Open LinkedIn on any given day and you will see it: another post about how AI will revolutionize your business. Another vendor promising their tool will “transform your operations.” Another consultant urging you to adopt machine learning before your competitors do.
The message is clear: AI is the future, and if you are not implementing it now, you are falling behind.
But here is what nobody is talking about: most businesses are not ready for AI. Not because they lack budget or technical talent — but because they have not done the foundational work that makes AI actually useful.
The Problem With Tool-First Thinking
Walk into most “AI consulting” engagements today and you will notice a pattern. The conversation starts with the solution, not the problem.
“Have you considered implementing a machine learning model for forecasting?”
“Our platform uses AI to automate your reporting.”
“Let us build you a predictive analytics dashboard.”
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These are not bad tools. But they are answers to questions that were never asked. And when you implement a sophisticated solution for a problem you have not fully diagnosed, you get one of two outcomes:
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Expensive shelfware. The tool gets built, the team gets trained, and six months later nobody is using it because it does not actually fit how work gets done. |
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Automated inefficiency. You have now made your broken process faster. Congratulations — you are producing garbage at scale. |
Neither outcome is what you were sold.
What Gets Skipped: The Unsexy Work
Before any technology decision, there is work that needs to happen. It is not flashy. It will not make for a good LinkedIn post. But it is the difference between a tool that transforms your operations and one that collects dust.
Process mapping. How does work actually flow through your organization? Not how it is supposed to flow — how it actually flows. Where do things get stuck? Where do people create workarounds? Where does data get re-entered, reformatted, or reconciled manually?
Waste identification. Lean Management principles teach us to look for seven types of waste: overproduction, waiting, transportation, overprocessing, inventory, motion, and defects. Most organizations have never systematically examined their data workflows through this lens. When they do, they are often shocked by what they find.
Root cause analysis. That report that takes three days to produce every month — why? Is it a technology problem, or is it that the source data is unreliable? Is it a skills gap, or is it that the process requires twelve unnecessary approval steps? The answer determines the solution.
This is the work of Lean Management and Continuous Improvement. It has been transforming manufacturing and operations for decades. And it is exactly what is missing from most AI conversations.
Why Process Improvement Comes First
Here is a scenario we see often:
A company wants to “use AI for forecasting.” They have heard competitors are doing it. Their vendor is pushing a new module. Leadership is asking why they are not more innovative.
So they engage a consultant, build a predictive model, and deploy it.
Six months later, the forecasts are wrong more often than they are right. The team does not trust the outputs. They are back to doing things the old way, except now they have an expensive AI tool running in the background that nobody looks at.
What went wrong?
Usually, it is one of two things:
The input data was bad. Machine learning models are only as good as the data they are trained on. If your historical data has quality issues — inconsistent categorization, missing fields, manual entry errors — the model learns those patterns. It does not magically fix them.
The problem was not a prediction problem. Sometimes what looks like a forecasting challenge is actually a process challenge. The issue is not that you cannot predict demand — it is that your demand signal is distorted by a dozen handoffs and manual adjustments before it reaches the planning team.
In both cases, Lean principles would have uncovered the real issue before any technology was selected. And in both cases, the right solution might have been simpler, cheaper, and more effective than an AI model.
The Right Sequence
None of this is to say AI does not have value. It absolutely does — in the right context, applied to the right problems, with the right foundation in place.
But context matters. Sequence matters.
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First:
Understand the current state. Map the process. Talk to the people doing the work. Document what is actually happening, not what the org chart says should happen.
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Second:
Identify waste and inefficiency. Where are the bottlenecks? Where is value being created, and where is time being lost? What would this process look like if we stripped it down to only the essential steps?
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Third:
Fix what can be fixed without technology. Sometimes the answer is eliminating unnecessary approvals. Consolidating data sources. Standardizing definitions. Creating clear ownership. These changes cost almost nothing and often deliver immediate results.
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Fourth:
Now — and only now — ask: what technology would make this better process even more efficient? Maybe it is AI. Maybe it is simple automation. Maybe it is a dashboard that surfaces the right information at the right time. The answer emerges from the analysis, not from a vendor’s sales pitch.
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Cutting Through the Noise
The AI hype is not going away. If anything, it is getting louder. And there is real pressure on business leaders to show they are “doing something” with AI.
But the companies that will actually benefit from these technologies are the ones willing to do the unglamorous work first. They are the ones asking “what problem are we solving?” before “what tool should we buy?”
They are the ones who understand that the goal is not to implement AI. The goal is to run a better business. AI might help with that — but only if you have built the foundation first.
Before you buy the tool, understand your process.
Before you automate, eliminate the waste.
Before you chase the future, fix the present.
That is not a popular message right now. But it is the right one.
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Oneta 