Who
Should Attend
Marketing, communications, and events professionals producing content, campaigns, and multi-language assets.
What participants can do by the end
Identify AI tools applicable to marketing and events, content, analytics, visuals, translation, and map each to a specific GCC use case. Apply structured prompt techniques to generate campaign copy, event communications, and multi-language content assets using AI tools. Examine AI-generated marketing content for accuracy, cultural alignment, tone, and brand compliance using a structured content audit checklist. Analyze AI-driven analytics outputs to identify audience trends, content gaps, and performance signals relevant to a campaign. Evaluate AI-enabled collaboration workflows for suitability, risk, and compliance before recommending implementation to a marketing team.
How the three days build
Day one maps AI tools to marketing functions, content, analytics, visuals, events, and CRM, covers which GCC sectors are leading AI marketing adoption, then goes hands-on with prompt engineering for copy, headlines, and CTAs, multi-language and Arabic content generation, and iterating outputs for genuine brand voice fit, before a dedicated content audit session covering cultural sensitivity, tone, brand compliance, and where AI translation most commonly fails in GCC language contexts. Day two covers reading AI analytics outputs, dashboards, trend reports, audience segments, distinguishing which signals are actionable from which are noise, then moves into AI tools for event planning, scheduling, logistics, vendor coordination, and registration, and how to evaluate those workflow tools for risk and data compliance. Day three brings it together into one campaign where AI adds measurable value, with a commitment to pilot and review.
Participants leave faster and more careful, not just faster.
Why this matters specifically for marketing
An AI investment decision made without a readiness assessment or a governance framework in place tends to look reasonable in the boardroom and expensive eighteen months later, once the data infrastructure gap, the talent shortfall, or the governance omission becomes visible. Boards that ask the right readiness and governance questions upfront catch these gaps before capital is committed, not after. This program exists so board-level AI decisions get the same rigor applied to any other major capital or strategic commitment the board is asked to approve.
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