From Pilots To Production: How The Middle East And Global Enterprises Are Industrializing AI

With global spending on AI systems expected to surpass $2 trillion in 2026, the spotlight is firmly on why this year is shaping up as a defining moment for enterprise AI adoption. Organizations worldwide are moving beyond pilots and proof-of-concepts, embedding AI into core operations, navigating regulatory requirements, and industrializing agentic systems at scale. In regions such as the Middle East and India, adoption is accelerating rapidly. Finance, energy, government, and digital services are leading the charge, with India emerging as a major AI talent and execution hub, while the Middle East is driving large-scale, strategic implementation of sovereign models, national data platforms, and sector-specific initiatives.

In this interview, Farida Gibbs, CEO of Gibbs Consulting, explores the forces driving enterprise AI adoption, the sectors leading the way, and provides practical guidance for CEOs and CIOs seeking to balance innovation, governance, and long-term business transformation in an era of autonomous and agentic AI.

2026 As The Inflection Point: How The Middle East Is Moving AI from Pilots To Production Across Key Sectors

You argue 2026 will be a turning point for enterprise AI adoption—what key developments are driving this inflection point?

“2026/2027 marks the pivot point because regulatory obligations are becoming enforceable, sovereign deployment patterns are becoming implementable, and AI is finally being embedded into core business processes rather than just pilot programmes.”

On the Middle East specifically, Farida notes: “In the Middle East we are seeing organisations industrialising AI as part of platform modernisation, product-aligned delivery teams, and a shift from innovation labs to operational ownership, even if initially at the trustworthy data level.”

The region is moving from AI ambition to action, with governments and enterprises investing in sovereign compute, national data platforms, and sector-specific programmes that push AI from pilots into production. Companies are transitioning AI from innovation labs to operational teams, focusing on trustworthy data and platform modernization to accelerate adoption across key sectors.

The Middle East is making large investments in national AI strategies. What are the most impactful initiatives you’re seeing in that region?

“The initiatives showing the greatest impact are sovereign compute and models, national data platforms, and sector-specific adoption programmes led by governments in the Middle East.”

These initiatives enable local model training and deployment under national policy frameworks, reduce latency and cross-border data friction, and provide public and private actors with the infrastructure to move workloads from pilot environments into sustained production.

What industries or sectors are currently leading in AI maturity across these geographies, and why?

“IT infrastructure, banking, government, and energy are leading due to a focus on strong data foundations, ROI-driven use cases, and central mandates.”

The outlined sectors benefit from large, structured datasets, clear efficiency or revenue levers (fraud detection, grid optimization, citizen services), and often direct regulatory or ministerial sponsorship—conditions that make enterprise-grade AI adoption both practical and measurable.

Navigating Agentic AI: Leadership, Governance, And Operational Strategies For Safe Enterprise Adoption

Autonomous and agentic AI systems are gaining traction. What new challenges do these technologies pose for leadership and workforce planning?

“Agentic systems introduce new challenges around operational risk, workforce redesign, and the need for continuous oversight of systems that can independently act. However, the human in the loop is an essential component, as is auditable transparency.”

Commenting on what new roles, reporting lines, and accountability mechanisms CEOs should create to safely operationalize agentic AI, Farida explains: “Businesses should create clear AI product ownership, independent risk accountability, and formal change controls so autonomous systems can be deployed safely, at scale, with full traceability of decisions.”

This leads naturally to the broader question of balancing innovation with governance: How can enterprises balance AI innovation with governance, especially in regulated sectors like finance or government?

“Enterprises must innovate and automate quickly in low-risk areas while embedding auditability, decision traceability, and tiered governance for regulated use cases from day one.”

As companies prepare for large-scale AI implementation, what should be the top priorities for decision-makers over the next 12–24 months?

“Decision-makers should prioritise high-value workflows, enterprise AI control planes, trustworthy data foundations, and operating-model redesign over chasing the latest hype.”

Scaling AI With Confidence: Strategic Guidance For CEOs And CIOs

Gibbs Consulting advises clients on aligning AI strategy with business transformation goals. Farida Gibbs shares: “At Gibbs Consulting, we align AI programmes to business outcomes by designing and combining trusted data platforms, regulation-first architecture, and safely governed and traceable agentic automation.”

With the pace of AI evolution accelerating, many executives feel pressure to act quickly yet responsibly. What advice would you offer to CEOs and CIOs who feel overwhelmed but don’t want to fall behind?

“My advice is to focus less on hype and more on business automation—something we have been doing since the industrial revolution. Building durable enterprise capability based on trustworthy data, quality controls, and operating models is the foundation for this. AI reasoning should only be implemented where it provides a clear business benefit and never to make automated decisions without subject-matter experts in the loop. That way, organisations can scale AI with confidence.”

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