Why Most AI Projects Fail (And How We Prevent It)

There’s a stat that gets thrown around a lot: somewhere between 70-85% of AI projects fail to make it to production. Having spent 15+ years building software systems — and the last several years focused specifically on AI — I can tell you that number feels about right.

But the reasons might not be what you expect.

Starting Without Clear Business Value

The most common failure mode is the simplest: building AI because it sounds impressive, not because it solves a specific problem. I’ve seen companies spend six figures on “AI transformation initiatives” that amount to a chatbot nobody uses.

The fix is boring but effective: start with the business problem. What process is costing you money? What manual task is eating 40 hours a week? What decision could be made faster with better data? If you can’t put a dollar figure on the problem, you’re not ready for an AI solution.

Skipping the Data Quality Step

Here’s the dirty secret of AI: your model is only as good as your data. And most organizations’ data is a mess. Duplicate records, inconsistent formats, missing fields, data scattered across fifteen systems that don’t talk to each other.

We’ve seen teams spend months fine-tuning models only to discover their training data was fundamentally flawed. The unsexy truth is that data cleaning, normalization, and pipeline work often accounts for 60-70% of a successful AI project.

Over-Engineering V1

Engineers love elegance. We want to build the perfect system from day one. But in AI work, that instinct is a trap. The technology moves so fast that the framework you chose three months ago might already be outdated.

Ship the simplest thing that works. Use an off-the-shelf model before training a custom one. Call an API before building infrastructure. You can always iterate — but you can’t iterate on something that never shipped.

Not Measuring ROI

If you’re not tracking the impact of your AI system against the baseline, you have no idea if it’s working. “It feels faster” isn’t a metric. “We reduced processing time from 4 hours to 12 minutes with 98% accuracy” is.

How We Avoid These Traps

At Odyssey Mercantile, every engagement starts with a discovery sprint. We spend the first week understanding your data, your processes, and your actual business goals — not your AI wishlist.

Then we build a proof of concept. Something small, focused, and measurable. If the POC doesn’t show clear value, we tell you before you’ve spent a fortune. And if it does, we have a clear path to production with metrics already in place.

No hype. No slide decks full of buzzwords. Just engineers who ship production systems that deliver measurable results.