The numbers that wake you up — Data and AI

Artificial intelligence is emerging as a major driver of transformation in businesses. But one observation remains: AI is never more efficient than the data on which it is based. Behind the talk about automation and productivity, the reality is often more nuanced. Too many organizations launch AI projects without controlling the quality of their data. The result: delays, hidden costs, and biased decisions. The numbers speak for themselves.

AI is progressing, but data is holding back

The promise of artificial intelligence remains held back by an essential link: data preparation.

• 42% AI projects are delayed or fail due to insufficient data preparation
  → Fivetran, “AI and Data Readiness Report”, 2025
• 58% of managers recognize that their strategic decisions are based on inaccurate data
  → GlobeNewswire, “Bad Data Makes Bad Decisions”, 2025
• 73% of organizations believe that the data quality remains the main obstacle to the success of their AI projects
  → Precisely, “Data Integrity Trends & Insights,” 2025

These figures remind us that the performance of a model depends first of all on the reliability of the data base. AI powered by fragmented or inconsistent data amplifies errors instead of correcting them.

The hidden cost of bad data

Bad data doesn't just cost time: it's expensive. Very expensive.

• Businesses lose on average $12.9 million per year due to poor quality data: errors, duplicates, inaccurate reporting
  → Integrate.io, “Data Quality Improvement Stats”, 2025
• 84% of businesses are suffering from inaccurate or duplicated data, which slow down their operations and degrade the customer experience
  → Melissa, “State of Enterprise Data Quality,” 2025
• The cost of data errors can be up to 20% of annual turnover in certain sectors (retail, logistics, finance)
  → Experian, “Global Data Management Research,” 2025

These invisible losses reflect a lack of governance and rigor. They show that data is not a technical subject: it is a strategic subject.

Businesses that are getting ahead

While some struggle to make their data reliable, others move quickly. The most successful companies are those that place the data quality and governance at the heart of their AI strategy.

• 67% European companies have already integrated AI into their data processing or analysis processes
  → EY, “European AI Barometer”, 2025
• The European AI market shows an average annual growth of +33% until 2030
  → Grand View Research, “Europe Artificial Intelligence Market Report”, 2025

These organizations understood that before investing in algorithms, you need to invest in The structuring and reliability. A well-fed AI then becomes a A real driver of performance and decision, not a tech gimmick.

Conclusion

The success of an AI project is based on three pillars: reliable data, a solid governance framework, and smart operations. Without this, even the best models will remain ineffective. The challenge is therefore not whether AI will transform your business — but to what extent will your data allow you to take advantage of it.