Why So Many AI Projects Fail

AI is supposed to make business easier - yet 95% of GenAI projects fail before they ever create real value.

The reason isn’t bad technology.
It’s bad planning.

Most teams (and even solopreneurs) jump straight into tools - building prompts, testing automations, or buying new software - without doing the groundwork.

The biggest mistakes usually look like this:

1. No Business Understanding
   → No clear goal, just curiosity. “Let’s try AI!” instead of “Let’s fix this bottleneck.”

2. No Data Understanding
   → The data is incomplete, messy, or not even collected.

3. Tool Chaos
   → Every new app promises magic, and suddenly you’re managing 10 dashboards instead of one.

4. Jumping Straight to Modeling
   → People skip the prep work, plug data into an AI tool, and hope for miracles.

And when results disappoint? AI gets blamed - instead of the missing process behind it.

The Framework That Prevents All This

Back in 1996, long before ChatGPT, a group of data experts created a simple process called CRISP-DM - the Cross Industry Standard Process for Data Mining.

It was designed to keep projects focused, efficient, and aligned with business goals.
And it still works today — especially for solopreneurs starting to experiment with AI.

CRISP-DM breaks down any AI or data project into six steps.

Here’s how it looks when you apply it to your own business 👇

1️⃣ Business Understanding - Start with Clarity

Before opening any AI tool, ask yourself:

What problem am I actually solving?

Is it about saving time, attracting better clients, or understanding your numbers?
Be specific - “I want more clarity about my top 20% of clients” is a strong start.

💡 Tip: Write your goal as one sentence that you can measure.
Example: “I want to reduce my reporting time from 8 hours to 2 using AI templates.”

Is the task repetitiv enough?

💡 Tip: Is the task even repetitive enough? Don't waste too much money and time for workflows, when you're doing it only once a year. Just because it's fancy.

2️⃣ Data Understanding - Know Your Inputs

Your business already runs on data - even if it doesn’t feel like it.
Invoices, calendar entries, emails, social posts, client notes - all contain useful information.

Ask yourself:

• Where do my data and ideas live?

• Are they complete, consistent, and accessible?

• Am I legally allowed to use them with AI tools (DSGVO check)?

✨ You don’t need more data - just better awareness of what you already have.

3️⃣ Data Preparation - Simplify Before You Scale

Here’s the secret no one talks about:
80–95% of the effort in AI projects happens before the model runs.

That means cleaning, organizing, and structuring your information - because messy data creates messy results.

Start small:

• Combine your client and revenue data in one spreadsheet.

• Standardize spellings, fill missing info, and tag key details (like industry or service type).

• Convert key content (like blog posts or client emails) into plain text or Markdown — easy for AI tools to “read.”

💡 Think of it as cleaning your workspace before working - everything works better. Puh… there is a lot the word "work" in it. But you know what I mean ;)

4️⃣ Modeling - The Fun Part (Finally!)

Now it’s time to bring in AI.

Because you’ve done the groundwork, you can use tools like ChatGPT, Notion AI, or Zapier more effectively.

You might:

• Ask ChatGPT to group client feedback into key themes.

• Use automation tools to summarize reports or emails.

• Build a prompt library for recurring content creation tasks.

This is where your creativity meets structure and that’s where AI truly shines. Rise & shine baby.

5️⃣ Evaluation - Check Before You Trust

Even if the AI result looks impressive, pause.
Ask yourself:

• Does this answer my original question?

• Does it sound like me?

• Is it correct and useful?

AI is like a mirror — it reflects the quality of your question and your data.

Refine your prompts, adjust the context, and test again until the result is consistent and helpful.

6️⃣ Deployment – Make It Part of Your Routine

This is where most people stop - but real transformation begins here.

Once you have an AI workflow that works, make it part of your weekly rhythm.

• Save your best prompts as reusable templates.

• Automate repetitive steps (e.g. using Make or Zapier).

• Review results every few months and adjust as your business grows.

AI isn’t a one-time project, it’s a living process that evolves with you