- IBM and E& announced a strategic collaboration at the World Economic Forum to deploy enterprise‑grade agentic AI for governance, risk, and compliance.
- The initiative demonstrates agentic AI operating at scale and embedded in core systems, raising questions about trust, accountability, and oversight.
What happened: enterprise‑grade agentic AI unveiled at the World Economic Forum
Global technology group E& (a telecommunications company formerly known as Etisalat Group) and IBM (NYSE: IBM) have unveiled a strategic collaboration to develop and deploy enterprise‑grade agentic AI focused on policy, risk, and compliance workflows. The announcement was made at the World Economic Forum Annual Meeting in Davos, Switzerland, on 19 January 2026.
Unlike traditional natural language processing (NLP)–based chatbots, agentic AI is designed to reason, act, and integrate autonomously within core enterprise systems. The solution is built on IBM watsonx Orchestrate and integrated with IBM OpenPages and the broader watsonx portfolio to deliver traceable, governance‑aligned AI responses for legal, regulatory, and compliance information.
A proof of concept developed by IBM, GBM (Gulf Business Machines), and e& within eight weeks showed the capability of agentic AI to operate at enterprise scale under real‑world conditions, helping employees and auditors interpret information quickly and consistently with governance requirements.
IBM’s Client Engineering team led the design and integration work, emphasizing the system’s potential to automate tasks, reduce response times, and provide 24/7 self‑service access to compliance resources. The deployment also aligns with watsonx.governance, enabling AI reasoning and task orchestration under enterprise controls.
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Why it’s important
This collaboration represents a notable shift in how organizations adopt advanced AI agents. While AI research often focuses on experimental or proof‑of‑concept work, this initiative embeds agentic AI directly into mission‑critical workflows such as governance, risk, and compliance—areas traditionally reliant on human judgement and oversight.
Agentic AI is defined by its ability to reason and act autonomously, not just respond to prompts. These systems extend beyond simple automation, incorporating decision‑making and task orchestration without constant human input. This transition suggests that AI agents are maturing beyond lab environments into practical, enterprise‑grade applications, representing what many see as the next evolution after generative AI models. Critics, however, caution that embedding AI into governance systems raises significant questions about accountability, auditability, and ethical oversight.
With governance and compliance work subjected to strict legal and regulatory scrutiny, it remains unclear whether autonomous AI can consistently uphold transparency and explainability in high‑stakes decision contexts. The proof of concept demonstrated scalability, but real‑world adoption will require robust governance frameworks to ensure that agentic systems do not inadvertently undermine the very compliance regimes they are designed to support.
Enterprise agentic AI also signals broader industry trends. Organizations are moving from isolated AI uses—such as chatbots or narrow automation—to systems where AI agents are deeply embedded in core operations. This may accelerate efficiency in areas such as regulatory interpretation, risk analysis, and policy enforcement, but it also raises questions about job displacement, ethical safeguards, and trust in autonomous systems.
Ultimately, the IBM and E& partnership illustrates both the technological potential of agentic AI and the organizational challenges it will face. As more enterprises explore similar deployments, a broader dialogue about governance, ethics, and human‑in‑the‑loop oversight will be necessary to ensure that agentic AI is both effective and responsible.
