In the Media

Artificial intelligence is rapidly moving from pilot projects into core business operations across the Middle East. From customer service and financial services to healthcare, energy, manufacturing and government, organisations are increasingly looking to AI to improve efficiency, decision-making and customer experiences.

Middle East organisations face significant challenges when scaling AI across their business operations. According to data from IDC, spending on artificial intelligence in the region is expected to exceed US$3 billion by 2026. However, deploying these systems in pilot projects is often much easier than expanding them across an entire enterprise. The primary challenge is no longer selecting a model, but ensuring the system has access to trusted, relevant, and timely information.

For years, the insurance industry has invested heavily in artificial intelligence (AI). Underwriters gained predictive models, claims teams deployed automation, contact centers introduced conversational AI, and fraud teams expanded analytics capabilities. Despite the investment, many insurers remain stuck in a familiar position: AI pilots are growing, but operational transformation is lagging.

As agencies scale AI initiatives, that distinction is becoming a crucial but often misunderstood factor in successful modernization. The real modernization challenge is creating an AI Data Layer that can provide Active Context: live, governed, and semantically trusted data from across the enterprise. 

Denodo has announced the release of Denodo Platform 9.5, introducing new capabilities designed to provide AI agents with richer enterprise context, trusted business semantics and governed access to live data. The latest version strengthens the company’s AI data layer, helping organisations improve the accuracy of AI, analytics and self-service data delivery.

Discover why insurers' AI investments underperform. Learn how data readiness and governance determine AI success in claims, underwriting, and fraud detection.

Without the ability to discover, integrate, and leverage data across the enterprise, even the most advanced AI agents struggle to deliver meaningful outcomes.

The White House’s National Security Presidential Memorandum, NSPM-12 is explicitly focused on cybersecurity governance for national security systems, but many of its requirements depend on agencies having trusted, governed, and timely access to data about systems, policies, incidents, metrics, risks, and operational responsibilities. To implement the memorandum effectively, agencies need more than isolated security controls; they need a governed data foundation that helps them understand their environment, coordinate across agencies and mission partners, enforce policy consistently, and support mission-critical decisions.

As enterprises move beyond generative AI towards autonomous AI agents, Kevin Bohan, Director of Product Marketing, Denodo, says success will depend less on model performance and more on providing trusted, governed and live business context that enables AI to operate safely at enterprise scale.

Artificial intelligence has entered a new phase: It’s no longer just chatbots answering questions. It’s moved towards autonomous agents that take action. Agentic AI systems can perceive conditions, decide what to do next, and execute tasks across enterprise systems. This move from insight to action is what makes AI transformational – but it’s also what makes it risky. When AI moves from answering to doing, the trust bar is raised significantly.