AI in practice: machine learning for strategic and operational planning

Professional Forum

Friday, April 03, 2020: 3:00pm - 3:50pm -

Angie Judge, Dexibit, New Zealand, Timothy Hart, Auckland War Memorial Museum, New Zealand

For years, cultural institutions have embraced artificial intelligence, becoming more dynamic in their use of technology behind-the-scenes. The newest trend sees museums and galleries using artificial intelligence to inspire core museum management decisions, shaping strategy and operations.

In this session, four art and history museums share their experiences implementing artificial intelligence and machine learning to predict and analyze visitor behavior. These museums discuss the practical applications of using data to make strategic and operational decisions spanning exhibition management, visitor experience design, staff scheduling, target setting, financial planning and more. While presenting real data and use cases, each speaker will offer implementation insights and lessons learned, such as countering data resistance, addressing data literacy and challenging business rules.

This panel will cover forecasting and analysis, including:
– An overview of artificial intelligence in museum management
– Identifying the roles and responsibilities of a data initiative
– Organizational readiness components to conduct a successful data program
– Achieving accuracy, granularity and automation with machine learning for forecasting
– Creating an insight inspired organizational culture by influencing people and process

Experiences will be shared by art and history museums of various sizes, including Eric Bruce, Head of Visitor Experience and Insights at Minneapolis Institute of Art; Daniel Stewart, Director, Guest Experience at Natural History Museum of Los Angeles County; Tim Hart, Director Public Experience at Auckland War Memorial Museum; and Amanda Peterson, Senior Director of Audience Engagement at Milwaukee Art Museum together with Angie Judge, Chief Executive at Dexibit.

Bibliography:
https://mw19.mwconf.org/proposal/ai-big-data-and-analytics-how-its-done/
https://mw18.mwconf.org/proposal/operational-excellence-innovation-with-data-analytics-three-federal-case-studies/
https://mw17.mwconf.org/proposal/big-data-and-analytics-what-weve-learned-so-far/