Certification in AI Workflow Automation in Dubai

How large language models work in simple terms: tokens, context window and temperature

Chatbots, AI assistants, AI agents and automated workflows: what is different

Rule-based automation, RPA and AI automation compared

The platform landscape: n8n, Make, Zapier, Microsoft Power Automate and Copilot Studio

Where AI automation pays off, and where it is the wrong answer

Shadow automation: unmanaged flows built outside the team's knowledge, and why they matter

A simple prompt framework: role, goal, context, steps, format and limits

System prompts and user prompts for workflow steps

Structured output: getting JSON, tables and fixed fields that the next step can read

Examples and few-shot prompting for consistent results

Prompt chaining: breaking a large task into small, testable steps

Testing, comparing and versioning prompts before they go live

Finding automation candidates: repetitive, rule-heavy and high-volume work

AI-readiness scoring: value, effort, risk and data availability

Mapping a current workflow and finding delays, hand-offs and rework

Choosing where AI fits, where plain automation fits and where a person must stay

Drawing the future-state workflow with triggers, steps, decisions and outputs

Setting a baseline and measuring time saved and quality gained

Triggers: schedules, webhooks, form submissions and new emails or files

Actions, nodes and modules: how data moves from step to step

Working with data: fields, lists, JSON, mapping and transformation

Conditions, branches, loops and routers

APIs and webhooks at concept level: requests, responses, keys and authentication

Connecting business apps: email, spreadsheets, CRM, chat and cloud storage

Credentials and permissions: connecting accounts safely with least access

Adding an LLM step to a workflow: model choice, prompt and output field

Classifying and routing emails, tickets and requests

Extracting fields from invoices, forms, contracts, PDFs and images

Summarising and drafting inside a workflow: emails, reports and replies

Handling confidence: when the AI is unsure, route the item to a person

Multilingual steps in English and Arabic

Workflow or agent: deciding how much freedom the AI should have

Agent building blocks: goal, instructions, tools, memory and steps

Agent patterns: tool use, planning, reflection and multi-agent teams

Giving an agent tools: search, calendar, email, databases and other workflows

Short-term and long-term memory: what to remember and what to forget

Setting limits: allowed actions, step limits and approval before sensitive actions

Testing an agent with normal, tricky and unexpected inputs

Why AI needs company knowledge: grounding answers in your own documents

How retrieval-augmented generation works, in simple terms: chunks, embeddings and vector search

Preparing documents: cleaning, splitting and tagging

Answering with sources, and saying "I don't know" when the answer is not in the documents

Model Context Protocol (MCP): connecting agents to tools and data in a standard way, and when to use it

Keeping knowledge fresh: updating and re-indexing documents

Human-in-the-loop checking: what to check and how

Spotting made-up facts, fake sources, wrong numbers and biased results

Prompt injection: how hidden instructions in emails and documents can hijack a workflow

Data leakage: what must never be sent to an AI step or an external tool

Approval gates and confidence thresholds inside an automation

Building a verification checklist for each automation

What goes wrong: API failures, rate limits, timeouts, bad data and model errors

Error branches, retries with back-off, fallback paths and failure alerts

Run history, logs and tracing a failed run

Testing before launch: test data, edge cases and regression checks

Evaluating AI steps: accuracy, completion rate and quality scoring

Versioning, change control and safe updates of live workflows

Maintaining workflows over time: model changes and output drift

What a token is and how tokens are counted: input tokens, output tokens and the context window

How AI usage is priced, and why output tokens usually cost more than input tokens

Estimating the cost of one run and of a month of runs for a workflow

Writing shorter prompts and limiting output length

Context management: trimming conversation history, summarising earlier turns and passing only the fields a step needs

Prompt caching: placing fixed instructions first so they can be reused across runs

Model routing: using smaller, cheaper models for simple steps and stronger models only where needed

Batching non-urgent work and caching repeated answers

Retrieval trimming: sending only the most relevant chunks to the model

Budget limits, usage tracking, cost alerts and step limits that stop runaway agent loops

Balancing cost, speed and quality using test results

Ranking automation use cases by value and risk

Build, buy or partner: a simple decision guide, including licence and usage costs

Setting a starting baseline and success measures (KPIs)

Counting time saved, errors avoided and quality gained, net of platform and token costs, and showing risks honestly

Rolling out an automation: pilot, handover, training and ownership of live workflows

Pitching to leaders

Participants join cross-functional teams, each placed in one of four industries: Professional Services and Consulting, Banking and Financial Services, Retail and FMCG, or Government and Public Sector.

Each team inherits a set of manual business processes and works on them through the whole course, finding the best candidates for automation, designing the workflows, building them, making them reliable and cutting their running cost.

A short checkpoint at the end of each phase moves the team forward. After the foundations phase, the team reviews its tools and prompts. After the design phase, it selects and maps its priority workflows.

After the build phase, it demonstrates working automations. After the reliability and cost phase, it shows test results and the cost per run. After the business phase, it presents its business case to a mock leadership panel.

Additional case studies from healthcare, energy and telecom help learners from other sectors see themselves in the examples.

Participants work in a structured sandbox with a no-code automation platform, sample business apps and realistic test data.

They automate one recurring business task, such as triaging a shared inbox, extracting data from documents or routing an approval.

They add an AI step or an agent, then test the workflow with normal, tricky and unexpected inputs.

Each workflow is finished with error handling, a human verification step and a token and cost log that shows the cost of every run.

Each learner keeps an automation blueprint with the prompt library, workflow design, test results, cost per run and expected business result.