# AI Workshop Saudi Arabia — reviewed programme portfolio # https://aiworkshop-saudi.com/llms-full.txt # Source version: 2026-09-11 This document describes the programmes published for Saudi Arabia. Content, canonical URLs, locations, and governance context are generated from the active market configuration and route allowlist. Workshop governance is adapted to applicable Saudi requirements, client policies, approved tools, data boundaries, and sector-specific expectations. Training does not constitute legal advice or certify compliance. ## Agentic AI Workshop: design a safe, testable pilot Canonical: https://aiworkshop-saudi.com/workshops/agentic-ai/ Markdown: https://aiworkshop-saudi.com/workshops/agentic-ai.md Choose one important workflow and leave with a safe, testable agent pilot. Your team moves beyond agent demos. Participants define the job, sources, permissions, decisions and human checkpoints for an agent that fits a real workflow. Audience: Business, product, operations, innovation and technical teams ready to examine one multi-step workflow together. Recommended format: Core workshop · 4–6 hours. Alternatives: Focused workshop · 4 hours. Delivery format: On-site / in person, online, or hybrid. Tools: - Agent Design Canvas: Define the job, sources, tools, permissions, decisions and human checkpoints before building an agent. - ChatGPT: Draft, analyse, research and turn recurring work into reusable team instructions. - Your approved AI tools: Exercises adapt to the systems, licences and data boundaries your organisation has approved. Outcomes: - Select an agent opportunity worth testing - Separate agent work from human decisions - Design permissions and review points - Plan a small pilot with clear tests Syllabus: 1. Choose the workflow: Map the current work, friction and decision owner. Output: Agent Design Canvas. 2. Define the agent’s job: Set the goal, boundaries, inputs and expected output. Output: Permission and approval map. 3. Connect sources and tools: Decide what the agent may read, use and change. Output: Failure-test checklist. 4. Design human control: Add approvals, escalation and a visible source trail. Output: Pilot brief with owner and next step. 5. Test before pilot: Create failure cases, success measures and a first pilot plan. Output: Agent Design Canvas. Take-away artefacts: - Agent Design Canvas - Permission and approval map - Failure-test checklist - Pilot brief with owner and next step ## AI Fundamentals Workshop for confident everyday use Canonical: https://aiworkshop-saudi.com/workshops/ai-fundamentals/ Markdown: https://aiworkshop-saudi.com/workshops/ai-fundamentals.md Move from uncertain experimentation to confident, responsible everyday use. Participants learn which tool to use, how to give useful context, how to check the answer and when not to use AI. The session ends with one workflow they have tested themselves. Audience: Non-technical teams, mixed departments and leaders who need a shared practical foundation. Recommended format: Focused workshop · 4 hours. Alternatives: Core workshop · 4–6 hours. Delivery format: On-site / in person, online, or hybrid. Tools: - ChatGPT: Draft, analyse, research and turn recurring work into reusable team instructions. - Microsoft Copilot: Work with approved Microsoft 365 capabilities across documents, meetings, email and business information. - Your approved AI tools: Exercises adapt to the systems, licences and data boundaries your organisation has approved. Outcomes: - Choose an appropriate tool for the task - Give clearer instructions and context - Check claims, numbers and omissions - Turn one recurring task into a reviewed workflow Syllabus: 1. Understand the modern AI workday: See what current tools do well, where they struggle and how work changes. Output: Reusable request template. 2. Choose the right tool: Match writing, research, document and data tasks to approved tools. Output: Human review checklist. 3. Ask for useful work: Practise context, examples, constraints and output formats. Output: One tested workflow. 4. Verify before using: Check sources, numbers, omissions, tone and sensitive information. Output: Shortlist of useful next opportunities. 5. Build one reusable workflow: Turn a real task into a repeatable request-review-improve loop. Output: Reusable request template. Take-away artefacts: - Reusable request template - Human review checklist - One tested workflow - Shortlist of useful next opportunities ## Generative AI for Business: build repeatable team workflows Canonical: https://aiworkshop-saudi.com/workshops/generative-ai-for-business/ Markdown: https://aiworkshop-saudi.com/workshops/generative-ai-for-business.md Turn scattered individual use into shared, repeatable team workflows. Participants apply generative AI to research, documents, files, presentations and recurring team work. They leave with shared instructions, a quality check and clear owners for the next experiments. Audience: Cross-functional business teams that have begun experimenting and now need consistency, quality and responsible boundaries. Recommended format: Core workshop · 4–6 hours. Alternatives: Focused workshop · 4 hours. Delivery format: On-site / in person, online, or hybrid. Tools: - ChatGPT: Draft, analyse, research and turn recurring work into reusable team instructions. - Microsoft Copilot: Work with approved Microsoft 365 capabilities across documents, meetings, email and business information. - AI search & deep research: Find, compare and cite current sources, then separate evidence from interpretation. Outcomes: - Choose business tasks where generative AI is genuinely useful - Create reusable context and instructions - Review quality, sources and sensitive information - Assign owners to a focused 30-day workflow plan Syllabus: 1. Find the useful work: Separate high-value team tasks from low-value novelty. Output: Team instruction pack. 2. Research and documents: Create sourced briefs, summaries and stronger first drafts. Output: Worked examples from real tasks. 3. Files, data and presentations: Work across tables, documents, slides and visual concepts. Output: Quality and review checklist. 4. Build reusable context: Turn individual prompts into shared projects, instructions and examples. Output: Prioritised workflow map with owners. 5. Adopt with control: Set review rules, owners and a practical 30-day plan. Output: Team instruction pack. Take-away artefacts: - Team instruction pack - Worked examples from real tasks - Quality and review checklist - Prioritised workflow map with owners ## AI for Business Intelligence & Decision-Making Canonical: https://aiworkshop-saudi.com/workshops/ai-for-business-intelligence/ Markdown: https://aiworkshop-saudi.com/workshops/ai-for-business-intelligence.md Move from a business question to a checked, explainable decision brief. Teams practise a complete data-to-decision workflow: frame the question, inspect the data, explore patterns, challenge assumptions and communicate what the evidence does—and does not—support. Audience: Analysts, managers, finance, operations and commercial teams that work with recurring reports, spreadsheets or management decisions. Recommended format: Core workshop · 4–6 hours. Alternatives: Focused workshop · 4 hours. Delivery format: On-site / in person, online, or hybrid. Tools: - Spreadsheets & data tools: Explore tables, test assumptions and create checked summaries without losing the source trail. - ChatGPT: Draft, analyse, research and turn recurring work into reusable team instructions. - AI search & deep research: Find, compare and cite current sources, then separate evidence from interpretation. Outcomes: - Start analysis with a decision rather than a chart - Use AI to explore tables without hiding assumptions - Check trends, anomalies and calculations - Create a concise decision narrative with limits Syllabus: 1. Frame the decision: Define the question, owner, timeframe and evidence needed. Output: Decision and analysis brief. 2. Prepare the data: Inspect columns, missing values, definitions and sensitive fields. Output: Reusable question library. 3. Explore patterns: Investigate trends, variances and anomalies with plain-language questions. Output: Verification checklist. 4. Test scenarios: Make assumptions visible and compare alternatives without presenting guesses as facts. Output: Recurring reporting pattern. 5. Communicate and sign off: Build charts and commentary with a source trail and human approval. Output: Decision and analysis brief. Take-away artefacts: - Decision and analysis brief - Reusable question library - Verification checklist - Recurring reporting pattern ## AI for HR Training Saudi Arabia: Copilot and governed adoption Canonical: https://aiworkshop-saudi.com/workshops/hr-training/ Markdown: https://aiworkshop-saudi.com/workshops/hr-training.md Help HR teams prepare, communicate and support people more consistently while keeping sensitive decisions human. HR teams in Riyadh, Jeddah and across Saudi Arabia practise approved Copilot workflows for employee support, onboarding, communication and knowledge work while keeping sensitive decisions with accountable people. Audience: HR business partners, people operations, talent, learning and HR leadership teams. Recommended format: Core workshop · 4–6 hours. Alternatives: Focused workshop · 4 hours, Core workshop plus optional follow-up. Delivery format: On-site / in person, online, or hybrid. Tools: - Microsoft Copilot: Work with approved Microsoft 365 capabilities across documents, meetings, email and business information. - ChatGPT: Draft, analyse, research and turn recurring work into reusable team instructions. - Your approved AI tools: Exercises adapt to the systems, licences and data boundaries your organisation has approved. Outcomes: - Identify useful low-risk HR workflows - Create clearer employee and policy communication - Use AI as preparation support, not decision-maker - Set review rules for fairness and sensitive data Syllabus: 1. Map HR work: Prioritise repeated, high-effort tasks that still need human ownership. Output: HR workflow cards. 2. Employee and policy communication: Draft clear answers, summaries and manager guidance from approved material. Output: Approved instruction pack. 3. Recruitment and onboarding support: Improve role briefs, interview preparation and onboarding content without delegating decisions. Output: Fairness and review checklist. 4. Learning and meeting follow-up: Create learning plans, summaries and actions that people can review. Output: Pilot map with human owners. 5. Fairness, privacy and adoption: Define data boundaries, review questions and a responsible pilot. Output: HR workflow cards. Take-away artefacts: - HR workflow cards - Approved instruction pack - Fairness and review checklist - Pilot map with human owners ## AI for Developers: from issue to reviewed change Canonical: https://aiworkshop-saudi.com/workshops/ai-for-developers/ Markdown: https://aiworkshop-saudi.com/workshops/ai-for-developers.md Establish a dependable AI-assisted development workflow from issue to tested, reviewed change. Developers practise how to give coding tools the right repository context, plan changes, inspect generated work, run tests and leave evidence for reviewers. The focus is team reliability, not faster typing. Audience: Software engineers, technical leads, platform teams and product engineers using or evaluating coding assistants and agents. Recommended format: Core workshop · 4–6 hours. Alternatives: Focused workshop · 4 hours, Optional follow-up clinic · 60–90 minutes. Delivery format: On-site / in person, online, or hybrid. Tools: - GitHub Copilot, Codex & coding agents: Move from issue to plan, change, tests and review inside the team’s approved development environment. - Agent Design Canvas: Define the job, sources, tools, permissions, decisions and human checkpoints before building an agent. - Your approved AI tools: Exercises adapt to the systems, licences and data boundaries your organisation has approved. Outcomes: - Give coding tools useful repository context - Move from issue to explicit implementation plan - Require tests and review evidence from agent work - Agree team boundaries, permissions and standards Syllabus: 1. Prepare repository context: Create instructions, architecture notes and constraints the tool can follow. Output: Repository instruction template. 2. Issue to plan: Turn an ambiguous request into a reviewable implementation plan. Output: Agent-ready development workflow. 3. Plan to change: Make focused edits, inspect diffs and keep the developer in control. Output: Test-evidence checklist. 4. Tests, security and review: Require evidence, test failure paths and examine security-sensitive changes. Output: Team standard and pilot backlog. 5. Team operating standard: Define approved tools, permissions, review rules and a pilot backlog. Output: Repository instruction template. Take-away artefacts: - Repository instruction template - Agent-ready development workflow - Test-evidence checklist - Team standard and pilot backlog ## AI for Finance: faster preparation without weaker control Canonical: https://aiworkshop-saudi.com/workshops/ai-for-finance/ Markdown: https://aiworkshop-saudi.com/workshops/ai-for-finance.md Make recurring finance work easier to prepare, explain and review without weakening control. Finance teams practise sourced research, reconciliation support, variance analysis, scenarios and management commentary. Every workflow keeps assumptions, evidence and final approval visible. Audience: FP&A, controlling, accounting, treasury, finance operations and finance leaders. Recommended format: Core workshop · 4–6 hours. Alternatives: Focused workshop · 4 hours. Delivery format: On-site / in person, online, or hybrid. Tools: - Spreadsheets & data tools: Explore tables, test assumptions and create checked summaries without losing the source trail. - AI search & deep research: Find, compare and cite current sources, then separate evidence from interpretation. - Microsoft Copilot: Work with approved Microsoft 365 capabilities across documents, meetings, email and business information. Outcomes: - Use AI for preparation while retaining finance ownership - Investigate variances with explicit assumptions - Create management commentary linked to evidence - Design a reviewed pilot for recurring work Syllabus: 1. Choose the finance workflow: Identify recurring work with clear inputs, controls and owner. Output: Finance workflow brief. 2. Research and source trails: Build current briefs that distinguish source facts from interpretation. Output: Source and assumption trail. 3. Reconciliation and variance: Use AI to structure checks, questions and explanations—not approve numbers. Output: Human review checklist. 4. Scenarios and commentary: Make assumptions visible and draft concise management narratives. Output: Reporting template and pilot plan. 5. Controls and pilot: Set data boundaries, review steps and a small controlled pilot. Output: Finance workflow brief. Take-away artefacts: - Finance workflow brief - Source and assumption trail - Human review checklist - Reporting template and pilot plan ## AI for Legal: accelerate preparation, keep legal judgement Canonical: https://aiworkshop-saudi.com/workshops/ai-for-legal/ Markdown: https://aiworkshop-saudi.com/workshops/ai-for-legal.md Accelerate preparation and comparison while keeping legal judgement with the professional. Legal teams practise matter preparation, clause comparison, source-grounded research and drafting support. Confidentiality, verification and final professional review are built into each workflow. Audience: In-house legal teams, law firms, contract professionals, compliance and legal operations. Recommended format: Core workshop · 4–6 hours. Alternatives: Focused workshop · 4 hours. Delivery format: On-site / in person, online, or hybrid. Tools: - AI search & deep research: Find, compare and cite current sources, then separate evidence from interpretation. - Claude: Review long documents, compare alternatives and support structured writing when it is an approved tool. - Your approved AI tools: Exercises adapt to the systems, licences and data boundaries your organisation has approved. Outcomes: - Select legal tasks suitable for AI support - Compare clauses against an approved playbook - Research with a visible source and date trail - Set confidentiality and final-review boundaries Syllabus: 1. Choose the legal task: Separate preparation and comparison from professional judgement. Output: Matter-preparation template. 2. Prepare the matter: Structure facts, questions, documents and missing information. Output: Playbook-based comparison workflow. 3. Compare clauses: Review language against an approved playbook and flag differences. Output: Research and citation checklist. 4. Research and draft: Use current sources, citations and controlled drafting patterns. Output: Confidentiality and review boundary. 5. Confidentiality and verification: Set data boundaries, review questions and a safe pilot workflow. Output: Matter-preparation template. Take-away artefacts: - Matter-preparation template - Playbook-based comparison workflow - Research and citation checklist - Confidentiality and review boundary Important: This is professional training, not legal advice. Outputs require review by a qualified legal professional. ## AI for Marketing: from brief to reviewed campaign workflow Canonical: https://aiworkshop-saudi.com/workshops/ai-for-marketing/ Markdown: https://aiworkshop-saudi.com/workshops/ai-for-marketing.md Move from disconnected content experiments to a coherent, reviewable campaign workflow. Marketing teams practise audience research, positioning, brand context, multimodal creation, localization, search and AI discovery, approval and performance learning—using one connected campaign story. Audience: Marketing, communications, brand, content, growth and customer teams. Recommended format: Core workshop · 4–6 hours. Alternatives: Focused workshop · 4 hours. Delivery format: On-site / in person, online, or hybrid. Tools: - ChatGPT: Draft, analyse, research and turn recurring work into reusable team instructions. - AI search & deep research: Find, compare and cite current sources, then separate evidence from interpretation. - AI visual tools: Create and refine visual concepts while keeping brand, rights and human approval visible. Outcomes: - Turn audience evidence into a stronger brief - Give tools useful brand context - Create and localize content with clear review - Design a campaign experiment that can teach the team Syllabus: 1. Audience and positioning: Turn research and customer language into a focused campaign brief. Output: Brand context pack. 2. Brand context: Create instructions, examples and boundaries tools can follow. Output: Search and GEO content brief. 3. Multimodal creation: Develop copy and visual concepts without losing rights or approval. Output: Localization and approval checklist. 4. Localization and AI discovery: Adapt messages for people, search engines and answer engines without keyword stuffing. Output: Campaign experiment plan. 5. Approval and learning: Set review, measurement and a useful campaign experiment. Output: Brand context pack. Take-away artefacts: - Brand context pack - Search and GEO content brief - Localization and approval checklist - Campaign experiment plan ## AI for Education & Teaching: save time, improve learning design Canonical: https://aiworkshop-saudi.com/workshops/ai-for-education/ Markdown: https://aiworkshop-saudi.com/workshops/ai-for-education.md Save preparation time while improving learning design, assessment and student guidance. Educators practise lesson planning, differentiation, feedback and assessment redesign while addressing academic integrity, student AI literacy, privacy and the need for human pedagogical judgement. Audience: Teachers, lecturers, learning designers, faculty teams and education leaders. Recommended format: Focused workshop · 4 hours. Alternatives: Core workshop · 4–6 hours. Delivery format: On-site / in person, online, or hybrid. Tools: - ChatGPT: Draft, analyse, research and turn recurring work into reusable team instructions. - AI search & deep research: Find, compare and cite current sources, then separate evidence from interpretation. - Your approved AI tools: Exercises adapt to the systems, licences and data boundaries your organisation has approved. Outcomes: - Use AI to strengthen lesson preparation - Adapt material without losing learning intent - Redesign assessment for an AI-rich environment - Give students clear responsible-use guidance Syllabus: 1. Teaching tasks and boundaries: Choose where AI helps preparation and where educator judgement remains essential. Output: Lesson-planning template. 2. Lesson design and adaptation: Create activities, examples and accessible variants tied to learning goals. Output: Redesigned assessment activity. 3. Feedback and assessment: Improve feedback and redesign tasks that reveal student thinking. Output: Student AI guidance. 4. Academic integrity and literacy: Set transparent expectations and teach students how to question AI outputs. Output: Privacy and review checklist. 5. Privacy and subject workflow: Define data boundaries and build one workflow for the educator’s context. Output: Lesson-planning template. Take-away artefacts: - Lesson-planning template - Redesigned assessment activity - Student AI guidance - Privacy and review checklist ## AI workshops for universities, faculty and students Canonical: https://aiworkshop-saudi.com/universities/ Markdown: https://aiworkshop-saudi.com/universities.md Give each university audience a practical, responsible way to use AI in its own work. Choose a track for students, faculty, researchers, professional services, executive education or alumni. Each format combines current tools, guided practice, academic judgement and artefacts participants can adapt after the session. Audience: Universities, schools, executive-education teams and academic communities planning practical AI learning for a defined cohort. Recommended format: Focused workshop · 4 hours. Alternatives: Core workshop · 4–6 hours, Core workshop plus optional follow-up. Delivery format: On-site / in person, online, or hybrid. Tools: - ChatGPT: Draft, analyse, research and turn recurring work into reusable team instructions. - Agent Design Canvas: Define the job, sources, tools, permissions, decisions and human checkpoints before building an agent. - Your approved AI tools: Exercises adapt to the systems, licences and data boundaries your organisation has approved. Outcomes: - Select the right track for the cohort - Practise tasks that match academic roles - Address integrity, privacy and human judgement - Leave with guidance and artefacts that can be reused Syllabus: 1. Choose the audience track: Align the session with students, faculty, research, services or executive education. Output: Track-specific workshop workbook. 2. Understand current capabilities: Use the tools available to the institution and make limits visible. Output: Responsible-use guidance. 3. Practise role-specific work: Work on teaching, study, research or service scenarios. Output: Ready-to-adapt workflow or agent brief. 4. Set responsible boundaries: Discuss integrity, privacy, disclosure, verification and human responsibility. Output: Cohort next-step plan. 5. Take the work back: Create guidance, templates and next steps for the cohort. Output: Track-specific workshop workbook. Take-away artefacts: - Track-specific workshop workbook - Responsible-use guidance - Ready-to-adapt workflow or agent brief - Cohort next-step plan ## Microsoft Copilot Training Saudi Arabia for Corporate Teams Canonical: https://aiworkshop-saudi.com/workshops/copilot/ Markdown: https://aiworkshop-saudi.com/workshops/copilot.md Help teams use the Copilot capabilities available in their organisation with clearer expectations and safer habits. Saudi teams confirm the Copilot experience, licences and permissions available in their Microsoft environment, then build practical workflows with source checking, human review and a realistic adoption plan. Audience: Teams and champions using or preparing to roll out Microsoft Copilot in a managed Microsoft 365 environment. Recommended format: Core workshop · 4–6 hours. Alternatives: Focused workshop · 4 hours, Core workshop plus optional follow-up. Delivery format: On-site / in person, online, or hybrid. Tools: - Microsoft Copilot: Work with approved Microsoft 365 capabilities across documents, meetings, email and business information. - Enterprise knowledge & workspaces: Organise shared context, approved knowledge and repeatable ways of working inside managed AI workspaces. - Your approved AI tools: Exercises adapt to the systems, licences and data boundaries your organisation has approved. Outcomes: - Understand which Copilot experience the team actually has - Use Copilot across approved Microsoft 365 work - Check sources, permissions and generated content - Create a practical 90-day adoption plan Syllabus: 1. Know your Copilot environment: Distinguish Copilot Chat, licensed Microsoft 365 Copilot and plan-dependent features. Output: Copilot use-case map. 2. Work across Microsoft 365: Practise available document, meeting, email and information workflows. Output: Role-based instruction pack. 3. Context, sources and permissions: See how access and document quality shape the answer. Output: Readiness and permission checklist. 4. From prompt to repeatable habit: Create role-based instructions and examples colleagues can reuse. Output: Champion kit and 90-day plan. 5. Adoption and measurement: Prioritise use cases, champions, support and a 90-day plan. Output: Copilot use-case map. Take-away artefacts: - Copilot use-case map - Role-based instruction pack - Readiness and permission checklist - Champion kit and 90-day plan ## ChatGPT Enterprise Adoption Workshop Canonical: https://aiworkshop-saudi.com/workshops/chatgpt-enterprise/ Markdown: https://aiworkshop-saudi.com/workshops/chatgpt-enterprise.md Turn access to a managed ChatGPT workspace into useful, governed team practice. Teams build practical habits for projects, research, data work, reusable GPTs or agents and approved company context where available. Capabilities are confirmed against the client’s plan, region and administrator settings before delivery. Audience: Organisations rolling out or improving adoption of a managed ChatGPT workspace for business teams. Recommended format: Core workshop · 4–6 hours. Alternatives: Focused workshop · 4 hours, Core workshop plus optional follow-up. Delivery format: On-site / in person, online, or hybrid. Tools: - ChatGPT: Draft, analyse, research and turn recurring work into reusable team instructions. - Enterprise knowledge & workspaces: Organise shared context, approved knowledge and repeatable ways of working inside managed AI workspaces. - Agent Design Canvas: Define the job, sources, tools, permissions, decisions and human checkpoints before building an agent. Outcomes: - Create consistent workspace and project habits - Design repeatable research and data workflows - Evaluate reusable GPT or agent behaviour - Agree practical governance and rollout decisions Syllabus: 1. Workspace habits and boundaries: Confirm available capabilities, data rules and team expectations. Output: Team workflow pack. 2. Projects, research and data: Organise context and practise sourced research, documents and analysis. Output: Evaluation examples and review set. 3. Reusable GPTs and agents: Define purpose, instructions, knowledge, tests and human control. Output: Governance decision log. 4. Company context and access: Examine approved apps or company knowledge where available and plan-dependent. Output: Adoption and rollout plan. 5. Evaluation and rollout: Create an evaluation set, ownership decisions and an adoption plan. Output: Team workflow pack. Take-away artefacts: - Team workflow pack - Evaluation examples and review set - Governance decision log - Adoption and rollout plan