Over the last four years, the amount of enterprises that have deployed AI has gone up from 10% to 37%. As confidence in the technology continues to grow, decision makers are accelerating adoption to stay ahead of the competition, innovate and grow businesses.

But how fast is too fast when it comes to adopting AI? What are the priorities? Is there a point where the rush for deployment turns into a treat for development? And what are the ethical implications of this nascent tech? As Machine Learning teaches us, these questions can be answered by looking at the past.

In this presentation, we go beyond the buzzwords and deep dive into practical implementations of AI. Whether it's analyzing recruitment in education, predicting delays and traffic on complex railway networks, or figuring out why self-driving cars can't see kangaroos, we look to the real-world for the five steps that characterize AI project engagement and share best practice procedures for deploying an AI leading solution

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