How to Avoid the Six Most Common Mistakes in Machine Learning

Machine learning has entered boardrooms as spreadsheets did in the past – quietly at first, then suddenly indispensable. Models are now being pitched by executives to make predictions on churn, automation of credit risk, dynamic product pricing, you name it. However, this enthusiasm masks a darker truth: the majority of ML projects are not, in fact, resulting in corresponding business value – not because the math is wrong, but because leadership is.

Technical quirks, in fact, are not among the most prevalent errors of machine learning. Errors instead rise from tactical blind points – mistakes in framing, oversight and implementation that transform good projects into sunk costs. The true art lies in being able to identify these traps before businesses’ cash – and credibility – run out.

Mistake 1: Pursuing Accuracy, not Relevance

It is often a rookie instinct to idolize the accuracy score. Teams boast their model is 95% accurate. Investors nod. Managers breathe easy. Yet, accuracy is more often than not the wrong measure to look at. Consider a bank which already forecasts that a loan will never default correctly 95% of the time in a portfolio with only 5% of the customers ever defaulting. The model appears brilliant – until the 5% defaults wring several millions of dollars more than expected.

Business executives should make the move to change the discussion from accuracy to impact. Does the model make better decisions that are significant: fewer defaults, increased retention, increased margins? Otherwise, you are polishing the wrong apple.

Mistake 2: Forgetting the Baseline

Machine learning can sometimes be magic, but the right comparison is not ‘model vs perfection’. Instead, it is ‘model vs. current practice’.

Consider a retailer who is making a decision on whether to use an ML demand forecast model. When the current baseline of a simple moving average already forecasts sales within a 10% accuracy threshold and the new model only has an incremental 2% forecast accuracy, is the complexity worth it?

Always ask: compared to what? Without a baseline, running complex and sophisticated models may just be chasing headlines for its own sake. Good strategy isn’t about using the most up-to-date thing – it is being superior to the alternative.

Mistake 3: Overlooking Data Quality

The silent killer of ML models is dirty data. You may employ PhDs, rent cloud servers, and tweak algorithms endlessly – however, when customer addresses are stale and transaction logs not in sync, then the model turns into garbage in, garbage out. This is an error brought about by impatience. Business leaders invest in modeling teams even before repairing the actual data pipeline those teams will be working with.

The solution is banal, yet essential: invest in data governance, management, cleaning and ownership. Models constructed on wobbly data do not merely fail – they mislead.

Mistake 4: Modelling as a Black Box

Most executives nod politely in response to model outputs they do not understand. That’s dangerous. Trusting ML outputs without questioning them is as foolish as signing an agreement you have not read.

While all executives do not need to be experts at gradient descent, they must insist on explainability. What were the reasons behind the decision? To what extent are the results sensitive to changes? What trade-offs were made?

This is key due diligence. You wouldn’t buy a company without going through their finances. Similarly, strategy cannot be bet on a model that has not been thoroughly interrogated.

Mistake 5: Overfitting Past Data

Machine learning models are usually very efficient at identifying historic trends. But the market, much like a game of poker, transforms completely when players adapt. Overfitting is when models overestimate yesterday and fail tomorrow.

Suppose an airline model of pricing is trained on pre-pandemic travel patterns – once COVID struck, all forecasts seemed laughable. The risk isn’t academic and reserved for once-in-a-century events either. Overfit models embed false certainties, encouraging leaders to double down on their bets on mirages.

The cure is humility. Out-of-sample stress-tests of models monitored in live environments and retrained consistently is the key. Markets evolve faster than models.

Mistake 6: Ignoring Organizational Fit

Even the best algorithm fails when the organization does not want to utilize it. A churn model that raises the red flag on at-risk customers is useless when sales teams do not heed the warning signs.

Too many projects fail due to leaders considering ML models as a plug-and-play solution instead of organization-level behavioral change. Adoption is a process – requiring alignment of incentives, redesigning processes and trust in the output.

Strategy implementation is far more difficult than strategy development. A model that is not suitable on the org chart is a playbook that no one employs.

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Machine learning does not require just blind coding. It’s about judgment. For future executives and business learners, it is more than just memorizing algorithms – it is about posing the right pointed questions:

• Are we measuring the most useful metric, not just accuracy?
• How does the model compare to current practices?
• Is the underlying data trustworthy?
• Do we have the ability to explain findings and assumptions and take action?
• Are we preparing for change instead of just stretching the past?

There is no technical checklist as such. It is a tactical mindset: sceptical, contextual and inexorably focused on business results.

Machine learning is not a silver bullet. It is an instrument – a mighty one at that – which entails discipline to accrue true comparative advantage. Leaders who do not mistake noise for signal will make models assets. Those who fail to do so will suffer.