The global landscape is changing. In the coming years, the ability of organizations to pivot their activities around Enterprise AI will fundamentally determine their fate. Those able to efficiently leverage data science and machine learning techniques to improve business operations and processes, as well as to find new business opportunities, will get ahead of the competition, while those unable to shift will fall behind, perhaps swept away with the tide of rising costs and diminishing revenue. In fact, three out of four C-suite executives believe that if they don’t scale artificial intelligence in the next five years, they risk going out of business entirely.
However, the key word is efficiently. It’s not enough for organizations to simply leverage Enterprise AI techniques at any price. Eventually, in order for Enterprise AI strategy to be truly sustainable, one must consider the economics; not just the gains, but the cost.
So, what does it mean to scale a data initiative? How can capitalization and reuse reduce costs, and what do these look like in the context of AI?
Join Dataiku and a select group of senior thought-leaders to share best practice on how to weigh the benefits and negatives of the economics of AI and to discuss the challenges we face in scaling, reuse and capitalization.