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

Operational Excellence

Recipe for managing the digital workforce effectively

Build a comprehensive RPA Bot governance model to reduce operation hassles, improve bot performance and scale automation programs

Service Providers are now riding the automation wave. Painful manual tasks, which burdened staff for ages, can be easily handled by the software bots. However, in the process of onboarding the digital workforce, most service providers have missed establishing robust and unified governance. In a survey done by Forrester Consulting, 69% of the respondents said they face difficulty in managing rules that guide bot behavior and 61% responded that control & operations of RPA bots are immature.

The lack of unified governance of the digital workforce significantly impacts different users such as the RPA Center of Excellence (COE), Business Unit Owners, Production Support, and Operations Team. These users face challenges such as managing bot license and application credentials, orchestrating bots across platforms and analyzing real-time bot performance and its utilization. They also lack real-time alerts on process failures & forecasts, which often lead to missing the SLA for critical deliveries.

Service providers must establish an effective RPA bot governance model by focusing on key areas. A few of them are listed below:

  • Integrated Visual Control Room- Provides a high level of collaboration & transparency while managing bots across processes and platforms. This helps to find the root cause of non-functioning bots
  • Delivery Forecast & Inflow Alert Mechanism: Helps to visualize key metrics in real-time to meet the SLAs
  • Automated Application Credential Management & Bot License Tracker: Prevents production outage by avoiding account lock and license expiry issues

Governance of the Digital Workforce is becoming a consistent challenge while adopting Robotics and Cognitive Automation. A Forrester Consulting report shows that 70% of service providers struggle with BOT performance and scalability issues.

Operational Excellence

Creating a smart field workforce with an AI-powered video guide

Leverage video AI to improve field engineers’ efficiency, reduce site visits, and accelerate install to commission cycle time by 3X

Inefficiencies in field services contribute the most to the capital expenditure of service providers. One of the major reasons for field service inefficiency is repeat site visits or rework, leading to a 5X increase in repair cost and delay in order delivery time.

In the case of field surveys, data shows that 40-60% of installation orders require a site survey, out of which 18% require repeat surveys. The sites survey is done manually, requiring manual data capture and physical audits leading to errors and incomplete data. Hence, the process becomes extremely time-consuming.

To overcome these challenges, service providers must leverage the power of video intelligence. An AI-powered video intelligence framework can create a smart field workforce. Surveyor captures a video and voices it over, using a guided storyboard. The framework auto-captures the details and sends alerts for missing details. A survey is submitted with 100% details and can be a point of reference for specific details or future changes. This leads to 3X acceleration in installation time and improved customer experience.

Enable field engineers with AI-powered devices to improve ‘right-first-time’ field work and enhance customer experience through reduced

The three main components of this framework are –

  • AI-assisted video guide – Provides a structured guided storyboard for field engineers to effortlessly capture the data
  • Recommendation engine – Enables guided actions to various business stakeholders. Gives AI-powered recommendations and real-time visibility into the jobs to supervisors, auditors, and field engineers
  • Smart dashboards – Provides end-to-end visibility into jobs driving smarter actions for management and business as a whole
Operational Excellence

Combining the power of RPA and AI to keep customer experience unharmed during network outages

Leverage RPA and AI to build and implement a proactive two-way Conversational Framework to reduce OpEx, boost agent productivity and improve NPS

According to recent statistics, 30% of the service providers’ contact center calls are network outage related. Their inability to predict these outages on time and provide prior information to the customers results in contact center call spikes, customer dissatisfaction and a low NPS score. This also increases the OpEx for contact centers and may lead to a reputational loss for service providers.

To overcome these challenges and improve NPS, service providers must create a central Intelligent platform capable of orchestrating seamless conversation between the contact centers and customers. This is established by implementing a “Two-way conversational Framework”. The steps involved are:

  • Step 1: Auto-identification of outage information
    Build a standardized process to identify relevant outages in the network monitoring systems. Integrate them with an outage monitoring dashboard for BOT to auto-extract outages and store them in a central database.
  • Step 2: Schedule notification
    Perform automated validation and intelligent scheduling to send proactive notifications to the impacted customers in a well-organized structure.
  • Step 3: Notify and engage with customers using a Conversational AI BOT
    Send proactive notifications, and if the customer has additional queries, the bot can engage in a conversation using the conversational AI

Conversational AI Bot orchestrates bi-directional communication and provides seamless customer experience during common network outages.

Operational Excellence

Improving the efficiency of your Field Service Workforce

Leverage machine learning to eliminate blind dispatches and improve the first-time fix rate (FTFR)

Field Technicians are the face of your service organization, and it is imperative to equip them with the right tools and knowledge to handle any field challenges. With efficient management and empowerment of technicians, your organization can deliver fast, effective, and efficient services to customers.

A business should strike a balance between the speed and accuracy of on-site customer requests to increase the productivity of technicians and improve customer satisfaction. But, in reality, technicians are frequently not able to deal with customer problems on time and are forced to make multiple trips to the client location due to process inefficiencies. Thus, instead of servicing new customers or optimizing current customer relationships, technicians invest valuable time and resources in non-revenue-generating activities.

Today, 70% of field technicians visit sites without prior information about the nature of the problem, issue location and solution recommendation. It leads to repeated dispatches, longer resolution time and high customer churn.

Going digital is the cornerstone of success for a modern services organization. Adopt the ‘AI-Powered Field Service Framework’ to optimize field services and increase technician productivity. The framework encompasses three vital components to achieve a higher First Time Fix Rate (FTFR) and reduce Mean Time to Resolve (MTTR):

  • Fault Location Classifier– Predicts the fault location and sends email/SMS notification via mobile app to technicians
  • Recommendation Engine– Suggests guided actions and next best resolution steps to improve technicians’ efficiency
  • Technician Dashboard– Provides a one-stop view of all dispatches and actionable insights to technicians

70% of field technicians visit the sites without prior information about the problem leading to repeated dispatches, longer resolution time and high customer churn.

Insights Operational Excellence

Go Beyond RPA to Speed Up Transaction Processing Time

Leverage effective continuous improvement techniques to achieve a high straight-through processing rate

Straight-through processing (STP) refers to the automated processing of transactions without manual intervention. Transaction processes are usually multi-staged, requiring multiple people across different departments and sometimes even involving paper checks. Companies often adopt RPA as a one-time solution to complete transactions and achieve a high STP rate. But is it really effective?

The estimated STP rate for any service provider in the connectedness industry is 75%-85%. However, the actual realization is only 30%-50%. One of the reasons that has contributed to the average rate is implementation of only RPA by service providers. Other widely used continuous improvement techniques like occasional continuous improvement and analytics-driven continuous improvement have proven to be less effective to achieve the targeted STP rate. Service providers must adopt effective continuous improvement methods to get more value from their existing RPA implementation.

Adopt the Automation Optimizer Framework, an efficient continuous improvement strategy to improve your STP rate. The framework identifies automation inefficiencies, root causes, and solutions for the identified gaps and continuously monitors the STP rates- all in an automated manner. Its key components are:

  • Intelligent RCA (Root-cause analysis) Engine: Drills down to transaction-level information to automatically identify the root-cause for fallout
  • Integrated Solutionizer: Constantly analyzes the output from an Intelligent RCA Engine and triggers respective action based on the identified root cause
  • Continuous Monitoring Tool: Tracks the STP rate progress over time for the defined objectives, KPIs and milestones

The estimated STP rate for any service provider is 75%-85%, however, the actual realization is only 30%-50%. Only RPA implementation will not suffice if the STP rate has to be improved.

Operational Excellence

Giving wings to your standard RPA bots

Combine the power of RPA with NLP to improve the automation potential of service provisioning

Most service providers in the Connectedness industry have started leveraging Robotic Process Automation (RPA) to automate various processes, especially in service provisioning. However, the standard RPA bot alone cannot automate the end-to-end provisioning process, as it involves a lot of unstructured data that requires manual intervention for processing. According to Gartner, “Today, 80% of enterprise data is unstructured”. Processing such a huge amount of unstructured data and performing end-to-end automation with a standard RPA is a major challenge for service providers.

To overcome this challenge, service providers can combine the power of RPA bot with a Natural Language Processing (NLP)-based engine capable of extracting information and processing the unstructured text. It further helps in deriving insights and providing the next best action, all in an automated way. This end-to-end automation helps the service providers to reduce the cycle time and provide efficient services to their customers.

According to Gartner, “Today, 80% of enterprise data is unstructured”. Standard RPA alone cannot process such a huge amount of unstructured data and perform end-to-end automation.

  • Madhusudhanan S
  • Velmurugan M
  • Gurunath L V
  • Mogan A.B.

Operational Excellence

Fiber is fast, but rollout needs to keep up

AI/ML can forecast delays before they occur, making the service delivery predictable and fast

The global pandemic has highlighted the fact that high-speed broadband is a necessity, not a luxury. And fiber is one of the ways to faster broadband. This appetite for fiber means that service providers need to roll out fiber-based connectivity services faster. However, with the rising complexities in the order management process, delivering the service within the specified timeline is becoming a nightmare. The main business issue is unpredictability, which may be as important as speed. Its absence means frustration for service providers and their customers.

The main cause of this lack of predictability stems from the structure of the process. In many cases, the enterprise service delivery process has evolved and grown organically. The most common causes of dysfunction are:

  • Multiple teams operating in silos prevent a clear view of the process and a single source of truth
  • Manual hand-offs leading to errors and delays
  • Dependency on external vendors, resulting in vendors operational issues being transferred to the service provider
  • Lack of strategies to forecast order delays
  • Lack of mechanisms for real-time tracking of service delivery flow

To overcome these challenges and tap into the next wave of opportunities, service delivery operations will require an advanced vision. AI/ML is at the heart of that vision. With AI/ML in service delivery, enterprises can predict and address delays before they impact the business. Enterprise AI can, over time, improve the prediction of potential delays and delivery dates at all points of the order journey. Over time, enterprises can achieve faster processing of orders with improved predictions.


The appetite for high-speed broadband demands a faster rollout of fiber-based connectivity services.

Operational Excellence

Accelerating Digital Transformation with Hyperautomation

Leverage the power of RPA, process mining and AI for end-to-end process automation to increase automation rate, reduce operational expenditures and improve customer experience

‘Hyperautomation’ is one of Gartner’s Top Strategic Technology Trends for 2022. Hyperautomation aims to identify, analyze, and automate business processes to the greatest extent possible. It involves orchestrating the use of multiple technologies, tools, and platforms to streamline business processes.

Legacy infrastructure and outdated processes can hinder an organization’s ability to compete. Automation of only task-based processes will not deliver the cross-functional results needed to drive business decisions and outcomes. By automating as many processes and tasks as possible, hyperautomation transforms an organization.

Increase connectivity, efficiency, and agility in business operations with hyperautomation.

As per Gartner, hyperautomation will lower operating costs by 30 percent or more by 2024, thereby increasing connectivity, efficiency, and agility of business operations. The businesses in the connectedness vertical can achieve end-to-end process automation and scale up the automation rate by building and implementing a hyperautomation framework that includes four key components:

  • Intelligent Process Orchestrator: Orchestrates bots, people, and IT applications for end-to-end integration of any business process.
  • Conversational AI: Automates all sub-processes that requires a conversation with humans. Conversational AI understands natural language and converses with the customer.
  • Low-code Applications: Helps to automate the sub-processes that require aggregating data from humans by building applications/interfaces rapidly.
  • Unified Hybrid Dashboard: Provides a real-time integrated view of the order completion process, resolution time, automation success rate, and many other KPIs. It also highlights the actionable insights.
Operational Excellence

Accelerate cash flows by faster order processing

Managed Digital Transformation to reduce Order-to-Activate (O2A) cycle time and increase new business wins

The Order-to-Activate (O2A) process is at the heart of every business operation. Simply put, it refers to the end-to-end process of receiving, processing, and fulfilling a customer’s order. A smoother and more efficient order flow will allow the company to process more orders, thus allowing the business to grow more quickly.

The Order-to-Activate process cannot be conducted in isolation; it depends upon numerous roles, departments, and systems. For example, a typical digital service provider takes 15+ teams to traverse through 55+ systems to complete one order. These complexities and increasing inefficiencies in the O2A process leads to longer cycle time, delayed revenue realization, and higher cost.

The complexities and increasing inefficiencies in the Order-to-Activate process lead to longer cycle times, delayed revenue realization, and higher costs.

Businesses need to ensure that their business runs smoothly, and the orders are delivered efficiently and accurately, with minimal chances of error. Adopt the Managed Transformation Model to achieve long term sustainable business benefits like reduced cycle time, accelerated revenue, enhanced customer experience, and maximized cost savings. By doing this, a business can transform its operations holistically and address all the challenges in the O2A process.

Businesses can ensure a reliable and undisrupted high-speed broadband service by adopting the ‘Zero-touch service assurance’ framework. This framework enables continuous remote monitoring to detect connectivity issues proactively and provide automated resolutions.

The model encompasses transformation levers such as:

  • Agile Work Cell: Consolidates multiple functional roles into one hence, reducing the touchpoints in the O2A process. It ensures better control, promotes transparency and eliminates handoffs
  • Process Optimization & Automation: Analyzes the current performance and cycle time elongation factors to identify and implement improvement opportunities
  • Operational Accountability: Provides a Dashboard with end-to-end visibility into each order and the milestones. It also helps in governance, performance tracking and reporting