Middle East AI scaling: Five mistakes organisations must avoid

AI investment across the Gulf is accelerating, but data readiness is emerging as the defining factor between successful enterprise adoption and stalled deployments. IDC forecasts AI spending across the Middle East will surpass $3bn by 2026, driven by programmes such as Saudi Arabia’s Vision 2030, the UAE National AI Strategy, sovereign AI investments and rapid expansion of hyperscale datacentre infrastructure. As governments, banks, healthcare providers, energy companies and manufacturers move beyond proofs of concept, many organisations face the challenge of scaling AI from pilots into trusted, business‑critical systems.

“The conversation around enterprise AI has changed,” says Gabriele Obino, vice‑president for Southern Europe and the Middle East at Denodo. “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.”

Obino and other practitioners are identifying a consistent set of barriers that prevent AI projects from reaching enterprise scale. The article highlights five common mistakes that organisations in the region must avoid if they want to move beyond experimentation: treating AI as a technology project rather than a business transformation, conflating data volume with data quality, relying on replicated or batch data for live decisions, postponing governance until after deployment, and building isolated use cases that duplicate datasets and business definitions.

• Treating AI as a technology initiative: Many organisations begin by evaluating models and tools before defining the business problem. The piece stresses that business outcomes — whether reducing fraud, improving customer experience, optimising operations or accelerating decision‑making — should determine AI strategy and success metrics.
• Assuming more data means better AI: Rapid growth of enterprise data has led to the misconception that ingesting larger volumes automatically yields better models. Instead, AI needs relevant, trustworthy and fit‑for‑purpose data; multiple versions of customer, financial and operational records can undermine decision accuracy.
• Using stale or batch data for live decisions: Traditional analytics tolerated delays, but AI increasingly supports live business processes. When models rely on replicated or out‑of‑date information they can produce plausible but incorrect outputs, a critical risk for customer service, financial services and healthcare.
• Postponing governance: As Gulf governments roll out AI policies and data protection frameworks, governance is becoming strategic rather than merely compliance. Without clear data ownership, consistent business definitions and transparent access controls, organisations may struggle to explain AI decisions and maintain trust.
• Building isolated use cases: Rapid, siloed deployments can accelerate early wins but create long‑term technical debt. The article recommends creating reusable, governed data products and a common data foundation to scale AI efficiently across departments.

Looking ahead, the region’s large public and private investments — including references to Saudi Arabia’s multibillion‑dollar AI programmes and the UAE’s sovereign activity — will continue to fuel adoption. Yet success will hinge less on model sophistication than on the ability to supply AI with live, trusted and business‑ready information. “The organisations that succeed will not necessarily be those using the most advanced AI models,” Obino warns. “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.”