AI in telecommunications for service providers has been saved
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AI in telecommunications for service providers
Reinventing operations and customer care using AI in telecom
Communication service providers (CSPs) face the challenge of managing complex networks while providing seamless customer experiences. With the right strategy in place, they can use AI to enhance operations across the enterprise and build competitive advantage.
AI for telecommunications transformation
Explore use cases and learn how CSPs can create a winning strategy to reinvent customer care, field service and network operations through AI-enabled solutions.
Building competitive advantage through enhanced efficiency and customer experience
For CSPs, delivering the right solution at the right time is critical in a highly competitive market, requiring a combination of automation, data-driven insights, and creativity across operations.
AI in telecommunications, particularly Generative AI, can help CSPs enhance operations by automating tasks, enhancing insights, and creating data for machine learning and scenario planning. But to avoid wasting resources and ending up with a slew of disjointed, ineffective use cases, CSP leaders must develop a sound AI strategy that aligns to their business goals.
AI in telecom is a game changer for CSPs
In addition to creating new content like text, code, voice, images, and processes based on past data and patterns, Generative AI can also gather data from siloed sources to easily create insights, predict outcomes, and uncover anomalies. These benefits empower CSPs to reinvent several parts of their business operations.
Personalized customer self-service
Network planning & deployment
Network stress testing
Network operations & maintenance
Decision-making in the field
Personalized customer self-service
Enable on-the-fly customer support and propose new product/service recommendations and offers to increase satisfaction and retention.
Network planning and deployment
Simulate service quality and subscriber experience at potential deployment sites based on user behavior and, environmental and historical usage data.
Network stress testing
Create scenarios to simulate future network consumption patterns to stress test load and provision resources, given specific bandwidth and network constraints.
Network operations and maintenance
Identify network faults through digital twins, and provide on-field technicians with remediation prompts for faster resolution.
Advanced decision-making in the field
Augment technicians with additional solutions and advanced problem-solving capabilities to test new solutions prior to implementation.
Elevating customer care through differentiated loyalty experiences
The low cost of switching CSPs is driving the need for standout experiences. Conversational AI chatbots and predictive models create nuanced, personalized encounters to anticipate problems, solve technical issues, and provide recommendations in addition to helping agents provide better service by summarizing complex customer information.
Enhanced customer care outcomes
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Increased personalization in customer experience
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Reduced costs deflecting more calls to virtual agents
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Labor effectiveness with live agents handling complex issues
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Three primary enterprise service delivery goals
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Increased compliance with script and solution suggestions
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Improved customer acquisition and retention
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Transforming field service and logistics for network reliability
An increase in at-home networks and customer expectations for reliability have exacerbated challenges for field service. In addition to helping CSPs source, manage, and pack inventory, AI tools can efficiently equip and schedule technicians by skill set, dynamically routing based on schedule and weather changes. Generative AI can act as a copilot for technicians and logistics managers, helping them troubleshoot and make more informed, proactive decisions.
Optimized field service and logistics performance
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Increased field service technical productivity
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Reduced time to resolution for jobs
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Decreased dispatch processing time
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Demonstrated benefits
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Lower costs for gas and maintenance with predictive capabilities
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Decreased time to train and onboard technicians
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Increased customer retention
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Optimizing network operations with proactive maintenance
AI-enabled network operations centers (NOCs) with AI can drive value by supporting downstream AI use cases, relying less on human intervention and manual processes for greater agility, precision, and proactiveness. This, in turn, can make them more efficient, resilient, and scalable. Predictive models can train on historical maintenance and fault data to identify potential issues; automate resolution; and raise alarms for quick interventions, notifications, and backup solutions to improve customer experience.
Improved network operations efficiency
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Reduced operational complexity
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Reduced network downtime
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Increased outage and incident predictability
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Preventive care for network access
How to create a winning AI in telecommunications strategy
AI for telecommunications offers CSPs the opportunity to enhance operations with automation, precision, and personalization. While many organizations are already seeing significant cost savings and revenue generation, other CSPs must avoid the siloed approach that fails to extract value from AI in telecom.
The journey must begin with a broad but cohesive AI strategy that aligns to business objectives and encompasses risk mitigation, governance, talent and more. Read the report for details on how to realize these benefits for your organization and establish a clear path forward.
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