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Google Cloud Retail United States

Kohl's

Enterprise Knowledge Search · Marketing Personalization

Kohl’s needed to make governed enterprise data easier to use across the business and also wanted customer-facing shopping assistants that could guide discovery without forcing teams to build separate AI stacks for each use case.

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For more than 60 years, leading omnichannel retailer

Reported impact

Business outcome

Kohl’s accelerated data-driven decision-making for associates and used the same AI stack to build more intuitive shopper experiences.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

Kohl's uses Google Cloud to address this challenge: Kohl’s needed to make governed enterprise data easier to use across the business and also wanted customer-facing shopping assistants that could guide discovery without forcing teams to build separate AI stacks for each use case. Gemini acts as the front door to AI for internal agents and customer-facing shopping experiences, serving as the serving layer. Gemini platform hosts custom agents that turn analysis into active conversation and support guided gifting experiences, serving as the serving layer. Looker provides the trusted semantic layer that grounds agent responses in business logic and governed metrics, serving as the serving layer. BigQuery stores the governed data foundation that the agents query conversationally, serving as the storage layer.

Data flow

Enterprise data is modeled in Looker on top of BigQuery, Gemini agents translate natural-language questions into governed insights for associates, and the same agentic foundation powers customer gifting guidance and multimodal product discovery.

Solution architecture

4 components · 2 layers
  1. Storage
    • BigQuery stores the governed data foundation that the agents query conversationally
  2. Serving
    • Gemini acts as the front door to AI for internal agents and customer-facing shopping experiences
    • Gemini platform hosts custom agents that turn analysis into active conversation and support guided gifting experiences
    • Looker provides the trusted semantic layer that grounds agent responses in business logic and governed metrics

Architecture clues

  • Gemini’s multimodal search lets shoppers upload images and stay inside the chat flow
  • Looker’s semantic layer ensures insights are grounded in governed data rather than raw tables
  • The AI platform lets teams build custom agents on one standardized architecture

Evidence from the source

For more than 60 years, leading omnichannel retailer
Using Looker’s trusted semantic layer on top of its data foundation in BigQuery, Kohl’s can define the structure of its data and business logic.
Using the Gemini platform, Kohl’s built a custom agent that turns analysis into an active conversation accessible to anyone.
We’re using Gemini for customer experience to build an AI-powered gifting agent.
has been serving and celebrating families of all kinds, taking care of them through all the realest moments of everyday life. In today’s fast-moving retail landscape, delivering on that promise means making the right decisions while moving at the speed of the customer — no small task given Kohl’s operates over 1,100 physical stores across the United States and supports millions of shoppers online.
Transparency Evidence & methodology 66/100 reference value · 77% 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 value66/100
  • 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 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 confidence77%
  • 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 provenance0/10
    The publication date has no documented provenance.
  • 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. Google Cloud

    Kohl's customer story

    First-party
    https://cloud.google.com/customers/kohls
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Aug 12, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    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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