DevOps teams in the telecom sector often struggle to bring their ideas to market faster, adapt to evolving trends, and deliver world-class customer experiences. As new technologies emerge, it is imperative to integrate these innovations into products and services while ensuring faster rollouts, robust change management, and better customer satisfaction.

However, manual approaches and legacy systems often hinder business agility and growth. To transform operational efficiency and enhance IT service management, it has become imperative for DevOps teams to embrace Artificial intelligence (AI).

Read on as we examine the top AI use cases in telecom and provide recommendations on overcoming adoption challenges.

Reinventing Telecom with AI – Understanding Use Cases

AI in telecom is witnessing an exponential rise, having grown at a CAGR of 40.1% from a $2.48 billion market in 2023 to a $3.47 billion market in 2024. This growth can be attributed to several factors, including rising cybersecurity concerns, evolving customer expectations, and surging competition that requires telecom companies to act fast or be left behind.

AI is playing a critical role across various areas such as –

• Effective customer support: AI allows for highly efficient customer service. Using  Jira Service Management, for example, telecom companies can set up a customizable service desk tailored to their specific needs. Using AI-powered workflows and tailored automation rules, they can prioritize tasks, triage and respond to issues quickly, and deliver excellent service experiences.
• Building a knowledge-centric organization: When it comes to creating dynamic documentation and finding quick answers, AI opens doors to seamless knowledge management. Atlassian Intelligence features in Confluence, for instance, enable teams to accelerate their searches for content, answer questions with auto-suggestions, and share meeting and document summaries to save time. Real-time notifications further allow teams to stay up-to-date with the latest information, quicken review cycles, and streamline version control.
• Driving successful change initiatives: AI is highly effective for streamlining change management, especially in complex telecom environments. AIOps tools can enhance visibility, automate decision-making, and reduce the risks typically associated with changes. Teams can use these tools to analyze failure rates, get recommendations on changes with higher success, and correlate incidents with a recent change.
• Faster rollouts of voice, data, messaging, and video communication features: Integrating AI into CI/CD pipelines can help improve code quality, reduce errors, and accelerate deployment cycles. Bringing intelligence to the entire software development lifecycle, AI allows teams to detect inefficiencies early, initiate automatic rollbacks, and automate feedback loops for continuous improvement in code quality.
• Agile service delivery: AI empowers DevOps teams with capabilities that accelerate development and enable agile IT service management and delivery. For instance, Sonar offers several AI-assisted code generation tools that function as intelligent collaborators, providing insights, suggestions, and automation to streamline development. Jira Service Management’s AI features enhance service delivery by automating repetitive tasks, enabling faster issue resolution, and improving decision-making with intelligent insights. These tools significantly improve the efficiency and speed of building applications, enabling teams to unlock new possibilities and build software faster.
• Real-time vulnerability and anomaly detection: AI can enable real-time monitoring of code repositories, checking for abnormal patterns that could suggest malicious activity or fraud. Through intelligent vulnerability and anomaly detection, teams can ensure code and dependencies in software pipelines are secure, which is essential in the telecom sector, where fraud prevention and data security are critical. JFrog, for example, establishes automated policies, allowing teams to detect instances of fraud and ensuring only those features that meet the necessary criteria to advance toward release and distribution.
• Greater visibility into security vulnerabilities: Artificial Intelligence also allows for greater visibility into vulnerabilities, offering teams a consolidated view of application security. Using AWS, teams can perform real-time threat identification. Machine learning and threat intelligence models can help identify unauthorized activities, analyze security issues, and keep environments secure through proactive measures. Similarly, GitLab offers AI capabilities throughout the software development lifecycle, allowing developers to build and deliver secure software faster and automate compliance at scale.

Telecom giant achieves high-velocity ITSM with enreap & JSM Cloud. Read the case study.

Challenges and Considerations

AI empowers telecom DevOps teams with capabilities that transform development practices and deliver value faster. Yet, AI integration in the telecom DevOps environment is not without challenges.

Several considerations must be kept in mind while adopting this cutting-edge technology:

• Legacy systems with limited processing capabilities restrict AI integration. Companies looking to make the most of AI innovations must plan to modernize their legacy applications. They can either rehost, re-platform or completely refactor their proprietary tools and technologies to ensure higher success with AI integration.  
• Poor data quality can negatively impact AI model outcomes, limiting teams’ ability to achieve the best results from AI investments. Teams must establish robust data governance programs to ensure AI tools are fed accurate and updated data. They must also invest in tools that help with data cleansing and augmentation, enable data labeling and annotation, and monitor data quality metrics regularly.
• Talent and skills gaps often delay AI adoption. Since finding professionals with the necessary AI expertise can be challenging, teams must engage with Managed Services Partners who can offer on-demand access to skilled AI professionals. These experts can design, implement, and maintain AI solutions effectively and ensure timely and cost-effective deliveries.
• Compliance with international telecom regulations is another hurdle DevOps teams find difficult to cross. To keep pace with a constantly evolving regulatory landscape, teams must stay updated with varying data privacy, cybersecurity, and service quality requirements and automate compliance wherever possible to avoid penalties and safeguard business reputation.
• Operational complexities pose significant challenges in AI adoption, often delaying implementation and impacting overall effectiveness. These complexities range from integrating AI into existing IT infrastructure to managing large volumes of diverse data and ensuring seamless collaboration across different business units. To streamline this, teams must craft a clear AI strategy that outlines all aspects of AI adoption. From adoption plan to roadmap, tools to roles and responsibilities, clearly documenting these details can help simplify adoption while allowing teams to pivot as necessary.

As the telecom industry struggles to adapt to new trends and expectations, Artificial Intelligence offers several advantages in accelerating product development, promoting operational efficiency, and improving customer experiences. From finding information faster to automating Tier 1 support issues, automating code reviews to accelerating change management, improving fraud detection, and enabling knowledge management, tools like Atlassian AI allow telecom companies to revolutionize their operations and differentiate themselves in a competitive landscape.