Large Language Models are astonishingly capable. They summarize, translate, reason, and code (better than me). But unlike me, they have also become notorious for inventing facts with unnerving confidence.
Whilst tongue-in-cheek, I’ve always found the advice ‘never make predictions, especially about the future’ to be solid, and never more so than in AI-land. Three years ago AI was touted as an accelerant for modern, cloud-native software companies; super-charging well-staffed, industry-leading developer teams to deliver better products at an increasingly accelerated rate.
As we move through the second quarter of 2026, the global dialogue around AI has reached a critical turning point. The initial fascination with generative models has matured into a demand for measurable industrial impact. In the Middle East, a region currently serving as a global laboratory for the world’s most ambitious digital giga- and megaprojects, the question is no longer about AI’s potential, but about its performance at scale.
As "transformation debt" paralyzes traditional marketing, the strategic imperative for 2026 shifts from simple content generation to autonomous execution. Learn how Agentic AI is dismantling silos, reducing campaign latency, and freeing human creativity for high-level strategy.
From the breakout of DeepSeek R1 to the viral “Raising a lobster” trend (OpenClaw adoption), all within just a year, AI in China is being adopted and scaled in a fundamentally different way, rapidly translating into tangible commercial value.
How did a premium beauty brand significantly reduce its "zombie products," boost catalog visibility, and slash traffic costs in just a few weeks? This article examines the strategic deployment of AI that made it possible.
Watch the Replay About Jean-François Mazaud: Currently COO France
The strategy must reject a pure "technology push" and focus entirely on customer impact. Within 3 to 5 years, it is projected that over 50% of customers will possess their own intermediate AI agents to assist with purchasing decisions and financial choices. This shifts the banking paradigm: the bank must not only market to humans but ensure its services are discoverable and selectable by these customer-side agents. The challenge lies in building an infrastructure where the bank's systems can seamlessly interact with these personal digital intermediaries.
The drive is about efficiency, scope, and capability. AI allows banks to execute existing tasks faster, cover a broader spectrum of risks (covering risks A, B, and C instead of just A), and perform previously impossible tasks, such as analyzing massive document volumes for sentiment analysis in equity research.
