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There’s a pattern we see quite often. A business invests in machine learning, the project gets built, the data scientists are happy with the model’s accuracy, and then… not much changes. Costs stay the same. Revenue doesn’t shift. The project quietly fades from the agenda.
It’s more common than you might think. Research suggests that only around 25% of AI and machine learning initiatives deliver the ROI that was originally expected. That’s a striking number, especially given how much enthusiasm, budget, and goodwill typically goes into these projects.
So what’s going wrong, and more importantly, what does it look like when it goes right?
The honest truth about why ML projects stall
Most machine learning projects don’t fail because the technology doesn’t work. They fail because the project was never properly connected to a business problem worth solving.
We see this pattern consistently: a team builds a model, it performs well in testing, and then it gets handed over with no real plan for how it fits into day-to-day operations. Nobody owns it. No process changes around it. And without that connection, even the most technically impressive model delivers nothing.
Data quality is another recurring culprit. Around 70% of AI project failures are linked directly to data issues, whether that’s incomplete records, inconsistent formats, or simply data that doesn’t reflect what’s actually happening in the business. Garbage in, garbage out.
Where the real returns come from
When machine learning does deliver, it tends to show up in a handful of predictable places: cost reduction, revenue improvement, and risk mitigation.
In operations, predictive models can cut costs by 15% or more by catching inefficiencies before they escalate. In commercial functions, businesses that use ML for pricing and demand forecasting report sales ROI improvements in the range of 10 to 20%. In logistics, we’ve seen examples of organisations saving tens of millions through smarter inventory and routing decisions.
The common thread isn’t the sophistication of the model. It’s the clarity of the use case. The businesses getting results started with a specific, measurable problem and built their ML solution around it, not the other way round.
The build-vs-buy question (and why it’s often a distraction)
One thing we’re asked about a lot is whether to build ML capabilities in-house or work with a specialist partner. There’s no universal answer, but the question itself is often a distraction from what actually matters.
What matters more is whether you have the right data, the right problem definition, and a clear route to production. A beautifully engineered model that never leaves a development environment won’t move any needles. A simpler model that’s embedded into a real workflow, used daily, and iterated on over time almost certainly will.
The questions worth asking before your next project
If you’re trying to justify investment in machine learning, or trying to understand why a previous investment hasn’t paid off, here are the questions we’d start with:
- Is the problem clearly defined in business terms, not technical ones?
- Do you have sufficient, reliable data to support it?
- Is there an owner on the business side who will champion the output?
- How will success be measured, and over what timeframe?
These aren’t glamorous questions, but they’re the ones that consistently separate projects that deliver from those that don’t.
Getting to value that actually shows up
Machine learning genuinely can transform how businesses operate: reducing waste, improving decisions, and creating real competitive advantage. We’ve helped clients make that happen, and we know what it takes. But it works when it’s grounded in a real problem, supported by good data, and built with business outcomes front and centre.
If you’re looking at your ML investments and wondering whether you’re getting the returns you expected, we’d love to have that conversation.
