According to past research (by Gartner, VentureBeat, etc.), nearly 80% of AI projects fail. For an executive, this number isnโt just a statistic. It represents wasted budget and lost opportunities.
When an Artificial Intelligence project derails, the easy excuse is “tech complexity.” The reality, however, is much more blunt. We focus on buying the machine and ignore the quality of the data.
AI is the ultimate mirror of an organization’s data maturity. When the risks surrounding data (quality, sources, compliance) remain invisible and teams operate in isolated silos, failure is predetermined.
Experience shows that AI success requires executive Risk Management, not just engineering:
- Align Your Teams: Project Managers, BAs, Architects, and Privacy Specialists must sit at the same table from Day One.
- Data Governance for ROI: Governance is not a bureaucratic brake; it is the safety mechanism that allows the project to run fast and within budget.
If you want to ๐ฐ๐ข๐ง, stop looking only at the model. ๐๐จ๐จ๐ค ๐๐ญ ๐ฒ๐จ๐ฎ๐ซ ๐๐๐ญ๐!
To the leaders driving data projects: Are you seeing the same “trap” in the market, or have the challenges evolved?