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Scout
Customer Stories
Ecosystem
Work Graph
Scout cuts effort per order and effort disparity for a F500 auto company, saving manual effort by 47%.
Challenges
The customer support team in Barcelona faced the following set of challenges:
Inconsistent processes and unclear handoffs
Delays and rework due to execution gaps
Lack of visibility into workflow variants to drive imrpovements
- Teams
- Location
- Key runtime metrics
Effort per Order, Effort Disparity
Enter Scout
The AI model revealed insights into the actual processes driving the work.
Scout Insights
Process inefficiencies
47% time was lost yearly to manual tasks- pointing to system gaps and lack of automation.
Operational bottlenecks
30% of orders got rescheduled, with most of them due to service technicians unavailability, causing delays
Effort disparity
1.3x variance in order creation and 2.5x in scheduling among user groups highlighted process inconsistencies.
Multi touch orders
36% of orders required multiple touches, driving a 20% increase in average effort per order.
Runtime metrics
Recommendations
Self service portal & chatbot
Introduce a self service portal & chatbot to handle common inquiries, reducing effort and enhancing customer experience.
Automate call scripting & logging
Use speech-to-text for call scripts and automatic note logging into downstream systems.
RPA bot for email order creation
Use a structured request form and RPA bot to extract details and create orders in downstream systems.
User training
Standardize note-taking templates, search guides and call scripts to reduce effort variation and improve efficiency across users.
Conclusion
Scout identified a ~47% potential for manual effort reduction, and an additional ~29% throughput boost through targeted automation and user training, significantly reducing effort per order and effort disparity.