Oil and Gas

Global energy leader sees a 64% boost in Accounts Payable throughput


The Challenge

A leading oil and gas company with a workforce of over 5,000, faced significant challenges within its Accounts Payable department. With around 4,000 invoices pending at any given time, the team struggled with an effort-intensive and time-consuming process, impacting overall efficiency and financial performance.

Oil and Gas


Asia Pacific



Attempted Solution before Scout

Traditional methods like

RPA and manual discovery

added to the team’s workload
Prior to Scout's involvement, the organization's leadership attempted to address these challenges through RPA and manual discovery. However, these efforts did not lead to the desired improvements in invoice processing efficiency, on the other hand it added more workload to the already stretched teams resulting in decreased productivity.


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The Head of Transformation introduced Scout to tackle these challenges and to gain a deeper understanding of the actual processes in place. Scout's AI model could be quickly put into action because it didn't need complicated integration with current systems.

The AI analysed how the Accounts Payable team interacted with various applications like SAP, Coupa and other applications like email and chat to map how work happens and identify inefficiencies.

Scout was

easily deployed

as there were no complex integrations involved.

Scout to “find and fix”

Step 1: Find

As the first step, the Scout’s AI model decoded the work patterns of the Accounts Payable team, distinguishing between core and non-core activities by analysing their interactions with business management applications.
Based on this analysis of 9 sub processes in 5 weeks of fresh data collection,​ Scout’s AI model provided the following insights:

Harvard Business Review

Do You Know How Your Teams Get Work Done?

Scout analysed 9 sub-processes over 5 weeks and
provided deep insights.

33% of the team’s effort was spent on correcting activities which should have been ‘First Time Right’ including manual data entry and handling exceptions, due to information silos and disconnected systems. 40% of these were repetitive actions like rule-based validations in SAP.

This startling statistic pointed to a work recall gap across the team, highlighting the disparity between the team’s understanding of how work is being done and how work was actually being done.

Harvard Business Review

How Much Time Does Having Too Many Apps Really Waste?

Step 2: Fix

Based on the above insights, Scout’s AI model recommended two levels of fixes:

Quick Fixes

These were ‘no-code’ fixes based on standardisation and user training.
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Enhance the 3-way bot
Enhance the 3-way bot
Prioritize enhancing existing 3-way bot capability to further automate exception handling cases with a potential to deliver 64% higher throughput.
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Cognitive automation
Cognitive automation
Programs to integrate front and back-office workflows and enhance system functionalities were launched, addressing unintegrated systems, data duplication, and fragmentation.

Deep Fixes

Systemic and long-term fixes planned across the organisation
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Reduce disconnected debt
Reduce disconnected debt
Scout’s findings led to a recommendation of a single case management system (either COUPA or SAP) to streamline invoice journey across one single flow. This can reduce the disconnected debt arising from using disparate and disconnected systems.

Harvard Business Review

What’s Lost When Data Systems Don’t Communicate

It is important to note that in all these recommendations, privacy was of 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 below:

Business outcomes

With Scout's insights and the subsequent interventions, the following improvements were achieved:


increase in throughput, significantly reducing invoice backlogs


decrease in manual effort, lowering cost to serve.


improvement in first time right cases, enhancing operational efficiency

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’.
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The 'Dark Side Of The Moon' In Enterprises

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