The Myth of AI Magic
Since the explosion of ChatGPT and generative models, AI seems capable of everything: writing, coding, creating, analyzing. But behind every success lies a much more down-to-earth truth: data quality.
The greatest models are not technological miracles; they are machines fed with astronomical volumes of clean, structured, sorted, and verified data. Without these foundations, no intelligence stands strong. Yet most companies neglect this step, deeming it too "technical" or "secondary".
This is a strategic mistake.
The Invisible Work
Data preparation is like a building's foundation. You don't see it, but without it, everything collapses. It includes:
Collection
identifying, centralizing, securing sources
Cleaning
removing duplicates, correcting errors, handling missing values
Labeling
giving meaning to data
Governance
defining who does what, with which rules, and within what legal framework
This process is long, sometimes thankless, but it determines everything. Poorly labeled or biased data can lead to an inaccurate or even dangerous model.
And contrary to popular belief, AI doesn't "correct" these biases. It amplifies them.
The 80/20 Equation
Practitioners know it: 80% of AI project time is dedicated to data preparation, and only 20% to modeling. But in many companies, the inverse ratio is applied to budgets. Massive investment in models, very little in data.
Result: promising prototypes, but impossible to industrialize. Data teams then spend months "catching up" on problems—cleaning, documenting, recoding… Meanwhile, business units lose confidence in the project.
And that's often where initiatives stop.
The Silent Gold: Governing Data
The key isn't having lots of data, it's having reliable and governed data. This means:
- knowing where it comes from
- knowing what it's used for
- being able to trace it
- and most importantly, making it understandable to everyone
Good governance is also a culture. It's established over time, with clear roles: Data Owners, Data Stewards, Data Engineers. It rests on simple principles: quality, transparency, regulatory compliance.
Responsible AI starts with responsible data.
What About Generative AI?
With generative AI, the issue becomes even more critical. Models like GPT or Claude rely on heterogeneous corpora, often from the web. In an enterprise context, this isn't enough: you need internal data that's high-quality, reliable, consistent, and secure.
Organizations succeeding in this field understand that data preparation is no longer a "technical prerequisite" but a competitive advantage. An internal generative AI built on a well-constructed corpus offers:
- more accurate responses
- reduced legal risk
- and faster team adoption
From Data to Trust Capital
At Ti Ael Mat, we consider data a living asset. It must be cultivated, nurtured, audited. It's the fuel of digital performance, but also the key to ethical and sustainable AI.
Our conviction: companies that take the time to structure their data today will be the only ones able to fully leverage AI tomorrow. Because trust isn't programmed: it's built.
AI won't replace humans, but it will reveal the value of those who know how to organize their knowledge.