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Yesterday, I was sitting in a room listening to colleagues talk about the latest AI developments. New models. New capabilities. New promises about what AI will be able to do. It was energetic, enthusiastic, and genuinely well-informed.
And somewhere in the middle of it, a quiet thought surfaced: does any of this actually matter right here and right now?
It took me back to a book I read years ago when studying Business and Finance. The Goal by Eliyahu Goldratt. If you haven’t read it, it’s a business novel — a plant manager called Alex Rogo, a factory on the verge of closure, and a series of deceptively simple questions from an old professor that eventually save the business. The central insight is the Theory of Constraints: every system has a bottleneck. Find it, fix it, and the whole system improves. Ignore it, and no amount of optimisation elsewhere will save you.
It took me back to a book I read years ago when studying Business and Finance. The Goal by Eliyahu Goldratt. If you haven’t read it, it’s a business novel — a plant manager called Alex Rogo, a factory on the verge of closure, and a series of deceptively simple questions from an old professor that eventually save the business. The central insight is the Theory of Constraints: every system has a bottleneck. Find it, fix it, and the whole system improves. Ignore it, and no amount of optimisation elsewhere will save you.
Sitting in that room, I realised the AI industry has a Goldratt problem. It is endlessly fascinated by what is possible at the frontier, and not nearly interested enough in the bottlenecks that are quietly strangling real businesses today.
That is when the idea of the Magpie Effect crystallised for me.
There is a bird famously distracted by shiny things. It spots something glittering, drops whatever it was doing, and goes to investigate. Every few weeks, a new AI headline lands. A new model. A new capability. A new promise that this is the breakthrough that changes everything. Autonomous agents. Artificial General Intelligence. AI that writes code, runs your supply chain, manages your workforce. The glittering object rotates. The industry pivots. And somewhere in a conference room, a leadership team starts asking whether they should be doing that instead.
The Magpie Effect is quietly one of the biggest obstacles to real AI progress in business today. And Goldratt, I think, would have had little patience for it.
Goldratt was writing about factory floors. Machines, production lines, throughput, inventory. But swap the factory for a financial services firm, a retailer, or a healthcare provider, and the principle holds perfectly. The production lines just look different now. They are the workflows, the approval chains, the data pipelines, the customer journeys that run your business every single day. Constraints in those processes cost just as much as a jammed machine on a shop floor. They are just harder to see.
The Cost of the Chase
Chasing possibilities isn’t free. It has a price, and businesses are paying it in three currencies.
Time. Every pivot towards the next shiny object means restarting conversations, rewriting roadmaps, and pulling teams off work that was already delivering. The opportunity cost is real even when it’s invisible.
Money. Proof of concepts that never graduate. Vendor relationships built on promises. Platforms bought for futures that haven’t arrived. Science projects with no measurable return dressed up as innovation investment.
Momentum. Perhaps most damaging of all. Teams that are perpetually chasing the new never build the deep competency that comes from doing one thing properly, learning from it, and scaling it. They become generalists in hype rather than specialists in value.
The AI industry is not entirely to blame. Vendors need differentiation. Analysts need narratives. Media needs clicks. But the businesses that get swept up in it are making a choice — and there is a different choice available.
Practical AI Is Already Here. It’s Just Less Exciting.
Here is the uncomfortable truth: the AI that will genuinely move the needle for most organisations is not frontier. It is not exotic. And it is definitely not on the cover of a technology magazine.
It is the model that reduces manual data entry by 70%. The system that flags anomalies in financial transactions before a human would spot them. The tool that summarises a week of customer feedback into ten actionable themes in minutes. The scheduler that optimises field service routes and saves 15% on fuel costs.
None of those are science fiction. All of them are in production somewhere today. All of them have a measurable ROI. And none of them required waiting for the next frontier model to drop.
Practical AI solves a real business problem with available technology, accessible data, and a return you can put on a spreadsheet. That is not a consolation prize. That is the goal.
Start With Your Golden Process
Before you evaluate a single AI use case, ask one question: what is the one business process that, if it stopped tomorrow, the business would stop with it?
Not the most complex process. Not the most talked-about. The one that everything else depends on. The process that runs quietly in the background, holding the whole operation together. That is your Golden Process.
It might be order management. It might be claims processing. It might be production scheduling, customer onboarding, or logistics coordination. Every business has one. Most businesses have never explicitly named it.
Name it. Then look at it seriously.
Where does it slow down? Where does it depend on human heroics to keep running? Where does data get re-entered, reformatted, or chased across systems? Where do errors creep in? Those friction points are not just operational annoyances — they are your AI use case shortlist.
This is the starting point of a genuinely business-aligned AI strategy. Not a vendor briefing. Not a technology roadmap. Not a list of capabilities from the latest model release. The Golden Process and the friction within it.
The achievable business outcome — faster, cheaper, more reliable execution of the thing the business depends on most — is worth more than any amount of AI possibility. And it is almost always more achievable than it looks, because the process is already understood, the data already exists, and the ROI is not hypothetical.
Start there. Everything else follows.
How to Tell the Difference: The Practical AI Test
When an AI use case lands on your desk — from a vendor, from a consultant, from an enthusiastic team member who just watched a keynote — run it through these five questions.
1. What specific business problem does this solve? If the answer starts with “it could potentially…” or “imagine if we…” that is a possibility, not a solution. A practical use case has a named problem with a named owner.
2. Do we have the data to support it? AI without good data is not AI. It is noise with a marketing budget. Before you evaluate any use case, ask whether the relevant data exists, whether it is clean, and whether it is accessible. If the answer to any of those is no, the use case is not ready — regardless of how impressive the demo looked.
3. Do we have the skilled resources to support it? Good intentions and good data are not enough. Someone has to build it, maintain it, and own it when something goes wrong. That might be internal talent, a partner, or a combination — but the answer needs to be honest. An AI use case with no clear resource plan is not a use case. It is a wish list item.
4. Can we measure success? Define the KPI before you build anything. Handling time. Error rate. Cost per transaction. Customer satisfaction score. If you cannot articulate what winning looks like in a number, you are not running a business initiative. You are running an experiment with an open-ended budget.
5. Can this be in production within 90 days? Not perfect. Not scaled. But in the hands of real users, generating real outputs, against real data. If the answer is no, the scope is wrong or the foundations are not ready. Either problem needs solving before you proceed.
6. Would this still matter if nobody wrote about it? Strip away the hype. Imagine the use case is completely unglamorous — no AI branding, no press release, just a quiet improvement to a business process. Does it still make the list? If yes, it is probably worth doing. If the answer depends on how it sounds in a strategy presentation, be careful.
Make AI Boring. That Is the Win.
The organisations that are getting the most from AI right now are not the ones chasing the frontier. They are the ones that picked three or four use cases, proved the value, scaled what worked, and moved on to the next problem on the list. Quietly. Methodically. Without waiting for permission from the hype cycle.
They have made AI boring. And boring, in this context, is the highest compliment.
Boring means repeatable. Boring means trusted. Boring means it runs on a Tuesday afternoon without anyone noticing, because it has just become part of how the business works.
The magpies are still circling. The glittering objects will keep appearing. The organisations building durable capability are the ones that have learned to look away — and get back to the work that actually pays.
One Final Thought on Foundations
None of this works without getting data right. Practical AI is only as good as the data that feeds it. The organisations that move fastest are not necessarily the ones with the most advanced models — they are the ones whose data is clean, governed, and ready to use.
That is a less exciting conversation than whatever was announced at the last major AI conference. But it is the conversation that separates progress from theatre.
Data first. Practical use cases second. Everything else can wait.
At the end of The Goal, Alex Rogo doesn’t walk away with a new machine or a bigger budget. He walks away with a better question. Not “how do I optimise everything?” but “what is the goal — and what is stopping us from reaching it?” That shift in thinking is what saved the factory. It is the same shift that separates businesses building real AI capability from those still circling the next shiny object. Find your Golden Process. Name the constraints within it. Ask the right questions about where AI can remove them. The rest follows.
If you found this useful, the Getting AI Right First Time post covers the five steps to moving from AI experiments to durable enterprise capability.
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