In 2023, generative artificial intelligence (AI) has become mainstream, sparking excitement among companies looking to integrate enterprise-grade versions into their processes. By 2024, 60% of executives plan to promote or support generative AI in some way, suggesting that publicly available generative AI platforms have made the world aware of their groundbreaking capabilities. Generative AI in particular has enormous potential to improve all types of processes and customer loyalty. Generative AI would particularly change customer service, IT and network optimization, and digital work – all areas in which automation can particularly contribute to increasing agility and efficiency. CSPs and NEPs typically have huge support centers, and IBM has the potential to help transform the workflows of all ecosystem participants. Here are some ways AI can help transform the telecom ecosystem:
lifecycle management and service innovation
Customer relationship management is traditionally reactive: answering phone calls, responding to emails and developing solutions. Introducing generative AI into these interactions helps drive the shift toward more proactive support that can increase customer satisfaction and create new revenue streams. Allowing customer service representatives to focus on complex issues by eliminating routine questions and answers is a perfect example of how to address
Net Promoter Score and employee satisfaction at the same time.
chatbots have been around for a while, but they can often be a frustrating experience for customers. Generative AI can go beyond basic questions and answers, but can also train itself to detect negative comments and report reviews to the appropriate agent, limiting further escalation and allowing agents to respond quickly and appropriately.Chatbot technology can also be applied to phone interactions, further improving the customer service process.
AI can also help generate automated outreach that anticipates customer needs and problems, as well as personalized marketing that can increase sales and optimize customer service. For example, AI can analyze a variety of inputs to create offers such as current usage plans and pricing, device ownership lifecycle, service experience, and expand upgrade offers, tiers, and incentives to purchase additional services or maintain services based on the offers. This can help reduce churn, increase revenue per
users, and reduce subscriber acquisition costs.
Network Optimization
AI can help improve the efficiency, effectiveness and reliability of telecommunications networks necessary to meet the ever-increasing demands of various customer segments.With real-time data analysis and predictive forecasting, AI tools can help network operations center staff and network engineers mitigate congestion and outages. As 5G networks continue to expand, the need for intelligent load balancing and intelligent traffic management is likely to increase.
AI-based
network optimization can benefit CSPs in several ways: not only can it increase a company’s competitive advantage by improving customer service, but it can also help manage operational costs by reducing pressure on resources and CSP and NEP is supported to avoid excessive orinsufficient costs. resources provided.
ISPs can use watsonx.ai to train, validate, optimize and deploy artificial intelligence and machine learning to optimize network performance.Watsonx’s open source platforms, SDKs and APIs are designed to make it easier to implement AI into existing software platforms that telecommunications companies already use to monitor.
Digitize operations with AI talent
One of the key benefits of AI is its power as a productivity tool, enabling theautomation of more mundane and time-consuming tasks, allowing employees to focus on higher-level tasks and work. Many of today’sworkers use a shocking number of manual processes or isolated tools in their daily work, constantly switching between screens. A good example is the use of IBM Watson Orchestrate, which uses robotic process automation to streamline workflows and connects to applications to help employees complete various tasks more easily.
Path to implementation
Before implementing AI improvements, it is important that CSPs and NEPs develop organizational strategies to ensure that these powerful tools are aseffective as possible.
AI
is data-driven, but many organizations still use multiple siled repositories.CSPs and NEPs must define and establish a hybrid information architecture that facilitates the flow of data across multi-cloud environments and provides visibility into the quality of that data. Watsonx.data facilitates this process by enabling CSPs and NEPs to scale AI in a data warehouse built on open lakehouse architecture that supports query, management, and transparent access to data. With watsonx.data, business functions within CSP and NEP can access their data through a single
entry point and connect to storage and analytics environments to build trust in their data and work from controlled sources.
CSPs and NEPs that develop detailed organizational and data strategies will not only be able to maximize the capabilities and ethics of their AI frameworks, but also be able to apply these methods to help their clientcompanies along their journey accompany and release potential. for additional sources of income in this process.
As AI capabilities continue to evolve, organizations will need to choose between two paths: some organizations will view AI as an additional tool in various aspects of their business, and others will prioritize AI. CSPs and NEPs that choose the latter path will be able to realize competitive advantages in cost savings, service quality and customer experience, and these advantages will become even more evident as AI matures over the next decade.
Bringing artificial intelligence to life
To learn more about how IBM AI platforms like Watsonx can contribute to the telecommunications industry, visit our booth (#1010) at MWC Las Vegas, September 26-28 at the West Hall Las Vegas Convention Center.