What challenges did Harness face before AI implementation?

Harness needed a scalable BI experience with governed metrics and role-based access. It also wanted an AI assistant to speed up developer tasks, automate remediation, and support customer and internal analytics without maintaining a parallel custom dashboarding solution.

What was the impact of the AI solution?

Developer efficiency increased by 75%. Customer query response time fell from minutes to seconds. Customers can reduce cloud expenses by up to 70%.

Source

GCPEvidence: Medium50/100

Harness (US) — AIDA AI assistant for BI development and automation using Vertex AI

Use case typeDeveloper productivityUpdated Jun 3, 2026

Harness, a modern software delivery platform, embedded Looker and BigQuery into its Cloud Cost Management product and built a unified semantic layer with LookML to support governed dashboards, self-service BI, and an AI-powered BI development assistant called AIDA. The solution uses Vertex AI to automate development and remediation tasks and to power natural-language queries, dashboard generation, and predictive insights for customers and internal teams.

Organization: Harness
Industry: Tech & Comms
Location: United States
Published: June 2026

Reported outcomes

Cloud expense reduction: Up to 70% lower

Cost savings

Planned next steps

The source says the organization aims to achieve Projected development time: −50%.
Projected development time was reduced by 50%.

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

Normalized claim

Projected development time: 50% decrease

Google Cloud Customer StoriesJun 3, 2026Customer storyExplicit claimMedium evidence strength

"Reduces projected development time by 50%"

Normalized claim

Developer efficiency: 75% increase

Google Cloud Customer StoriesJun 3, 2026Customer storyExplicit claimMedium evidence strength

"Increases developer efficiency by 75%"

Normalized claim

Dashboard load times: 98.3% decrease

Google Cloud Customer StoriesJun 3, 2026Customer storyInferred claimMedium evidence strength

"decreased their dashboard load times from tens of minutes to less than 10 seconds"

Normalized claim

Cloud expense reduction: 70% decrease

Google Cloud Customer StoriesJun 3, 2026Customer storyExplicit claimMedium evidence strength

"customers reduce their cloud expenses by up to 70%"

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

Customer: Harness
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 4

1Developer productivity
2Real-time analytics
3Workflow automation

Harness built a unified semantic data layer in Looker/LookML on BigQuery.
It used Vertex AI to automate core tasks, generate dashboards, answer natural-language questions, and support AIDA for vulnerability detection and suggested fixes.

Technologies

Vertex AI Looker LookML BigQuery

Developer efficiency increased by 75%.
Customer query response time fell from minutes to seconds.
Customers can reduce cloud expenses by up to 70%.

Architecture

Harness embedded Looker into Cloud Cost Management and built a unified semantic layer with LookML on BigQuery. Vertex AI powers AIDA for natural-language BI development, automated vulnerability detection and suggested fixes, and dashboard generation. The solution uses aggregate tables and hub-and-spoke semantic modeling for governed, role-based access and faster queries.

Sources & evidence1

Evidence: Medium50/100Evidence strength