Manufacturing
Manufacturing Lead Collection with Azure OpenAI
Primary Area
AI & Innovation
Service Area
Business Development / AI Vision Model
Products Used
Azure OpenAI Vision
Roles Impacted
Sales Director, Account Managers, Sales Representatives
Impact Highlight
Leveraged Azure OpenAI for Sales Enablement
Opportunity
A manufacturing sales organization saw a chance to grow by spotting industrial structures that matched its ideal equipment and service buyers across new regions, but it lacked a unified way to mine and score geographic data. Disconnected GIS files, inconsistent CRM records, and manual spreadsheet research slowed outreach and left high‑value facilities unseen.
Pain Points
Location intelligence scattered across legacy ESRI shapefiles, vendor CSV drops, and field‑rep notebooks with no governed source of truth.
CRM held partial account data that didn’t map cleanly to geographic assets or installed‑base attributes.
Limited ability to classify facility types from imagery; relied on individual judgments.
Missed expansion territories because data doesn’t refresh.
Solution
We convened sales leadership, data stewards, and IT to translate the growth vision into governed data definitions, and territory rules. The team implemented a Microsoft‑centric, cloud‑native architecture anchored by Azure OpenAI Vision for facility image analysis and Azure Blob Storage as the durable geospatial data lake, integrating pipeline orchestration, enrichment, and downstream sales activation.
People, Process, Technology Highlights
Facilitated working sessions to codify what constitutes a "qualified structure" (size thresholds, process indicators, power infrastructure, sector tags) and mapped these to standardized data fields.
Established data stewardship roles and a bi‑weekly data quality review.
Built ingestion using Azure Data Factory to land satellite/imagery, 3rd‑party industrial datasets, and legacy ESRI exports into Azure Blob Storage.
Applied Azure OpenAI Vision to detect structural signatures and classify probable types; confidence scores written back as metadata.
Geocoded and enriched locations with Bing Maps services.
Exposed governed datasets to Microsoft Fabric / Power BI semantic model.
Integrated with Dynamics 365 Sales to auto‑create and route leads by territory, structure-of-interest match, and AI confidence threshold.
Private Blob containers used for sensitive imagery.
Impact
The client now operates a governed, geospatial lead identification pipeline directly embedded in Power BI reports, eliminating manual hunting and accelerating territory coverage. Automated visual classification cut the response window from discovery to first qualified contact by more than half, enabling reps to act on opportunities faster with AI‑driven insights.
Results
Qualified leads closed quicker within six months of go‑live.
Average lead qualification cycle reduced by half if coming from system.
Faster sales team response time for identified opportunities.
Conversion from first contact to sales‑accepted lead improved in pilot regions after adding AI structure-of-interest type scoring.
Manual data prep hours for sales ops by consolidating feeds in Azure Blob and automating transforms.
Improved account coverage mapping accuracy.