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Accenture Commercial and Professional Services Global

Accenture

Marketing Personalization

Accenture's marketing organization spent too much time on research, analysis, and campaign coordination across a large global operation, so the company needed a way to reduce manual steps, speed campaign preparation, and help marketers focus on strategic work instead of repetitive process tasks.

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25-35%
25-55%
55%
6% cost savings

Reported impact

Business outcome

Accenture reduced manual marketing effort, accelerated campaign preparation and decision-making, improved governance and visibility across programs, and created an agentic operating model that lets marketers spend more time on creative and strategic work.

  • Cost Reduction
  • Productivity Improvement
  • Speed to Market

Solution

Accenture used AI Refinery and SynOps to turn marketing execution into an agentic workflow instead of a chain of manual tasks. SynOps first created a unified operational layer for campaign governance, cross-system data integration, centralized KPIs, and real-time performance reporting, already reducing manual campaign-management steps by 55 percent. Accenture then layered AI Refinery on top as an enterprise framework built on NVIDIA AI Foundry, NVIDIA AI Enterprise, and NVIDIA Omniverse Enterprise. Powered by SynOps, proprietary data, and selected third-party sources, the solution gives marketers a shared interface to 14 AI agents covering skills such as research, analytics, editorial scheduling, critical thinking, and forecasting. Those agents collaborate in intention-driven workflows, pulling information from internal research, Moody's Analytics NewsEdge, and the web to create campaign briefs, recommendations, and timing guidance in minutes instead of days or weeks.

Data flow

Campaign data and KPIs from disparate systems are integrated through SynOps into a unified operational layer. Marketers then interact with AI Refinery, whose agents draw from proprietary research, third-party sources such as Moody's Analytics NewsEdge, and web content to generate briefs, research summaries, scheduling guidance, and recommendations that are surfaced back through the shared interface for human review and refinement.

Solution architecture

6 components · 3 layers
  1. Compute
    • NVIDIA AI Foundry Supplies part of the technical foundation Accenture uses to customize and deploy enterprise AI models and solutions.
    • NVIDIA AI Enterprise Provides enterprise AI infrastructure capabilities supporting the agentic marketing environment.
    • NVIDIA Omniverse Enterprise Forms part of the AI Refinery stack used to support scalable enterprise AI solutions.
    • Research Agent Summarizes internal and external sources into concise campaign intelligence for marketers.
  2. Serving
    • AI Refinery Serves as the shared interface and execution framework that gives marketers access to multiple AI agents in one environment.
  3. Orchestration
    • SynOps Provides the unified operational and governance layer for campaign data, reporting, and workflow visibility.

Architecture clues

  • Agents can huddle autonomously and expose their reasoning to marketers in real time.
  • Marketers interact through one shared interface rather than separate AI apps.
  • The solution uses 14 AI-powered agents with specialized marketing skills.
  • The system is powered by proprietary data plus selected third-party and web sources.

Evidence from the source

"A campaign strategy brief used to take up to three weeks to complete; now marketers can produce an initial draft in minutes."
"AI Refinery houses 14 AI-powered agents"
"The AI Refinery is built on NVIDIA AI Foundry, NVIDIA AI Enterprise and NVIDIA Omniverse Enterprise"
"The platform provides a single, unified view of all programs that has reduced duplicative content production—saving valuable employee hours and reducing manual steps for managing a campaign by 55%"
"Where previously they would have spent upwards of two days manually researching internal and external data sources ... Within seconds, marketers receive a concise summary"
Transparency Evidence & methodology 95/100 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 value95/100
  • 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 impact25/25
    4 metrics describe measurable impact.
  • Deployment scale5/10
    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. Accenture

    Accenture customer story

    First-party
    https://www.accenture.com/en/case-studies/technology/ai-refinery-smarter-faster-marketing
    Source type
    Vendor-published customer story
    Published
    Mar 27, 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 Mar 27, 2025 · Accenture · 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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