Seth Dobrin, vice president and chief data scientist, IBM has a lot of experience in leading and executing artificial intelligence and data science projects; he has been doing this for the last seven years. Furthermore, he has been leading the IBM Data Science Elite Team of 60 people since 2017.
The IBM Data Science Elite Team is a group of data science and artificial intelligence experts who work with companies to help them kick-start or build an AI minimum viable product.
With this wealth of experience, Dobrin can easily recognise what are the key features of a successful AI project. These are the use of an agile methodology, a hub and spoke configuration, a focus on teamwork as well as a focus on the end-user.
“Nothing should be in an ivory tower.”
The benefit of agile working is that allows the teams to have the flexibility and the ability to choose their own tools however there does need to be oversight from the executive. “Some of these tools can get you into trouble so there need to be guard rails that are set by the central organisation in conjunction with the business so nothing is in an ivory tower,” he said.
In addition, one of the benefits of agile working is peer learning. “A side effect of agile is you can bring people into this team who may not have a depth of understanding but through the activity of executing these projects, they get up to speed.”
“They’ll sit side by side with them and get skilled up.”
In fact, he said this is how knowledge transfer takes places when members of his team are embedded in other organisations. He said: “We want to skill up in the space. They’ll join the team, they’ll sit side by side with them, they’ll do the work with them and they’ll get skilled up.”
Dobrin said that it is important to bear in mind the need to choose the most appropriate form of agile working that makes the most sense for different phases of the project.
He said that when you look at the successful implementation of AI projects in large enterprises, it is typically in a hub and spoke model, with a centralised team and other teams in the business unit.
Dobrin said there are two reasons for this. The first is that those who are in the business units retain
a sense of urgency and understand the need for the outcome of the project.
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