# AI Workshop UAE — reviewed programme portfolio
# https://aiworkshop.ae/llms-full.txt
# Source version: 2026-09-05

This document describes the programmes published for the United Arab Emirates. Content, canonical URLs, locations, and governance context are generated from the active market configuration and route allowlist.

Workshop governance is adapted to applicable UAE requirements, client policies, approved tools, data boundaries, and sector-specific expectations. Training does not constitute legal advice or certify compliance.

## Agentic AI Workshop UAE: governed pilots for enterprise teams

Canonical: https://aiworkshop.ae/workshops/agentic-ai/
Markdown: https://aiworkshop.ae/workshops/agentic-ai.md

Choose one important workflow and leave with a safe, testable agent pilot.

UAE teams design an agent around a real decision or workflow, then define approved knowledge, tool access, permissions, evaluation, security boundaries and human approval before planning a governed pilot.

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.ae/workshops/ai-fundamentals/
Markdown: https://aiworkshop.ae/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.ae/workshops/generative-ai-for-business/
Markdown: https://aiworkshop.ae/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.ae/workshops/ai-for-business-intelligence/
Markdown: https://aiworkshop.ae/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: practical workflows with human judgement

Canonical: https://aiworkshop.ae/workshops/hr-training/
Markdown: https://aiworkshop.ae/workshops/hr-training.md

Help HR teams prepare, communicate and support people more consistently while keeping sensitive decisions human.

The workshop applies approved AI tools to employee communication, policies, recruitment support, onboarding, learning and meeting follow-up. Fairness, privacy and review are part of every exercise.

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.ae/workshops/ai-for-developers/
Markdown: https://aiworkshop.ae/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.ae/workshops/ai-for-finance/
Markdown: https://aiworkshop.ae/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 Marketing: from brief to reviewed campaign workflow

Canonical: https://aiworkshop.ae/workshops/ai-for-marketing/
Markdown: https://aiworkshop.ae/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

## Microsoft Copilot for Teams: useful habits and a realistic adoption plan

Canonical: https://aiworkshop.ae/workshops/copilot/
Markdown: https://aiworkshop.ae/workshops/copilot.md

Help teams use the Copilot capabilities available in their organisation with clearer expectations and safer habits.

The workshop distinguishes Copilot Chat from licensed Microsoft 365 Copilot, then applies available capabilities to documents, meetings, email and business information. Permissions, source checking and adoption are addressed before automation.

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.ae/workshops/chatgpt-enterprise/
Markdown: https://aiworkshop.ae/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