Professional Services Managed Technology

Automating the Mundane: How RPA & AI Workflows Reduced Admin Overhead by 40%

Microsoft Power Automate and Azure OpenAI eliminated 200+ hours of manual data entry per month and cut errors by 95% — billed only for architecture and deployment.

OceanSoft Solutions
automationaipower-automaterpa
Hours saved per month
200+
Reduction in data entry errors
95%
Admin overhead reduction
40%

The Client's Problem

A company was drowning in manual data entry, struggling to process unstructured data from emails and PDFs into their core systems.

The OceanSoft Solution

Deployed Microsoft Power Automate integrated with Azure OpenAI to intelligently read, extract, and route data. This automated the intake process from start to finish without requiring expensive custom software development.

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The Measurable Outcome

Saved 200+ hours of manual labor per month and reduced data entry errors by 95%, billing only for the architecture and deployment time.

Power Automate and Azure OpenAI workflow dashboard showing automated document intake and data extraction pipeline
From manual copy-paste to AI-powered intake — unstructured emails and PDFs routed into core systems automatically.

The Intake Bottleneck

The client — a mid-sized professional services firm handling hundreds of client submissions weekly — had outgrown manual intake. Staff spent hours each day copying information from inbound emails and PDF attachments into CRM and ERP records. Variations in document format made template-based OCR unreliable, and manual review introduced delays and transcription errors.

Operations manager Claire described the daily grind: "Every morning someone opened the intake mailbox and started copying fields from PDFs into Dynamics. Vendor invoices looked nothing like client onboarding forms, which looked nothing like compliance certificates. Same task, different layout, every single time."

Rework cycles from transcription errors cost an estimated 15 hours per week. Senior staff reviewed entries that should never have needed human touch.

The Automated Workflow

We built a Power Automate flow that:

  1. Monitors a dedicated intake mailbox for new messages and attachments
  2. Sends PDF and image content to Azure OpenAI for structured field extraction
  3. Validates extracted fields against business rules (required fields, format checks)
  4. Routes clean records into the core system; flags exceptions for human review

Exception handling keeps a human in the loop only when confidence scores fall below threshold — typically less than 5% of submissions.

Workflow stage Trigger Action
Document intake New email with attachment in intake mailbox Classify document type (invoice, onboarding, compliance)
Field extraction Classified PDF or image Azure OpenAI extracts structured fields with confidence score
Validation Extraction complete Business rules check required fields, formats, duplicate detection
Auto-route Confidence ≥ 90% Create record in Dynamics/ERP with extracted data
Exception queue Confidence < 90% or validation fail Card in review inbox with extracted preview and edit fields

Why Power Platform Over Custom Code

The client needed rapid deployment without a six-month custom build. Power Automate integrates natively with Microsoft 365 and their existing Dynamics environment. Azure OpenAI handles unstructured document variance that traditional OCR cannot.

A custom Python extraction pipeline was scoped at twelve weeks and ongoing maintenance. Power Automate delivery: four weeks from kickoff to production, with flows the client's IT team can inspect and modify without a developer on retainer.

Total project delivery: architecture, deployment, and handover documentation — no ongoing retainer required for day-to-day operation.

Exception Handling That Scales

The review inbox shows the original attachment alongside extracted fields. Staff correct mismatches and approve — resolution averages under 90 seconds per exception. Common correction patterns feed back into prompt tuning, improving auto-route rates over time.

After three months in production, auto-route rate climbed from 78% to 95% as the extraction prompts were refined against real document variance.

Results After Six Months

Metric Before After 6 months
Manual data entry hours per month ~240 ~35 (exceptions only)
Data entry error rate ~8% <0.5%
Average intake-to-record time 4–6 hours <15 minutes (auto-route)
Admin FTE on intake tasks 1.5 0.3 (redeployed to client-facing work)
Rework hours from transcription errors ~15/week ~1/week

Claire: "We didn't replace people — we stopped paying skilled staff to be copy machines. The exceptions inbox is the only place anyone touches intake now, and it's a fraction of what we used to do."

What They Did Not Change

Staff kept their existing Dynamics environment and email infrastructure. The automation layer sits on top — monitoring the intake mailbox and posting structured records. No rip-and-replace of core systems, no migration of historical data beyond a forward-looking cutover date.

Engagement Model

Phased delivery over four weeks: intake workflow design (week 1), Azure OpenAI extraction configuration and testing (week 2), Power Automate flow build and Dynamics integration (week 3), hypercare and handover (week 4). Ongoing support on an hourly basis — no bloated MSP retainer.


This engagement sits within our Managed Technology practice — business automation, AI workflows, and Microsoft ecosystem integration.