How Tech Mahindra used Scout AI to streamline operations for a UK telecom giant, reducing processing effort by 34%


The Challenge

One of Tech Mahindra’s leading telecom customers in UK, having a global presence, was grappling with inefficient and variable order fulfillment, 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 85000+ employees.

The company had already used a myriad of discovery tools to identify end-toend opportunities for reducing the cost of operations but had not yet achieved the desired level of optimization.








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.


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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: the root of the problem was excessive manual intervention in order pre-validation, generation, and validation processes.


6 weeks

Scout’s 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, the 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, Scout AI model provided the following insights:


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 was actually being done.


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

Harvard Business Review

Do You Know How Your Teams Get Work Done?

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 ~18,300 hours annually
Manual verification and authentication Scout AI identified that the team was spending close to ~20,000 hrs annually i.e. 16% of process effort just for verifying & authenticating the right customer
Composing similar emails Scout identifies the end users from different teams and takes notes in notepad, which is prone to errors, poses a limitation to further automation and can’t be shared with other teammates. End user spends around 8% of process effort leading to collaborative effort of ~8,000 hours annually
And more …

Step 2: Fix

Based on the above insights, Scout AI model recommended the following fixes:

Quick Fixes

Identified 100+ user training
Produced As-Is & To-Be process snapshots for different processes
Produced 70+ L5 process-designed documents/training documents to drive standardization of 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.
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.

Introduction of IVR-based customer validation to free up users’ bandwidth and improve case handling & call wait time.

Introduction of self-serviceable channels to drive correct routing of workflows within teams, saving them collaborative ~50,000+ hours.
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.
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The company focused on centralised governance to track proactive process variation anomalies & follow-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, also discovering 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.
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These insights helped Tech Mahindra deliver to its telecom customer 90% reduction in digital gaps, 90%+ process accuracy, and a 34% reduction in manual processing effort
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.

Business outcomes

In summary, Tech Mahindra through Scout AI was able to drive the following business outcomes:
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Significantly improve how employees experience work and improve their morale


reduction in digital gaps


improvement in payroll accuracy


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.
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The 'Dark Side Of The Moon' In Enterprises

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