From ‘reactive and manual’ to ‘proactive and automated’ is almost a mantra of the times we live in. This phrase has never been truer than with Artificial Intelligence (AI) deployed in the telecommunications sector, particularly in operations and customer care. Both functions have been virtually transformed by the predictive, autonomous, and hyper-personalized experiences ushered in by AI across the full customer and network lifecycle.
AI, along with its popular variant Generative AI, are now firmly at the core of a new intelligence layer for Communication Service Providers (CSPs). AI has helped improve key operational parameters such as efficiency and reliability, as well as Customer Experiences (CX), while simultaneously enabling new revenue streams for telecom companies.
telecom business process automation
CSPs are actively deploying AI and Machine Learning (ML), as well as advanced analytics in telecom business process automation. AI/ML is deployed to automate monitoring and fault detection, and for remediation across radio, transport, and core networks. Leveraging these technologies, CSPs have evolved their operations from static rules-based Network Operation Center (NOC) practices to proactive and increasingly autonomous operations that optimize performance and cost. All of this is being done at scale.
The key impacts of this ongoing evolution are being felt across organizations:
- Predictive maintenance reduces unplanned outages via anomaly identification, component degradation, or traffic patterns that precede failures. This allows for planned interventions rather than crisis-driven responses.
- AI-driven optimization dynamically tunes parameters such as power levels, handover thresholds, and capacity allocations. It also improves the quality of service in quantum jumps, while lowering energy and spectrum costs.
- Intelligent assurance that correlates alarms, telemetry, and topology data. This cuts down noise and accelerates root-cause analysis, thereby freeing up operations teams to focus on high-value incidents.
reduced incident resolution times
The next phase of telecom efficiency and reliability will depend on AIOps and AI in CSP network operations. AI agents and closed-loop control are increasingly embedded into OSS (Operational Support Systems) and BSS (Business Support Systems), Software-Defined Networking (SDN), and cloud-native cores to support partial autonomy by mid‑decade.
Several CSPs are testing advanced AIOps platforms that are able to reduce incident resolution time by up to 60%. This drives higher uptime and better customer experience. A combination of anomaly detection, capacity forecasting, and automated remediation is being deployed for large-scale, multi-vendor environments. It is estimated that by 2026, 30% of CSPs could achieve partial autonomy in their core networks, allowing AI agents to reroute traffic, trigger maintenance, and adjust configurations with limited human approval.
omnichannel experiences
AI is moving telecom customer care from call-heavy, reactive support into omnichannel, data-driven experiences supported by virtual agents, agent-assist tools, and AI-powered knowledge management. These align with broader shifts in customer service technology, where self-service and live chat are expected to overtake traditional channels by 2027.
Key focus areas include:
- Generative AI and virtual agents that handle a large share of routine inquiries, eliminating wait times for a substantial portion of requests and improving perceived responsiveness.
- AI-powered agents that assist in real-time recommendations, next-best-actions, and contextual knowledge, shortening handle times and improving first-contact resolution.
- AI-enabled orchestration, which unifies Contact Center as a Service (CCaaS) and Customer Relationship Management (CRM) systems. The end result is consistent, personalized interactions across voice, chat, messaging, and apps.
lower Op costs, higher NPS
There are immense productivity and quality gains when AI is embedded into service operations and telecom customer journeys. For a large-scale customer care organization, this can translate into both lower operating cost and higher Net Promoter Score (NPS).
The numbers are telling: per research by McKinsey, a 5,000-agent service organization using gen AI saw issue resolution per hour increase by 14% and average handle time drop by 9%. Per other reports, applying gen AI across customer care functions can unlock productivity equivalent to 30–40% of current function costs, enabling either cost takeout or redeployment of capacity to higher-value work.
a new intelligence layer
AI in telecommunications has moved beyond being an efficiency lever to becoming the foundation of a new telecom intelligence layer — a layer that underpins growth and provides competitive differentiation. CSPs that effectively industrialize AI across operations and customer care can position themselves to move from legacy connectivity to become platform-based, experience-centric businesses.
how Infosys BPM can help
Infosys BPM has been at the forefront of transforming digital operations and customer care for CSPs. With innovative BPM solutions for network operators such as Communication as a Service (CaaS) and Communication Platform as a Service (CPaaS), Infosys BPM empowers CSPs to drive digital transformation and promote operational excellence through the strategic use of automation.
