General Motors
service voice assistant + genAI web agent
General Motors' OnStar Interactive Virtual Assistant, based on Google Cloud Dialogflow, handles more than one million customer requests per month in the United States and Canada, on GM vehicles connected to OnStar from model year 2015.
Objective
Absorb at scale the non-urgent requests of OnStar members (routing, navigation, vehicle questions) through conversational AI, and extend the use to the buying experience through genAI chatbots on GM sites.
The deployment
The OnStar Interactive Virtual Assistant, launched in 2022, relies on Google Cloud's intent recognition (Dialogflow) to help OnStar members with non-urgent requests: directions, navigation, routing. It is available on most OnStar-connected GM vehicles from model year 2015. GM is extending the approach to genAI chatbots on its websites to answer buyer questions about vehicles, and is piloting generative AI use cases with Google Cloud for the buying and ownership experience.
Results Proof C
Public usage figure (more than one million requests per month) reported by several established outlets naming GM and Google Cloud; the genAI web component is more recent and less quantified. Reference data dated 2023.
How it works
Documented architectureThe stack in detail
- plateforme Google Cloud Dialogflow Intent recognition (NLU) at the core of the OnStar Interactive Virtual Assistant: classifies non-urgent requests (directions, navigation, routing) and decides how to handle them
- llm IA generative Google Cloud Google Cloud genAI components for the GM site chatbots and the pilot use cases on the buying experience; exact model not named in the sources
- infra OnStar GM's embedded connected services platform, the voice channel of the assistant on model year 2015 and newer vehicles
- integrateur Google Cloud (partenariat GM) Provider of the conversational and generative AI components and partner in the rollout
How it runs, concretely
For ops teams-
1Receiving the request customer
A member calls on OnStar by voice, or a visitor opens the chatbot on a GM site.
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2Intent recognition AI
Dialogflow classifies the request (directions, navigation, vehicle question) and decides how to handle it.
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3Response or routing AI
The assistant answers directly for non-urgent requests, or routes to the right service.
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4Human escalation customer service
Out-of-scope or urgent cases are transferred to an advisor.
The intent detected in the request. If the intent is poorly recognized, the request is misrouted or falls back to a human agent, which degrades call deflection.
How your customers perceive this type of use
Sourced studiesLes consommateurs n'acceptent pas les chatbots par defaut : 64% prefereraient que les entreprises n'utilisent pas d'IA dans leur service client (Gartner, 2024) et pres d'un utilisateur sur cinq du service client par IA n'en retire aucun benefice (Qualtrics, 2025). L'acceptation se construit sur trois conditions mesurees par Salesforce : savoir qu'on parle a une IA, pouvoir escalader vers un humain, comprendre la logique de l'agent.
Acceptance conditions
- Etre informe qu'on parle a une IA et non a un humain (pres de 75% le demandent, Salesforce 2024)
- Un chemin d'escalade clair vers un agent humain (45% plus enclins a utiliser l'agent IA, Salesforce 2024)
- Une logique de l'agent clairement expliquee (44% plus enclins, Salesforce 2024)
Red lines
- Rendre l'humain injoignable : c'est la premiere inquietude des consommateurs sur l'IA dans le service client (Gartner 2024) et 50% craignent que l'IA les coupe du contact humain (Qualtrics 2025)
- Remplacer le service client par l'IA sans alternative : 53% envisageraient de partir chez un concurrent (Gartner 2024)
Sources: Salesforce 2024 · Gartner 2024 · Qualtrics 2025
How to replicate
Inference, not sourcedData prerequisites
- history of requests and intents
- vehicle and service knowledge base
- routing flow reference
Org prerequisites
- customer service team
- governance on human escalation
Possible stack
- Dialogflow or equivalent NLU
- cloud LLM for the genAI responses
- CRM/service connectors
The plan, step by step
- Step 1Map the customer service intents from the request history and keep those that are high volume and low risk.Deliverable: Prioritized list of intents with volumes and escalation rules
- Step 2Build the agent on Dialogflow (or an equivalent NLU) for the selected intents, connected to the vehicle and service knowledge base.Deliverable: Working conversational agent in a test environment
- Step 3Integrate the channels (embedded voice, web chat) and the routing to an advisor for out-of-scope or urgent cases.Deliverable: Assistant in pilot on one channel with human escalation working
- Step 4Measure volume handled and call deflection, correct poorly recognized intents, then add the genAI layer for open questions.Deliverable: Quantified deflection review and framed genAI extension
First step: Map the most frequent customer service intents and first automate those that are high volume and low risk.
The same pattern, proven in other industries
ADT
genAI customer agent
Air India
genAI customer agent (automated customer service)
AT&T
Governed enterprise genAI assistant: an LLM connected to internal knowledge, open to tens of thousands of employees and called at scale by automated systems
Sources
- S1 GM, Google Cloud bringing AI to millions of vehicles Established press archive pending
- S2 GM details its work with Google in advancing AI capabilities Secondary archive pending
- S3 GM Teams Up with Google Cloud on AI Initiatives Established press archive pending
An error, newer info, a source?
This page lives on its accuracy. If a figure has moved, if the deployment has changed, or if you have a higher-quality source, tell us. Every sourced correction is verified before publication.