As organizations evolve through their digital transformation path, they become increasingly aware of the need for translating the value of data into actionable and timely insights for decision-making.

Yet, many organizations that have been actively building up their data science capabilities are finding them- selves in analytical silos where scalability remains the main challenge.

Al Governance aims at unlocking the value of data insights by tackling siloed Al and fostering synergies across Al efforts.

It focuses on centralizing current Al capabilities which tend to rely on operating models where experimentation is either isolated in labs or scattered across business areas.

Through our Al governance framework we define three key levers for organizations to implement an operating model that oversees the complete algorithm lifecycle, embedding responsible Al guidelines and best practices across each domain's objectives.