In just a few years, generative AI has moved from novelty to default front door. Large language models (LLMs) already attract close to 45 billion visits per month, the equivalent of 56% of traditional search volume, which has plateaued for years.
Most CRM and CDP projects I work on in Germany don't fail because of the technology. They fail before the first data pipeline is built. CMOs still largely lead marketing strategy within silos, brand A, channel B, campaign C. But the customer doesn't see silos. They are a business traveller booking an upgrade on Monday. A family buyer hunting a weekend sale on Friday. An insurance policyholder renewing in October. The same person. Three distinct micropersonas. Three different touchpoints. And in the database: a flat profile with a date of birth and a list of all transactions.
Wealth management is stuck in the past. Clients live in a world of effortless, hyper-personalised recommendations from YouTube, TikTok, or Amazon, yet banks push products through rule books, clunky segmentation, and advisor guesswork. Banks struggle to keep up with client expectations and the complexity of today’s portfolios. Hybrid AI can change this. By fusing machine learning, portfolio optimization discipline, and the contextual intelligence of large language models, governed by an Agentic AI layer, wealth managers can deliver recommendations that feel smart, personal, and well-timed.
Wealth management is stuck in the past. Clients live in a world of effortless, hyper-personalised recommendations from YouTube, TikTok, or Amazon, yet banks push products through rule books, clunky segmentation, and advisor guesswork. Banks struggle to keep up with client expectations and the complexity of today’s portfolios. Hybrid AI can change this. By fusing machine learning, portfolio optimization discipline, and the contextual intelligence of large language models, governed by an Agentic AI layer, wealth managers can deliver recommendations that feel smart, personal, and well-timed.
The Middle East’s digital transformation market is projected to reach $205 billion by 2031. National strategies across the Gulf, from the UAE AI Strategy 2031 to Saudi Vision 2030 and Qatar National Vision 2030, are anchoring unprecedented investment in AI infrastructure, sovereign capability, and economic diversification. The commitment is real. But the defining question for every large organization is whether that investment produces enterprise-wide transformation or stays a collection of disconnected pilots.
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.
The public sector stands at a decisive moment. With global public debt reaching an unprecedented $102 trillion in 2024, governments are caught between opposing structural forces. On one side, fiscal space is shrinking rapidly, with over 3.4 billion people living in countries that spend more on debt interest than on health or education. On the other side, citizen expectations are soaring, demanding digital services that rival the speed and personalization of Big Tech.
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.
