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Microsoft Retail United Arab Emirates

Majid Al Futtaim Retail

Data Lake / Data Warehouse Modernization · Document Processing · Real-time Analytics

Majid Al Futtaim Retail faced fragmented data systems and slow manual reporting, causing delayed insights and inefficiencies.

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Majid Al Futtaim (MAF Retail), a leading shopping malls, communities, retail, and leisure pioneer across the Middle East, Africa, and Central Asia, holds exclusive rights to the Carrefour brand in 32 countries. It also operates over 450 stores in 14 nations, employing more than 37,000 people and serving over 770,000 customers daily. Majid Al Futtaim’s flagship stores are especially notable in the Emirates.
Majid Al Futtaim offers a range of 7 million SKUs across its 14 markets, requiring significant commercial and technological effort to manage pricing effectively. The new AI engine, "Spider," ensures competitive pricing by considering various scenarios, recommendations, and periodic promotions, ensuring customers receive the best value and price. Additionally, the engine enhances SKU specifications and information, enabling customers to make better choices and easily navigate their specific needs—whether it’s gluten-free, allergy-friendly, organic, or sustainably packaged.
With over 465 stores and up to 120 legal documents per location, Majid Al Futtaim manages a large volume of contracts using “Excellence.” The team can extract relevant information and map it to financial data for analysis and action. Previously, the legal team spent up to seven working days on each contract inquiry compared to now where they simply search and extract relevant information automatically within minutes.

Reported impact

Business outcome

From seven days to three minutes: Majid Al Futtaim boosts customer centricity with Azure OpenAI Service

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Majid Al Futtaim Retail, a Retail organization in United Arab Emirates, modernized this data lake / data warehouse modernization workload with Azure OpenAI Service, Azure, Power BI, Power Apps, and Microsoft Purview. Majid Al Futtaim Retail faced fragmented data systems and slow manual reporting, causing delayed insights and inefficiencies. Azure OpenAI Service runs the large language model workloads that power summarization, generation, or conversational reasoning in the solution. Azure provides the scalable cloud foundation hosting the customer’s modernized applications, data, and AI workloads. Power BI publishes dashboards and self-service analytics so business users can act on fresher insights. Data from operational systems is ingested into Azure OpenAI Service, processed across Azure, and surfaced through Power BI so teams can act on fresher operational insight. Applies automated information governance before broader AI adoption.

Data flow

Data from operational systems is ingested into Azure OpenAI Service, processed across Azure, and surfaced through Power BI so teams can act on fresher operational insight.

Solution architecture

5 components · 3 layers
  1. Compute
    • Azure OpenAI Service runs the large language model workloads that power summarization, generation, or conversational reasoning in the solution.
    • Azure provides the scalable cloud foundation hosting the customer’s modernized applications, data, and AI workloads.
  2. Serving
    • Power BI publishes dashboards and self-service analytics so business users can act on fresher insights.
    • Power Apps delivers low-code applications tailored to frontline or business-process use cases.
  3. Governance
    • Microsoft Purview provides data governance, classification, and compliance controls across the information estate.

Architecture clues

  • Applies automated information governance before broader AI adoption.

Evidence from the source

By implementing Azure Synapse, Power BI, and Azure OpenAI Service, Majid Al Futtaim Retail centralized data, automated reporting, and integrated advanced AI for better targeting and feedback analysis.
From seven days to three minutes: Majid Al Futtaim boosts customer centricity with Azure OpenAI Service
Majid Al Futtaim (MAF Retail), a leading shopping malls, communities, retail, and leisure pioneer across the Middle East, Africa, and Central Asia, holds exclusive rights to the Carrefour brand in 32 countries. It also operates over 450 stores in 14 nations, employing more than 37,000 people and serving over 770,000 customers daily. Majid Al Futtaim’s flagship stores are especially notable in the Emirates.
Majid Al Futtaim Retail faced fragmented data systems and slow manual reporting, causing delayed insights and inefficiencies.
The company saved USD1 million annually, cut feedback processing time from seven days to three hours, and improved geographic targeting, boosting efficiency with AI-driven solutions.
Transparency Evidence & methodology 85/100 reference value · 89% 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 value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference 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. Microsoft

    Majid Al Futtaim Retail customer story

    First-party
    https://www.microsoft.com/en/customers/story/21059-majid-al-futtaim-retail-azure-open-ai-service
    Source type
    Vendor-published customer story
    Published
    Jan 16, 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

Date evidence

  • Published on source page Jan 16, 2025 · Microsoft · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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