Organizations are at different levels of AI maturity, and to complicate matters further the same is true within each organization. Some business stakeholders already understand the benefits because you speak the same language, while with others your attempts to convince them of your value seem to get lost in translation. In this session Shaun McGirr, AI Evangelist at Dataiku, will facilitate a discussion among your peers to share insights and lessons on how to overcome this problem. He will share Dataiku’s framework on “Economics of AI” that helps anyone, regardless of maturity or technology, tell a compelling value story across the lifecycle of their AI initiative.

● Early stages: making the trade-off between intense investment in identifying use cases vs getting started with an experimental mindset
● Middle stages: maintaining momentum after initial success, when the low-hanging fruit are gone
● Transformation: embedding productivity gains to keep driving down the cost of new use cases

Speaker:

Shaun McGirr, AI Evangelist, Dataiku

Shaun McGirr is a data leader with experience across official statistics, academia, consulting, and now data science in a large automotive services company. He recently achieved minor stardom in a documentary Data Science Pioneers, coining the phrase “things that happen 35% of the time, happen ALL the time” to explain why quite likely outcomes are often dismissed out of hand. Shaun believes the toughest part of doing data well is finding the right questions and ensuring the answers will actually push a lever to change the world, a theme developed further in his podcast Half Stack Data Science.