The $2 Million PowerPoint

Why most AI projects fail (and what actually works)

I watched a VP present an AI pilot to the C-suite last month. Slick deck. Impressive accuracy metrics. Glowing testimonials from the data science team.

The CEO asked one question: “What problem does this solve?” Silence.

That pilot died three weeks later. Along with $2 million and six months of work.

We’ve been here before.

Ten years ago, every company was building dashboards. Beautiful dashboards with real-time data, interactive charts, and stunning visualizations.

Most of them sat idle. Nobody looked at them after the first week.

Why? Because we built them before asking what decisions they would actually support. We optimized for “cool” instead of “useful.”

Now we’re doing the exact same thing with AI. Different technology, same mistake.

Here’s what nobody tells you:

Automating a mess just creates a faster mess.

That sexy LLM you’re implementing? It’s going to hallucinate at scale if your underlying data is garbage. That computer vision model? Useless if the process it’s optimizing shouldn’t exist in the first place.

The pattern I see failing everywhere:

Engineers fall in love with tools before anyone defines the outcome

We build something “cool” that never connects to actual value

We measure adoption rates instead of business impact

We treat AI like it’s magic, not a tool

What actually works:

Start with the business problem. Work backward to the technology.

Every successful AI implementation I’ve seen had three things:

A business owner and technical lead working as equals from day one. Not a handoff. A partnership.

Clear ROI metrics before a single line of code. Even if it’s just “this saves Sarah 4 hours a week.”

An obsession with the problem, not the solution. The question isn’t “what can we build with GPT-4?” It’s “what’s costing us money that we could solve better?”

Sometimes the answer is embarrassingly simple.

A retail client wanted to use a transformer-based deep learning model to predict inventory stockouts. The data science team was excited—this was cutting-edge stuff. They planned months of work training the model, tuning hyperparameters, building pipelines.

I asked to see their data first. Turns out, 80% of stockouts happened when orders exceeded 2x the rolling 30-day average. That’s it. A simple rule in their existing system could catch most problems.

We implemented that instead. Took two days. Reduced stockouts by 70%. Saved them $1.2M in the first year.

No transformers. No neural networks. Just basic logic that actually solved the problem.

Another client wanted to predict customer churn using complex ensemble models and neural networks. They were planning a six-month project with feature engineering, model training, A/B testing—the works.

We started by just analyzing their historical data. Turned out customers who hadn’t logged in for 14 days and had only one product were churning at 78%. A simple logistic regression with three features (days since login, product count, account age) got us to 82% accuracy. Deployed in three weeks instead of six months.

The best AI project is sometimes no AI project.

Why I’m writing this:

Because I’m tired of watching good people waste money on AI theater.

Because we’re repeating the dashboard mistake at scale, and someone needs to say it.

Because the hype is exhausting and someone needs to talk about what actually works.

Because failures teach more than successes, and those lessons are worth sharing.

So here’s my question:

What’s your real AI story? Not the deck version. The one that actually moved the needle.

Or better—what’s your failure story? What dashboard is gathering dust? What AI pilot quietly died?

That’s where the learning happens.

Hit reply and tell me.

Welcome to the mess. Let’s figure it out together!