AI-powered document processing
Turn incoming documents into structured information and a clearer approval process.
Illustrative solution concept
This example is a design concept, not a completed client engagement. No client identity or measured performance claim is implied.
Business problem
Teams manually copy details from incoming documents, check completeness and chase approvals. Inconsistent formats and missing fields make reliable automation difficult.
Requirements and consultant responsibilities
Assess the current workflow, identify sensitive data, define mandatory fields, design integration boundaries and agree a human review process. Ownership includes architecture, implementation planning, testing and handover.
Solution architecture
Incoming document → private storage → extraction service → validation → human approval → business application. Power Automate coordinates the workflow; Azure AI extracts fields. Dataverse or an existing system holds approved records.
Implementation approach
Begin with a small representative document set. Compare extraction against reviewed reference data. Route low-confidence fields to people. Record decisions and retain only necessary documents.
Challenges and safeguards
Variable layouts, sensitive information and ambiguous fields require careful validation. Treat AI output as untrusted and prevent document content from controlling workflow actions.
Outcomes and lessons
Measure review effort, processing time and error rates during a pilot. No numerical outcome is claimed. Expand only when the evidence supports it and the team can maintain the workflow.
Related support
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