e& and IBM announced a strategic collaboration at the World Economic Forum in Davos, aiming to leverage enterprise-grade agentic AI for governance, risk, and compliance (GRC). This initiative seeks to deploy IBM’s watsonx Orchestrate and watsonx.governance to create action-oriented agents that can integrate seamlessly with enterprise systems. The collaboration is particularly significant for telcos operating in the Middle East and Africa, addressing the growing regulatory complexity and operational scale that these companies face in a rapidly evolving market.
Telcos have long grappled with GRC issues, which have only intensified due to increasing regulatory demands across various jurisdictions. The partnership between e& and IBM targets this challenge directly by introducing AI that can not only respond to queries but also reason through regulatory tasks and execute them within defined guardrails. However, the real test lies in whether this technology can fulfill its promises without introducing new risks. In the realm of compliance, failures can lead to severe consequences, including missed deadlines and misinterpretations that expose companies to liability.
The partnership was unveiled at the World Economic Forum, marking a significant move for e&, the UAE-based technology group formerly known as Etisalat. With operations in 38 markets and a customer base exceeding 200 million, e& is positioning itself at the forefront of AI governance in the region. The initiative aims to transcend traditional chatbot capabilities by employing agentic AI, which is designed to reason and manage compliance workflows rather than merely retrieve information about them.
Hatem Dowidar, CEO of e&, emphasized the transformative nature of this collaboration, stating, “Our ambition is to move beyond isolated AI use cases toward enterprise-scale agentic AI that is trusted, governed, and deeply integrated into how the organization operates.” The goal is to embed intelligence directly into risk and compliance processes, facilitating faster decision-making and consistent policy interpretation.
At the heart of this solution is IBM’s watsonx Orchestrate platform, which boasts over 500 tools and customizable agents tailored to specific domains. The technology integrates seamlessly with IBM OpenPages and other components of the watsonx suite, including watsonx.governance, which e& had already adopted prior to this announcement. Notably, the platform’s hybrid model allows large language models to operate on customer-managed infrastructure, addressing concerns about data sovereignty and security—particularly relevant for telecom operators managing sensitive regulatory data.
IBM is keen to emphasize its “compliance by design” principles throughout this deployment, insuring that every AI-driven action is traceable, auditable, and explainable. Ana Paula Assis, IBM’s Senior Vice President for Europe, the Middle East, Africa, and Asia Pacific, highlighted the importance of governance and accountability as companies transition from experimental AI use to integrating it into their core operations. She stated, “Through our collaboration with e&, this proof of concept intends to demonstrate how agentic AI can be designed and validated for enterprise-scale use.”
Facilitated by IBM’s Client Engineering team, the proof of concept was developed in just eight weeks. While this rapid turnaround is commendable, it raises questions about the thoroughness of the testing phase against the complex scenarios that compliance environments often present.
Importantly, the system is structured to support human-led decision-making rather than allowing for fully autonomous AI actions. In high-stakes governance applications, where the stakes are significant and regulators demand human accountability, this distinction is critical. However, as the system evolves, there may be pressure to automate more aggressively, which could challenge the human oversight framework.
The compliance landscape for telecom operators is increasingly challenging and is unlikely to ease soon. Companies like e& navigate a myriad of regulatory frameworks across the Middle East and Africa, each with its own reporting requirements and enforcement mechanisms. Manual management of these obligations demands considerable resources and introduces a risk of inconsistent policy interpretation within the organization.
The deployment aims to enhance response times for policy and regulatory inquiries, with 24/7 self-service capabilities particularly beneficial for large-scale operations where compliance questions arise outside of standard business hours. This initiative also serves a competitive purpose, positioning e& as an early adopter of sophisticated AI governance in a sector where regulatory compliance can significantly influence market access and licensing opportunities.
This partnership reflects a broader trend in the telecom sector, where operators are beginning to adopt AI not just as a customer-facing tool but also as a means to automate internal processes. Such an approach marks a maturation in AI adoption, which carries significant implications, particularly in areas prone to errors.
Nonetheless, the telecom industry has seen numerous ambitious technology partnerships announced at high-profile venues that ultimately fell short of expectations. A proof of concept is merely a step toward full-scale implementation, and while an eight-week development timeline is impressive, it leaves questions about long-term reliability and handling edge cases unaddressed. As e& and IBM strive to mitigate potential pitfalls, the deployment of AI in these critical domains may create new failure modes even as it resolves existing challenges.
See also
OpenAI’s Rogue AI Safeguards: Decoding the 2025 Safety Revolution
US AI Developments in 2025 Set Stage for 2026 Compliance Challenges and Strategies
Trump Drafts Executive Order to Block State AI Regulations, Centralizing Authority Under Federal Control
California Court Rules AI Misuse Heightens Lawyer’s Responsibilities in Noland Case
Policymakers Urged to Establish Comprehensive Regulations for AI in Mental Health















































