Recently, a client came to us with a common issue:
Recently, a client came to us with a common issue:
"We invested $50K in an AI project, but it didn’t work."
"We invested $50K in an AI project, but it didn’t work."
- Deadlines were missed.
- The budget was exhausted.
- Management was disappointed.
After analyzing the project in detail, the reason became clear:
It wasn’t AI that failed – it was poor planning.
Many companies expect AI to solve problems on its own.
There’s a belief that you can just upload data, apply some algorithms, and everything will work.
In reality, AI is just a tool that requires clear instructions and a well-structured process.
Why AI Isn’t Magic
Think of AI as a highly productive junior employee:
- Works 24/7.
- Handles repetitive tasks.
- Processes large amounts of data.
But, like any junior employee, it can’t make decisions on its own.
To make AI useful, it needs:
1. Clear instructions – What exactly needs to be done?
2. A structured process – What steps are required to solve the problem?
If these steps are skipped, there’s a high risk of wasting the budget without achieving meaningful results.
Two Key Questions to Ask Before Starting an AI Project
Before launching an AI project, we ask clients two important questions:
- What Does the Ideal Process Look Like?
Describe what you want to automate without using technical terms. For example:
- A user uploads a photo, and the system instantly recognizes it.
- An email arrives and is automatically routed to the right team.
- How Does the Process Work Now?
This question helps identify problem areas:
- What slows the process down?
- Where do errors occur most often?
- Which tasks take up too much time?
These questions help objectively assess current workflows and determine which processes are truly worth automating.
Why AI Projects Sometimes Don’t Deliver Results
In practice, we often encounter these situations:
- The Process Isn’t Ready for AI
- Some tasks can be solved more easily and cheaply with simple scripts or standard tools.
- Key Problems Haven’t Been Identified
- Sometimes, the biggest issues go unnoticed. For example, manual handoffs between teams can take more time than the actual data processing.
- Trying to Automate Too Much at Once
- Instead of focusing on one critical process, companies attempt to implement complex, end-to-end solutions. This approach requires more resources and time, increasing the risk of failure.
How We Solved the Client’s Problem
After analyzing the situation, we revised the project, spending just $10K. This allowed us to:
- Automate a key step that saved 15 hours of work per week.
- Reduce errors by 60%.
- Simplify the process, making it more manageable.
What to Keep in Mind When Starting AI Projects
Before starting an AI project, it’s important to:
- Understand the current process in detail.
- Identify the main problems that need to be solved.
- Realistically assess what AI can and can’t do.
In some cases, the best solution isn’t an AI, but a clear planning and process improvement. Only after that should you consider technology.
