Causal AI

Assembly Line

What Georgia-Pacific Is Doing With Causal AI Is Remarkable

📅 Date:

✍️ Author: Steve Banker

🔖 Topics: Causal AI

🏢 Organizations: Georgia-Pacific, Parabole AI, Vassar Labs


Using words to describe Causal AI only takes you so far. Seeing the layers of knowledge modeled in a knowledge graph is more powerful. The following figure helps demonstrate the depth of causality that is modeled with these systems. For GP, softness is one of 12 critical product attributes for any of their paper products. Softness itself has 10 attributes called Influencing Attributes (IA) that can affect the Softness of the product. Further, each Influencing Attribute has many items that can affect them. BULK is one of those Influencing Attributes. But BULK, in turn, has many “Conditional Attributes” that affect it.

Georgia-Pacific used technology from Parabole.ai to build its Causal AI solution, and Vassar Labs built the interface. Before working with GP, Parabole.ai provided solutions for the financial industry.

GP’s goal was to see whether Causal AI could combine subject matter experts’ tacit knowledge with production data to make more intelligent and automated decisions. They demonstrated a 10X increase in touchless order throughput. Some order management errors that used to take days to resolve were now resolved in seconds. Of course, getting a promise right is vital. However, good customer service also demands quick answers to customers’ order inquiries.

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