OpenAI Media and Entertainment Singapore

CNA

Enterprise Knowledge Search · IT Operations Automation · Risk Management

CNA needed to improve newsroom productivity and investigative reach without compromising public-service standards, because journalists faced labor-intensive reporting tasks while the organization also had to manage misinformation and AI governance risks.

150 million homes and connected devices
more than 500 enterprise licenses across CNA and another 2,000 at group level
over twenty custom GPTs

Reported impact

Business outcome

CNA improved newsroom efficiency, uncovered election anomalies it might have missed otherwise, and established a governed pattern for scaling AI across reporting, research, and editorial workflows.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

CNA built an AI-enabled newsroom around ChatGPT, internal GPTs, and OpenAI reasoning models, but only after putting governance first. The organization spent a year defining newsroom-specific AI guidelines, cross-functional oversight, human-in-the-loop rules, and restrictions on cloned voices or AI-generated footage before scaling usage. In daily work, reporters use internal GPTs with verified information as a second brain for context, while Parliament AI recognizes more than 90 MPs, transcribes speeches, and generates searchable summaries to make legislative coverage more efficient. During Singapore's General Election, CNA also used OpenAI reasoning models to analyze manipulative social media campaigns and surface suspicious account links in real time. The stack now supports more than twenty custom GPTs, including Newsroom Buddy for brainstorming and style-guide checks, making AI part of reporting, research, and verification rather than a disconnected experiment.

Data flow

Verified reference material flows into internal GPTs for reporter context, live Parliament and election signals move into specialized models for transcription and anomaly analysis, and outputs are routed back to journalists under human-in-the-loop controls for publication decisions.

Solution architecture

5 components · 2 layers
  1. Compute
    • advanced reasoning models Analyze election and social-media patterns to surface suspicious connections in real time.
  2. Serving
    • ChatGPT Acts as the general-purpose AI workspace CNA uses across newsroom workflows.
    • internal GPTs Provide reporters with verified contextual information for active story development.
    • Parliament AI Recognizes MPs, transcribes speeches, and generates searchable summaries for Parliament coverage.
    • Newsroom Buddy Helps journalists brainstorm ideas and check work against the CNA style guide.

Architecture clues

  • CNA spent a year drafting AI guidelines before scaling adoption
  • Human-in-the-loop processes are enforced across newsroom use cases
  • Parliament AI recognizes over 90 MPs
  • Training, hackathons, and cross-functional teams are part of the rollout

Evidence from the source

ChatGPT surfaced the anomaly and helped us uncover hidden connections in real time
The team has created over twenty custom GPTs, including a general purpose GPT 'Newsroom Buddy'
We built internal GPTs with verified information to provide context for stories
We spent a year drafting and refining our AI guidelines, setting up cross-functional oversight, enforcing human-in-the-loop processes
Transparency Evidence & methodology 70/100 reference value · 90% confidence · 3 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 value70/100
  • 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 scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence90%
  • 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
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    3 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. OpenAI

    CNA customer story

    First-party
    https://openai.com/index/cna-walter-fernandez/
    Source type
    Vendor-published customer story
    Published
    Jul 23, 2025
    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. Mediacorp

    WALTER FERNANDEZ - Mediacorp

    First-party
    https://www.mediacorp.sg/author/walter-fernandez

    Walter Fernandez 'oversees news and current affairs across all platforms, focusing on the global expansion of CNA (Channel NewsAsia)'.

    Source type
    Official leadership profile
    Supports
    • Company size
  3. Prospeo

    Mediacorp Revenue, Funding & Valuation

    Independent secondary
    https://prospeo.io/c/mediacorp-revenue

    The page shows '2001-5000 Employees' and its Organization JSON-LD sets numberOfEmployees value 2001.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

  • Published on source page Jul 23, 2025 · OpenAI · First-party
    Open date source
Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.