Artificial intelligence has moved rapidly from experimentation to expectation across the Gulf. Governments have embedded it into national strategies, boards have elevated it to a standing agenda item, and enterprises across sectors are deploying AI tools in daily operations. Yet beneath this momentum lies a quieter, less discussed reality – a significant proportion of AI initiatives are failing to progress beyond early trials.
Last year, a survey found that while 84 percent of organisations had adopted AI in at least one business function across the GCC, only a minority have succeeded in scaling these systems across the enterprise or translating them into measurable business value. This gap between adoption and impact is becoming one of the defining challenges of the region’s digital transformation.
Many organisations are spending countless millions on AI initiatives, racing to modernise operations, enhance productivity and accelerate growth. And yet, for all this momentum, returns have been uneven and failure rates for early AI pilots remain staggeringly high. One recent report found that 95 percent of AI pilots globally flat-out fail. This global pattern is reflected regionally as well. In the GCC, only 31 percent of organisations report having reached a level of AI maturity where initiatives are being scaled or fully deployed across the enterprise.
Despite this, investments in AI continue unabated. Why? Because AI represents more than automation. It represents a strategic advantage for the GCC countries. However, buried inside that promise is a paradox. The very technology designed to simplify work is also introducing unprecedented operational complexity.
AI does not live in isolation. It runs inside an already-dense digital ecosystem. Today, most network traffic is not driven by people but is, in fact, driven by systems talking to systems. AI adds a powerful new layer to that environment, intensifying the demand for data, compute, and connectivity. As agentic AI systems take on independent decision-making, that pressure will only grow. Managing that risk depends on observability.
AI failures are often the result of infrastructure problems
Most AI initiatives do not fail because the models are wrong. They fail because of one invisible failure point in a long, interconnected digital chain that brings the entire system down. Because dependencies between systems and applications are often buried within services and APIs, it becomes nearly impossible to determine what went wrong.
Without high-definition visibility into what is actually happening, leaders are left navigating one of the most complex transformations in business history with partial information at best and blind faith at worst.
What’s more, when an AI initiative stumbles, the consequences extend far beyond the technology team. The reputational risk lands squarely on the shoulders of the executives who championed it. The organisations that will truly separate themselves in the age of AI are not the ones that spend the most or move the fastest. The real winners will be the ones that mitigate risk and uncertainty better and can optimize the efficiency and effectiveness gains of AI without the negative downside. That begins with end-to-end visibility.
Considerations for de-risking AI adoption
Most AI failures originate in the surrounding infrastructure, dependencies and data flows rather than the model itself. Without observability, AI risk cannot be managed effectively.
The ability to eliminate blind spots can be achieved via packet-level insight into workloads. When teams can capture activity in real time, they gain the power to identify performance issues, security risks, and system failures before they escalate.
Visibility also extends to behaviour. Shadow use of generative AI is widespread across enterprises today. Innovation is happening everywhere, but not always safely, compliantly, or strategically. Without insight into who is using AI and how, executives cannot distinguish between productive experimentation and unmanaged risk.
Then there is the matter of dependency. AI systems rely on vast webs of services, APIs, and infrastructures. When one component breaks, leaders must immediately understand what else is affected. Without real-time mapping of these connections, small issues can cascade into major disruptions that directly impact customers and core operations.
Foundationally, the quality of data feeding AI systems probably plays the most critical role. High-quality data gives AI the context it needs to make reliable predictions, adapt to real-world complexity and earn trust in high-stakes environments. In contrast, low quality data produces flawed outcomes that are hard to recognise and costly to correct. As AI systems increasingly shape decisions, data quality is no longer a technical hygiene issue – it is the foundation of responsible, explainable and high-impact AI.
Data also shows just 11 percent of GCC organisations qualify as ‘value realisers’ — meaning they have adopted AI, scaled deployment and can attribute at least five percent of earnings directly to it, highlighting how poorly ROI is still understood.
Spending heavily without knowing whether applications are delivering real business impact turns strategy into speculation. True confidence comes not from tracking usage or outputs alone, but from linking performance directly to business outcomes. Without that alignment, AI becomes a risky gamble instead of a growth engine.
All of this is happening under constant pressure to move faster. Speed matters, but without confidence it creates instability. Leaders must deliver AI-driven services rapidly without disrupting the systems customers rely on.
Expect more complexity, not less
As GCC governments accelerate AI and digital transformation – with investment expected to add around $320 billion to regional GDP – a defining truth is emerging. Complexity is a permanent condition of modern digital business. It cannot be eliminated, only managed and that starts by making the invisible visible.
AI is a foundational bet on the future of the enterprise. The organisations that succeed will understand what is always happening inside their systems — spotting issues before they spread and tracing failures before customers feel them. They move forward with confidence, not hope.
When it comes to AI, certainty is the real competitive advantage.
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