- Soroco Tops Everest PEAK Matrix® for Digital Interaction Intelligence 2024 - Third Year in a Row
Customer Stories
Ecosystem
Work Graph
Company
Government council
CGI leveraged Scout to identify opportunities within a large local council to reduce its operational workload by 67%
The Challenge
In response to mounting pressure to maintain customer service standards amid budget constraints, one of Scotland’s largest historic counties, with over 18,000 employees, initiated a digital transformation program. The goal was to achieve 5% efficiency savings by streamlining operations, reducing manual effort, and improving work efficiency.
However, the council faced significant hurdles in identifying and prioritizing processes for automation and improvement. They needed a solution that could:
However, the council faced significant hurdles in identifying and prioritizing processes for automation and improvement. They needed a solution that could:
- Identify the most resource-intensive processes and determine inefficiencies
- Quantify the potential benefits of process optimization and prioritize them for transformation
These objectives had to be met without placing substantial burden on their business teams.
Industry
Government council
Location
UK
18000+
Employees
Attempted Solution before Scout
CGI analysed that roughly
80%
of the council’s cost was spread across 20% of business functions
Business leaders across multiple functions within the council attempted to identify and shortlist processes for transformation by holding workshops, interviewing process SMEs and by examining existing documentation. The SMEs struggled to quantify potential benefits and prioritize processes for improvements due to lack of empirical process data.
This led the council to seek recommendation from CGI - one of the largest IT and business consulting services firms in the world, who analyzed that roughly 80% of the council’s cost was spread across 20% of business functions amongst desk-based teams.
This led the council to seek recommendation from CGI - one of the largest IT and business consulting services firms in the world, who analyzed that roughly 80% of the council’s cost was spread across 20% of business functions amongst desk-based teams.
However, manually exploring these functions would have been a mammoth exercise. Therefore, they recommended that the council consider adopting an AI solution to achieve the following objectives:
Provide a detailed understanding of the process through interactive flowgraphs and auto generated documentation
Identify resource intensive processes
and quantify process inefficiencies
Recommend & prioritize digital transformation change initiatives
Enter
To achieve a comprehensive understanding of processes, CGI leveraged its domain expertise and onboarded Scout to find and fix problems. In this case, the council shortlisted the accounts payable function for the pilot.
The Scout AI model was quickly put into action because it didn't require complicated integration with current systems and was able to cater to the customer’s application stack. The Scout AI model analyzed how teams interacted with various applications, mapping out how and why work happens the way it does, within the accounts payable function of the council.
The Scout AI model was quickly put into action because it didn't require complicated integration with current systems and was able to cater to the customer’s application stack. The Scout AI model analyzed how teams interacted with various applications, mapping out how and why work happens the way it does, within the accounts payable function of the council.
Within a week, Scout discovered
70%
of the team’s
effort was spent on 4 major processes
Within a week, Scout discovered the following key insights:
- Usage of core applications was 20% higher than other similar functions.
- 70% of the team’s effort was spent on 4 major processes.
Scout to “find and fix”
Step 1: Find
As the first step, the Scout AI model analyzed interactions of the accounts payable team with business applications and recognized repeated patterns of work. This helped determine that certain processes take up most of the teams’ effort.
Scout then deep dived into the shortlisted processes and delivered the following key insights to guide the business in its transformation journey.
Scout then deep dived into the shortlisted processes and delivered the following key insights to guide the business in its transformation journey.
- The intelligent document processing (IDP) solution implemented for select suppliers was found to deliver 7% processing effort savings per invoice compared to invoices that were processed using the legacy system.
- On the legacy system, 0.5% of suppliers accounted for 15% of the effort spent.
- 2.3x time was spent on invoices that require re-work versus straight-through invoices.
- >80% of effort was spent across major processes on structured applications, leading to identification of significant savings through automation.
Step 2: Fix
Based on the above insights, two levels of fixes were recommended:
Quick Fixes
Efficient supplier migration
Scout identified that the effort per invoice could be reduced by 7% by transitioning suppliers to the IDP solution. By prioritizing the top 0.5% who currently contribute 15% of the effort on the legacy system, the council could achieve substantial savings within a short timeframe.
Streamlining process documentation initiative
By leveraging Scout, the council reduced operational workload and achieved standardization in training while automating the creation of process documentation across different processes and variations.
Deep Fixes
Systemic and long-term fixes to improve operational efficiencies.
Enhance existing IDP system to match invoice lines accurately
Scout highlighted that there were significant digital gaps in invoice processing; and these were predominantly due to manual effort being spent to match invoice lines and to upload invoices in the system. The existing system automatically updated invoice headers and users had to manually match invoice line items. They also had to send out emails to suppliers in case of any discrepancies. By enhancing the system to match invoice lines and sending out automated responses to suppliers, the council could significantly improve efficiency and save 70% of manual effort spent on processing invoices.
Deploy RPA bots
Detailed insights were generated in the Statements and Non-PO invoicing processes. Scout assessed a saving of at least 40% when RPA bots were deployed to service these requests.
Improvement Prioritization
The AI prioritized the assessed process for improvement based on the effort spent on operation, the ease of actionability and potential savings. This approach facilitated the development of an automation pipeline guided by data-driven recommendations.
It is important to note that in all of these recommendations, privacy was of the utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues i.e.
- Getting teams to work together across functions
- Fixing disconnected systems
- Simplifying and optimizing work processes
Business outcomes
In summary, CGI leveraged the Scout AI model and was able to achieve the following business outcomes:
07%
reduction in processing effort per invoice by migrating suppliers to the IDP solution
67%
efficiency savings across all scouted processes
65%
efficiency savings on invoice processing by enhancing the existing solution
40%
savings by deploying RPA bots on supporting processes
How AI connects interaction data to business outcomes
Your business generates billions of data points from human-machine interactions. Scout, our AI model, deciphers this interaction data to unveil what often remains unseen—the hidden challenges your teams face at work and how they affect business outcomes, whether it's cost optimization, revenue growth, customer or employee experience, or business continuity.
The AI then provides data-based recommendations for the necessary interventions to address these challenges, paving the way for improved outcomes.
We call this lighting up the ‘dark side of the moon’.
The AI then provides data-based recommendations for the necessary interventions to address these challenges, paving the way for improved outcomes.
We call this lighting up the ‘dark side of the moon’.
See Scout in action.
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