Over the past year, I’ve had countless conversations with founders, CTOs, and board members across the Middle East about their AI ambitions. The story is often the same — they know AI is here, they know they can’t afford to ignore it, and yet they don’t know where to start. Most organizations are stuck in what I call the POC quagmire: an endless cycle of pilots and proofs-of-concept that look promising but never reach production.
The frustration is palpable. Leaders are being bombarded daily by consulting firms, vendors, and “AI transformation specialists” promising instant readiness. Slide decks are flying faster than software is being written. But under the surface, most companies don’t have the foundational data infrastructure, engineering practices, or internal capabilities required to scale even the most basic AI workloads.
I’ve seen this movie before — during my time leading engineering transformations across global tech companies. The pattern repeats itself every time there’s a technological inflection point. Whether it was cloud computing, mobile apps, or data analytics — the early rush always attracts big promises before the plumbing is ready. AI is no different, except the gap between ambition and readiness is even wider this time.
Let’s be clear — you can’t build skyscrapers without laying the foundation. AI infrastructure isn’t glamorous. It’s the digital equivalent of sewage systems, power lines, and roads — invisible, but essential. In too many boardrooms, however, this groundwork is seen as an afterthought rather than a strategic asset. Data pipelines are fragmented, governance is reactive, and engineering workflows are still designed for yesterday’s problems.
In previous chapters of the Ikigai Series, I’ve written about three stages of AI maturity:
AI-Aware – organizations beginning to understand what AI can do for them, exploring ideas and concepts.
AI-Ready – those that have built the data and infrastructure foundations necessary for consistent experimentation.
AI-Enabled – companies where AI is deeply embedded in everyday processes and decisions.
Most companies in the Middle East — even the most ambitious ones — are still hovering between AI-Aware and AI-Ready. They have the will, they have funding, and they have external partners. But what they lack is internal fluency — teams who understand how to design data architectures for AI, how to monitor and govern models responsibly, and how to integrate insights back into real business processes.
This is where leadership courage becomes critical. AI transformation is not something that can be outsourced. You can hire vendors to help you, but you cannot outsource your organizational learning. The companies that will lead the next decade of the Middle East’s digital economy will be those that invest in their own data maturity — that train their engineers, rethink their platform design, and establish internal AI stewardship that bridges business and technology.
As I’ve written before, the hardest part of AI is not the algorithm — it’s the alignment. Leadership alignment, data alignment, process alignment. When these three come together, technology becomes a force multiplier. Without them, AI remains a headline, not a capability.
The Ikigai Series is evolving along with this movement. Now that my personal journey building AI at Property Finder has reached its conclusion, this platform will focus on something much broader: how leaders across the Middle East can build the next generation of AI infrastructure and talent ecosystems. Through Be Human Capital and our ecosystem of founders, investors, and technologists, my goal is to help companies move past the noise and focus on the fundamentals that matter.
In the coming chapters, we’ll go deeper into practical frameworks — from setting up scalable AI data pipelines to designing responsible governance systems and redefining organizational structures that enable sustainable AI innovation. This is where the real transformation happens — not in the demos, but in the discipline.
The Middle East has all the right ingredients — capital, ambition, and visionary leadership. What it needs now is patience, persistence, and a clear blueprint for AI maturity.
Let’s build that together.
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