Telephone companies (Telcos) have been struggling for years with insipid growth. Has the quest for catalysts to propel growth reached a tipping point amidst the rise of Generative AI in Telecom Industry?

Let’s deep dive to look at the data from the sector and early green shoots of growth by Gen-AI.

Telcos’ growth has been measured on many parameters. Let’s look at the KPIs.

• ARPU
• Churn Rate
• New Business Revenue
• Repeat Business Revenue
• Subscriber growth rate
• CSAT/NPS.

Majority of India-based telcos are facing headwinds and long periods of stagnation.

Courtesy: IBEF’s recent study

Intense competition has made it tough to tackle industry challenges, resulting in financial decline and increased consolidation. This has led companies to either exit tower assets or refocus on core operations.

Now, some of the initial studies are encouraging adoption of Gen-AI. A Latin American Telco increased call center agent productivity by 25% and improved the quality of its customer experience by enhancing agent skills and knowledge with recommendations from a Generative AI Consulting Services.

Another European telco recently increased conversion rates for marketing campaigns by 40% while reducing costs by using Gen-AI to personalize content.

Is this one-off or does it indicate the change for revolutionizing the growth issues encountered by the telcos?

A recent study by a leading consulting firm suggests that Gen-AI over “Traditional AI”, with its capacity to interact in human-like ways, is a tipping point. This tipping point is further summarized as capturing a significant share of nearly 100 billion USD in incremental value and about 140 billion USD in productivity to Gen-AI. Phew! This sounds unbelievable, but it looks like it surely leads to significant value addition. But the beauty of this Gen-AI adoption over Traditional AI is that it’s available now to telcos of any scale that’s moving towards -democratization.

Customer services are undoubtedly leading the adoption trend, but the potential in Network and IT remains largely untapped gold mines. CXOs today may understandably be cautiously skeptical about enterprise adoption, fearing disruption to current initiatives. However, smaller telcos are more open to embracing adoption across functions to gain a competitive advantage that scale of operations didn’t offer in the past.

A summary of the use cases for Telcos picking up the volume is listed below.

1. Network Operations: AI copilots evaluate images from technicians, provide accurate recommendations for remedies, and automatically initiate interventions or work orders.
2. Sale: AI models conduct sentiment analysis on customer calls in real-time and guide sales representatives on recommended responses, altering sales strategies based on customer interactions. Impacting the sales outcome in a significant way.
3. Customer Services: Chatbot interactions that are human-like, personalized, and real-time.
   Next set of questions that arise are – What is the recipe to adopt these changes and what could be the key building blocks, to begin your journey now?

Next set of questions that arise are – What is the recipe to adopt these changes and what could be the key building blocks, to begin your journey now?

1. Business Roadmap for Gen-AI adoption – Today most of the telcos are pro do-it-yourself approach limiting the possibility and slowing down the adoption. Instead of partnering with a Generative AI Consulting Company or a Gen-AI Consulting Services, the provider will accelerate the adoption.
2. Talent– Upskilling and expanding internal expertise to innovate with Gen-AI is going to be a critical factor fueling the growth. Building the culture from bottom to top with certifications, discussions, and embracing the change will be key.
3. Operating model: Setting up an organization to fuel Gen-AI investments. This includes setting up a dedicated process to prioritize the use-case pipeline, identifying opportunities to build future pipelines, prioritizing the reusability between the use cases, setting up performance matrices to measure the success/impact, and managing the associated risk to the programme.
4. Technology: The changes coming to Gen-AI technology are advancing rapidly. COEs and teams must stay updated with new technological developments and proactively test them in labs. Additionally, technology should be designed with interoperability in mind to support diverse use cases. Building components that generate data can help data scientists create use cases across various domains like customer services, sales and marketing, and network operations. Lastly, sustaining the technology requires investment in LLMOps.
5. Data: The crucial aspect of the entire puzzle lies in how data is acquired, how synthetic data is generated, and how it’s validated before being used by the system. It’s essential to bring together business, legal, and security teams to assess and validate the importance and exposure of this data to different models.
6. Change Management: The productivity boost brought by Gen-AI in personal life is contagious, and professionals should be introduced to similar tools in the workplace to enhance daily tasks. The Learning and Development team, acting as business strategists, plays a crucial role in facilitating the rapid adoption of Generative AI in Telecom Industry. Additionally, the increased productivity will result in more leisure time, which may be unfamiliar to many employees. Keeping them focused will be essential for managers.

Source:-

• IBEF
• McKinsey Insights
• The Judge Group research