Reported impact
Business outcome
This boosted productivity by 67%, enabling their teams to focus on customer engagement.
- Customer Experience
- Productivity Improvement
- Revenue Growth
- Risk Reduction
MicrosoftSoftware / SaaSSales AutomationGenerative AI / LLMSales AutomationCustomer ExperienceProductivity ImprovementRevenue GrowthRisk Reduction2025 MicrosoftMS#0078d4Software / SaaSJapanBoth new and seasoned staff faced challenges in identifying current data sources, calibrating and consolidating them from across Fujitsu's vast infrastructure, and creating accurate, client-specific proposals.The AI agent integrates with existing Microsoft tools familiar to around 38,000 employees, retrieves dispersed…Mar 1, 20255Sales AutomationAzure AI Agent Service · Azure AI Foundry · Azure AI Search0 2025-03-01 5
Fujitsu
Sales Automation
Both new and seasoned staff faced challenges in identifying current data sources, calibrating and consolidating them from across Fujitsu's vast infrastructure, and creating accurate, client-specific proposals.
The AI agent integrates with existing Microsoft tools familiar to around 38,000 employees, retrieves dispersed knowledge, and lays the foundation for broader AI-driven innovation.
Solution
Fujitsu built its Microsoft solution to improve seller-productivity and pipeline workflows. Both new and seasoned staff faced challenges in identifying current data sources, calibrating and consolidating them from across Fujitsu's vast infrastructure, and creating accurate, client-specific proposals. Key components included Azure AI Foundry coordinated workflows, prompts, or deployment, and Azure AI Agent Service coordinated workflows, prompts, or deployment. User prompts and domain content are orchestrated through Azure AI Foundry, processed by Azure AI Foundry, and then delivered through Azure AI Foundry so Fujitsu can return context-aware answers or automation in the flow of work. The governed Microsoft stack gave Fujitsu a reusable foundation for generative ai / llm, sales automation while keeping deployment, security, and day-to-day operations inside the Microsoft estate. This boosted productivity by 67%, enabling their teams to focus on customer engagement.
Data flow
User prompts and domain content are orchestrated through Azure AI Foundry, processed by Azure AI Foundry, and then delivered through Azure AI Foundry so Fujitsu can return context-aware answers or automation in the flow of work.
Solution architecture
- Serving
- Azure AI Search Indexes enterprise content so copilots and applications can retrieve grounded answers.
- Orchestration
- Azure AI Foundry Provides the build and management layer for enterprise AI agents and applications.
- Azure AI Agent Service Hosts and coordinates enterprise agents that automate specialized sales and support tasks.
Architecture clues
- Delivering impact and shaping the future of AI at Fujitsu The launch of the agent has yielded measurable results.
Evidence from the source
"We are using Microsoft's Semantic Kernel and Azure AI Agent Service to orchestrate multiple specialized AI agents and an orchestrator AI to coordinate them to answer questions as a team," he adds.
Both new and seasoned staff faced challenges in identifying current data sources, calibrating and consolidating them from across Fujitsu's vast infrastructure, and creating accurate, client-specific proposals.
Fujitsu evaluated Azure AI Agent Service to automate sales proposal creation.
Leveraging Azure AI Agent Service for intelligent automation Building on its long-standing partnership with Microsoft, Fujitsu used Azure AI Agent Service within Azure AI Foundry to develop an intelligent, scalable AI agent for sales automation.
The AI agent integrates with existing Microsoft tools familiar to around 38,000 employees, retrieves dispersed knowledge, and lays the foundation for broader AI-driven innovation.
This boosted productivity by 67%, enabling their teams to focus on customer engagement.
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
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 clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth20/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity16/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact0/250 metrics describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/206 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity8/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://www.microsoft.com/en/customers/story/21885-fujitsu-azure-ai-foundryMicrosoftFirst-party
Fujitsu 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
- Publication date
Date evidence
- Published on source page Mar 1, 2025 · Microsoft · First-partyOpen 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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