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.
According to IDC, AI spending across the Middle East is expected to exceed US$3 billion by 2026. Yet many organisations are discovering that deploying AI is far easier than scaling it successfully across the enterprise.
The challenge is no longer selecting the right AI model. It is ensuring AI has access to trusted, relevant and timely information that reflects the reality of the business.
Gabriele Obino, Vice President for Southern Europe and the Middle East at Denodo, outlines five common mistakes organisations make as they move AI from experimentation into production.
1. Treating AI as a technology project instead of a business initiative
Many organisations begin with the AI model rather than the business outcome they want to achieve.
Whether the objective is reducing fraud, improving customer experiences, increasing operational efficiency or accelerating decision-making, organisations should first define the business problem they are trying to solve. This determines the information AI requires and how success should be measured.
2. Assuming more data automatically leads to better AI
AI does not simply need more data. It needs the right data.
Many organisations store multiple versions of customer, financial and operational information across different systems. While each version may be technically correct, not all of them are relevant to the decision AI is being asked to make.
The challenge is no longer data volume. It is helping AI identify information that is accurate, trusted and fit for purpose.
3. Giving AI yesterday’s information to solve today’s problems
Many AI applications continue to rely on copied, replicated or batch-processed information.
As a result, AI can generate answers that appear accurate but are based on information that is already out of date.
As organisations increasingly use AI in operational environments, access to real-time business information is becoming essential for producing reliable and relevant outcomes.
4. Building governance after AI has already been deployed
Governance is often viewed as a compliance requirement that can be addressed after deployment.
In reality, organisations need clear ownership of business data, consistent definitions and transparent access controls from the outset. Without them, it becomes difficult to ensure AI-generated outputs are explainable, trusted and aligned with evolving regulatory expectations.
5. Rebuilding the same data foundation for every AI project
Many organisations continue to approach AI one use case at a time, creating new integrations, duplicating information and redefining business terms for every initiative.
This slows deployment, increases complexity and creates inconsistent outcomes across the business.
Building reusable, governed data products and enabling access to distributed information through a common data foundation can help organisations scale AI more efficiently while maintaining trust in the information underpinning business decisions.
Gabriele Obino, Vice President for Southern Europe and the Middle East at Denodo, said:
“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.”
“The organisations that succeed will not necessarily be those using the most advanced AI models. They will be the ones that give AI access to live, trusted and business-ready information. When AI understands the context behind the data it is using, organisations can move from experimentation to confident, enterprise-wide adoption.”
About Denodo:
Denodo is the AI data layer company that powers trustworthy agents and applications. The award-winning Denodo Platform enables that layer, transforming enterprise data into reliable insights for analytics and self-service. Organizations worldwide use Denodo alongside their data lakehouses to deliver AI-ready, business-ready data in a fraction of the time, achieving up to 4x faster time-to-insight, 345% ROI, and 10x better performance.
