Generative AI as a catalyst for change in the telecommunications industry

Generative artificial intelligence (AI) burst into the mainstream in 2023, lighting a fire under businesses to integrate enterprise-grade versions into their processes. By 2024, 60% of C-suite executives are planning to pilot or operate generative AI in some way, indicating that generative AI’s public-facing platforms have awakened the world to its groundbreaking capabilities

For Communications Service Providers (CSPs) and Network Equipment Providers (NEPs), in particular, generative AI holds tremendous potential to help improve all manner of operations and customer engagement. Specifically, generative AI would transform customer care, IT and network optimization and digital labor—all areas in which automation can notably help increase agility and efficiency. CSPs and NEPs usually have huge support centers and IBM has the potential to help transform workflows between all ecosystem players. Here are some ways AI can contribute to transformation in the telco ecosystem:

Customer Lifecycle Management and service innovation

The job of managing customer relationships is traditionally a reactive one: fielding calls, responding to emails and working out solutions. Infusing generative AI into these interactions helps support the shift to more proactive care that has the potential to improve customer satisfaction and unlock new revenue streams. Enabling customer care agents to focus on complex cases by removing routine types of Q&A is a perfect case for concurrently addressing Net Promoter Score and employee satisfaction.

Chatbots have been around for some time, but can often create frustrating experiences for customers. Generative AI can go beyond basic Q&A, and can also train to identify negative sentiment and triage the ticket to the right agent, reducing further escalation and enabling agents to respond quickly and appropriately. Chatbot technology can also be applied to phone interactions, driving additional refinement to the customer care process.

AI can also help drive automated outreach that anticipates customers’ needs and issues, along with personalized marketing that can drive boosted sales and optimize the customer experience. For example, AI can look at a range of inputs to build offers, such as current usage and tariff plans, lifecycle of device ownership, service experience and extend offers to upgrade and be incentivized to buy more or retain service based on offerings. This has potential for helping reduce churn, improve revenue-per-user and lower the cost of subscriber acquisition.

Network optimization

AI can help to improve the performance, efficiency and reliability of telecommunications networks, which is essential to satisfy ever-increasing demands of different customer segments. Through live data analysis and predictive forecasting, AI tools can help employees working in network operations centers and network engineers to mitigate congestion and downtime. As 5G networks continue to expand, the need for intelligent load balancing and traffic shaping will likely grow.

AI-enhanced network optimization could benefit CSPs in a multitude of ways: not only can it add to a company’s competitive advantage by enhancing service for customers, but it can also help manage operating costs by addressing the strain on resources and helping CSPs and NEPs alike to avoid over-or under-provisioning resources.

CSPs can take advantage of watsonx.ai to train, validate, tune and deploy AI and machine learning capabilities to help optimize network performance. Watsonx’s open-source frameworks and SDK and API libraries are designed to make it easier to implement AI into existing software platforms that telcos already use to oversee their networks.

Digitalizing operations with AI talent

One of AI’s chief benefits is its power as a productivity tool to automate more mundane and time-consuming tasks, freeing up employees to focus on higher-order activities and work. Many of today’s employees utilize a staggering number of manual processes or fragmented tooling in their day-to-day jobs, with constant screen switching. A good example is the use of IBM Watson Orchestrate, using robotic process automation to streamline workflows, and connect to apps to help employees tackle a variety of tasks more easily.

The path to implementation

Before embarking on implementing AI enhancements, it’s crucial that CSPs and NEPs take care to develop organizational strategies to make these powerful tools most effective.

AI relies on data, but many organizations still operate various siloed repositories. CSPs and NEPs should define and establish a hybrid information architecture that facilitates the easy flow of data across multicloud environments and provides insights into the quality of that data. Watsonx.data helps make this process easy, allowing CSPs and NEPs to scale AI across a data store built on an open lakehouse architecture that supports querying, governance and fluid access to data. Using watsonx.data, business functions within the CSP and NEP can access their data through a single point of entry and connect to storage and analytics environments to  build the trust in their data and work from auditable sources.

CSPs and NEPs that develop thorough organizational and data strategies will not only be  positioned to maximize the capabilities and ethics of their AI frameworks, but they can also apply these methodologies to guide their own enterprise customers along their own journeys—opening up the potential for additional revenue streams in the process.

As AI’s capabilities evolve, companies should choose from two paths: There will be organizations that see AI as an additional tool for various aspects of their business and organizations that are AI-first. CSPs and NEPs that take the latter route will bepositioned to realize advantages over competitors in terms of cost savings, service quality and customer experience—and this advantage can only deepen with the maturation of AI over the coming decade. 

Source: ibm.com