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.

Earn AI grows into a team of specialised AI agents that help property managers scale operations, automate renewals and embed financial services while keeping people in control of every decision.

Dubai, United Arab Emirates: Rentify, a cutting-edge fintech & proptech startup, announced the launch of Renewal Command Center, the third specialised AI agent within Earn AI, its AI-native operating system for rental operations.

Today, Earn AI supports property portfolios representing more than AED 22 billion (USD 6 billion) in real estate assets and over AED 1.3 billion in annual rental value, demonstrating how some of the region’s largest property portfolios are already adopting AI-native rental operations.

Unlike traditional software that only records information, Earn AI detects what needs attention, prepares the required work and coordinates execution. Property managers can upload spreadsheets, lease agreements and fragmented portfolio information and see it transformed into a live, structured portfolio view in under 60 seconds.

Each specialised Earn AI agent has a clearly defined responsibility while operating within approval workflows that ensure property managers remain in complete control of every important decision.

Nearly 80% residential tenants renew their tenancy agreements each year, making renewals one of the largest recurring operational responsibilities for property managers. Across the real estate industry in the UAE, experienced property managers are expected to oversee larger portfolios while managing renewals, collections, tenant onboarding, documentation, compliance and financial coordination across multiple disconnected systems. As portfolios grow, operational work grows faster than teams.

Through Rentify’s product Rent Shield in partnership with YallaCompare, landlords and tenants can seamlessly access rental insurance during the renewal process. Integration with Spare, a leading open finance provider, enables Open Finance-powered affordability assessments and Pay-by-Bank payment orchestration that supports Rentify’s existing digital payment infrastructure. The platform will also make rent renewal seamless with embedded financial services.

Rajneel Kumar, Co-founder, Rentify, said, “We built Earn AI around a simple idea that technology should handle the operational heavy lifting so property managers can focus on outcomes for landlords and tenants. For decades, the only way to scale was to hire more people, but AI changes that. Renewal Command Center takes that idea into one of the most repetitive workflows in property management, connecting renewals, tenant intelligence, Open Banking, embedded insurance and payments in a single flow, with every critical decision still made by a person.”

Rashed Hareb, Co-founder & CEO, Rentify, said, “Property managers don’t need more dashboards. They need greater operational capacity. Over the next decade, every major operational function  within the property management business will gain a specialised AI counterpart. Our role is to build an operating system that enables those teams to work together seamlessly.”

Rentify believes the future of property management is not about replacing people with artificial intelligence. It is about giving every property manager a specialised AI workforce that quietly handles repetitive operational work, allowing people to focus on relationships, judgement and portfolio growth.

Alteryx, Inc., an AI-ready data and analytics company, today released its “2026 IT Leader Research: The State of AI Ownership, Agents, and ROI” report, revealing that organizations are entering a new phase of AI maturity where success is no longer defined by AI adoption alone, but by the ability to translate AI investment into measurable business outcomes.

Among 1,400 IT leaders surveyed globally, 80% expect AI spending to increase over the next two years, while 69% report moderate or significant ROI from their AI investments. Yet despite growing investment and early returns, more than half (53%) say their organization struggles to translate business context into AI systems and workflows. At the same time, 77% agree business context is critical to producing accurate and relevant AI outputs, underscoring a widening gap between AI ambition and operational readiness.

Key Findings at a Glance:

• 80% of organizations expect AI spending to increase over the next two years.
• 69% report moderate or significant ROI from their AI investments.
• 93% of IT leaders are confident agentic AI could deliver measurable ROI for their enterprise within the next two years.
• 77% agree business context is critical to accurate, relevant AI outputs.
• 53% say their organization struggles to translate business context into AI systems and workflows.
• Only 18% of organizations have achieved fully self-service access to cloud data for business users.

AI Investment Is Accelerating. Expectations Are Rising Even Faster.

AI investment continues to accelerate as organizations move beyond experimentation toward enterprise-scale deployment. Eighty percent of organizations expect AI spending to increase over the next two years across infrastructure, workflow automation, data platforms, and governance.

With that investment comes greater accountability. Technology leaders are increasingly measuring AI success through productivity improvements (53%), cost reduction (45%), and revenue growth or broader business impact (39%). More than one-third (35%) say the ability to measure AI ROI will be one of the capabilities that most distinguishes technology leaders from their peers.

The findings suggest AI has entered a new phase where organizations are no longer asking whether AI works. They are asking whether it consistently delivers measurable business value.

Business Context Is Emerging as a Barrier to Enterprise AI

As AI becomes embedded in everyday business processes, organizations are discovering that models alone are not enough. According to the research, 77% of IT leaders agree that business context, including the rules, definitions, and operational knowledge that shape how their organizations operate, is essential for producing accurate and relevant AI outputs. Yet more than half (53%) say their organization struggles to translate that business context into the systems and workflows AI depends on.

The challenge isn’t simply giving AI more data. It’s giving AI the business logic that tells it how the business actually works. While AI can analyze information and generate responses, it cannot consistently apply company-specific rules, policies, thresholds, and decision criteria unless that knowledge is built into the workflows it uses to make decisions.

Much of that business logic still lives in spreadsheets, macros, documentation, email threads, and the expertise of the people closest to the work. A financial forecast depends on assumptions. A tax process depends on rules and exceptions. A supply chain decision depends on inventory thresholds and timing. AI should be grounded in the rules and logic the business already trusts.

“Our research highlights a growing gap between AI ambition and enterprise-scale execution,” said Andy MacMillan, CEO of Alteryx. “Organizations have proven they’re willing to invest in AI, and many are already seeing returns. But scaling AI requires more than better models. It requires making the business knowledge people use every day available to the systems making decisions.”

Limited Data Access Continues to Slow AI Adoption

Despite years of investment in data democratization, only 18% of organizations report that business users have fully self-service access to cloud data. Most organizations continue to rely on IT or data teams for routine data access and analytics, with 38% describing a mixed model and 15% saying business users remain largely dependent on technical teams.

The findings suggest this dependency extends beyond productivity. The employees with the deepest understanding of how the business operates are often the same people waiting on IT to access the data needed to build, validate, and improve AI workflows. That disconnect makes it more difficult to embed business context into enterprise AI systems, limiting AI’s ability to generate meaningful business outcomes.

Enterprise AI Works Best When IT and the Business Work Together

The research also points to a growing consensus that AI performs best when technical expertise and business expertise work together. Two-thirds of technology leaders say AI and agent-based systems are most productive when managed within the line of business. Additionally, 71% believe AI initiatives are most successful when IT and business teams collaborate closely.

This sentiment correlates with past Alteryx research. Yet, strategy (37%) and delivery (38%) remain concentrated within IT, while business teams are most often responsible for defining requirements (30%). That disconnect between where business knowledge resides and where AI systems are built continues to slow enterprise AI adoption.

MacMillan concluded, “The organizations creating lasting value from AI will be the ones that operationalize their business logic so it becomes visible, governed, repeatable, and ready for AI.”

To learn more and explore the full findings, download the “2026 IT Leader Research: The State of AI Ownership, Agents, and ROI” report.