By Tareq Masoud, Country Manager, UAE, Snowflake
As AI capabilities advance, leaders across business and government in the Middle East are shifting from exploration to execution, racing to unlock value from data sources that were previously out of reach. AI is expected to influence the core architecture of how organizations operate, from customer satisfaction to product innovation. The UAE is projected to gain $96 billion from AI by 2030, and in Dubai, more than 100 high-impact AI use-cases across urban planning, healthcare, procurement, and other sectors have been developed or are in development. In Saudi Arabia, the government has committed $100 billion to developing its AI ecosystem in order to deliver more citizen-friendly public services across healthcare, education, and social sectors.
Earlier this year, 69% of local organizations committed to increasing investment in AI, driving adoption ahead of many global markets. Such momentum is setting the stage for a new operating model that is powered by AI and anchored in data. Against this backdrop, several AI trends will strongly influence organizations in the coming year.
AI change management becomes CEO priority
The most complex part of adopting AI will be the human transition. As enterprises introduce AI copilots and agents across functions, the challenge shifts from building technology to redesigning the way people interact with it. Reskilling, trust, clear lines of accountability, and revised incentives become issues that leadership must address directly.
Employees will need guidance on when to rely on AI, when to intervene, and how to maintain responsibility for decisions supported by automated systems. This calls for structured change management rather than ad hoc training. Organizations that treat AI as a behavioural and cultural shift will drive stronger adoption and reduce friction across teams, evolving change management to a CEO-level priority.
CDOs evolve into enterprise AI COOs
AI agents only perform well when the data beneath them is accurate, governed, trusted, and available. This reality is transforming the Chief Data Officer into a more front-line role. Today’s CDO is expected to run the enterprise’s AI capability, ensure the reliability of outputs, and manage the governance frameworks that protect customers and revenue.
As AI becomes essential for business survival, the emerging AI COO role will balance innovation velocity with foundational investments, design the organizational structures for AI success, and manage the internal roadmap. Snowflake research shows that 92% of early adopters report ROI from AI investments, highlighting how strong data leadership drives measurable value.
AI Quality Control becomes essential
As organizations move beyond AI experimentation, validation becomes the priority. By 2026, enterprises will develop dedicated AI Quality Control (AI QC) teams responsible for accuracy, consistency, and alignment with business objectives.
The stakes are higher than in traditional analytics. Poor data quality no longer affects reports alone; it drives flawed decisions, erodes customer trust, and hits revenue. AI QC functions will define launch thresholds for internal agents, run continuous evaluation cycles, and monitor performance with the same rigor applied to core business systems. This will position AI QC into the heart of enterprise AI to avoid reputational risk and ensure that AI delivers durable benefits rather than unpredictable results.
Analytics engineering anchors AI success
Many organizations are accelerating AI deployment without resolving underlying inconsistencies in their data environments. Without a shared understanding of data, organizations risk creating armies of AI agents with conflicting views of reality—an automated chaos that undermines trust.
The Analytics Engineer will play a pivotal role in teaching AI to speak the language of the business. Their focus is the semantic layer: a shared map of core business definitions that gives AI meaningful context. Concepts such as customer lifetime value or net retention require precise definitions before AI can improve them. By establishing a common language for data, Analytics Engineers ensure that every model, dashboard, and decision-maker works from a single, trusted source of truth. This groundwork enables AI to scale responsibly
AI-ready talent drives advantage
The region’s AI momentum is backed by national programmes developing homegrown talent and digital infrastructure. AI’s contribution to the Middle East economy is expected to grow between 20% and 34% annually, adding $320 billion to the GDP by 2030. This trajectory increases demand for specialized roles that support AI operationalization.
Organizations will compete for skills that did not exist a few years ago: governance specialists, AI auditors, prompt engineers, and AI product owners. Companies that invest early in the people and processes that turn data into a reliable, shared reality will be better positioned to unlock the true potential of AI at scale.
While AI is redefining how organizations work, how decisions are made, and how value is created, none of this can be possible without a strong data foundation. Modern cloud data platforms provide businesses the ability to harness structured and unstructured data that previously sat unused at scale. Enterprises that succeed will combine trustworthy data with skilled teams and disciplined governance to convert AI’s potential into results and build operating models designed for the next decade of growth.
