- Soroco Tops Everest PEAK Matrix® for Digital Interaction Intelligence 2024 - Third Year in a Row
Customer Stories
Ecosystem
Work Graph
Company
Telecom
How Tech Mahindra used Scout to streamline operations for a UK telecom giant, reducing processing effort by 34%
The Challenge
One of Tech Mahindra’s leading telecom customers in the UK, having a global
presence, was grappling with inefficient and variable order fulfilment, ITSM,
Sales Ops, and billing processes. This inefficiency led to increased time
expenditure, revenue leakage, reduced process accuracy by over 50%, a
suboptimal end-user experience, and eventually high cost of operations with
severe operational issues impacting the morale of several employees.
The company had already used a myriad of discovery tools to identify end-to-end opportunities for reducing the cost of operations but had not yet achieved the desired level of optimization.
The company had already used a myriad of discovery tools to identify end-to-end opportunities for reducing the cost of operations but had not yet achieved the desired level of optimization.
Location
UK
Industry
Telecom
12
Subsidiaries
85,000+
Employees
Attempted Solution before Scout
Consultants requested an additional
1,200 hours
for Manual Discovery Workshops
The company engaged multiple discovery tools to overcome the operational shortcomings. They went about this in a traditional manner, but these tools operated in silos, leading to fragmented insights that failed to provide a holistic view of process inefficiencies. This traditional approach lacked the integration and sophisticated analytics needed to pinpoint critical bottlenecks
and did not account for the nuanced variations in workflows across different
teams and locations.
Enter
The business heads of the company then worked with Tech Mahindra’s BPS Team for evaluating and onboarding Soroco to find and fix these problems. Soroco’s Scout was brought into play, offering a suite of automation and standardization recommendations tailored to tackle the specific challenges faced.
Scout AI model could be quickly put into action because it didn’t need complicated integration with current systems. The AI analysed how their customer teams interact with various applications, mapping out how and why work happens the way it does within the said processes.
This led Scout to discover a key insight: there was excessive manual intervention in order pre-validation, generation, and validation processes.
Scout AI model could be quickly put into action because it didn’t need complicated integration with current systems. The AI analysed how their customer teams interact with various applications, mapping out how and why work happens the way it does within the said processes.
This led Scout to discover a key insight: there was excessive manual intervention in order pre-validation, generation, and validation processes.
Within
6 weeks
the Scout AI model uncovered a critical insight - the problem lay with high manual processing effort.
Scout to “find and fix”
Step 1: Find
As the first step, AI decoded the work patterns of the different teams and
connected them to business activities, simply by analysing interactions between
the team and their software. It then automatically classified these work patterns as
either core business activities or non-core activities.
Based on this analysis, within two weeks, the Scout AI model provided the following insights:
Based on this analysis, within two weeks, the Scout AI model provided the following insights:
40%
time was spent on core business apps and activities.
Contrary to popular belief, only 40% of the team’s effort was spent on core business
apps and activities. This startling statistic pointed to a massive work recall gap
across the team, highlighting the disparity between the team’s understanding of how work was being done
and how work should actually be done.
60%
time was spent on non-core activities
such as pre-validation, managing workflow, manual order creation, duplicate billing
note generation, one-off adjustments, sales ops validation activities, and more
The AI further helped Tech Mahindra in determining the following:
| Key Observation | Impact |
|---|---|
| Managing spreadsheets and custom reporting | The team was manually managing data across 100+ spreadsheets and creating custom reports for management leading to ~10,000 hours of manual work |
| Application switch | A common trend was identified between different teams as they spent so much time on non-core activities – information silos and disconnected systems, forcing the team to incessantly toggle between different applications ultimately resulting in manual inefficiency and loss of productivity to the tune of 18500 hours annually |
| Manual verification and authentication | Scout identified that the team was spending close to ~20,000 hrs annually i.e. 16% of process effort just for verifying & authenticating the right customers |
| Composing similar emails | The Scout AI model was able to identify similar topics across several email chains and found a high number of email topics which if automated could help to save ~8,000 hours annually |
Step 2: Fix
Based on the above insights, Scout AI model recommended the following fixes:
Quick Fixes
Identified 100+ user training
opportunities
opportunities
Produced As-Is & To-Be process
snapshots for different processes
Produced 70+ L5 process-designed
documents/training documents to drive
standardization in training
Created conformance view - gold
standard (what’s known to the business)
vs what standard users follow (unknown
to the business)
Introduce an excel-based template to take notes which can be used across teams and reduce
time to analyze issues if it is not resolved in first time
Deep Fixes
Build RPA bots across
Customer order, validation,
and ITSM processes
Build RPA bots across
Customer order, validation,
and ITSM processes
By introducing RPA-based query resolution across
different processes, the company was able to
increase the self-serviceability of online portals and reduce manual effort. This led to expedited process efficiency of the team’s bandwidth, allowing them to focus on core business activities.
Standardise
workflows
workflows
Standardise
workflows
workflows
Leadership also made a concerted effort to standardise
excel-based templates and introduce chatbot-based
self-serviceable channels to handle simple queries from
customers and later scale up in other areas.
IVR based customer validation was introduced to free up users’ bandwidth and improve case handling & call wait time.
Self-serviceable channels was also introduced to drive correct routing of workflows within teams, saving them collaborative ~50,000+ hours.
IVR based customer validation was introduced to free up users’ bandwidth and improve case handling & call wait time.
Self-serviceable channels was also introduced to drive correct routing of workflows within teams, saving them collaborative ~50,000+ hours.
Standardise
data inputs
data inputs
Standardise
data inputs
data inputs
The leadership enforced standardisation of
data inputs, which reduced the effort and time
spent on organising and validating the input
data received by different teams.
Centralized
Governance
Governance
Centralized
Governance
Governance
The company focused on centralised governance
to track proactive process variation anomalies &
followed up with customers by dedicated teams to
reduce overall wait time.
The root cause of all issues was that there were significant digital gaps in the overall process, and these were
predominantly because systems did not talk to each other, and because data was duplicated/fragmented
Scout also provided detailed insights into the cost of unintegrated ERP systems and, discovered revenue leakages in the organisation.
Based on these insights, the leadership kicked off a transformation program, to build an end-to-end automation and workflow system by integrating information inputs, business process rules, and reporting & compliance.
Scout also provided detailed insights into the cost of unintegrated ERP systems and, discovered revenue leakages in the organisation.
Based on these insights, the leadership kicked off a transformation program, to build an end-to-end automation and workflow system by integrating information inputs, business process rules, and reporting & compliance.
These insights helped Tech Mahindra identify potential opportunities to reduce digital gaps by 35%, generate accuracy in identifying bottlenecks by 90% in identifying bottlenecks, and decrease manual processing effort by 34% for its telecom customer.
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, Tech Mahindra through Scout AI was able to drive the
following business outcomes:
Significantly improve how employees experience work and improve their morale
90%
reduction in digital gaps
90%
improvement in payroll accuracy
34%
reduction in manual processing
effort across 261 users
How AI connects interaction data to business outcomes
Scout lights up the ‘dark side of the moon’.
Your business generates billions of data points from team-machine interactions. Scout, our AI model, deciphers this interaction data to reveal 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 to address these challenges, paving the way for improved outcomes.
Your business generates billions of data points from team-machine interactions. Scout, our AI model, deciphers this interaction data to reveal 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 to address these challenges, paving the way for improved outcomes.
See Scout in action.
Schedule your demo now!
