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Microsoft Education United States

Georgia Tech

Document Processing · Real-time Analytics · Sales Automation

Dr. Omar Asensio, an Associate Professor at Georgia Tech and Visiting Fellow at Harvard Business School, leverages Microsoft's Azure OpenAI Service to enhance the electric vehicle (EV) charging infrastructure. By analyzing global EV charging behavior data in 72 languages

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A decade ago, electric vehicle sales barely accounted for 1% of the total new vehicles sold. Fast forward to 2023, and EVs make up a remarkable 14% of all new car sales worldwide.
Asensio's work spans 10 years, addressing climate change by devising solutions to curtail greenhouse gas emissions with technology and behavioral strategies.
Dr. Omar Asensio, an Associate Professor at Georgia Tech and Visiting Fellow at Harvard Business School, leverages Microsoft's Azure OpenAI Service to enhance the electric vehicle (EV) charging infrastructure.
In his work to better understand this experience, Dr. Asensio's team found an active community of EV drivers who are connected through a variety of apps and platforms, creating a treasure trove of unstructured data.

Reported impact

Business outcome

Dr. Omar Asensio, an Associate Professor at Georgia Tech and Visiting Fellow at Harvard Business School, leverages Microsoft's Azure OpenAI Service to enhance the electric vehicle (EV) charging infrastructure.

  • Cost Reduction
  • Productivity Improvement
  • Revenue Growth
  • Scalability

Solution

Dr. Omar Asensio, an Associate Professor at Georgia Tech and Visiting Fellow at Harvard Business School, leverages Microsoft's Azure OpenAI Service to enhance the electric vehicle (EV) charging infrastructure. By analyzing global EV charging behavior data in 72 languages, Asensio's team uncovers insights crucial for policy development and infrastructure improvement. In the global landscape of electric vehicle (EV) adoption, one question endures: What constitutes the elusive "if you build it, they will come" moment? Is it the vehicles themselves, or is it necessary to first build public trust in electric vehicle charging infrastructure? This is the challenge Dr. Utilizing Microsoft's Azure OpenAI Service, Asensio was able to automatically discover EV charging behavior in 72 languages globally. By partnering with Microsoft, Georgia Tech's Institute for Data Engineering and Science (IDEaS) created the Cloud Hub to advance the use of Azure for research and education.

Solution architecture

1 components · 1 layer
  1. Compute
    • Azure OpenAI Service Runs the large-language-model features for classification, summarization, retrieval, or conversational responses in the solution.

Architecture clues

  • A decade ago, electric vehicle sales barely accounted for 1% of the total new vehicles sold. Fast forward to 2023, and EVs make up a remarkable 14% of all new car sales worldwide.
  • Asensio's work spans 10 years, addressing climate change by devising solutions to curtail greenhouse gas emissions with technology and behavioral strategies.
  • Azure OpenAI Service: Runs the large-language-model features for classification, summarization, retrieval, or conversational responses in the solution.
  • Dr. Omar Asensio, an Associate Professor at Georgia Tech and Visiting Fellow at Harvard Business School, leverages Microsoft's Azure OpenAI Service to enhance the electric vehicle...
  • In the global landscape of electric vehicle (EV) adoption, one question endures: What constitutes the elusive "if you build it
  • Utilizing Microsoft's Azure OpenAI Service, Asensio was able to automatically discover EV charging behavior in 72 languages globally.

Evidence from the source

Dr. Omar Asensio, an Associate Professor at Georgia Tech and Visiting Fellow at Harvard Business School, leverages Microsoft's Azure OpenAI Service to enhance the electric vehicle (EV) charging infrastructure.
In the global landscape of electric vehicle (EV) adoption, one question endures: What constitutes the elusive "if you build it, they will come" moment? Is it the vehicles themselves
Utilizing Microsoft's Azure OpenAI Service, Asensio was able to automatically discover EV charging behavior in 72 languages globally.
Transparency Evidence & methodology 71/100 reference value · 82% 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 depth15/25
    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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence82%
  • 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 evidence15/20
    3 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 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. Microsoft

    Georgia Tech customer story

    First-party
    https://www.microsoft.com/en/customers/story/1800038623596257192-gatech-azure-ai-studio-higher-education-en-united-states
    Source type
    Vendor-published customer story
    Published
    Aug 14, 2024
    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 Aug 14, 2024 · 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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