Category: Operational Excellence

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Operational Excellence

Robotic Process Automation: Rise of the machines

Automate processes and achieve faster ROI with full-featured RPA

Employing staff is the biggest operating expense for service providers in the Connectedness industry. Employees are also their greatest asset. Automation technology advances enable service providers with new ways to maximize employee productivity, revenue, and customer satisfaction while minimizing human error. One of those advances is robotic process automation (RPA), a broad, deep category of tools for automating business, network, and operational processes.

RPA can automate mundane processes which are tiring and boring for a human to do all day long – the kind of fatigue that results in mistakes and expensive turnover. By providing what customers seek faster than a live agent can, RPA eliminates one of their major complaints: wasting minutes on hold to speak with an agent or hours or days for a work order to be processed manually. The big advantages delivered by RPA are customer satisfaction, employee satisfaction, greater revenues and profits, security, and reliability. But the biggest obstacle to achieving these ends through RPA misunderstands how and where it can be deployed. This insight, sponsored by Prodapt and published by TM Forum, elaborates on how to embrace RPA across the business processes to achieve greater efficiencies, improved customer experience, and faster ROI.


RPA automates mundane processes which are tiring for a human to do all day long, thereby avoiding mistakes and expensive turnover

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Operational Excellence

Enhancing your business operations with AI/ML

Leverage AI/ML to enhance the efficiency of your day-to-day business operations and improve customer experience

Today’s digital-savvy customers demand advanced and ultra-rich experiences. Therefore, factors like service availability, turnaround time, and quality of service (QoS) are becoming increasingly important. But the question remains how can Service Providers bridge the huge gap that currently exists between customer demands and their fulfillment?

Service providers’ current infrastructure and service delivery approach are unable to match up with customer expectations. There is a dire need to enhance their key operational areas such as customer service, service assurance and network automation.

Artificial Intelligence/Machine Learning (AI/ML) technology, with its recent advancements, is fast becoming the choice of service providers to bridge this gap and improve operational efficiency. AI/ML systems, together with Bigdata, can process huge amounts of historical and real-time data from various systems such as CRM, billing systems, NMS/EMS, and product catalogues to provide actionable insights and predictions.

Service providers must adopt AI/ML solutions in their infrastructure to offer next-generation services and experiences.

  • Intelligent software-defined approach for operations and delivery of services (virtualization, self-healing, and self-learning networks)
  • Automation of customer service and customer experience improvement (chatbots, virtual assistants)
  • Predictive maintenance and agile operations (automated problem detection, troubleshooting, and optimization of networks)
  • Innovation in subscriber profiling, usage analysis, and personalized offers


Approximately 63.5% telecom companies are committing investments on AI systems to improve their infrastructure and enhance operational efficiency – IDC.

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Operational Excellence

Artificial Intelligence in Action

Adding intelligence to robotic process automation

Robotic Process Automation (RPA) is a low-code, low-cost option for the service providers in the connectedness industry to automate high-volume manual processes, delivering cost, efficiency, accuracy, and transparency. By automating a large part of day-to-day activities, service providers can drive accuracy, improve employee morale and productivity, and ensure reliability and consistency of operations. However, to drive the intended benefits from their RPA initiatives, service providers need to understand the difference between the three primary levels of RPA maturity: Basic RPA, Cognitive RPA, and Intelligent RPA.

The Basic RPA relies on easy-to-implement and understanding fundamental technologies such as macro scripts and workflow automation. It is rule-based, does not involve much coding, and uses an ‘if-then’ approach to processing. Cognitive RPA, on the other hand, is a knowledge-based approach. It uses complex technologies such as natural language processing (NLP), text analytics, and data mining to automate parts of the process that Basic RPA cannot. But service providers can primarily benefit from Intelligent RPA, which uses AI/ML technology for decision making. With AI/ML, Intelligent RPA can go beyond data processing (gathering, sorting, calculating, and reporting), automate processes based on continuous analysis of incoming information, and learn to act smarter over time. This is especially beneficial for service providers dealing with large volumes of unstructured data. Furthermore, Intelligent RPA can gather insights and improve them over time while working together for the best results.

The insight elaborates on the three maturity levels of RPA and how to adopt them across the customer engagement lifecycle to help build out and deliver high-value use cases.


With RPA, service providers can drive accuracy, improve employee morale and productivity, and ensure reliability and consistency of operations

Three main levels of RPA maturity

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Operational Excellence

Transforming Telecom Business Processes Using RPA

Leverage Robotic Process Automation (RPA) to accelerate business process transformation and innovation

Robotic Process Automation or RPA is widely used by companies around the globe to streamline their business processes. RPA creates software robots to automate the processes that are highly manual, voluminous, repetitive, and rule-based. Process automation increases work quality, minimizes errors, and allow organizations to scale rapidly.

The service provider’s operational landscape has many mundane processes like service fulfillment, service assurance, billing, revenue management, and network management. By adopting RPA, service providers can quickly and easily automate the manual and tedious processes, without much investment or hassle. As a result, service providers can reduce cost, improve data quality, boost customer service, and drive operational efficiency.

Figure 1: Some examples of processes from the Order-to-Activate(O2A) cycle suitable for RPA

RPA is being widely accepted across industries and serves as a guide to help service providers in their RPA implementation, right from business process assessment till rollout. Topics covered in the insight:

  • Why is RPA becoming popular in the telecommunication industry?
  • Preview of telecom processes with RPA potential
  • The RPA journey of a communications service provider – How to implement RPA successfully?
  • Common challenges faced during RPA implementation

By 2025, 3 out of 10 jobs will be done by software, robots, or smart machines allowing replaced employees to do more crucial jobs. -Gartner.

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Operational Excellence

Data-driven Process Optimization to Accelerate Digital Transformation

With most Digital Service Providers (DSPs) investing in digital initiatives today, transformation is no longer a choice but a must-have!

According to a Celonis study, “Most organizations are struggling with transformation initiatives because they are diving into executions without understanding what to change first. In a rush to innovate, 82% admit that they do not review their internal business processes while setting initial goals for a transformation program”.

Also, DSPs experience increasing process inefficiencies due to the ever-changing landscape. Traditional approaches to handling these processes are more focused on process discovery. They do not provide an accurate view of these processes and the real bottlenecks. Hence DSPs need to embrace a data-driven process optimization approach to look beyond discovery.

This whitepaper details on leveraging a process optimization framework for the DSPs to set business objectives across the transformation journey. It helps identify critical processes, accelerates cost savings by 60%, and improves customer satisfaction by 30%.

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Fig. Key steps of process optimization approach

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Operational Excellence

Robotic Process Automation: The key to accelerate digital transformation

To stay relevant in today’s digital world, the service providers in the connectedness industry should simplify their business and transform themselves into a digital organization.

The road towards digital transformation is a business-critical one, and the service providers embarking on this journey will need to consider how each aspect of their business can be optimized to fulfill the new digital objectives. To optimize the existing processes and keep pace with the competition, service providers should bring the power of RPA in digital transformation.

Automation is not new and Robotic Process Automation (RPA) with its highly evolved level of sophistication, has made it a lot easier to automate processes across a variety of systems and technologies and reap tangible ROI in a very short time. RPA is a delightful journey, and the end-to-end lifecycle needs to be planned across the below phases to accelerate digital transformation.


To optimize the existing processes and keep pace with the competition, service providers should bring the power of RPA in digital transformation.

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Operational Excellence

Increasing the pace of your process automation programs

Leverage bot development framework to empower RPA Centre of Excellence with bot velocity

Most service providers across the globe have been leveraging robotic process automation (RPA) to increase their operational efficiency and are at different stages in their journey. RPA helps an organization automate repetitive and mundane tasks using the development and deployment of bots or software robots.

But scaling RPA and increasing bot velocity to make it an organization-wide success is a big challenge. One definite solution to this challenge is setting up a robust RPA Centre of Excellence, which not only defines the best practices but also strives to achieve the organization’s goal.

An ideal RPA CoE has the following 7 components:

This insight focuses on the third component, i.e., bot development, specifically on the bot development framework. It throws light on best practices to create a robust development framework for driving bot velocity by standardizing development across the organization. By following these guidelines, organizations can reduce the bot development, testing, deployment, and review time.

A robust bot development framework helps in reducing development, testing, deployment, and review time by 30%

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Operational Excellence

Building a successful Technical Support Center

Use proven tools and techniques to improve the efficiency of Technical Assistance Center (TAC), reduce inbound repeat calls and customer trouble tickets

Today, telecommunication is no longer limited to voice. In the past few years, the industry has rapidly grown to accommodate multiple services. In this era of multi-play, service providers in the connectedness industry are adding various services to their catalog, such as voice, messaging, broadband, IPTV, DTH, VAS etc. And to support these services, service providers have technical assistance centers (TACs) to help customers resolve issues related to specific services.

However, service providers have been facing challenges in maintaining and improving the efficiency and productivity of TACs as much of their efforts go into non-value-adding (NVA) tasks causing resource wastage. NVA tasks can be classified into following categories:

Figure : NVA Waste Classification

Further analysis of NVAs (over-processing, rework, waiting, etc.) shows that managing a high volume of inbound repeat calls and tickets and operating with distributed tools are the major challenges of a TAC.

To mitigate these challenges, service providers must explore innovative and field-proven tools and techniques, including robot-assisted screening, Proactive Network Analyzer etc. By implementing these techniques, service providers can easily realize a 30- 40% reduction in inbound repeat calls and customer trouble tickets.

“In a typical service provider’s technical assistance center (TAC) landscape, many tasks are NVA (non-value adding), leading to resource wastage”

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Operational Excellence

Ride the fiberization wave with a lean and scalable operating model

How fiber operators could build a lean and scalable operating model to deliver with speed, keeping an eye on cost

Deep fiberization remains a strategic objective for global fiber operators to meet the data consumption demands. As per the EY report, global fiber deployment will double during CY2018-2026, majorly led by 5G. The global fiber optic cable market is expected to reach USD 20.8B by 2026 at a projected CAGR of 14.5% between 2020-2026. To ride on this fiberization wave, fiber operators must act quickly, keep an eye on cost and check data accuracy before making planning decisions.

Fiber operators need to rethink their business operations to overcome the challenges in their journey towards accelerating fiber rollouts. The three key domains of a fiber operator and the associated challenges are listed below:

  • Plan & Build Massive coverage targets, high cost to build, shortage of skilled labor
  • Service Delivery Longer cycle time, siloed and disconnected customer journeys, repeat visits and rework
  • Service Assurance Operational Efficiency due to lack of automation and standardization, reactive approach in network management

Fiber operators must build a lean and scalable operating model to overcome all the above challenges and achieve their fiberization goals efficiently. Here’s a proven 4-step approach to building a lean and scalable Target Operating Model by transforming your business capabilities:

  • Step 1: Perform due diligence and discovery of as-is fiber journeys
  • Step 2: Benchmark capabilities using Capability Maturity Assessment
  • Step 3: Collaborate and identify change initiatives
  • Step 4: Plan implementation and define a roadmap

Successful implementation of these steps can help fiber operators to reduce their operational expenses by 53% (in just 3 years) and create a lean and scalable organization.


A lean and scalable operating model will enable fiber operators to achieve fiberization goals efficiently by transforming their business capabilities.

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Operational Excellence

Bridging the gap between demand and capacity

Leverage AI-powered capacity planning to modernize field services

Most service providers face challenges in planning and allocating field technicians based on the demand vs capacity. According to Gartner, “Balancing available resources against the demand for those resources is essential to successful initiative completion“. Inefficient capacity planning often leads to over-staffing or under-staffing of field technicians. This further results in order fallouts and dissatisfied customers. The most common sources of dysfunction are:

  • Unavailability of tools to estimate capacity in real-time
  • Lack of strategy to identify the key influencing factors that impact the capacity planning process
  • Lack of mechanisms to assign the right technician for the right service
  • No end-to-end visibility into field service capacity


According to Gartner, “Balancing available resources against the demand for those resources is essential to successful initiative completion“.

To overcome these challenges and handle the diverse field data, service providers in the connectedness industry should move towards intelligent capacity planning, which helps in the real-time mapping of dispatches and the optimal usage of resources. Leveraging an AI-powered capacity planning framework helps the service providers to reduce resource wastage by 20% and improve the effectiveness of service response and customer satisfaction. Enterprise AI can, over time, improve the prediction of field technician work hours by considering the key factors such as weather, season and maintenance data.

Fig: Leverage AI-powered capacity planning framework for real-time field tech resource management