Schneider Electric: Predictive Analytics is Driving Change
Schneider Electric's Vanessa Miler-Fels explains how using AI and predictive analytics can transform corporate sustainability into real operational impact
TL;DR
- Predictive analytics uses AI and historical data to forecast future trends and drive strategic decisions in corporate sustainability.
- The focus is shifting from reactive, backwards-looking ESG measures to predictive and preventative environmental impacts.
- To implement predictive analytics effectively, organizations must start with clearly defined sustainability outcomes, not just technological capabilities.
- Data readiness, including reliable, granular, and connected data with clear ownership and context, is crucial for accurate insights.
- Strong executive leadership is essential to connect AI for sustainability to strategic priorities and establish accountability.
- Involving employees closest to operational decisions from the beginning and fostering trust through training and transparent communication are key to adoption.
- Model success is measured not just by accuracy, but by a balanced value framework encompassing business KPIs (cost savings, productivity) and sustainability outcomes (emissions reduction, safety improvements).
- Emerging technologies like AI enable organizations to anticipate demand, optimize operations, and act on insights more effectively.
- Schneider Electric's Beijing campus exemplifies successful implementation, achieving significant reductions in energy consumption and management hours through a smart energy and carbon management platform.
- Getting started with predictive analytics involves starting small, focusing on a clear outcome, and building incrementally, rather than replacing human judgment.