Category: Insights

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Cloud

AI strategy to build customer resilience during outages

Adopt a GenAI-powered Outage Management Framework for near real-time, personalized notifications and Estimated Time of Resolution (ETR) to build trust and transparency during service disruptions

As converged networks become the norm, AI-driven customer expectations rise, and regulatory demands for transparency intensify, the traditional outage management strategies are falling short. While network disruptions are inevitable, how enterprises respond can make or break customer trust, brand loyalty, and regulatory compliance.

With network outages costing enterprise customers, including CSPs, over $1.2 million on average, and 21% of telecom users switching providers after just one negative experience, the stakes have never been higher. In today’s digital age, millions turn to platforms like Downdetector and Twitter to express frustration in real time. This makes transparent, timely communication during outages not just a best practice, but a business imperative.

Companies across sectors face growing challenges in maintaining customer satisfaction during outages, hindered by fragmented systems and manual processes. Key gaps include:

  • Lack of proactive outage updates to customers, including ETR
  • Absence of Self-Service Tools for Independent Service Status Verification
  • Inconsistent communication for unified outage updates across customer touchpoints

These shortcomings lead to increased inbound calls, customer frustration, anxiety, brand damage, and ultimately, higher churn.

Revolutionize outage management with the AI-powered Total Outage Management Framework (TOMF), a scalable, intelligent, self-learning system designed for proactive customer engagement. Powered by AI Outage Managers, TOMF automates outage detection, generates personalized updates adapting to customer segments (B2B, B2C) and communication channels, enables triage, notifications, and multi-platform streaming with minimal human intervention. The key components of TOMF are as follows:

  • Automated Outage Monitor: Periodically extract outage details like ticket & dispatch status, enabling timely insights and proactive responses
  • Smart Notification (SmartNoti): Use GenAI to create context-aware and persona-tailored outage summaries and ETR updates, to curb customer uncertainty
  • Notification Review System (NRS): Provide human-in-the-loop oversight for high-stake scenarios while auto-approving low-risk cases, hence safeguarding sensitive data
  • Outage Streaming Engine: Create a centralized outage data storage of all approved outage summaries, ensuring seamless access across digital channels, CRM platforms, and customer-facing teams


“In network outage scenarios, AI/Gen-AI can enhance responsiveness and personalization, providing tailored recommendations and solutions to individual customers.”

Categories
Operational Excellence

Transform field service operations with Generative AI

Leverage GenAI-powered field service framework to reduce technician assist calls and dispatch costs by 50%

In today’s experience-driven market, field service is a critical differentiator for enterprises. Despite the significant digitalization efforts, traditional field services remain reactive, inefficient, and costly. Enterprises are grappling with rising volumes of field technician dispatches, increasing customer churn, and operational overhead resulting from repeated visits and prolonged issue resolution cycles. “20–30% of all field service dispatches are repeat visits, costing $100–140 per visit, primarily due to unresolved issues during the initial service attempt” – Aberdeen report. This is typically due to inadequate access to service history, a lack of contextual intelligence, and an over-reliance on backend support teams. Additionally, field technicians rely more on support centers for installation and repair services, leading to an increase in calls for guidance, diagnostics, or additional information. The support agents charge an average of $20-30 per call, thereby straining resources and increasing operational costs.

Enterprises should shift towards a Generative AI (GenAI)-powered field service approach to address these issues and redefine how field services are delivered, making them intelligent, autonomous, and proactive. Whether it’s a fiber technician addressing last-mile connectivity, a smart meter installer troubleshooting edge device faults, or a hardware engineer servicing campus Wi-Fi deployment, a holistic GenAI-powered approach can help deliver faster, more innovative, and effective field interventions.

Implementing a GenAI-powered field service framework can equip technicians with AI-generated, real-time, and contextual insights for installations, repairs, and diagnostics, thereby reducing the time spent on calls or rework and improving installation and repair services by 40%. It also facilitates 2x acceleration in assisting field technicians, 50% reduction in dispatch costs , and a 50% improvement in efficiency with quick and easy access to customer and facility data.


“Heavy reliance on support centers for field fixes drives up assist calls. Support agents charge an average of $20-30 per call, thus increasing the costs for enterprises.”

Categories
Product Engineering

Elevate your Software Solutioning game with Generative AI

Utilize DesignMate to cut design cycle time by 30%, speeding up product time to market by 15%, and lowering design phase costs

Solution design is a crucial phase in software development, defining product architecture and requirements. Creating these software solution blueprints has traditionally been a complex and manual process. Gathering comprehensive requirements from diverse stakeholders while ensuring scalability and performance is challenging, often leading to cost overruns, potential errors, and inefficiencies.

Key challenges in the Software Solution Design (SSD) creation process include:

  • Knowledge Fragmentation: Design info is scattered across formats and systems, causing inconsistencies
  • Knowledge Concentration: Reliance on a few experts creates bottlenecks & risks if they are unavailable; organizations with high dependency on key individuals experience 26% more project delays
  • Technology Evolution: Frequent tech changes outpace manual design methods
  • Inconsistent Quality: Manual designs are prone to human error, leading to inconsistencies; ~15 -20% increase in time-to-market due to delays in design retrieval and inefficient design processes

Failure to address these challenges can lead to missed revenue opportunities, loss of market share, or inability to scale innovations. Leverage Generative AI to address these challenges and revolutionize SSD creation by facilitating rapid knowledge gathering, retrieval, and content generation for designers. Implement the DesignMate Framework, powered by Generative AI, for an interactive and standardized automated SSD generation process. This framework systematically understands user demands, retrieves relevant data, conducts impact analysis, and generates tailored solution designs. As a result, it boosts design efficiency by 30%, accelerates time to market by 15%, and ensures consistent, rapid software releases to maintain a competitive edge.

Figure 1: DesignMate Framework and its three key components


“Generative AI can revolutionize the software development lifecycle across various phases, including requirement analysis and design phase, enhancing employee productivity and accelerating time to market for software products.

Categories
Software Intensive Networks

Optimize Telecom Network Management with Generative AI

Reduce Opex by 30% and enhance customer experience with AskNetwork

Telco networks, spanning 5G, fiber, IoT, and cloud, feature intricate topologies, diverse devices, and multi-layered services, making traditional network management increasingly burdensome. Telcos allocate 15-20% of their revenue to network operations, primarily due to manual processes and outdated systems. For instance, managing alarms in a telco network is complicated by network complexity, fragmented monitoring systems, and a high volume of alarms. These inefficiencies drive up costs and impact customer satisfaction, with outages resulting in lost revenue and potential churn.

Telcos should adopt a comprehensive Generative AI (GenAI)-driven solution to address these issues. “GenAI in Telecom network management can significantly reduce $20 billion spent annually on network outages and service degradations” – TM Forum.

Implementing AskNetwork, a GenAI-powered framework, facilitates seamless, real-time interaction with network data through a conversational interface. It consolidates fragmented systems, simplifies alarm management, provides automated root cause analysis, and eliminates the need to maintain multiple dashboards, platforms, and integrations. It helps to efficiently manage network complexity, leading to enhanced performance and customer experience.

Fig: Leverage AskNetwork for automated Root Cause Analysis (RCA) and network outage resolution


“GenAI in Telecom network management can significantly reduce $20 billion spent annually on network outages and service degradations” – TM Forum.

Categories
Operational Excellence

From speech to insights: Harness the power of human voice

Transform contact centers with GenAI-powered Voice Intelligence framework to cut costs and reduce repeat calls by up to 85%

In today’s digital landscape, contact centers serve as the frontline of customer service, handling millions of interactions across various channels. As customer expectations evolve, deriving insights from the call recordings becomes essential for improving customer experience and boosting operational efficiency. According to McKinsey, “85% of business leaders say it’s essential to analyze call data for insights. However, only 16% of enterprises fully utilize the data generated from call recordings”. Most contact centers struggle to derive actionable insights from calls, which leads to prolonged resolution times, inconsistent service quality, risk of churn, and customer dissatisfaction.

To overcome this, service providers should implement Voice Intelligence, an automated, GenAI-powered framework to extract actionable insights from calls and enhance contact center efficiency. It helps reduce repeat calls and call handling time, thereby improving customer experience. Further, it helps service providers improve the First-Call Resolution rate and achieve huge cost savings.

Fig: Leverage a GenAI-driven Voice Intelligence framework to gain real-time actionable insights from customer calls


“ Only 16% of enterprises fully utilize the data generated from call recordings” – McKinsey.

Categories
Operational Excellence

Maximizing Agent Productivity: The Power of Gen AI with Agent Genie

Leverage Generative AI to enhance your agent productivity by 30% and cut your Business Process Services (BPS) costs by 20%

Outdated Business Process Services (BPS) operations are a critical barrier to efficiency and profitability for service providers. Legacy systems across crucial BPS functions, such as Order Management, Billing & Revenue Assurance, Network Ops, etc., create bottlenecks, forcing agents to navigate slow, error-prone processes.

Key pain points affecting the agent journey (plan & build, service delivery, service assurance)—spanning roles such as Assigners, Tech Support Specialists, Provisioning Specialists, etc., include:

  • Delayed Training Deployment: Manual creation of training materials hinders timely deployment, leaving agents without access to essential knowledge when they need it most
  • Lack of Intelligent Assistance: Insufficient tools for effective guidance, content generation, and summarization reduce agent productivity and diminish the quality of customer service
  • Manual Information Consolidation: Reliance on manual processes to gather and validate information from multiple sources introduces errors, leading to rework and delays in order completion and service
  • Inefficient Audit Mechanisms: Traditional audit processes lack real-time validation capabilities, resulting in unaddressed inefficiencies and missed opportunities for performance optimization

Agents hampered by these challenges face rising costs and inefficiencies. Manual processes add 20% to operations costs, and training gaps leave 30% of content uncovered.

The path forward is to reimagine BPS with modern, agile solutions. Leverage Generative AI to transform service provider agent journeys—across planning, service delivery, and assurance—boosting productivity. Implement the Agent Genie Framework , powered by Gen AI, to enhance the agent experience at every stage, reducing effort by 30% and cutting operational costs by 20%. The key components of the framework are:

  • Empowerment Hub: Rapidly generate personalized training materials for swift and effective live deployment of agents; reduce agent onboarding time by 50%
  • Intelligent Agent Playbook: Assist agents with a quick guide for the next best solution, content generation, and summarization of key information; Enhance response accuracy by ~98% while reducing agent effort by ~25%
  • Audit Nexus: Employ an AI-driven audit process that minimizes manual effort and provides instant feedback to the agents. Ensure 100% audit coverage and improve productivity by 20%


“ As per McKinsey Generative AI is projected to cut back-office costs and boost employee productivity by 30% through faster procurement, reduced recruitment costs, and automated content generation.”

Categories
Operational Excellence

Harmonizing operating models to attain M&A goals

Deploy Prodapt’s industry-leading Unified Operations Framework (UOF) as a strategic solution for a streamlined operating model transformation — Achieve a 25% OpEx reduction with Zero service disruptions

M&As in the Connectedness segment are expected to rise through 2024 as technology advancements and the release of pent-up deal appetite lead to a flurry of investments, according to this PWC report. Pursuing this route, Communications Service Providers (CSPs) may adopt shortcuts towards operating model transformation. However, a contrasting perspective from McKinsey emphasizes the importance of careful planning and execution when transitioning from two separate operating models to a newly integrated one. Even in ideal conditions, harmonizing operating models is complex, time-consuming, and challenging.

Several key challenges hinder the effective unification of operating models post-M&A. These include streamlining role redundancy, improving maturity levels across merged entities, and standardizing evaluation systems. Neglecting these aspects can lead to a sharp rise in operational expenditures, customer churn rates, and loss of competitive edge.

To address these challenges and unlock the full potential of M&A, CSPs can leverage the Unified Operations Framework (UOF). UOF offers a comprehensive approach to streamline processes, optimize resources, and integrate systems and teams effectively post-M&A. It employs a function and objective-driven approach to pinpoint areas for operational change and provides specific steps for implementation.

The three key implementation steps recommended by UOF are:

  • Redesign business teams aligning with TM Forum’s eTOM framework
  • Track automation and outsourcing maturity to enhance operational efficiency
  • Define, measure, and monitor Key Performance Indicators (KPIs) to ensure performance transparency and accountability

By adopting UOF, CSPs can achieve a significant reduction of approximately 25% in Opex, and accelerate migration by 3X. This strategic approach ensures sustainable growth and competitiveness in the rapidly evolving connectedness landscape, strengthening the merged entity.


“McKinsey – Transitioning from two existing operating models to a new, combined operating model requires thoughtful transition planning and execution.”

Categories
IT Agility

Unleash the power of cloud modernization

Accelerate migration of complex data pipelines to modern cloud services using a holistic approach

Communications Service providers face several challenges in managing and processing massive amounts of data generated every day from Call Detail Records (CDRs), networks, and application logs from various sources. Big data platforms like Hadoop help manage, analyze, and derive insights from extensive data but performance limitations, scalability challenges, and high maintenance efforts make it a tough challenge.

Service providers must move towards a cloud-based Hadoop ecosystem to overcome these challenges. While there are different approaches to cloud migration, the serverless route provides several benefits when compared to the traditional cloud.

According to Forrester, more enterprises are frustrated with the complexities of Hadoop’s on-premise systems and want to shift to the public cloud. Serverless and Hadoop alternatives in the public cloud will gain more traction in the near future.

This insight sheds light on cloud modernization of service providers’ ML use cases to facilitate efficient handling of large volumes of ML data, real-time data analysis, and faster decision-making.

Fig: Cloud modernization approach to maximize the value of migration


According to Forrester, many enterprises are frustrated with the complexities of Hadoop’s on-premise systems and want to move to the public cloud. Serverless and Hadoop alternatives on public clouds will gain traction in the future.

Categories
Operational Excellence

Accelerating fibre rollouts by pre-empting order delays

Leverage AI/ML to forecast delays and reduce customer churn

Fibre to the Premises (FTTP) service delivery includes deploying high-speed fibre optic connections directly to the customer premises, which involves several complexities and unexpected delays in order fulfillment. These delays can lead to missed SLAs, high customer churn, and compensation liabilities for Communications service providers.

According to Forrester, “70% of customers are likely to churn if orders are delayed, and proactive information about orders are missed”. Hence, an intelligent FTTP service delivery becomes imperative for service providers in the Connectedness industry.

Leveraging an AI/ML-powered FTTP service delivery framework can help service providers predict and address order delays before they impact the business. With the predictions from the ML model, the operations team can gain a view of the expected delays, root causes, and ways to overcome them. This helps reduce operational overload and customer churn.

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Fig: Leveraging an AI-powered FTTP service delivery framework for on-time provisioning and improved customer experience


“70% of customers are likely to churn if orders get delayed, and proactive information about orders is missed”. – Forrester

Categories
Software Intensive Networks

Predicting network faults with ultimate precision using AI

Service providers ditch rule-based firefighting and embrace proactive AI to anticipate and prevent outages, saving millions and boosting customer satisfaction.

Network Operations Centers (NOCs) are pivotal for service providers in ensuring seamless connectivity and optimal performance. However, a surge in 5G, IoT, and virtualization technologies has brought unprecedented challenges. NOCs grapple with the overwhelming influx of alarms, struggling to differentiate critical issues from irrelevant ones. Manual reduction methods and rule-based approaches lead to delays and false alarms, inflating costs and hindering response efficiency.

To overcome these challenges, service providers must embrace a proactive approach, harnessing machine learning (ML) for precise prediction and resolution of network faults. Service providers can leverage ML to analyze extensive and diverse data sets, extract crucial insights and promptly implement preventative measures in real time.

Hence, adopting a network event prediction model becomes imperative to anticipate and proactively mitigate potential network failures and outages. It also enables service providers to ensure precise predictions, reduce network downtime, and cut operational expenses.

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Fig: Key steps to leverage ML model for ticket prediction
and prioritization


Leveraging ML to analyze extensive and diverse data sets, service providers can extract crucial insights and promptly implement preventative measures in real time.