We help you complete

  your approach to ai

Don’t expect them to behave like a traditional software project. Product managers need to ensure that data scientists are delivering results in efficient ways so business counterparts can understand, interpret, and use it to learn from. This includes everything from the definition of the problem, the coverage and quality of the data set and its analysis, to the presentation of results and the follow-up.

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   Artificial Intelligence

Bain Public SOAP AI part one

Part one: We’ll dive into the importance of having solid data, model and problem understanding as a product manager. We’ll also look at how a product manager can create the conditions for success with AI.

» Tomorrow’s Product Managers Need to Create the Conditions for Success With AI
Bain Public SOAP AI part two

Part Two: Why does collaboration between product management, data science and engineering really matter?

» Your Guide to Executing AI and Machine Learning Projects

Bain Public can help you uncover the main value
drivers for implementing Data Intelligence initiatives.

Data Intelligence projects generally have longer timelines, higher costs and contain a number of moving parts that can sometimes be challenging. In short, AI is a lifecycle that requires the integration of data, machine learning models, and the software around it. It covers everything from scoping and designing to building and testing all the way through to deployment — and eventually requires frequent monitoring. It's a bottom-up process; you attempt to solve a problem with the team, get a signal early and use it to construct the bigger picture.

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