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.
Gabriele Obino, Vice President for Southern Europe and the Middle East at Denodo, outlined five common errors that organisations make during this transition.
Treating scaling AI as a technology project
Many organisations begin their journey by focusing on the technology model rather than the specific business outcome they want to achieve. Whether the objective is reducing fraud, improving customer experiences, or accelerating decision-making, businesses must first define the problem. This definition determines the exact information the system requires and establishes how to measure success.
Assuming more data means better results
An enterprise does not simply need a larger volume of data to succeed. Instead, it requires the correct data. Many organisations store multiple versions of customer, financial, and operational information across separate systems. While each version may be technically accurate, not all of them are relevant to the specific decisions the system must make.
Using outdated information for real-time problems
Many applications continue to rely on copied, replicated, or batch-processed information. Consequently, the system may generate answers that appear correct but are based on outdated facts. As businesses increasingly use these tools in operational environments, access to real-time business information is essential for producing reliable outcomes.
Delaying governance until after deployment
Governance is often treated as a compliance requirement to be addressed after a system goes live. In reality, organisations require clear ownership of business data, consistent definitions, and transparent access controls from the very beginning. Without these measures, it is difficult to ensure that outputs are explainable, trusted, and aligned with regulatory expectations.
Rebuilding the data foundation for every project
Many businesses approach each use case individually, creating new integrations and duplicating information for every initiative. This practice slows down deployment and increases operational complexity. Building reusable, governed data products through a common data foundation helps organisations perform scaling AI processes more efficiently.
The importance of trust in enterprise data
The conversation around enterprise technology has shifted from model selection to data trust. Organisations must ensure their systems can access live, business-ready information to support critical decisions.
“The conversation around enterprise AI has changed. Two years ago, organisations were asking which model they should adopt. Today, they are asking whether they can trust AI to support real business decisions.”
Gabriele Obino, Vice President for Southern Europe and the Middle East at Denodo
According to Denodo, successful scaling AI strategies require giving systems direct access to live, trusted, and business-ready information. When the system understands the context behind the data, businesses can transition from experimentation to confident adoption across the economy.
