Denodo identifies five AI scaling pitfalls for Middle East enterprises

Data virtualisation and AI data layer vendor Denodo has published a practitioner-oriented advisory identifying five recurring mistakes enterprises in the Middle East make when attempting to scale artificial intelligence beyond pilot projects. The guidance comes from Gabriele Obino, Vice President for Southern Europe and the Middle East at Denodo, and arrives as regional AI investment accelerates sharply.

IDC forecasts that AI spending across the Middle East will exceed US$3 billion by 2026, according to figures cited by Denodo. Despite that capital commitment, the company says a significant number of organisations are discovering that production-scale AI deployment exposes structural weaknesses in how their data is organised, governed and accessed.

The five failure modes

Obino's framework groups the most common errors into five categories. The first is treating AI as a technology initiative rather than a business-led one, a pattern where teams optimise model selection before defining the business outcome they actually want to achieve. The second mistake is the assumption that data volume alone improves AI quality. Denodo argues that relevance and trustworthiness matter more than scale, particularly where organisations hold multiple conflicting versions of the same customer or financial record across different systems.

A third error is feeding AI on stale information. Many production deployments still rely on batch-processed or replicated data, which can produce outputs that appear plausible but reflect a business reality that no longer exists. The fourth pitfall is bolting governance on after deployment rather than embedding ownership, consistent definitions and access controls from the outset. Without pre-deployment governance, Denodo argues, organisations struggle to make AI-generated outputs explainable under increasingly demanding regulatory expectations. The fifth mistake is rebuilding a bespoke data foundation for every individual AI use case, which compounds complexity and creates inconsistent results across the business.

Obino said: "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."

Market context and competitive landscape

Denodo operates in the data integration and virtualisation market, where it competes with a range of vendors offering data fabric, data mesh and semantic-layer approaches, including Informatica, Talend (now part of Qlik), and cloud-native offerings from the major hyperscalers. The advisory is consistent with a broader industry shift: as generative AI and retrieval-augmented generation workloads move into production, the data pipeline underneath the model has become as commercially significant as the model itself.

The Middle East context adds a regional dimension. Governments across the Gulf Cooperation Council have made AI central to their economic diversification agendas, with Saudi Arabia's Vision 2030 and the UAE's AI Strategy 2031 both driving large-scale public and private AI investment. Organisations operating across those markets face a patchwork of emerging data-localisation and AI-governance requirements, making the governance points in Denodo's advisory particularly timely.

Regulatory read-across

The EU AI Act, which carries extraterritorial implications for multinational organisations, and evolving Gulf-region data protection frameworks such as the UAE Personal Data Protection Law are both increasing pressure on enterprises to demonstrate explainability and auditability in AI-generated decisions. Denodo's emphasis on building governance before deployment aligns with the direction regulators in both regions are taking, even if the advisory does not reference specific frameworks by name.

The company positions its platform as a reusable data layer that sits across distributed enterprise data sources, enabling AI applications to draw on governed, real-time data without duplicating it. Whether that architectural argument converts to pipeline wins in a competitive regional market will depend on named enterprise references, which the company did not provide in this release.