Erick Brethenoux is a Key Initiative leader in the AI team, responsible for managing the research agenda for AI. He specializes in machine learning, artificial intelligence and applied cognitive computing. Mr. Brethenoux guides organizations on the strategic, organizational and technology aspects of using advanced analytics as a driving force of their growth. In particular, his research focuses on the deployment of machine learning insights through integration with advanced analytics solutions, data science and next-generation, decision-making and management systems (decision intelligence).
Mr. Brethenoux has extensive experience in the domains of AI, data science and cognitive computing. Prior to rejoining Gartner, he was IBM Director of Machine Learning & Analytics Strategy. Prior to IBM, he was VP of Corporate Development at SPSS, pioneer in predictive analytics, and before that VP at Lazard Freres, working with institutional investors, analyzing startups for venture capital funding and merger and acquisition activities. Before Lazard, Mr. Brethenoux was Research Director of Advanced Technologies at Gartner focusing on decision support systems, knowledge management and advanced software engineering. Prior to Gartner, he conducted a scientific mission for the French Embassy in the U.S. Mr. Brethenoux is also currently an Adjunct Professor at Illinois Institute of Technology.
Director, Business Analytics & Decision Management Strategy
Vice President, Corporate Development
Applications and Software Engineering Leaders
Analytics, BI and Data Science Solutions
Data and Analytics Programs and Practices
Ph.D., Cognitive Science, University of Delaware
M.S., Artificial Intelligence, West Chester University of Pennsylvania
1How to leverage data science and artificial intelligence technologies and methodologies to achieve competitive differentiation
2Define and deploy best practices in artificial intelligence and machine learning development through the right skills and the creation of analytical assets (and identify the appropriate vendors)
3Define and apply best practices around data science and machine learning operationalization to deploy, scale and maintain solutions using machine learning
4Understand and decipher the market trends for data science, machine learning, deep learning, advanced and predictive analytics, real-time analytics, natural language processing, and other AI systems
5Unveil emerging trends, technologies and markets in the cognitive computing, artificial intelligence, intelligent systems and man-machine collaboration (including IoT analytics)