The investment playbook in the Gulf Cooperation Council is fundamentally changing. For decades, institutional investors relied on financial engineering and strategic leverage to create value. But elevated interest rates, localization mandates, and pressure to diversify away from oil have forced a reckoning. Today, the most successful GCC investors, sovereign wealth funds, and family offices are redefining value creation around a single capability: using artificial intelligence to scale revenue without proportionally scaling headcount or costs .

This shift represents a meaningful departure from how institutional investors have traditionally approached portfolio management. Rather than optimizing deal structures and capital stacks, they are now evaluating whether their portfolio companies can support autonomous, AI-driven workflows that deliver measurable returns on investment.

What Is Operational Alpha and Why Does It Matter to Investors?

IBM's research across GCC client engagements points to a consistent finding: organizations achieving the most durable results are not simply deploying AI tools. They are redesigning how they operate with AI at the center, automating high-value workflows, eliminating structural cost inefficiencies, and building systems that scale revenue without proportionally scaling headcount or fixed costs. IBM calls this capability "Operational Alpha," and it is becoming a central topic in portfolio management conversations across the region's sovereign wealth funds, private equity firms, and family office conglomerates .

The implications for deal economics are significant. IBM's data across client engagements shows that strategic AI orchestration is delivering between 10% and 25% EBITDA improvement. These gains come from automating high-value workflows across enterprise operations. For portfolio operators focused on earnings expansion, this shift is a direct and measurable lever .

How Are GCC Investors Evaluating AI Readiness in Portfolio Companies?

The diligence process itself is evolving in response to these dynamics. Increasingly, acquirers and investors are evaluating what might be called the intelligence layer of a target asset. This assessment reflects the degree to which its data, workflows, and decision-making processes are structured to support autonomous, scalable operations. Three factors have become prominent in this assessment :

• Data Readiness: Assets with fragmented or siloed data architectures face meaningful discounts because those structures cannot support the agentic workflows that drive Operational Alpha at scale.
• AI Governance: With Saudi Arabia's data residency framework under SDAIA now a regulatory baseline, transparent and auditable AI governance is no longer a differentiator but a prerequisite for a successful exit.
• Workflow Architecture: The ability to execute multi-step business processes with minimal human oversight, such as rerouting supply chains in response to disruption signals or conducting automated financial auditing cycles, is increasingly becoming a determinant of an asset's valuation ceiling.

This represents a fundamental shift in how institutional investors assess risk and opportunity. An asset's capacity to support agentic workflows, its data architecture, integration layer, and governance model are increasingly becoming determinants of its valuation ceiling .

From Generative AI to Agentic AI: What Changed in 2026?

The AI adoption conversation has shifted in a meaningful way. IBM's EMEA productivity study surveyed more than 3,500 senior business leaders across ten markets including the UAE and Saudi Arabia. In the UAE, 77% of senior leaders reported significant productivity improvements from AI deployment, well above the EMEA average of 66%. At the same time, 92% of leaders surveyed expected agentic AI specifically to deliver measurable ROI within two years .

The distinction between generative AI and agentic AI matters operationally. Generative AI assists humans in completing tasks. Agentic AI executes multi-step business processes with minimal human oversight. Examples include rerouting supply chains in response to disruption signals, conducting automated financial auditing cycles, and managing customer recovery workflows at a personalized level. For portfolio operators focused on EBITDA expansion, this shift is a direct and measurable lever .

How Can Investors De-Risk the AI Transition?

The most common barrier to AI adoption is not strategic intent but execution confidence. Leaders understand the opportunity. What creates hesitation is the absence of a proven, de-risked path to achieve it. IBM's answer to this challenge is what the company calls the "Client Zero" approach. Before recommending any AI or automation strategy to a client, IBM deploys it internally across its own USD 60 billion plus enterprise and validates the outcomes at scale .

The results from IBM's internal agentic AI journey are concrete. By the end of 2025, IBM unlocked USD 4.5 billion in productivity gains through internal AI orchestration. In finance and journal processing alone, the company achieved a 90% reduction in cycle time. AI agents now resolve 94% of HR inquiries and 86% of IT queries without human intervention, redirecting significant human capital toward higher-value activities .

For a GCC sovereign wealth fund or general partner evaluating a digital transition for a portfolio company, this framework provides something the market typically lacks: evidence-based execution at enterprise scale, with documented outcomes that translate directly into deal economics.

Why Does Building New Give GCC Enterprises a Competitive Edge?

One of the more underappreciated dynamics in the GCC's transformation story is the competitive advantage that comes from building new rather than modernizing legacy systems. Western enterprises are navigating decades of technical debt, fragmented data systems, patchwork infrastructure, and integration complexity that constrains how quickly AI can be deployed at scale. GCC enterprises, new entrants, and greenfield investments do not carry that burden. They can design AI-native operations from the outset, a capability that, once established, is genuinely difficult for incumbents to replicate .

Riyadh Air is the clearest illustration of what this looks like in practice. Announced at IBM Think Riyadh 2025 as the world's first AI-native airline, Riyadh Air was built from day one without legacy IT architecture. Working with IBM Consulting across 59 workstreams and more than 60 ecosystem collaborators, Riyadh Air deployed IBM watsonx Orchestrate to build agentic AI capabilities into its core operations .

These capabilities include a proactive guest concierge that anticipates and responds to traveler needs in real time. They also extend to an employee experience platform where AI agents handle HR self-service and crew enablement across a rapidly scaling workforce. ModelOps capabilities ensure that AI models remain accurate and compliant as the airline grows, preventing the performance drift that commonly stalls large-scale AI programs. The result is an enterprise that scales from day one on an AI-first architecture rather than retrofitting intelligence into systems designed for a different era .

Steps to Assess AI Readiness in Your Portfolio

• Audit Data Architecture: Evaluate whether portfolio companies have centralized, accessible data systems or fragmented silos that would require significant investment to support agentic workflows.
• Review AI Governance Frameworks: Assess compliance with regional data residency requirements and the transparency of AI decision-making processes to ensure regulatory readiness.
• Map High-Value Workflows: Identify business processes that consume significant labor or capital and could be automated to improve EBITDA margins.
• Benchmark Against Peers: Compare portfolio companies' AI maturity against regional and global competitors to identify gaps and opportunities for value creation.

The shift from financial engineering to operational architecture is not a temporary trend. It reflects a structural change in how institutional investors create value in an environment where traditional leverage has become less effective. For GCC investors, the question is no longer whether to invest in AI capabilities, but how quickly they can build the operational infrastructure to support them .