Every enterprise wants to unlock AI’s potential, yet most initiatives stall – not because of models, but because data foundations are unfit. Across large organizations, we’ve observed recurring pitfalls: treating data as a byproduct instead of a product; emphasizing catalogs and monitoring rather than true ownership; enforcing governance after the fact; and focusing on tools while neglecting best practices and guardrails.

The outcome? Data that seems fine on paper but can’t be trusted in practice, projects that generate debt instead of value, and AI that never scales beyond prototypes.

In this talk, Paolo will share the common failure patterns, why they persist, and the practices that help organizations avoid them. He will explore how to build a sustainable path to AI-ready data – combining ownership, quality, intelligibility, and automation. The goal is not a one-size-fits-all recipe, but guiding principles to transform data from a liability into AI’s true foundation.

• • • Reveal the most common pitfalls that prevent AI initiatives from scaling in enterprises
    • Show why governance, ownership, and product thinking matter more than tools
    • Share concrete practices to build trustworthy, AI-ready data
    • Highlight principles that turn data from a liability into a strategic asset