Section
Practical coverage of how organisations evaluate, deploy and govern artificial intelligence — from model selection and data readiness to measurable business outcomes.
A model that performs well on English benchmarks can still fail on Arabic instructions, dialects, business terminology and culturally specific context. Gulf deployments need native evaluation before production claims are credible.
Xonique Editorial Team9 min read
Editorial Desk
A practical engineering baseline for production AI systems informed by the UAE's 2026 national cybersecurity policy direction for artificial intelligence.
Xonique Editorial Team10 min read
The useful question is not whether an AI demo looks impressive, but whether the system has been tested against the failures, permissions and operational limits that matter in production.
Xonique Editorial Team12 min read
The useful procurement question is not whether an AI demo looks impressive. It is whether the buyer can verify what the system does, what happens to company data, how quality is measured, who is responsible when things change, and how to exit cleanly.
Xonique Editorial Team12 min read
Getting an AI feature into production is less about model choice and more about data ownership, evaluation and the operating model around it.
Xonique Editorial Team9 min read
Small teams rarely need a platform. They need two or three narrow automations that remove repetitive work without creating a new maintenance burden.
Xonique Editorial Team7 min read
Mid-sized companies can build workable AI governance with a register, a review route and clear disclosure rules — long before they need a formal programme.
Xonique Editorial Team6 min read
