Accenture Public Sector Saudi Arabia

Saudi Data and Artificial Intelligence Authority (SDAIA)

Real-time Analytics

SDAIA needed national-scale infrastructure, governance, and practical AI services to move Saudi Arabia from strategy into operational data sharing, analytics, and Arabic-language AI adoption across government entities.

50 Billion SAR in value
more than 200 key government systems
more than 85 government entities
trained more than 45,000 professionals

Reported impact

Business outcome

SDAIA established a practical data-and-AI operating foundation for Saudi Arabia, giving agencies secure data sharing, national analytics, and an operational Arabic AI service while enabling broader digital-economy adoption across the Kingdom.

  • Productivity Improvement
  • Scalability

Solution

Accenture worked with SDAIA on a broad public-sector data and AI build-out that combines infrastructure, data sharing, analytics, and language AI. The program established national cloud infrastructure and the National Data Bank so government entities can share data securely and efficiently instead of operating in isolated silos. On top of that foundation, the team supported AI research and development for an Arabic natural-language-processing mobile app and helped create Estishraf, a national insights and analytics platform used for planning and decision support. Accenture also assisted SDAIA in launching the National Data Management Office to set foundational regulations for data and AI. The resulting architecture ties governed cross-agency data exchange to analytics and citizen-facing AI capabilities, giving SDAIA a practical operating model for public-sector AI rather than a strategy document alone. It also pairs the platform work with national talent-building efforts so more agencies can adopt the new services safely and at scale.

Data flow

Government-system data is connected into the National Data Bank through the national cloud infrastructure, where it can be shared securely across entities. Estishraf consumes that governed data for planning and insight generation, while the Arabic NLP app applies AI capabilities in an end-user service built on the same national foundation.

Solution architecture

5 components · 4 layers
  1. Compute
    • national cloud infrastructure Hosts the public-sector data and AI environment at national scale.
  2. Storage
    • National Data Bank Provides the shared government data layer for secure cross-entity data exchange.
  3. Serving
    • Estishraf Serves as the national analytics platform for planning and data-driven decision making.
    • Arabic natural language processing (NLP) mobile app Applies AI research and development in a citizen-facing Arabic language service.
  4. Governance
    • National Data Management Office Sets foundational regulations and governance for national data and AI usage.

Architecture clues

  • Estishraf operates as a national insights and analytics platform.
  • The National Data Bank is connected to more than 200 government systems.
  • The program combines infrastructure, regulation, analytics, and Arabic NLP services.

Evidence from the source

"AI research and development for the strategy of an Arabic natural language processing (NLP) mobile app"
"Today the National Data Bank is connected to more than 200 key government systems"
"established the national cloud infrastructure as well as the National Data Bank"
"more than 85 government entities benefited from Estishraf's services, resulting in an estimated 50 Billion SAR in value"
Transparency Evidence & methodology 71/100 reference value · 87% confidence · 1 source

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 value71/100
  • 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 specificity16/20
    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 confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability19/25
    9 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 transparency10/10
    1 source reference 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. Accenture

    Saudi Arabia Transforms With Data and AI | Accenture

    First-party
    https://www.accenture.com/en/case-studies/artificial-intelligence/reimagining-saudi-arabia-economy
    Source type
    Vendor-published customer story
    Published
    Oct 19, 2023
    Supports
    • 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

  • Published on source page Oct 19, 2023 · Accenture · First-party
    Open date source
Not separately sourced Customer, Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.