As AI moves from experimentation into core business operations, organisations across the Middle East are discovering that trusted, timely and contextual data not simply sophisticated models is becoming the foundation for enterprise-scale AI.
Artificial intelligence is rapidly moving beyond the experimental phase across the Middle East. Governments, banks, healthcare providers, energy companies, manufacturers and retailers are increasingly deploying AI to improve decision-making, automate processes, strengthen customer engagement and unlock new sources of business value.
The region’s AI ambitions are reflected in its growing technology investments. According to IDC, AI spending across the Middle East is expected to exceed US$3 billion by 2026.
But spending on AI does not automatically translate into successful enterprise adoption.
For many organisations, the difficult part begins after the pilot. Moving an AI application from a controlled proof of concept into a production environment requires access to reliable data, real-time information, appropriate governance and an architecture that can support multiple use cases at scale.
The question is therefore shifting from which AI model to use to whether the organisation can trust the information that AI uses to make decisions.
Gabriele Obino, Vice President for Southern Europe and the Middle East at Denodo, identifies five common mistakes organisations make when scaling AI and explains why addressing the data foundation is essential to moving from experimentation to enterprise-wide adoption.
1. Treating AI as a Technology Project Instead of a Business Initiative
One of the most common mistakes is starting with the technology rather than the business problem.
Organisations can become focused on selecting models, platforms and infrastructure before establishing what they actually want AI to achieve.
But successful AI initiatives should begin with a clearly defined business outcome.
For a bank, the objective might be reducing fraud or improving customer service. For a healthcare organisation, it could involve improving operational efficiency or supporting clinical decision-making. A manufacturer might use AI to predict equipment failures, while a government agency could deploy it to improve citizen services.
The business objective determines what information AI needs, how that information should be accessed and how success should ultimately be measured.
Starting with the outcome also helps prevent organisations from deploying AI simply because the technology is available.
As AI moves into production, the focus needs to shift from “What can this model do?” to “What business problem can this capability solve reliably and repeatedly?”
“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.”
— Gabriele Obino, Vice President, Southern Europe and the Middle East, Denodo
2. Assuming More Data Automatically Means Better AI
The second mistake is equating data volume with data quality.
Large enterprises often have enormous quantities of information distributed across customer databases, ERP systems, data warehouses, data lakes, SaaS applications and operational platforms.
Yet more data does not necessarily produce better AI.
In many cases, organisations have multiple versions of the same customer, financial or operational information. Each may be technically accurate within its own system, but the information may not be equally relevant to the decision an AI application is being asked to make.
This creates a different challenge from traditional data management.
The issue is no longer simply collecting or storing more information. It is ensuring that AI can identify trusted, relevant and business-ready information.
This requires organisations to understand where critical data resides, how it is defined, who owns it and whether it is appropriate for a particular AI use case.
Without this context, even an advanced AI model can produce an answer that is technically plausible but commercially or operationally wrong.
3. Giving AI Yesterday’s Information to Solve Today’s Problems
Timeliness is becoming just as important as accuracy.
Many organisations continue to feed AI applications with copied, replicated or batch-processed information. That approach may have been sufficient for traditional reporting, but it becomes problematic when AI is being used to support real-time business decisions.
An AI system can produce an apparently accurate response while relying on information that is already outdated.
Consider a financial institution using AI to assess potential fraud. If the system is working with information that does not reflect the latest transaction activity, its recommendation may already be behind the situation it is intended to address.
The same principle applies to retail inventory, supply chains, energy operations, customer service and government services.
As AI becomes embedded into operational workflows, organisations increasingly need access to information that reflects the current state of the business.
The ability to connect AI with timely enterprise information can therefore become a competitive advantage.
4. Building Governance After AI Has Already Been Deployed
Governance is sometimes treated as something that can be added once an AI project has demonstrated value.
That approach can create significant problems.
As AI becomes connected to more enterprise systems and sensitive information, organisations need to establish data ownership, access controls, consistent definitions and accountability from the beginning.
Governance is particularly important in the Middle East, where organisations are navigating increasingly sophisticated requirements around data protection, sovereignty, security and responsible AI.
A lack of governance can also undermine confidence in AI outputs. Employees need to understand where information came from, whether it is current and whether they are authorised to use it.
The objective should therefore be to make governance part of the AI architecture rather than an administrative layer added later.
This includes defining who owns business data, establishing common terminology, controlling access and ensuring that information used by AI can be traced and understood.
For organisations scaling AI across departments and geographies, this becomes increasingly important.
5. Rebuilding the Data Foundation for Every AI Project
The fifth mistake is approaching every AI initiative as an entirely separate project.
An organisation may build one data integration for customer service, another for fraud detection and another for operational analytics. Over time, this creates duplicated information, additional integrations and competing definitions of the same business concepts.
The result is an AI environment that becomes increasingly complex as more use cases are introduced.
Instead, organisations should focus on creating reusable and governed data foundations that can support multiple AI applications.
A common data layer can help organisations access information across distributed environments without repeatedly moving or duplicating data.
This approach can make it easier to introduce new AI use cases while maintaining consistent definitions, governance and security.
For enterprises across the Middle East, this is particularly relevant as AI adoption expands from individual pilots into enterprise-wide programmes.
From AI Experimentation to AI Confidence
The common thread running through all five mistakes is data.
The AI model may be sophisticated, but its value ultimately depends on the information available to it.
“The conversation around enterprise AI has changed,” says Obino. “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.”
That change in mindset is significant.
The first phase of enterprise AI was largely about experimentation—testing models, identifying use cases and demonstrating what generative AI could accomplish.
The next phase is about operationalisation.
Organisations now need to determine how AI can be embedded into business processes while maintaining accuracy, security, governance and accountability.
This requires a shift from model-centric thinking to data-centric AI strategies.
AI needs access to information that is not only available, but also relevant to the context of the decision being made. It needs data that can be trusted and, increasingly, information that reflects the current state of the business.
Building an AI-Ready Data Architecture
For CIOs and data leaders, the implications extend beyond individual AI projects.
Organisations need a data architecture capable of connecting information across existing environments without creating unnecessary duplication and complexity.
Many enterprises will continue operating with a mixture of data warehouses, data lakes, lakehouses, cloud platforms, SaaS applications and legacy systems. The objective is not necessarily to replace all these environments.
Instead, organisations need ways to make distributed data accessible and useful to AI applications while maintaining governance and control.
This is where a modern data layer can play a role, enabling organisations to bring together information from different sources and provide AI applications with a more consistent view of business data.
For Middle East enterprises, the approach can also help address data sovereignty and compliance requirements by providing greater control over how information is accessed and used.
The Road Ahead for Middle East AI
The Middle East has no shortage of AI ambition. National strategies, digital transformation programmes and enterprise investments are creating strong momentum across the region.
The next challenge is turning that ambition into sustainable, enterprise-scale value.
That will require organisations to move beyond the assumption that the most advanced AI model automatically creates the best outcome.
Instead, success will increasingly depend on whether AI can access the right information at the right time, understand the business context behind that information and operate within clearly defined governance boundaries.
The organisations that make this transition successfully will be better positioned to scale AI beyond isolated pilots.
As Obino concludes, “When AI understands the context behind the data it is using, organisations can move from experimentation to confident, enterprise-wide adoption.”
For Middle East organisations, that may ultimately be the difference between simply deploying AI and genuinely transforming the enterprise with AI.
