Cognizant Telecommunications North America

Cox Communications

Customer Service Automation

Cox Communications needed to modernize a high-volume chatbot and IVR estate so customers could get accurate, relevant answers across preferred channels without overloading live support teams.

17% improvement in first-contact resolution for chatbot
32% resolution rate within the first month
8x increase in customer use of the digital channel
Delivery of gen AI-powered chatbot in three months

Reported impact

Business outcome

Cox raised automated resolution across chatbot and IVR channels, sharply increased customer adoption of self-service, and created a scalable AI-first contact-center model that frees live agents for more complex issues.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Cognizant rebuilt Cox Communications' conversational support stack around Google Cloud Contact Center as a Service (CCaaS) and Google's Gemini Enterprise for Customer Experience so chat and voice bots could serve customers consistently across digital, SMS, chat and IVR channels. The team migrated Cox's Oliver virtual assistant off the legacy Khoros environment and onto an AI-first architecture using UJET for agent-assisted support, Google Dialogflow CX and playbooks for deterministic flows, and intelligent routing that decides when to use rule-based workflows versus generative responses. SmartHelp troubleshooting and bill-pay automations handle common requests in self-service, while the shared AI foundation also powers Olive, a generative IVR bot that launched first for service appointments and then expanded to dozens of intents. The platform creates a single operating model for self-service, escalation and continuous bot optimization across Cox's contact center.

Data flow

Customer questions arrive through chat, digital, SMS and IVR channels in Google CCaaS. Intelligent routing and Dialogflow CX send each request into rule-based playbooks or Gemini-powered responses, while SmartHelp and bill-pay automations resolve common needs in self-service. When the request is voice-based, the same foundation powers Olive; when it needs assisted care, UJET supports the handoff to live representatives.

Solution architecture

8 components · 3 layers
  1. Compute
    • Gemini Enterprise for Customer Experience (CX) Supplies the generative AI capabilities behind Cox’s virtual service agents and cross-channel customer interactions.
  2. Serving
    • Google Cloud Contact Center as a Service (CCaaS) Provides the omnichannel contact-center foundation for Cox across voice, digital, chat and SMS support.
    • UJET Replaces Cox’s prior customer-service platform with an AI-powered cloud contact center for agent-assisted support.
    • Oliver virtual assistant Handles customer-facing chatbot conversations on the relaunched AI-first platform.
    • Olive voice bot Extends the same AI foundation into IVR so voice interactions can automate appointment scheduling and other intents.
    • SmartHelp Lets customers resolve common technical issues without agent intervention.
  3. Orchestration
    • Google Dialogflow CX Runs structured conversational flows and helps route requests into the right automated path.
    • playbooks Define rule-based workflows for structured customer requests inside the hybrid AI design.

Architecture clues

  • Continuous feedback from production use enabled Cox to implement more than 500 bot improvements.
  • Oliver and Olive share a common AI foundation so chat and IVR automation can evolve on one platform.
  • The design uses a hybrid AI model that combines deterministic workflows with generative responses.
  • The migration replaced the legacy Khoros customer-service environment with UJET on Google CCaaS.

Evidence from the source

“Cognizant created an AI-first channel to ensure the telecom giant’s chat and voice bots deliver accurate, relevant information to customers.”
“Cox decided to adopt Google’s Gemini Enterprise for Customer Experience (CX) for its flexibility.”
“Hybrid AI approach with Google Dialogflow CX and playbooks, using rule-based workflows for structured customer requests and gen AI for more complex conversational interactions”
“The bot’s increased context awareness drove an 8x increase in customers’ use of the chatbot channel.”
“The voice bot debuted with a single intent: allowing customers to schedule appointments for service.”
Transparency Evidence & methodology 100/100 reference value · 83% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

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 value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    7 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence83%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency10/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Cognizant

    Cox Communications’ bold vision redefines the contact center experience | Cognizant

    First-party
    https://www.cognizant.com/us/en/case-studies/cox-communications-bold-vision-redefines-the-contact-center-experience
    Source type
    Vendor-published 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
  2. Revelio Labs

    Cox Communications employees

    Independent secondary
    https://www.reveliolabs.com/companies/cox-communications/employees

    Revelio states Cox Communications, Inc. employs 15,726 people worldwide as of March 2026.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

  • First archived Oct 9, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Aug 14, 2026 · Cognizant · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.