The Myth of the Singular AI Project: A Tech Lead's Rant
Ah, the term "AI project"—a phrase that gets tossed around like confetti at a tech conference. But let's be real: behind this buzzword lies a multitude of distinct realities. It's like calling every dish in a restaurant "food" without acknowledging the difference between a gourmet meal and a microwave dinner.
The Many Faces of AI Projects
"Derrière l'expression 'projet IA' se cachent des réalités très différentes," as the French would say. And they're not wrong. Each AI project is its own beast, demanding a unique set of tools, skills, and financial commitments. Yet, here we are, lumping them all together as if they're interchangeable. Spoiler alert: they're not.
Why Differentiation Matters
The importance of distinguishing between these types of projects can't be overstated. "Les distinguer permet de mieux choisir ses outils, ses compétences et ses investissements," says the wise voice of reason. But who listens to reason when there's hype to be had?
- Tools: Not every AI project needs the latest, shiniest tool. Sometimes, the old reliable ones work just fine.
- Skills: Hiring a data scientist to do a data analyst's job is like hiring a chef to make toast. Sure, they can do it, but it's overkill.
- Investments: Throwing money at a project doesn't make it successful. It just makes it expensive.
The Realities of AI Projects
So, what are these "realities" we're talking about? Well, they range from simple automation tasks to complex machine learning models. Each requires a different approach, and pretending otherwise is a recipe for disaster.