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Customer Stories
Ecosystem
Work Graph
Company
Government council
CGI leverages Scout to identify opportunities within a large local council and reduce its operational workload by 67%
The Challenge
CGI, a global leader in consulting and IT services with over 80,000 employees, encountered a significant challenge with one of its clients. Scotland’s third-largest historic county, employing over 18,000 people, was under pressure to uphold customer service standards while dealing with budget constraints. To address this, CGI launched a digital transformation program for the council, aimed at achieving a 5% efficiency gain by streamlining operations, reducing manual effort, and enhancing work efficiency.
However, CGI faced significant hurdles in identifying and prioritizing processes for automation and improvement for the council. They needed a solution that could:
However, CGI faced significant hurdles in identifying and prioritizing processes for automation and improvement for the council. 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 burdens 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 within the council attempted to identify and shortlist processes for transformation by holding workshops, interviewing process SMEs, and examining existing documentation. The SMEs struggled to quantify potential benefits and prioritize processes for improvement due to a lack of empirical process data. .
This led the council to seek recommendation from CGI, who analysed that roughly 80% of the council’s cost came from 20% of business functions which were handled by desk-based teams. Essentially, a few key functions were costing a lot, so focusing on these areas could help in managing or reducing costs.
This led the council to seek recommendation from CGI, who analysed that roughly 80% of the council’s cost came from 20% of business functions which were handled by desk-based teams. Essentially, a few key functions were costing a lot, so focusing on these areas could help in managing or reducing costs.
However, manually exploring these functions would be a mammoth exercise. Therefore, CGI recommended adopting an AI solution to:
Provide detailed process insights through interactive flowgraphs and auto generated documentation
Identify resource intensive processes and quantify process inefficiencies within the business functions, and
Prioritize digital transformation initiatives
Enter
To achieve a comprehensive understanding of processes, CGI leveraged its domain expertise and onboarded Scout to find and fix problems. The council chose 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 compatible with the customer’s application stack. Scout analysed 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 compatible with the customer’s application stack. Scout analysed 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 that
70%
of the team’s
effort was spent on 4 major processes
Within a week, Scout discovered the following key insights:
- 70% of the team’s effort was spent on 4 major processes.
- 20% higher reliance on core invoice processing apps was observed compared to other similar functions across Scout customers, indicating streamlined operations
Scout to “find and fix”
Step 1: Find
As the first step, the Scout AI model analysed interactions in the accounts payable function with business applications and recognized repeated patterns of work. This analysis revealed which processes consumed most of the team’s 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% savings in processing effort per invoice compared to the ones that were processed using the legacy system.
- 0.5% of suppliers accounted for 15% of the effort spent in the legacy system.
- 2.3x time was spent on invoices that required re-work versus time spent on straight through invoices.
- >80% of effort was concentrated on structured applications across major processes, highlighting significant potential 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% if suppliers were transitioned 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 by migrating suppliers within a short timeframe.
Streamlining process documentation initiative
The council could reduce operational workload, standardize training, and automate the creation of process documentation across various processes and variations by leveraging Scout.
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. While the existing system automatically updated invoice headers, users had to manually match line items and send emails to suppliers for 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 savings of ~ 40% when RPA bots were deployed to service these requests.
AI driven Process Prioritization
The AI prioritized the assessed process for improvement based on operational effort, ease of implementation, 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:
7%
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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