having to change out any of these is very time consuming and expensive should you make a mistake early on.)

For (an easily understood) example, if you think invoice processing sucks (because you only looked at three vendors as you are too clueless to do market research, like many vendors that started during COVID because they all of a sudden realized that the business back-office should be capable of running 100% online, distributed, and remote), what else are you likely going to do after that. (Unless you’re a world leader in invoice processing technology, no one is going to buy just that!) In other words, are you going to support invoice analysis and predictive payment analytics, payment platform integration, contract and PO data extraction and matching, enhanced procurement (platform) support, etc. All of these capabilities will dictate data model, orchestration, and stack requirements.

Again, the point is not to plan out a detailed release schedule, but understand where your customers might ask you to go, where you want to go, where you want to hire a guide (to provide you with the expertise you need), and where you might want to hire a service to take your customers there (because a certain capability is best done by a specialist). This, along with constant monitoring of customer functionality uptake, customer feedback, and user forums will give you the complete picture you need to create the high level development plan for the year and the detailed functional specification for the final release of the next quarter (which might be built incrementally using agile methodology).

To put this in terms non-technical people will understand, you can’t build a twenty-story high-rise on a foundation for a two-story house. By thinking ahead, you’re building a solid foundation, and when you start building, you’re building the frame for the twenty-story high-rise that you can then build out and complete floor-by-floor once you know what the tenants you are signing on want on their floor.

By thinking at a high level years into the future, you are visualizing how you are going to fit into and evolve with the organizational ecosystem you want to sell into, and you are making good architectural decisions as you will be able to build that understanding of what you’ll need to support!

Moreover, as one commenter pointed out, and we noted above, watching how users work with the system is key! That not only helps you understand the depth and configurability of workflow process management required, the breadth of the data models that will be needed, and what systems they will want interfaces to (based on what they use before and after), but how to design a good UX based on now they work and what they are adopting! (It should be noted that designing a good UX, including a good UI, can be harder than the model and controller algorithms — which, if you need advanced analytics, optimization, and higher performance, might take a PhD to get right — because it doesn’t matter how good the application core is if no one uses it!)

Roadmaps are key. That’s how your Chief Software Architect and Chief Technology Officer build great applications. It ensures that once you select a destination, they know the route they have to navigate to get there!

AI vs No AI – Let’s Make This Clear!

There are valid uses for AI, and valid AI models you should use. (LLMs are rarely one of them, having only a handful of reliable applications, but, when you push them aside, there are lots of other AI technologies that actually work if you don’t get blinded by the hype.) But there are invalid cases, and AI models you shouldn’t use. So to make it easy-peasy for you, here’s a simple guide!

USE AI WHEN DON’T USE AI
It’s a well constrained use-case where AI has been successfully deployed in industry, where the confidence is proven, and where you have access to the right technology. It’s a poorly defined use case, AI has not yet been successful for the use case, the confidence is unproven, and/or the tech you have access to is still in alpha!
It works as well as previous gen tech but with significantly shorter training cycles and easier integration and utilization. Previous gen tech still works better, costs less, and/or has no impediments to integration or UX.
It can bring value beyond what last generation tech can bring. All of the value can be achieved using traditional rules-based (A)RPA, (decision) optimization, analytics, or (classical) machine learning.
It’s a cost effective solution that can be run predictably based on a predictable cost model. It’s based (primarily) on LLM(-based) models that have unpredictable compute costs and, with the wrong request, can eat up thousands of dollars on a single request.
You have a valid use case for agentic tech! You think you have a valid usre case for agentic tech. (If you think you’re ready for AI, you’re NOT ready for AI.)
You’ve mastered current generation tech. You’re still a generation (or three) behind on tech.
You have in-house expertise on what AI is, and isn’t; where it can, and can’t be successfully deployed; and what “AI” is typically appropriate in a given situation. You’re relying entirely on (junior) consultants from the Big X promising it’s gonna “change your life“.
It’s designed to augment human performance and make your employees more productive and more effective super humans (able to do the work of 3, 5, 7, and even 10 regular humans). It’s designed to replace humans. (This doesn’t mean it can’t reduce the number required to do a task, just that at least one is still maintained to handle exceptions and make decisions.)
The firm is selling augmented intelligence! The firm is selling AI Employees. (There are none! And any firm that makes this claim is dehumanizing your employees! But hey, it’s your choice if you want to lose all your money.)

Get it yet?

C-Suite Only Has Budget for AI? Then Lie its AI and Buy Solutions That Work!

You need modern tech more than ever, but with unemployment on the rise (and people buying less), recessionary fears, tariff wars, etc., the C-Suite doesn’t want to give you the increased budget you need to buy the tech you desperately need to be more productive.

On the flip-side, they are finding millions of dollars for “AI” (in the hopes the lies are true and they can replace you, even though any attempts to do so will result in massive negative repercussions) despite the fact you’re not ready for AI and most AI-first vendor solutions SUCK, and trying to bring in in big consultancies (who will propose multi-year big-bang projects that, like all big bang projects that came before, will result in big busts and possibly create some of the biggest supply chain disasters of all time). You know the tech doesn’t work. You saw the 6% success rate from the recent McKinsey study and the 5% from the recent MIT study (both in late 2025). That’s a 94% failure rate, which is even worse than the general tech failure rate of 88% (as per a Bain 2024 study)!

You know you need modern tech, and you know, for the vast majority of those needs, AI ain’t it. Especially since you know that there is no such thing as Artificial Intelligence. (Artificial Idiocy for sure — it’s called Gen-AI — but not Artificial Intelligence. The best you can get is Augmented Intelligence, but that’s always narrowly focussed and quite rare due to too much research, and promotion, of Gen-AI LLMs that will never work [as the models are foundationaly flawed and there is no more data to train them on].)

So what you do?

Frankly, you lie and say its AI!

The fact of the matter is, if the C-Suite is insisting on AI, it’s because they don’t actually know what AI is. (This should be abundantly clear by the fact we have a lot of vendors, and consultancies, claiming AI Employees, and we all know that’s pure bullcr@p!) And the reason they are insisting on it is because everyone else is lying to their face. The Big (and small) “AI” Tech Vendors. (Big) Analyst firms. Big Consultancies. The Media publishing the fake claims and fake stories (and presenting what are bullcr@p-filled advertorials as vetted and verified case studies). Influencers spreading the hype for their own profit.

And since this C-Suite has a lower TQ (Technological Quotient) than an average elementary school child (who can probably use your phone and tablet better than you can), it’s not like they have any real clue what AI is.

And if the C-Suite wants “proof” that the vendor you select has AI, ask the C-Suite what that means to them. Nine times out of ten it is a chatbot interface. If the vendor you select hasn’t done so already, just have them hook in Chat-GPT through an API, let the execs play for 5 minutes, get the C-Suite approval, and then have the vendor disable the interface before delivery (in platforms where it truly is useless, or limit the chatbot interface to help queries where there is very little downside to it screwing up).

When Chat-GPT first became the rage in Procurement, C-Suites insisted on “AI Guided Buying” even though, with a well designed federated catalog (that support standard service forms) — that supported contracts, preferred vendors, (learned) business rules, and budgets — it was five to ten times faster to use the integrated search bar and filters (as per our twelfth entry of our 2025 Myth-busting series where we illustrated just how dumb the Gormless AI could be). After losing out on a few deals due to this lack of functionality (even though they were the buyer’s choice), a fed-up vendor built a chat-bot led guided buying offering on standard LLM libraries. They can turn it on, demo it for the pointy-haired bosses, and turn it off again.

This is critical when the real value of

  • analytics is exploration
  • sourcing is optimization (not error-prone calculations by an LLM that might erroneously multiply a number by -1 because it’s interpretation of the request is to satisfy an arbitrary savings number anyway it can)
  • supplier management is potential issue detection and human review and remediation
  • procurement is honouring contracts, using vetted suppliers, and following rules designed to prevent risk
  • etc.

So take advantage of the fact that they don’t have a clue what (real) AI is and tell them whatever tech you need to solve your problem, regardless of how much AI it does, or doesn’t, have is the latest and greatest AI if that’s what it takes to get the tech you need to solve your problem.

I know you don’t want to lie, but the reality is that is now what you have to do to keep your job, because if you don’t get the tech you need, you’ll fail and be made redundant. And since everyone else is lying too, chances are someone is already saying the tech is AI and you can just point to them (and blame them for the lie).

End of the day, it’s whatever makes you the most productive. That might be a new AI solution, might be a classic ML or NN solution, or it might be two-decades old rule-based automation where you can encode a few rules and have the solution do 95% of your work on auto-pilot without any worry of it ever screwing up (and costing someone their job).

Remember, you’re hired to get things done, not listen to bullsh!t that comes straight from the A.S.S.H.O.L.E.. Don’t get blinded by the hype!