John Sigmon is a highly experienced technology executive and thought leader, currently serving as the Chief AI Officer at ServiceNow. With a distinguished career spanning over two decades, he has been at the forefront of leveraging data analytics, artificial intelligence, and machine learning to drive business transformation and innovation. Prior to ServiceNow, John held significant leadership roles, including Chief Data & Analytics Officer at GE Digital, where he was instrumental in shaping their industrial IoT and data strategies. He is also recognized for his academic contributions, having served as an Adjunct Professor at Northwestern University, sharing his expertise in data science and analytics. John is passionate about building high-performing teams and translating complex technological capabilities into tangible business value and customer success.
John Sigmon's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Appointed in 2023, leads ServiceNow's global AI strategy, innovation, and development efforts, embedding AI capabilities across the Now Platform to enhance productivity, intelligence, and user experience for customers worldwide.
Led GE's global data and analytics strategy, building and scaling capabilities for the Industrial Internet of Things (IIoT). Drove the development of data-driven solutions to optimize industrial operations, asset performance, and create new digital revenue streams.
Taught and mentored graduate students in the Master of Science in Analytics (MSiA) program, focusing on practical applications of data science, machine learning, and big data technologies in business contexts.
Consistently recognized for guiding large organizations through complex digital transformations by effectively integrating data science, AI, and analytics into core business processes and strategies.
Played a key role in advancing the application of AI and IoT in industrial sectors, helping companies leverage data from machines and operations to improve efficiency, reliability, and safety.
The University of Texas at Austin
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