Incident Report Review Automation
Blackbook AI implemented a Power Platform solution that automated incident report reviews, reduced manual workload, and improved processing accuracy and timeliness.
Client Context
A large Australian emergency services organisation managing thousands of incident reports each month across multiple brigades faced increasing difficulty reviewing and managing reports within its Fire Incident Report System. Timely and accurate reporting was critical for compliance, operational oversight, and brigade accountability.
As report volumes grew, the review process became harder to manage at scale. Manual tracking, fragmented workflows, and system limitations created delays and operational risk.
The Challenge
The incident review process was constrained by several operational inefficiencies:
- Email notifications provided limited context, requiring manual searching within FIRS
- Work allocation was managed through shared mailboxes, slowing down processing
- Navigating FIRS was time-consuming, even for experienced staff
- No in-system tracking capability for reviews, with Excel used to manage report status
- High report volumes meant support reports were often delayed or missed
The Solution
Blackbook AI designed and deployed an end-to-end Power Platform solution to streamline and automate the FIRS review process:
- Developed a custom rule engine to pre-assess and categorise reports, reducing manual review workload
- Implemented Azure Data Factory pipelines to automate daily data synchronisation from FIRS, eliminating manual data entry
- Built two Power Apps: one to guide assessors through a structured review workflow, and another to manage and update business rules
- Enabled one-click actions for follow-ups, priority setting, and incident allocation
- Replaced fragmented tools and manual tracking with a structured, systemised workflow
The Outcome
- Automated Assessment at Scale:
60% of reports now automatically pass the rule engine, with a further 10% requiring only minimal manual checks - Reduced Processing Times:
Faster review cycles enabled more reports to be assessed accurately and on time - Eliminated Manual Tracking:
Excel-based status management and manual searching were replaced with guided, in-system workflows - Improved Staff Experience:
Structured review tools and self-serve allocation improved efficiency and reduced administrative burden - Enhanced Compliance and Communication:
Automated follow-ups with brigades improved response rates and oversight
Client Context
A large Australian emergency services organisation managing thousands of incident reports each month across multiple brigades faced increasing difficulty reviewing and managing reports within its Fire Incident Report System. Timely and accurate reporting was critical for compliance, operational oversight, and brigade accountability.
As report volumes grew, the review process became harder to manage at scale. Manual tracking, fragmented workflows, and system limitations created delays and operational risk.
The Challenge
The incident review process was constrained by several operational inefficiencies:
- Email notifications provided limited context, requiring manual searching within FIRS
- Work allocation was managed through shared mailboxes, slowing down processing
- Navigating FIRS was time-consuming, even for experienced staff
- No in-system tracking capability for reviews, with Excel used to manage report status
- High report volumes meant support reports were often delayed or missed
The Solution
Blackbook AI designed and deployed an end-to-end Power Platform solution to streamline and automate the FIRS review process:
- Developed a custom rule engine to pre-assess and categorise reports, reducing manual review workload
- Implemented Azure Data Factory pipelines to automate daily data synchronisation from FIRS, eliminating manual data entry
- Built two Power Apps: one to guide assessors through a structured review workflow, and another to manage and update business rules
- Enabled one-click actions for follow-ups, priority setting, and incident allocation
- Replaced fragmented tools and manual tracking with a structured, systemised workflow
The Outcome
- Automated Assessment at Scale:
60% of reports now automatically pass the rule engine, with a further 10% requiring only minimal manual checks - Reduced Processing Times:
Faster review cycles enabled more reports to be assessed accurately and on time - Eliminated Manual Tracking:
Excel-based status management and manual searching were replaced with guided, in-system workflows - Improved Staff Experience:
Structured review tools and self-serve allocation improved efficiency and reduced administrative burden - Enhanced Compliance and Communication:
Automated follow-ups with brigades improved response rates and oversight
