Certification in AI Workflow Automation in Dubai
40-hour prompt design & reusable library training
Generative AI fundamentals, agents & conversation management
Workflow redesign & ROI business case development
Output verification, responsible AI & governance
Industry simulations & automation sandbox practice
4.9/55348Enrolled Learners
4.9/55348Enrolled Learners
Overview
This course will help you in:
- Gaining AI automation literacy and selecting suitable no-code platforms
- Writing structured prompts that deliver consistent machine-readable results
- Evaluating processes and redesigning workflows for AI suitability
- Creating no-code automations using triggers, logic, and API connections
- Adding AI steps for classification, data extraction, and content drafting
- Building agents equipped with tools, memory, limits, and RAG grounding
- Applying testing, error handling, monitoring, and token cost management
- Developing ROI-focused business cases and practical adoption plans
Upcoming sessions
Curriculum
1
How large language models work in simple terms: tokens, context window and temperature
2
Chatbots, AI assistants, AI agents and automated workflows: what is different
3
Rule-based automation, RPA and AI automation compared
4
The platform landscape: n8n, Make, Zapier, Microsoft Power Automate and Copilot Studio
5
Where AI automation pays off, and where it is the wrong answer
6
Shadow automation: unmanaged flows built outside the team's knowledge, and why they matter
AI Integration
- Use an LLM to compare automation platforms against a stated business need.
- Generate a plain-language glossary of automation terms for the participant's own role.
Activities/Case Study
- Hands-on: run the same business task on two automation platforms and compare setup effort, features and cost.
- Case study: a manual approval process cut from days to hours with AI automation.
- Simulation: sort ten business tasks into AI-ready, rule-based only and not for automation.
1
A simple prompt framework: role, goal, context, steps, format and limits
2
System prompts and user prompts for workflow steps
3
Structured output: getting JSON, tables and fixed fields that the next step can read
4
Examples and few-shot prompting for consistent results
5
Prompt chaining: breaking a large task into small, testable steps
6
Testing, comparing and versioning prompts before they go live
AI Integration
- Compare structured and unstructured prompt results side by side in a live LLM.
- Ask an LLM to critique a prompt and suggest changes, then test whether the changes helped.
Activities/Case Study
- Hands-on: rebuild five weak prompts, then write one prompt that returns clean JSON from a messy email.
- Case study: an automation that failed because the prompt returned free text instead of fixed fields.
- Simulation: build and test a five-step prompt chain for a real task from your own job.
1
Finding automation candidates: repetitive, rule-heavy and high-volume work
2
AI-readiness scoring: value, effort, risk and data availability
3
Mapping a current workflow and finding delays, hand-offs and rework
4
Choosing where AI fits, where plain automation fits and where a person must stay
5
Drawing the future-state workflow with triggers, steps, decisions and outputs
6
Setting a baseline and measuring time saved and quality gained
AI Integration
- Paste a process description into an LLM to draft a step list and flag the steps suited to automation.
- Ask an LLM to find missing exceptions and edge cases in the future-state workflow.
Activities/Case Study
- Hands-on: score three workflows with a readiness matrix and redesign the best one from trigger to final output.
- Case study: a finance reporting process redesigned with AI steps and human checks.
- Simulation: present the future-state map to a process owner and answer challenges.
1
Triggers: schedules, webhooks, form submissions and new emails or files
2
Actions, nodes and modules: how data moves from step to step
3
Working with data: fields, lists, JSON, mapping and transformation
4
Conditions, branches, loops and routers
5
APIs and webhooks at concept level: requests, responses, keys and authentication
6
Connecting business apps: email, spreadsheets, CRM, chat and cloud storage
7
Credentials and permissions: connecting accounts safely with least access
AI Integration
- Ask an LLM to explain an API response and suggest the field mapping.
- Generate realistic sample test data for the workflow with an LLM.
Activities/Case Study
- Hands-on: build a workflow that reads a form entry, branches on a condition and updates a spreadsheet.
- Hands-on: call a public API from a workflow and map the response into a table.
- Case study: a lead-routing automation broken by a changed field name.
- Simulation: find and fix a failing workflow using the run history.
1
Adding an LLM step to a workflow: model choice, prompt and output field
2
Classifying and routing emails, tickets and requests
3
Extracting fields from invoices, forms, contracts, PDFs and images
4
Summarising and drafting inside a workflow: emails, reports and replies
5
Handling confidence: when the AI is unsure, route the item to a person
6
Multilingual steps in English and Arabic
AI Integration
- Use an LLM to label a sample set of messages as test data.
- Generate two prompt versions for the extraction step and score the results.
Activities/Case Study
- Hands-on: build an email triage workflow that classifies each message, drafts a reply and routes unclear cases to a person.
- Hands-on: extract fields from five sample invoices into a spreadsheet.
- Case study: mixed Arabic and English forms in a bank or government office.
- Simulation: measure accuracy on a test set and fix the prompt.
1
Workflow or agent: deciding how much freedom the AI should have
2
Agent building blocks: goal, instructions, tools, memory and steps
3
Agent patterns: tool use, planning, reflection and multi-agent teams
4
Giving an agent tools: search, calendar, email, databases and other workflows
5
Short-term and long-term memory: what to remember and what to forget
6
Setting limits: allowed actions, step limits and approval before sensitive actions
7
Testing an agent with normal, tricky and unexpected inputs
AI Integration
- Use an LLM to draft the agent's instructions and tool descriptions, then refine them after testing.
- Generate a test set of twenty inputs, including edge cases, to check the agent.
Activities/Case Study
- Hands-on: build an agent with two tools and memory for a recurring request, such as meeting follow-up.
- Case study: an agent that looped and used up its budget with no step limit.
- Simulation: stress-test the agent with tricky inputs and fix its weak answers
1
Why AI needs company knowledge: grounding answers in your own documents
2
How retrieval-augmented generation works, in simple terms: chunks, embeddings and vector search
3
Preparing documents: cleaning, splitting and tagging
4
Answering with sources, and saying "I don't know" when the answer is not in the documents
5
Model Context Protocol (MCP): connecting agents to tools and data in a standard way, and when to use it
6
Keeping knowledge fresh: updating and re-indexing documents
AI Integration
- Ask an LLM to generate test questions from the documents and check the answers against the sources.
- Use an LLM to suggest chunk sizes and tags for a given document set.
Activities/Case Study
- Hands-on: build a knowledge assistant over a set of business documents that answers with sources.
- Hands-on: connect an agent to a tool through an MCP server or connector.
- Case study: a support assistant that gave outdated answers from stale documents.
- Simulation: test retrieval quality with ten questions and improve the document setup.
1
Human-in-the-loop checking: what to check and how
2
Spotting made-up facts, fake sources, wrong numbers and biased results
3
Prompt injection: how hidden instructions in emails and documents can hijack a workflow
4
Data leakage: what must never be sent to an AI step or an external tool
5
Approval gates and confidence thresholds inside an automation
6
Building a verification checklist for each automation
AI Integration
- Add a second LLM step that checks the first step's output against the source and flags differences.
- Generate adversarial test inputs for the workflow with an LLM.
Activities/Case Study
- Hands-on: audit an AI-generated workflow output that contains hidden errors and bias.
- Hands-on: add an approval gate and a confidence check to the email triage workflow.
- Case study: a prompt injection hidden in a customer email.
- Simulation: test a classmate's workflow with malicious and misleading inputs
1
What goes wrong: API failures, rate limits, timeouts, bad data and model errors
2
Error branches, retries with back-off, fallback paths and failure alerts
3
Run history, logs and tracing a failed run
4
Testing before launch: test data, edge cases and regression checks
5
Evaluating AI steps: accuracy, completion rate and quality scoring
6
Versioning, change control and safe updates of live workflows
7
Maintaining workflows over time: model changes and output drift
AI Integration
- Use an LLM to read an error log and suggest likely causes.
- Generate edge-case test inputs, and use an LLM judge to score outputs against a rubric.
Activities/Case Study
- Hands-on: add retry, fallback, and alert paths to an existing workflow, then break it on purpose to test them.
- Hands-on: build a test set and score an AI step before and after a prompt change.
- Case study: a workflow that stopped silently for a week with no alert.
- Simulation: handle a live failure from alert to fix, and write a short incident note.
1
What a token is and how tokens are counted: input tokens, output tokens and the context window
2
How AI usage is priced, and why output tokens usually cost more than input tokens
3
Estimating the cost of one run and of a month of runs for a workflow
4
Writing shorter prompts and limiting output length
5
Context management: trimming conversation history, summarising earlier turns and passing only the fields a step needs
6
Prompt caching: placing fixed instructions first so they can be reused across runs
7
Model routing: using smaller, cheaper models for simple steps and stronger models only where needed
8
Batching non-urgent work and caching repeated answers
9
Retrieval trimming: sending only the most relevant chunks to the model
10
Budget limits, usage tracking, cost alerts and step limits that stop runaway agent loops
11
Balancing cost, speed and quality using test results
AI Integration
- Ask an LLM to compress a long prompt, then compare the output quality with the original.
- Use an LLM to summarise a long conversation into a short memory note, and test that the key facts are kept.
Activities/Case Study
- Hands-on: measure the token use and cost of an existing workflow, then reduce both without losing output quality.
- Hands-on: apply model routing to a multi-step workflow and compare cost and accuracy before and after.
- Case study: an agent loop that multiplied a monthly AI bill.
- Simulation: build a cost forecast for a workflow running at ten times its current volume and propose savings.
1
Ranking automation use cases by value and risk
2
Build, buy or partner: a simple decision guide, including licence and usage costs
3
Setting a starting baseline and success measures (KPIs)
4
Counting time saved, errors avoided and quality gained, net of platform and token costs, and showing risks honestly
5
Rolling out an automation: pilot, handover, training and ownership of live workflows
6
Pitching to leaders
AI Integration
- Use an LLM to draft the business case structure, then ask it for counter-arguments to test the assumptions.
- Rehearse the pitch with an LLM acting as a sceptical sponsor.
Activities/Case Study
- Hands-on: complete a business case template for one automation, with costs, benefits, and payback.
- Case study: an automation that was switched off after launch because nobody owned it.
- Simulation: defend the business case in a short question round with a mock leadership panel.
1
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.
2
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.
3
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.
4
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.
5
Additional case studies from healthcare, energy and telecom help learners from other sectors see themselves in the examples.
1
Participants work in a structured sandbox with a no-code automation platform, sample business apps and realistic test data.
2
They automate one recurring business task, such as triaging a shared inbox, extracting data from documents or routing an approval.
3
They add an AI step or an agent, then test the workflow with normal, tricky and unexpected inputs.
4
Each workflow is finished with error handling, a human verification step and a token and cost log that shows the cost of every run.
5
Each learner keeps an automation blueprint with the prompt library, workflow design, test results, cost per run and expected business result.
Meet your Trainer
Our Trainers
Learners Point has a reputation for high-quality training that makes a difference in people's lives. We undertake a practical and innovative approach to working closely with businesses to improve their workforce. Our expertise is wide-ranging with ample support from our expert trainers who are globally recognized and hold a diverse set of experiences in their field of expertise. We are proud of our instructors who take ownership of our distinctive and comprehensive training methodologies, help our students imbibe those with ease, and accomplish gracefully.
We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.
Learning Outcomes
After finishing this course, you will be able to:
1
Deliver a fully working AI automation built on a real business process
2
Create and maintain a tested prompt library for consistent machine-readable outputs
3
Design bounded AI agents grounded in organisational knowledge using RAG
4
Track and lower token costs without reducing the quality of workflow results
5
Use error handling, testing and monitoring to keep live automations dependable
6
Prepare and present a leadership-ready business case with clear ROI metrics
KHDA Certificate
Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.
Learners Point Certificate
Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.
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Frequently asked questions
The Certification in AI Workflow Automation in Dubai covers eleven modules that take participants from LLM fundamentals, such as tokens, context windows and temperature to triggers, webhooks and APIs, connecting accounts with least access. Early modules also compare RPA with AI automation and explain shadow automation.
Participants also cover agent memory and step limits, workflow versioning, output drift monitoring and a build, buy or partner decision guide. Each module closes with a stated skills outcome.
This AI Workflow Automation Course in Dubai suits working professionals with 2+ years of experience and basic AI awareness who use AI occasionally and want to automate real tasks in their own teams. No coding or programming background is needed.
It also fits process owners who must justify automation spend to leaders, and participants from healthcare, energy or telecom, where extra case studies reflect their sectors. It is designed for no-code practitioners, not developers.
No, coding experience is not required for this course. Participants build in a no-code automation platform and meet APIs and webhooks at concept level. The stated entry requirement is 2+ years of work experience and basic AI awareness.
Technical ideas are taught through practice. Participants learn JSON and data mapping by moving a form entry into a spreadsheet, then use an LLM to explain an API response and suggest field mapping. The apps involved are email, spreadsheets, CRM and chat.
This AI Workflow Automation course in Dubai teaches participants to design automated workflows, not just to use GenAI tools occasionally. It targets the gap in design, reliability and cost control, so every automation ends with a verified result and a measured cost.
Participants also learn where AI is the wrong answer, choosing between plain automation, an AI step or an agent, and deciding when a person must stay in the loop. They build the habit of testing before launch and monitoring after launch.
This training in the UAE runs for 40 learning hours. The course is delivered online with live facilitation, breakout rooms, polls, and shared digital workspaces to ensure interactive, practical learning.
Sessions are scheduled on weekends to accommodate working professionals in Dubai, Abu Dhabi, and the UAE. Participants engage in industry simulations and an automation sandbox, designed to fit into a compact, high-impact format without disrupting weekday commitments.
This training supports AI adoption by building a shared way of designing, testing and running automation across teams, from operations to finance and HR. It supports faster processes, fewer manual errors, controlled AI spend and clearer visibility of workflow performance.
Adoption also needs ownership. Participants plan a pilot, handover, training and ownership of each live workflow, after studying an automation switched off because nobody owned it. The result is a pipeline of proven use cases ready to scale across the organization.
Participants use no-code automation platforms, live LLMs and everyday business apps inside a structured sandbox with realistic test data, so participants do not need to write any code.
Key tools used during this AI training:
- n8n, Make, Zapier, Microsoft Power Automate and Copilot Studio, compared on setup effort, features and cost
- A no-code automation platform in the sandbox to build, test and cost-log one recurring task
- LLMs to critique prompts, generate test data and score outputs against a rubric
- Email, spreadsheets, CRM, chat and cloud storage, connected as sample business apps
- An MCP server or connector to link an agent to tools and data
- Run history, test sets and a business case template to trace failures and calculate payback
Learners Point delivers this AI Workflow Automation Training in Dubai with a strong focus on practical application and measurable business outcomes for professionals and organizations across the UAE.
Key reasons to choose Learners Point for this training:
- Industry-aligned curriculum covering generative AI, AI agents, workflow automation, and responsible AI governance
- Experienced facilitators with real-world expertise in AI adoption, process redesign, and business transformation
- Hands-on learning through an industry simulation, automation sandbox, and capstone project for job-ready skills
- Flexible delivery with live weekend online sessions to suit working professionals in Dubai and the UAE
- Actionable outputs including prompt libraries, workflow maps, verification checklists, and ROI-backed business cases
- Continued support for individuals and corporate teams seeking to scale AI-first practices across functions
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