Artificial Intelligence (AI) and digital agents are revolutionizing technology and software operations for telcos, introducing unprecedented efficiency, automation, and intelligence across the software development life cycle (SDLC). Key areas impacted by these technologies include IT strategy, governance, software development, and support functions.
The potential applications of generative AI are vast and varied. Generative AI and IT is improving back-office and strategic functions outcomes, such as maintaining and updating IT strategy or creating a transparency layer to better understand enterprise IT architecture, which is often highly complex in telecommunications due to the presence of thousands of applications. It is also enhancing IT operations by automating such routine tasks as system monitoring, incident response, and predictive maintenance. This automation not only improves efficiency and customer experience but also allows product and tech professionals to focus on more strategic initiatives.
The integration of AI and generative AI into technology functions is already yielding significant benefits. We estimate that generative AI has potential to optimize IT-spend by 14% to 35%, which is a material opportunity for telecom operators where IT spend as a percentage of revenue accounts for 3% to 7%.
Revolutionizing the technology landscape with AI and digital agents
Imagine a future where your organization’s technology landscape is not just reactive but preventive, driven by AI and digital agents. Organizations at the forefront of using AI are integrating the three key elements of the value chain:
1. Enhancing IT strategy and governance
For strategy and governance, AI and digital agents are being used to synthesize market insights and summarize trends impacting Telecom operations, empowering leadership teams to make better informed and strategic decisions. Automated risk monitoring systems are being deployed to detect unusual security anomalies, ensuring that organizations adopt a proactive approach to risk management and safeguarding vital assets.
2. TRANSFORMING the software development lifecycle (SDLC)
Across the software development life cycle, generative AI transforms project planning by converting objectives into actionable tasks, recommending and allocating resources and costs, and proactively optimizing your IT project portfolio for maximum efficiency. AI-driven tools assist in generating and writing functional and technical requirements based on user feedback, ensuring consistency and quality while prioritizing tasks according to urgency and business importance. It enables the rapid modernization of complex legacy system landscapes, such as field operations, service delivery platforms, or even billing.
During the design phase, teams leverage AI for ideation and writing, facilitating design reviews, and enabling early detection of flaws. During the build and application development phases, AI can augment work done by engineers by fast-tracking build activities, enabling them to develop code 30% to 40% faster, refactor code 20% to 30% faster, and complete code documentation up to 45% to 50 % faster, according to Oliver Wyman analysis. Technology has matured to the point where it can rival the performance of a mid-level engineer. AI can also help reduce a company's technology debt by modernizing legacy IT systems, which often represents over 30% of the total technology debt in large telecom operators. As a result, managing technical debt becomes more feasible, allowing teams to concentrate on innovation instead of being hindered by legacy challenges.
Testing and deployment processes are augmented as well, with AI and digital agents automating the creation of test case descriptions and generating test data to ensure the robustness of software and applications. Automating security scans help identify critical vulnerabilities before they can impact operations.
3. ENHANCING SUPPORT FUNCTIONS EFFICIENCY
Functions that support technology undergo a transformation as well, with automated financial analysis and forecasting optimizing IT capital expenditures and resources in real time. Enhanced customer support capabilities deliver fast, personalized assistance, while the knowledge base is continuously updated to reflect the latest information and FAQs.
This is the vision of a telecom technology function that fully embraces the capabilities of AI and digital agents, creating a technology ecosystem that not only meets current demands but also anticipates future needs.
Navigating the AI transformation for technology leaders
As the use of AI technologies evolve, so will the roles of chief information officers (CIOs), chief technology officers (CTOs), Chief network officers (CNO) and software engineers. These leaders are instrumental in ensuring that technology strategies align with business objectives and fostering a culture of continuous learning and adaptation.
The advent of generative AI and digital agents necessitate a strategic shift for technology leaders. CIOs, CNOs, and CTO, are now tasked with integrating AI capabilities into their organizations' core functions. This involves developing a comprehensive understanding of AI technologies, identifying relevant use cases, overseeing the implementation of AI-driven solutions, building platforms and orchestration layers to manage all agents seamlessly, while collaborating with other business leaders to think through how AI and generative AI can create new sources of value.
Another challenge that leaders face revolves around talent and skills. Rapidly transforming technology operations and adjacent functions necessitates a transformation of the workforce. Data-related skills are in high demand and will continue to be, while skills related to testing, maintenance, and support are increasingly being automated. For most technology executives, achieving this future state requires a thoughtful reevaluation of current capabilities and the mix of skills needed in the future. Leaders will also have to determine if they should reskill portions of their workforce or outsource certain functions.
Succeeding with generative AI requires building the right foundations to run models, scale use cases, ensure adoption, and optimize costs. These foundations go beyond the technical to encompass operating models, systems and data, talent and culture, and change management.
Accelerating an AI-driven transformation requires leaders to clearly define the business problem from the outset and regularly review the sequencing of use cases. Other essential strategies include embracing an iterative process that prioritizes progress over perfection, allowing for continuous improvement, and focusing on seamless integration rather than optimizing each individual component.
Reimagining business processes using AI and digital agents
Unlocking the true value of AI is fundamentally about enhancing business outcomes. For technology leaders in telecom, this means reimagining business processes and operating models from end-to-end. This involves designing streamlined processes and workflows, simplifying business rules, redefining maintenance procedures, and automating activities wherever possible. By leveraging AI and generative AI, technology organizations can accelerate this transformation journey. However, the primary challenge lies not in the technology itself, but in establishing sustainable practices, addressing misconceptions, and mitigating associated risks. Effectively managing change at scale will be crucial to overcoming this challenge and truly position technology as an asset.
