Summary
Let's start with an uncomfortable truth: most AI projects fail. Not "fail to meet expectations" fail. Properly fail. Money spent, nothing to show for it, everyone pretends it never happened fail.
The numbers vary depending on who you ask, but they're consistently grim. Gartner says 85% of AI projects don't deliver. MIT Sloan found that only 10% of companies see significant financial benefit from AI. And yet, according to every CEO keynote and LinkedIn thought leader, AI is going to revolutionise everything by next Tuesday.
Something doesn't add up.
The Problem Isn't AI. It's How We're Approaching It.
Here's what we see, over and over again.
A business reads about ChatGPT. The board starts asking questions. Someone gets tasked with "doing something with AI." A big consultancy gets called in. Six months and a few hundred thousand pounds later, there's a strategy deck, a proof of concept that impressed everyone in the demo, and absolutely nothing in production.
Sound familiar?
The problem isn't that AI doesn't work. It does. We've seen it extract six months of manual data processing work in six weeks. We've seen it eliminate fifteen hours of analyst time per week on report generation. We've seen it turn chaotic document archives into searchable, queryable knowledge bases.
But those successes all had something in common: they started small, solved a real problem, and proved value before scaling.
The Three Ways AI Projects Die
1. The "Boil the Ocean" Approach
Someone decides AI is going to transform the entire business. Customer service, operations, finance, HR - everything. A massive programme gets kicked off. Requirements gather dust while the technology moves on. By the time anything gets built, the original problem has changed, the budget's gone, and everyone's exhausted.
Transformation programmes have a 70% failure rate even without AI. Adding experimental technology to the mix doesn't improve those odds.
2. The "Solution Looking for a Problem" Approach
A vendor demos something impressive. It uses AI, it looks slick, the sales team are persuasive. Someone signs a contract. Then comes the awkward realisation that nobody actually knows what business problem this solves or how it fits with existing systems.
AI is a tool, not a strategy. You wouldn't buy a forklift and then wander around looking for things to lift.
3. The "Perfect Data" Trap
"We can't do AI until we fix our data." This sounds sensible. It's also a convenient way to delay forever while spending millions on data warehousing projects that never quite finish.
Yes, you need decent data. No, you don't need perfect data. And often, the act of building something useful reveals exactly which data problems actually matter - as opposed to the theoretical ones consultants love to document.
What Actually Works
The AI projects that succeed tend to share a few characteristics.
They solve a specific, painful problem. Not "improve efficiency" or "enable digital transformation." Something concrete. "Our analysts spend two days a month copying data between spreadsheets." "We can't find anything in our document archive." "Customer queries take 48 hours to answer because we're checking three different systems."
They start embarrassingly small. One process. One team. One spreadsheet. Prove it works, measure the impact, then expand. This feels slow, but it's actually faster than spending eighteen months on a grand plan that never launches.
They work with existing systems. Most businesses don't need to rip out their infrastructure. They need their existing systems to talk to each other properly. The best AI projects enhance what you've got rather than demanding you replace everything.
They have a clear owner. Not a steering committee. Not a cross-functional working group. One person who cares whether this thing actually gets done and has the authority to make decisions.
How to Not Be a Statistic
If you're thinking about AI, here's our advice.
Don't start with AI. Start with the problem. What's the most painful, time-consuming, error-prone process in your business? What makes your team groan on Monday mornings? That's your starting point.
Quantify the pain. How many hours does this waste? What does it cost? What's the error rate? If you can't put numbers on it, you can't measure whether AI actually helped.
Pick the smallest viable win. Not the biggest opportunity. The smallest thing that would still be valuable if it worked. Get a win under your belt. Build confidence. Then go bigger.
Talk to someone who's done it. Not someone who wants to sell you a platform. Not someone who's going to spend three months on discovery. Someone who can look at your problem and tell you honestly whether AI is the right solution - and if so, what the fastest path to value looks like.
A Sensible First Step
We run something called a Lunch & Learn. It's exactly what it sounds like: an hour over lunch where we demystify AI and help you identify where it might actually be useful in your business.
No sales pitch. No commitment. No strategy deck that costs six figures. Just a practical conversation with people who've implemented this stuff in the real world - in financial services, infrastructure, construction, recruitment, and everything in between.
Sometimes the answer is "AI isn't right for this." That's a useful answer. It saves you a lot of time and money chasing the wrong thing.
Sometimes the answer is "There's a quick win here that could be live in weeks." That's an even more useful answer.
Either way, you'll leave with a clearer picture of what's possible and what's not - which is more than most AI initiatives deliver after months of work.
If that sounds useful, get in touch. We'd rather have an honest conversation now than watch another AI project fail later.
Xerini is an AI and data consultancy that helps businesses get actual value from their data - without the transformation drama. We built Xefr, a data orchestration platform that connects your existing systems instead of replacing them. Find out more or book a Lunch & Learn.
