What challenges did Elanco face before AI implementation?

Elanco needed an independent, secure IT ecosystem after separating from its parent company. The company wanted to accelerate deployment of generative AI while reducing manual routine work. Employees needed faster access to SAP order data and a better way to handle large volumes of compliance documents. Pharmacovigilance case documentation and translation were slowed by an English-only legacy SaaS solution.

What was the impact of the AI solution?

Elanco estimated a 70% reduction in time spent on routine tasks. The company reported about $2.3M in first-year efficiencies from Elanco.ai. PV translation automation is expected to save about $750K once fully adopted. Employees can get answers from SAP data in around 10 seconds. Elanco expects up to 15% annual savings and up to 50% efficiency improvements through time and cost savings.

Source

GCPEvidence: Medium50/100

Elanco built a private generative AI framework (Elanco.ai) using Gemini, Cortex Framework, and BigQuery

Use case typeDocument automationUpdated Jun 13, 2026

Elanco rebuilt its IT ecosystem after separating from its parent company and adopted Google Cloud to support secure, scalable data analytics and AI across the business. The company developed Elanco.ai, a private and secure generative AI framework that uses Gemini models and Google Cloud Cortex Framework to ground responses in enterprise data. Elanco.ai supports routine employee tasks, pharmacovigilance case documentation and translation, SAP order-data lookup, and compliance-document processing across thousands of policy documents.

Organization: Elanco
Industry: Manufacturing
Location: United States
Published: May 2026

Reported outcomes

10 seconds

quantified impactOther quantified impact

−70%time−15%time−50%time

Strategic outcomes

New product / capabilityBuilt a secure enterprise AI frameworkSpeed & agilityAccelerated routine employee tasksNew product / capabilityEnabled real-time SAP data accessRisk & complianceAutomated compliance document processing

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 70% decrease

Google Cloud Customer StoriesMay 12, 2026Customer storyInferred claimMedium evidence strength

Elanco estimated a 70% reduction in time spent on routine tasks.

Normalized claim

Quantified impact: 10 seconds

Google Cloud Customer StoriesMay 12, 2026Customer storyInferred claimMedium evidence strength

Employees can get answers from SAP data in around 10 seconds.

Normalized claim

Time: 15% decrease

Google Cloud Customer StoriesMay 12, 2026Customer storyInferred claimMedium evidence strength

Elanco expects up to 15% annual savings and up to 50% efficiency improvements through time and cost savings.

Normalized claim

Time: 50% decrease

Google Cloud Customer StoriesMay 12, 2026Customer storyInferred claimMedium evidence strength

Elanco expects up to 15% annual savings and up to 50% efficiency improvements through time and cost savings.

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

Customer: Elanco
Provider: GCP
Maturity: Unknown
Linked source: Google Cloud Customer Stories

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 5

1Generative AI assistant
2Enterprise search and analytics
3Document processing automation

Elanco needed an independent, secure IT ecosystem after separating from its parent company.
The company wanted to accelerate deployment of generative AI while reducing manual routine work.
Employees needed faster access to SAP order data and a better way to handle large volumes of compliance documents.
Pharmacovigilance case documentation and translation were slowed by an English-only legacy SaaS solution.

Elanco built Elanco.ai as a private, secure multi-model and multimodal framework using Gemini Flash and Gemini Pro.
The framework ingests Elanco data from multiple sources to provide contextually grounded answers.
Google Cloud Cortex Framework and BigQuery were used to analyze and interact with SAP order data in real time.
Translation Hub and Gemini were used to automatically generate and translate pharmacovigilance case documentation.
A Gemini-based agent was used to sort, summarize, compare, and restructure unstructured compliance documents.
Supporting Google Cloud services included Cloud Run, Cloud SQL, Cloud Armor, Cloud CDN, Secret Manager, Cloud Logging, and Cloud Storage.

Technologies

Gemini Google Cloud Cortex Framework Translation Hub BigQuery Cloud Run Cloud SQL Cloud Armor Cloud CDN Secret Manager Cloud Logging Cloud Storage

Elanco estimated a 70% reduction in time spent on routine tasks.
The company reported about $2.3M in first-year efficiencies from Elanco.ai.
PV translation automation is expected to save about $750K once fully adopted.
Employees can get answers from SAP data in around 10 seconds.
Elanco expects up to 15% annual savings and up to 50% efficiency improvements through time and cost savings.

Architecture

A private, secure generative AI framework built on Google Cloud using Gemini Flash and Gemini Pro, with enterprise data grounding through Cortex Framework and interactive analytics via BigQuery. Supporting services mentioned include Cloud Run, Cloud SQL, Cloud Armor, Cloud CDN, Secret Manager, Cloud Logging, and Cloud Storage.

Sources & evidence1

Evidence: Medium50/100Evidence strength