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.

Entrepreneurs are often encouraged to move quickly when bringing ideas to life, responding to market shifts and pursuing new opportunities. While that urgency can help a young company gain momentum, applying a move-fast mindset to every decision can create weaknesses that become harder and more expensive to fix over time.

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.

AI tools have become more affordable, accessible and capable, prompting businesses across industries to explore how they can improve productivity, decision-making and customer experiences. But when adoption moves faster than a company’s policies, expertise and oversight, AI can introduce risks that leaders may not recognize until problems surface.

Business transformation can help companies adapt to new technologies, changing customer expectations and evolving market conditions. But completing projects, introducing new tools or hitting implementation milestones doesn’t necessarily mean an initiative has improved performance. Without a clear connection to business results, even well-executed efforts can consume time and resources without delivering meaningful value.

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.