Reported impact
Business outcome
With ChatGPT Work and Codex, RingCentral builds AI product features faster and centralizes operational intelligence.
- Productivity Improvement
- Scalability
- Speed to Market
OpenAITelecommunicationsApplication RefactoringIT Operations AutomationDevOpsGenerative AI / LLMProductivity ImprovementScalabilitySpeed to Market2026 OpenAIOA#10a37fTelecommunicationsGlobal"When you put real AI tools in everyone’s hands, the whole company becomes a product organization. Every one of our products—including but not limited to our Agentic Voice AI portfolio of AIR, AVA, ACE—gets sharper as…With nearly three decades of innovation in business communications, RingCentral has grown into a global company…Aug 12, 20266Application Refactoring|IT Operations AutomationChatGPT Work · Codex0 2026-08-12 6
RingCentral
Application Refactoring · IT Operations Automation
"When you put real AI tools in everyone’s hands, the whole company becomes a product organization. Every one of our products—including but not limited to our Agentic Voice AI portfolio of AIR, AVA, ACE—gets sharper as we compress the distance between an idea and a shipped feature, and that’s exactly what AI-native...
With nearly three decades of innovation in business communications, RingCentral has grown into a global company generating more than $2.6 billion in annual revenue, with thousands of employees...
Solution
"When you put real AI tools in everyone’s hands, the whole company becomes a product organization. Every one of our products—including but not limited to our Agentic Voice AI portfolio of AIR, AVA, ACE—gets sharper as we compress the distance between an idea and a shipped feature, and that’s exactly what AI-native development lets us do." With ChatGPT Work and Codex, RingCentral builds AI product features faster and centralizes operational intelligence. With nearly three decades of innovation in business communications, RingCentral has grown into a global company generating more than $2.6 billion in annual revenue, with thousands of employees worldwide.
Data flow
To encourage AI fluency across a global engineering organization, RingCentral’s Office of the CEO sponsored an AI-Native Challenge. Every participant was given ChatGPT Work and Codex, and asked to build a complete, end-to-end project—with no mandated workflow or other constraints.
Solution architecture
- Compute
- Codex Implements, tests, reviews, or proposes code changes so engineers can move faster with human oversight.
- Orchestration
- ChatGPT Work Turns repeatable business processes into AI-assisted workflows with shared context and actions.
Architecture clues
- Implements, tests, reviews, or proposes code changes so engineers can move faster with human oversight.
- To encourage AI fluency across a global engineering organization, RingCentral’s Office of the CEO sponsored an AI-Native Challenge.
- Turns repeatable business processes into AI-assisted workflows with shared context and actions.
- With ChatGPT Work and Codex, RingCentral builds AI product features faster and centralizes operational intelligence.
- With nearly three decades of innovation in business communications, RingCentral has grown into a global company generating more than $2.6 billion in annual revenue, with thousands...
Evidence from the source
"When you put real AI tools in everyone’s hands, the whole company becomes a product organization.
To encourage AI fluency across a global engineering organization, RingCentral’s Office of the CEO sponsored an AI-Native Challenge.
With ChatGPT Work and Codex, RingCentral builds AI product features faster and centralizes operational intelligence.
With nearly three decades of innovation in business communications, RingCentral has grown into a global company generating more than $2.6 billion in annual revenue, with thousands...
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value80/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth20/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity20/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact15/251 metric describe measurable impact.
- Deployment scale5/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/204 direct source quotes retained.
- Date provenance8/10The publication date comes from publisher-controlled metadata.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity10/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://openai.com/index/ringcentral/OpenAIFirst-party
RingCentral customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date
Date evidence
- Publisher catalog date Aug 12, 2026 · OpenAI · First-partyOpen date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
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