In an exclusive interview with Telecom Review during the 18th edition of the Telecom Review Leaders’ Summit, Najla Al Kaabi, Head of AI (Acting), du, shared the company’s well-balanced approach to AI integration at both the enterprise and operator levels.
How can organizations/businesses best strategize their AI integration plans? How can du help?
Integrating AI into any corporate setting is, for the most part, a change-management process. It involves multiple dimensions, including transforming processes, mindsets, organizational thinking, and technology. Digital transformation is a journey with no final destination; it begins small and evolves over time until it becomes an intrinsic part of the organization.
At du, we have adopted a bottom-up approach to AI integration. We established a cross-functional team and task force responsible for identifying AI use cases and problem statements that can be addressed using AI. A framework was then developed to prioritize and execute these use cases.
Before execution, we conducted an AI Readiness assessment to evaluate our preparedness in terms of technology, skills, resources, and processes. This ensured scalability in the industrialization and productization of AI. With a roadmap in place, we are systematically implementing these use cases over the next three-to-four years. With this experience, we are eager to share the lessons we've learned and our achievements to accelerate AI integration for other enterprises and operators.
Please tell us about du's latest AI solutions for key sectors, including network autonomy.
Our AI journey began with a bottom-up approach by identifying domain-specific use cases. One key focus has been advancing network autonomy. Through multiple workshops and sessions with our network engineers across planning, implementation, and operations, we identified high-value processes and operations that could be automated using AI to enhance operational efficiency and the customer journey.
We have developed a roadmap of AI use cases, including a significant initiative, to predict customer complaints arising from technical and network issues. This use case aims to enable a proactive approach by alerting operations and network custodians to potential problems before they trigger customer complaints. Our goal is to achieve self-awareness and self-healing capabilities within the network to improve customer experiences swiftly and effectively.
How is du addressing the AI-governance element in the context of AI integration?
AI governance is a cornerstone of our AI framework, and we recognized its importance from the outset. However, governance is a complex and expansive topic that requires gradual development to achieve maturity.
We adopted a phased approach by prioritizing governance elements that accelerate the onboarding of AI use cases while ensuring trust and security. For example, we focused on model and machine learning (ML) operations as part of the AI model lifecycle, embedding these processes into our governance framework early. Additional governance layers will be developed and implemented as we scale AI adoption and onboard more use cases. This approach ensures that we balance speed with security and deliver scalable returns on investment (ROI).
Al Kaabi at TRS-24:
Women in ICT: Breaking Gender Barriers in the Industry
AI Empowering Highly Autonomous Networks
