Here's a stat that should make you uncomfortable. 95% of corporate AI projects fail to deliver real value. They get stuck in "pilot purgatory." Endless prototypes. Demos that impress in meetings. And then... nothing. Six months later nobody's using the thing. The project quietly dies. Everyone moves on.
I've seen this happen over and over. But here's the thing: AI adoption doesn't have to fail. The failures aren't about the technology. They're about how the project was set up from day one.
The Real Reason Your AI Project Failed
Most failed AI projects have the same root cause: they weren't solving a real business problem. This shows up in a few ways:
FOMO-driven adoption. Leadership reads about AI. The board asks questions. Someone mandates an "AI strategy" without defining what problem they're actually solving. The result? A solution looking for a problem. A chatbot nobody asked for, answering questions nobody has.
Building for the wrong scale. If you're building something that 2 people use occasionally, you're not going to see real results. You might technically "adopt AI," but you won't move any needle that matters.
Skipping the business case. Teams jump straight to building. They never ask the fundamental question: Why isn't your business bigger than it is right now? Where are the bottlenecks? What manual work is eating up your best people's time? What's slowing down revenue? If you can't answer those questions clearly, you're not ready to build anything.
What Actually Works: Discovery Before Development
When I work with a business on AI, we don't start with technology. We start with your business.
First, we find the bottlenecks.
- Where is work getting stuck?
- Where are skilled people doing repetitive tasks that don't need their expertise?
- Where are you losing deals because you can't move fast enough?
- What's hurting your top line? Your bottom line?
The goal isn't to use AI for its own sake. The goal is to solve a real problem in a way that actually gets used.
Rapid Prototyping That Proves Business Impact
Once we've found a real problem worth solving, I move fast, but not recklessly. I build functional prototypes. Not toy demos that look impressive but can't handle real work. Actual tools your team can use to do their jobs. Then we get it into end users' hands immediately.
This is where most AI projects succeed or fail. A prototype sitting in a sandbox proves nothing. A prototype being used by your team to do real work every day? That proves everything. You'll know within weeks whether you're onto something. If people are using it, if it's saving them time, if it's speeding up their work. Then we're ready to talk about production.
If they're not using it? We learned that cheaply, before you invested in infrastructure and integrations nobody wanted.
Crawl, Walk, Run
I think about AI adoption in three phases.
Crawl: Discovery. Define the problem. Understand the bottleneck. Build the business case. Sometimes this phase ends with a recommendation not to build anything, and that's a success, not a failure. It means we didn't waste months on the wrong thing.
Walk: Prototype. Build something real but scoped. Get it in front of users. Measure whether it actually changes how work gets done. This is where you prove (or disprove) the business case.
Run: Production. Only after you've proven value do you invest in scaling: integrations with your existing systems, proper security and compliance, infrastructure to support your whole organization.
Skipping phases is how projects fail. You can't run before you can walk.
Keep the Technology Simple
One more thing I've learned: the best AI solutions aren't the most technically impressive ones. They're the ones that actually solve the problem. That means:
- Simple UI/UX that people figure out without training
- Technical accuracy over flashy features
- Building something that fits how your team already works
A Note on AI Coding Tools
You might have heard that AI can "write code" now. Maybe you're wondering if you even need a developer anymore. Here's my take.
AI coding tools are genuinely powerful. I use them every day. They've made me way more productive. I can solo-build production applications that would have taken a team before. But here's why they work for me: I'm a software engineer who can evaluate what they produce. I can think through system architecture, catch mistakes, and make judgment calls about code quality and security.
A non-technical person using the same tools? They'd produce something that looks right but breaks in production. AI makes good engineers more productive. It doesn't replace engineering judgment.