Scale AI solutions responsibly and accelerate trustworthy decision-making by 4x with RAI 360

As Artificial Intelligence (AI) shifts from isolated experiments to essential business infrastructure, enterprises are recognizing a vital truth: AI without responsibility is unsustainable. Generative AI (GenAI) and Machine Learning (ML) are already transforming customer experiences, operations, and innovation. However, as these systems grow, so do their risks, including bias, opacity, data privacy issues, security threats, and regulatory hurdles.

Industries such as BFSI, Healthcare, Telecom, and Technology are integrating AI into their core decision-making processes, including automating insurance claims, optimizing network operations, enhancing diagnostics, and credit risk modeling. Yet, the pace of AI deployment is quickly surpassing the development of governance frameworks, leaving enterprises vulnerable to ethical, legal, and reputational issues.

Generative AI holds enormous transformative potential. Still, in the rush to deploy, governance struggles to keep pace. Organizations often focus on speed over safety, releasing AI solutions without adequate oversight, risk management, or ethical review. With trillions of dollars and reputations at stake, deploying AI without a responsible and well-governed framework is no longer just risky; it’s unsustainable.

To unlock AI’s full potential while managing its risks, enterprises must find a balance between innovation and accountability. This is where Responsible AI (RAI) becomes essential, not merely as a compliance requirement, but as a strategic necessity. RAI incorporates transparency, trust, and governance into every stage of the AI lifecycle, enabling organizations to turn risk into a long-term edge.

Leverage the RAI 360 framework, specifically designed for regulated and AI-sensitive enterprises, offering a structured approach to AI governance across four key areas: Assess, Align, Assure, and Accelerate. Supported by hyperscaler-native tools and Prodapt accelerators, RAI 360 enables real-time oversight, model accountability, and four times faster decision-making, empowering enterprises to scale AI responsibly and competitively.

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.

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

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%

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.

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.

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

Adopt the GenCARE Framework to raise efficiency, streamline processes, and reduce Opex in contact centers

Contact centers deploying AI tools for customer engagement continue to report low satisfaction scores and delayed resolution. A lack of deeper understanding of human languages and inability to comprehend nuances in text and audio messages conveying the need for support are the key reasons. This deficiency results in customer dissatisfaction and, eventually, tremendous damage to the CSP’s brand value. Key challenges faced by contact centers include:

• Natural Language Understanding (NLU): Inability to accurately understand and interpret human languages. For example, misinterpreting customer inquiries (billing, service complaints) containing colloquial expressions leads to inaccurate responses
• Context Retention: Struggle to retain context, leading to disjointed and frustrating exchanges, especially in longer or more complex conversations
• Multilingual Support: Requires additional resources, training, and coordination, especially for languages with limited training data
• Emotional Intelligence: Empathy and emotional understanding are challenging to replicate in AI systems

These challenges significantly raise multilingual support costs (20%-30%) for contact centers, with low chat containment (<20%) necessitating more live agents. This contributes to customer dissatisfaction and increased churn risk, mainly due to prolonged wait times.

As per McKinsey, generative AI can reduce the volume of human-serviced contacts by up to 50%, depending on a company’s existing level of automation. Use our GenCARE framework to enhance contact centers through Generative AI and achieve a 40% cost optimization while boosting customer satisfaction. The key components of the framework are:

• NLU-based intent identification-Integrate the chat platforms with domain customized Generative AI models for quick and accurate query handling
• Context-enhanced agent support-Leverage context retention capabilities to quickly identify customer issues and generate automated notes to boost agent productivity
• Real-time sentiment analysis -Classify and score sentiments using the sentiment analysis module. Respond as per the customer’s emotional status
• Multilingual query resolution-Use language translation to achieve zero wait time with a language-independent unified team

Utilize the FibrePro Analytics Maturity (FAM) Model for improved decision-making, enhanced customer satisfaction, and cost efficiency

While organizations have made substantial investments in data and analytics, an HRB report reveals that only 23.9% of companies identify as data-driven, and merely 20.6% have successfully cultivated a data-centric culture. The level of data analytics maturity is a critical element for fibre operators in transitioning from intuition-based decision-making to an insight-driven organization.

Below are the primary challenges faced by fibre operators in achieving data analytics maturity despite huge investments.

• Lack of data and analytics strategy aligning with business
• Cultivating a data culture that binds data talent, tools, and decisions
• Creating a robust data architecture that enables controlled, secured data access and utilization
• Building a skilled team with both domain and data analytics expertise

Employ the FibrePro Analytics Maturity (FAM) Model, a holistic framework for fibre operators to overcome these hurdles and build a fully integrated data-driven organization. FAM synchronizes data capability and adoption maturity to enhance data analytics maturity across the fibre journey. This model comprises 4 key stages: Descriptive, Diagnostic, Predictive, and Prescriptive & Cognitive.

This Insight delves into the journey of data analytics maturity for service provisioning use cases, underscoring its pivotal role in boosting revenue generation, competitiveness, and customer satisfaction for fibre operators.

Put your Fibre on the Fast Lane with the Fibre Design Framework (FDF) and get deliverables right the first time

As per Research & Market, the global Fibre-to-the-Home/Building (FTTH/B) market is projected to reach US$29.7 B by 2026, growing at a CAGR of 13.1%. Considering such enormous growth and demand for fibre, efficient and commercially viable fibre planning and design is becoming incredibly important.

However, fibre operators face several challenges in Plan and Design phase that lead to budget overruns, missed deadlines, and loss of competitive edge. Here are some challenges:

• Skill shortage: Slows down the fibre rollout
• Manual work: Takes longer duration due to multiple hand-offs and paperwork in the High-level Design (HLD) and Low-level Design (LLD) stages
• Unstandardized designs: Leads to quality/consistency issues in templates and documents
• Unstructured work culture: Generates incorrect/missed field inputs

To overcome these challenges, fibre operators must consider ways to automate the key fibre design processes. The Fibre Design Framework (FDF) discussed in this Insight can bring high levels of automation in the fibre design process, and accelerate rollout time by 2X. The framework encompasses key components such as –

• Automated HLD generator: Create an automated high-level design with defined design standards and parameters
• Task Collaborator: Manage workflows digitally, collaborating tasks across multiple teams, and systems
• Field Navigator: Capture video-enabled field inputs across existing and planned design network elements
• Quality Gateways: Integrate quality management gates at crucial junctures

Embed a Value Driven Continuous Improvement (VDCI) function in your operations organization for superior customer experience and operational efficiency

There is fierce competition between fibre operators to acquire the top spot. And one way to remain competitive is to constantly improve or advance their operations while keeping costs in check and ensuring a seamless customer experience.

However, 60% of all corporate six sigma initiatives fail. Talking specifically about fibre operators, we observed that they still rely on traditional operations improvement activities performed on a one-time or on-demand basis translating to a limited-time benefit only. Also, these activities need to catch up with the fast-changing fibre environment.

As a result, fibre operators suffer from various challenges, like the inability to meet coverage targets on time, overshooting budget, and delays in service delivery. Despite a lean operating model, they fail to sustain continuous improvement, resulting in poor customer experience, huge investments, and unsustainable strategies

The need of the hour for fibre operators is a massive shift in their culture towards continuous improvement. They must embed a Value-Driven Continuous Improvement (VDCI) function into their organization’s structure to instill a culture of constant advancement, reduce OpEx and stay ahead of the competition. The critical stages of the VDCI function are Discovery, Formulation, Execution, and Evaluation.