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AT&T

Document Processing · Enterprise Knowledge Search

AT&T wanted a secure way for employees to use generative AI with company data to improve productivity, creativity, and innovation without exposing corporate information to the public domain.

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Reported impact

Business outcome

AT&T says Ask AT&T will help employees be more effective, efficient, creative, and innovative, with early value in developer productivity and easier translation and simplification of documentation.

  • Productivity Improvement

Solution

AT&T built Ask AT&T as an internal generative AI assistant for employees, using OpenAI's chatGPT functionality inside an AT&T-dedicated Azure tenant. Employees submit prompts plus company data and information to the Ask AT&T interface, which layers AT&T knowledge and internal processes onto the model so responses are focused on enterprise tasks and returned in a controlled environment rather than through a public chatbot. The first production uses target developer productivity and multilingual document support, helping coders and software developers work more productively while also translating and simplifying customer and employee documentation. Microsoft-supported Azure isolation and leakage testing are used to keep corporate data from escaping to the public domain, while AT&T's AI Policy, operating guidelines, and user review requirements govern responsible use and response checking.

Data flow

Employees enter prompts and company information into Ask AT&T; the application applies AT&T knowledge and process controls, invokes OpenAI's chatGPT functionality within AT&T's dedicated Azure tenant, and returns generated answers, translations, simplified documentation, summaries, and action items back to employees in the secured internal environment.

Solution architecture

3 components · 3 layers
  1. Compute
    • OpenAI's chatGPT functionality The underlying generative model generates responses for Ask AT&T's first iteration.
  2. Serving
    • Ask AT&T AT&T's internal generative AI tool provides employees with a secured interface for enterprise prompts, answers, translations, summaries, and other work tasks.
  3. Uncategorized
    • Azure An AT&T-dedicated Azure tenant hosts the tool in an isolated environment that was pressure tested for leakage.

Architecture clues

  • AT&T governs usage through an AI Policy and AI Operating Guidelines.
  • AT&T says the tool includes 'special sauce' that incorporates AT&T knowledge and processes.
  • Ask AT&T is designed to be interoperable with other generative tools.
  • Runs in an AT&T-dedicated Azure tenant.
  • The tenant was 'pressure tested for leakage' to protect corporate data.
  • Users are responsible for checking that generated results are accurate and appropriate.

Evidence from the source

"AT&T employees can bring company data and information into Ask AT&T without worrying about that material leaking into the public domain."
"It's also helping to translate customer and employee documentation from English to other languages as well as to simplify that documentation and make it easier to use."
"Out of the box use cases are helping our coders and software developers across the company become more productive."
"The first iteration uses OpenAI's chatGPT functionality."
"We're deploying a generative AI tool for our employees over the next several weeks so they can be part of the first generation of true corporate users of this new capability."
"We've worked with Microsoft to make Ask AT&T secure and safe for our employees and our corporate data. It runs in an AT&T-dedicated Azure tenant that's been pressure tested for leakage."
Transparency Evidence & methodology 75/100 reference value · 95% 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 value75/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence95%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    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. AT&T

    AT&T customer story

    First-party
    https://about.att.com/blogs/2023/generative-ai.html
    Source type
    Customer-published story
    Published
    Jun 20, 2023
    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
  2. Built In

    AT&T Careers, Perks + Culture | Built In

    Independent secondary
    https://builtin.com/company/att

    The Organization JSON-LD lists streetAddress '208 South Akard Street', addressLocality 'Dallas', addressRegion 'TX', and addressCountry 'US'.

    Source type
    Structured company profile
    Supports
    • Region

Date evidence

Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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