How to Make Data Work for Agentic AI in the GCC

By Tejas Mehta, Senior Vice President & General Manager, Middle East & Africa at Qlik

For decades, organizations have worked to use data to make better decisions and drive better outcomes. Data has become the lifeblood of business, and AI now has the power to unlock it in new ways. With AI adoption across GCC organizations surging from 62% in 2023 to 84% in 2025, the paradigm is shifting from dashboards and visual interfaces to AI-driven experiences.

But too much data is still stuck in silos, incomplete, and inaccurate. Many analytics workflows remain manual, which slows time to value, limits insight quality, and raises costs. This challenge is visible across the GCC, where rapid digital transformation agendas are generating vast volumes of data, but organizations still struggle to unify and operationalize it effectively.

A common misstep among organizations is assuming that more AI or better models alone will solve this problem. In reality, the gap is not in intelligence, but in how data, context, and workflows are connected. Without that foundation, even the most advanced AI will fall short of delivering meaningful business impact.

But what if AI could do more of the heavy lifting, safely and reliably?

That’s the promise of agentic AI, and it’s quickly becoming reality. Agentic AI can reason through multi-step problems, adapt its approach, and engage the right capabilities to achieve a goal with minimal human involvement. Done right, it accelerates insight, lowers costs, and allows teams to focus more on running the business rather than managing manual processes.

Rethinking AI in Practice

Today, we are seeing the emergence of AI systems capable of handling structured analytics, unstructured knowledge, anomaly detection, and decision support, all within a unified experience. More importantly, these systems are becoming interoperable, allowing organizations to integrate AI into existing tools and workflows rather than replacing them entirely.

This flexibility is crucial in the GCC, where enterprises often operate across hybrid environments and must balance innovation with governance, compliance, and data sovereignty requirements.

Overall, there are effectively two entry points into this new AI paradigm:

First, embedded AI experiences within enterprise platforms are enabling faster, more contextual insights, grounded in trusted data and existing business logic.

Second, open integration layers are allowing organizations to connect AI capabilities into the assistants and environments they already use, ensuring flexibility while maintaining governance and control.

Making Data Work for AI

To move from fragmented data and isolated AI initiatives to true agentic systems, organizations need a clear operating model that connects data, insights, and action. This is where three practical priorities come into focus:

• Achieve AI: Organizations need trusted, explainable insights embedded directly into workflows, while maintaining governance and context.
• Accelerate AI: Many enterprises have already invested heavily in data models and business logic. The focus now is on building on that foundation to prove value quickly and scale efficiently.
• Adapt AI: The future will not belong to a single assistant, vendor, or ecosystem. Interoperability will define success, allowing organizations to evolve without starting over.

Across the GCC, this adaptability is especially important as governments and enterprises push for AI leadership while maintaining flexibility to adopt global innovations.

Lessons from Early Adoption

Early adopters of agentic AI are already demonstrating tangible value.

A commercial leader can ask what changed in renewals this quarter, and immediately see the drivers, segments, and recommended next steps in one place.

An operations team can move from identifying a spike in service issues to understanding where it is concentrated, what factors are correlated, and what actions to prioritize, without switching between multiple tools.

A finance team can reconcile narrative and numbers while maintaining traceability, ensuring every insight is backed by clear evidence.

These use cases are highly relevant in the GCC, where sectors such as banking, telecom, and government are under increasing pressure to deliver faster, data-driven decisions while maintaining transparency and accountability.

A Regional Perspective on What Comes Next

AI conversation is moving beyond models. The real challenge lies in making AI dependable, explainable, and useful within the flow of work.

If organizations cannot connect analytics with knowledge, they don’t have agentic AI. They simply have automation without accountability.

For the GCC, where trust, governance, and strategic national initiatives play a central role, this distinction is critical. AI must not only be powerful; it must be responsible, transparent, and aligned with long-term economic visions.

Ultimately, the opportunity is clear: organizations that can successfully unify their data, embed intelligence into everyday workflows, and enable AI to act with context and accountability will define the next era of digital leadership in the region.