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About Asabya ↗
← All programmes Asabya Generative AI for Leaders Structured professional-development programme for managers, executives, decision-makers, IT directors, and aspiring leaders who need a working command of generative AI to lead its adoption in their organisations. Builds leadership-level literacy across five domains: foundations, strategy and responsible adoption, the technology landscape, practical applications, and agentic transformation.
1 level 45 hours
Asabya original programme. Not affiliated with or endorsed by Google Cloud. Learners may separately pursue Google Cloud Generative AI Leader certification.
Who it's for
Open to all learners. The programme is designed for working professionals, MBA students, and aspiring leaders, but is open to anyone interested in learning how generative AI is led, governed, and adopted in organisations. Note that some third-party AI tools referenced in the curriculum have their own minimum-age and account requirements (typically 18+, with some platforms accessible from 13+ with parental consent); these are flagged within the relevant sessions.
What you'll learn L1
Present an AI initiative and handle peer challenge. Recognise and avoid demo-driven procurement. Identify the five layers of the AI stack. Apply a four-part prompting framework. Identify realistic production-grade AI agent use cases. Identify the main patterns by which generative AI creates value. The curriculum 30 sessions across the programme. Enrol to open each lesson, resources, and assessments.
L1
01 Generative AI in the Real World 02 AI, ML, and Generative AI — Cutting Through the Jargon 03 How Foundation Models Work — Without the Maths 04 Data, Modalities, and the Multimodal Future 05 Module 1 Knowledge Check — Foundations 06 Building a Generative AI Strategy 07 Use Case Discovery and Prioritisation 08 Limitations, Hallucinations, and Risk 09 Responsible AI in a GCC Context Recognise real-world generative-AI applications across sectors.
Build a generative-AI strategy aligned to organisational goals. Explain how AI agents differ from chatbots. Map sovereignty considerations across the AI stack. Defend an AI proposal and respond to critical questions. Write effective prompts for leadership tasks. Make layer-level decisions for an AI initiative. Assess where generative AI fits an organisation. Assess where autonomous AI systems add value. Set strategic priorities for AI adoption. Structure prompts for reliable outputs. Plan realistic timelines and buffers for AI change. Recognise the AI-for-its-own-sake anti-pattern. Apply advanced prompting for structured outputs. Assess infrastructure and compute-sovereignty options. Distinguish high-value from low-value AI use cases. Lead change management for AI adoption. Explain GPU and data-centre requirements for AI. Place generative AI within the broader AI and machine-learning landscape. Discover and surface candidate AI use cases. Use advanced prompting techniques for complex tasks. Model AI use to drive team adoption. Explain core AI and ML terms for a leadership audience. Evaluate compute-sovereignty trade-offs. Prioritise AI use cases by value and feasibility. Address resistance and build buy-in for AI. Apply the human-in-the-loop pattern to manage AI risk. Communicate AI concepts clearly without jargon. Compare frontier and open-weight foundation models. Account for the Arabic token premium in AI budgeting. Apply AI to leadership productivity tasks. Embed AI into everyday workflows. Resource AI initiatives across budget, talent, and time. Explain why Arabic workloads typically cost more per token. Recognise AI limitations and hallucination risk. Select foundation-model vendors for organisational needs. Plan talent and capability for AI delivery. Explain how foundation models work at a conceptual level. Mitigate organisational risk from AI errors. Weigh open-weight versus proprietary model trade-offs. Judge when retrieval-augmented generation is and is not appropriate. Explain how retrieval-augmented generation works.
10 Data Protection, PDPL, and AI Governance
11 Module 2 Knowledge Check — Strategy and Responsible Adoption
12 The Five Layers of the AI Stack
13 Infrastructure — GPUs, Data Centres, Compute Sovereignty
14 Foundation Models — Frontier Labs, Open Weights, Vendor Selection
15 Model-Serving Platforms
16 Edge AI and Sovereign AI Realities
17 Module 3 Knowledge Check — Technology Landscape
18 Prompting Fundamentals
19 Advanced Prompting
20 Productivity Applications
21 Retrieval-Augmented Generation (RAG)
22 Workspace and Tool Integrations
23 Mid-Programme Mock Assessment
24 AI Agents — From Chatbots to Autonomous Systems
25 Change Management for AI
26 Resourcing — Budget, Talent, Time
27 AI Maturity and the Innovation Journey
28 Measuring AI Success
29 Capstone Presentation and Peer Challenge
30 Final Assessment and Programme Close Asabya Generative AI for Leaders · Asabya Academy
