Machine Learning ROI Benchmark: What It Delivers by Use Case

Key points: What does machine learning really deliver? This benchmark lays out the results per use case, based on anonymised outcomes from our own projects, combined with published industry ranges. The figures are deliberately conservative and meant to calibrate an investment, not as a guarantee. For each application we name a concrete real-world example as a reference point, from a forecast correction that led to roughly €1.2 million in annual savings at a global manufacturer to a recommendation model with 84% accuracy at an automotive marketplace.

How this benchmark is built

Most "AI ROI" figures circulating online are marketing promises without context. This benchmark is built differently: for each use case we combine the outcomes we see in practice with independently published ranges, and we explicitly state what is being measured. Where we name an own result, it is anonymised but real.

Two notes upfront. First: a range is not a promise. The actual outcome depends on data quality, scale and how far the model lands in decisions. Second: value rarely arises from the model itself, but from its integration into your processes. An accurate prediction left in a standalone dashboard changes nothing.

The benchmark by use case

What the numbers mean

Demand forecasting delivers the most clearly quantifiable value for large or erratic assortments. The gain scales with your inventory value: a few percentage points of accuracy quickly becomes a large sum at high purchasing volumes. The strongest reference point from our practice comes from manufacturing, where correcting the systematic over- or under-estimation of the statistical forecast (forecast bias) led to an order of magnitude of €1.2 million in annual savings. The same approach translates to retail and e-commerce, where reorder points across multiple warehouses reduce over- and understock.

Predictive maintenance delivers the largest absolute value in capital-intensive environments. Where an hour of downtime on a critical line runs into the thousands to tens of thousands of euros, predicting failures pays back quickly. The common range is 30-50% less unplanned downtime.

Computer vision quality control replaces sample-based, manual inspection with continuous control across the full stream. Detection rates of 99%+ are achievable, and inspection shifts from after-the-fact to real-time across the full product stream instead of a sample.

Recommendation and cross-sell models tie directly to revenue. At an automotive marketplace, a combination of a cross-sell engine across 1.2 million product-vehicle relations and a quote-scoring model at 84% accuracy delivered both better recommendations and a priority list for sales.

Document intelligence (IDP) delivers the hardest FTE gains at large manual volumes. Automatically extracting and classifying unstructured documents typically lowers manual work by 50-70%. At a financial services provider this involved automatic classification of inbound email, generating draft replies from client data, and assembling case files.

What it costs, honest ranges

Costs vary with complexity and especially with data quality. As a guideline:

• Proof-of-concept (one scoped use case, validating feasibility): order of several thousand to tens of thousands of euros.
• Production project (data pipeline, integration, deployment and operation): tens to hundreds of thousands, depending on scale and number of systems.

The largest cost line is usually not the model but data preparation, often around 40% of the budget. An AI-ready data foundation structurally lowers this line on every subsequent project. For every business case, account for the full lifecycle, including post-launch operation; a model that is not maintained degrades and so destroys its own return.

How to use this benchmark

Use the ranges to calibrate your own expectation, not as a promise. Start with the low end of the range as the base scenario and build your business case on it. Validate the core assumption, does the model reach the required accuracy on your data? in a proof-of-concept with a pre-agreed go/no-go threshold, before releasing the full budget.

Want to know what machine learning could realistically deliver for a concrete use case in your organisation? Get in touch, we map your data, the expected outcome and the feasibility.

Frequently asked questions

What does machine learning deliver on average per use case?

It varies strongly by application. Demand forecasting typically lowers inventory costs by 20-30%; predictive maintenance reduces unplanned downtime by 30-50% and maintenance costs by 10-25%; computer vision quality control reaches detection rates of 99%+; document intelligence lowers manual processing by 50-70%. The actual value depends on scale, data quality and how far the result lands in processes.

What is this benchmark based on?

On a combination of anonymised results from our own completed projects and published industry benchmarks. The ranges are deliberately conservative and meant to calibrate an investment, not as a guarantee. For each use case we name a concrete, anonymised real-world example as a reference point.

What does a machine learning project cost?

Costs vary with complexity and data quality. A scoped proof-of-concept is typically in the order of several thousand to tens of thousands of euros; a full production project with integration and operation runs into the tens to hundreds of thousands, depending on scale. The largest cost line is usually data preparation, not the model.

Which use case pays back fastest?

Applications tied directly to a measurable KPI that introduce no extra cost pay back fastest, dynamic pricing and demand forecasting at large inventory values, and document intelligence at large manual volumes. Predictive maintenance delivers the largest absolute value in capital-intensive environments where downtime is expensive.