Key takeaways

• Telcos are shifting from exploration to targeted production across code, customer experience and network operations

• Grounded in telco data, GenAI reduces MTTR and AHT while lifting developer velocity and time-to-market

• Vodafone and AT&T show pragmatic wins in engineering productivity and network optimization

• Biggest hurdles: proving value, data/skills readiness and governance; success needs clear KPIs and ROI baselines

• Guardrails, including security, hallucination control and copyright hygiene, are essential for scale

• A platform approach plus disciplined measurement (AHT, FCR, MTTR, velocity) keeps programmes accountable

In 2023, telecom entered a transformation phase as Generative AI (GenAI) moved from exploration to early production across code, CX and network ops. When grounded in telco data, GenAI can cut MTTR, raise developer velocity and reduce AHT, while supporting faster go-to-market. This article covers adoption trends, named use cases (Vodafone, AT&T), barriers, risks/benefits and best practices, with KPIs and ROI guidance for telcos.

“By harnessing the transformative power of generative AI, we are not merely revolutionizing telco operations; we are redefining the paradigms of innovation and customer-centricity. This pivotal shift is enabling us to drive unprecedented efficiency and significantly enhance connectivity, thereby uplifting the entire experience for our telco customers. In doing so, we're setting new, higher standards of excellence and responsiveness in the telecommunications industry,” commented Priyadarshi A Das “PAD”, EVP at HCLTech.

Understanding Generative AI in telecom

Generative AI in telecom refers to foundation-model assistants and automations embedded across OSS/BSS, the NOC and customer experience channels. Where predictive ML forecasts demand, scores churn or detects anomalies from structured features, GenAI creates text, code, runbook steps and UI actions and can converse across channels; when grounded in telco data and policies.

• For a network engineer, a copilot can translate alarm storms into probable cause, draft change plans and config diffs, and execute safely with approvals

• For a care agent, a desktop sidekick can summarise customer history across CRM/BSS, pre-write responses, guide troubleshooting and update records automatically

• For a developer, code and test generation accelerate tickets, migrations and API scaffolding while improving documentation quality

GenAI adoption trends and use cases in the telecom industry

Telecommunication service providers (telcos) operate within their industry and as technology providers to other industries. To remain competitive and meet customer demands, telcos need to harness the potential of GenAI for both internal operations and in offering AI-powered solutions to industries they aim to serve. 

According to a Gartner IT Executive Webinar Poll, most telcos (70%) are in exploratory stage with GenAI, while 15% are piloting and 4% are in production. 

Vodafone in collaboration with Microsoft, for example, has been leveraging GenAI through the GitHub Copilot to test code writing. During trials involving 250 developers, Vodafone experienced productivity gains of between 30% and 45%.

In another use case, AT&T is using GenAI for network optimization and troubleshooting to improve QoS and reduce MTTR.

Within the telecom industry, there are several critical use cases for GenAI implementation. Content generation, content discovery, simulation and conversation AI are other areas where AI can have a significant impact. 

Leveraging AI for network optimization is also a game-changer, enabling telecommunication service providers to improve network efficiency, enhance performance and deliver seamless connectivity to their customers.

“By 2027, more than 50% of the GenAI models that enterprises use will be domain-specific — specific to either an industry or business function — up from approximately 1% in 2023,” said Kameron Chao, Senior Director Analyst, Gartner.

Key impact areas of Generative AI on telecom

• Network operations (assurance and optimisation): AT&T’s use of GenAI for network optimisation illustrates how assistants can summarise alarms, recommend remediation and auto-generate trouble-ticket updates. KPI focus: MTTR, incident deflection rate, SLA adherence/QoS

• Customer experience (care and sales): Agent copilots and grounded virtual agents accelerate discovery and resolution, harmonising knowledge scattered across CRM, billing and devices. KPI focus: AHT, FCR, CSAT and self-serve deflection

• Engineering productivity (software and network engineering): Vodafone’s Copilot trials show code and test generation can lift throughput without compromising quality. KPI focus: deployment velocity (lead time/cycle time), change-failure rate

• OSS/BSS modernisation: GenAI can draft product-catalogue entries, policies and orchestration templates, and automate order-care knowledge updates. KPI focus: order-fallout %, STP automation rate, time-to-launch

• 5G/edge services: Assistants help design slices, generate intents and validate policies for MEC workloads. KPI focus: QoS (latency/jitter), time-to-service activation

Guardrails are needed across all lanes to enforce security/PII controls, ground responses via retrieval, monitor hallucination rates, respect licensing/copyright and maintain human-in-the-loop for high-risk actions.

Barriers to adoption for GenAI (and how to overcome them)

Despite the tremendous potential of GenAI in the telecom industry, there are several barriers hindering widespread adoption.

According to a 2023 Gartner AI in the Enterprise Survey, the foremost challenges faced by telcos include estimating and demonstrating the value of AI (49%) and the lack of talent and skills in implementing AI technologies (46%). To address these barriers, companies like HCLTech have positioned themselves as value-driven partners, assisting telcos in showcasing the value of AI and providing the necessary skills and expertise to successfully implement GenAI solutions.

Other barriers include the lack of data, lack of confidence in the technological aspects of AI and a lack of business alignment and use cases. Overcoming these barriers requires telcos to invest in data collection and curation, provide training and education to build confidence in AI technologies and align their AI strategies with specific business goals and use cases.

The risks and benefits of GenAI in telecom

The risks and benefits of GenAI in the telecom industry are vast and far-reaching. According to a Gartner Executive IT Webinar Poll, risks include loss of confidential data, hallucinations and copyright issues.

However, AI can significantly improve operational efficiency, enhance customer experiences through automation and chatbots, optimize network performance and enable telcos to offer personalized services to their customers. 

By leveraging AI, telcos can streamline internal processes, reduce costs, deliver faster and more efficient customer support and gain a competitive edge in the market. However, it is crucial to address the risks associated with AI implementation, such as potential data privacy and security concerns, and ensure the ethical and responsible use of AI technologies.