I was speaking to my manager the other day over coffee and the conversation steered towards the topic of Artificial Intelligence (AI). While on that topic, he commented that, "In the field of AI, many people talk but don't deliver". While what he said was true in general, I feel that success or failure of AI projects are predetermined at the point of hiring. Over the past few months, I have been interviewing with various firms across different industries for data analyst, junior data scientist or AI engineer positions. While interviewing, I noticed many differences between these companies I interviewed for.

Artificial intelligence (AI) in the enterprise is poised for significant growth, so it's no wonder savvy IT professionals are looking for ways to align their career trajectories with it. Indeed, there was a 29 percent increase in the number of AI jobs listed on Indeed from May 2018 to May 2019, and a concurrent 15 decrease in candidate searches for AI roles, suggesting a potential shortage of AI experts on which IT pros could capitalize. "Machine learning and AI has become ubiquitous in our lives," says Timothy Havens, the William and Gloria Jackson Associate Professor of Computer Systems at Michigan Technological University's College of Computing and Director of the Institute of Computing and Cybersystems. "This marketplace is going to grow exponentially in the coming years; it is the perfect time for both young people and also those who wish to make a career change to become involved in a field that is going to change everything." For many IT professionals, a role in AI can be a logical next step that takes advantage of some of their existing skills.

Data about business processes, operational metrics, business performance, workforce productivity. However, it's all about intelligence and monetisation that can be done through this. This 1 hour session will provide overview on factors on which data monetisation depends.