What challenges did Georgia-Pacific face before AI implementation?

Knowledge loss as senior employees retire and scattered information across documents and databases hinder machine operator efficiency across many diverse facilities. Lack of centralized knowledge base reduced machine productivity and increased downtime, repair costs, and quality issues.

What was the impact of the AI solution?

Increased machine production efficiency with near real-time operator adjustments. Reduced quality issues and machine downtime. Accelerated troubleshooting and repair processes. Preserved operational knowledge from experienced employees through AI summarization and documentation. Estimated potential multimillion annual savings across facilities.

Source

ProductionEvidence: Medium55/100

Georgia-Pacific Optimizes Operator Efficiency with Generative AI Using Amazon Bedrock

Use case typeAI platformUpdated Jun 13, 2026

Georgia-Pacific, a leading global manufacturer of pulp and paper products, faced challenges with scattered knowledge across many facilities, leading to production inefficiencies and risk of knowledge loss from retiring employees. The company partnered with AWS and AWS Professional Services to develop ChatGP, a generative AI chatbot using Amazon Bedrock's Anthropic Claude large language model, integrated with IoT sensor data via Amazon Kinesis. ChatGP provides machine operators centralized, contextualized, and real-time troubleshooting guidance and knowledge access tailored to specific equipment and processes across 140+ facilities. Impact includes improved machine production, reduced quality defects, minimized downtime, accelerated troubleshooting, preservation of expert knowledge, and estimated multimillion-dollar annual savings across operations.

Organization: Georgia-Pacific
Industry: Manufacturing
Location: United States
Published: April 2026

Reported outcomes

Strategic outcomes

New product / capabilityCentralized real-time operator guidanceCost efficiencyReduced downtime and quality defectsBetter decisions & insightImproved machine production decisionsInnovation & culturePreserved expert operational knowledge

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Georgia-Pacific
Provider: AWS
Maturity: Production
Linked source: AWS Customer Stories

Preserved operational knowledge from experienced employees through AI summarization and documentation

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 2 of 2

1Generative AI for Knowledge Management
2Operational Efficiency Improvement

Knowledge loss as senior employees retire and scattered information across documents and databases hinder machine operator efficiency across many diverse facilities.
Lack of centralized knowledge base reduced machine productivity and increased downtime, repair costs, and quality issues.

Developed ChatGP, a generative AI chatbot powered by Anthropic Claude model via Amazon Bedrock, combining IoT sensor data to deliver accurate, contextual operator guidance.
Engaged AWS Professional Services to design and deploy the solution.
Delivered the chatbot via a user-friendly web app accessible on company network devices.

Technologies

Amazon Bedrock Anthropic Claude Amazon Kinesis

Increased machine production efficiency with near real-time operator adjustments.
Reduced quality issues and machine downtime.
Accelerated troubleshooting and repair processes.
Preserved operational knowledge from experienced employees through AI summarization and documentation.
Estimated potential multimillion annual savings across facilities.

Architecture

Architecture combines Anthropic Claude LLM via Amazon Bedrock with IoT sensor data processed through Amazon Kinesis, delivered via a custom web app for operator interaction.

Sources & evidence1

Evidence: Medium55/100Evidence strength