Certification in AI Automation Workflow
16-hour structured prompt design & reusable prompt library training
Generative AI fundamentals, agents & conversation management
Workflow assessment, AI-enabled redesign & ROI business cases
Output verification, responsible AI & governance practices
Industry simulations & automation sandbox for real workflows
Capstone-driven AI adoption planning with no coding required
4.9/54.9/5Overview
What you will learn:
- Master generative AI fundamentals and agent applications for business
- Design structured prompts and manage multi-step conversations
- Build reusable role-specific prompt libraries and configure AI agents
- Draft analyse and adapt business content using AI tools
- Assess workflows and redesign processes for AI-enabled productivity
- Verify AI outputs and apply responsible AI governance principles
- Construct AI business cases with clear ROI metrics and KPIs
- Lead AI adoption change management and deliver the capstone project
Curriculum
How generative AI and LLMs work
AI, agents, and chat: key differences
Capabilities, limitations, and context windows
When AI is not the right solution
Responsible AI principles introduced
AI Integration
- Analyze a set of business scenarios to classify which generative AI capability applies and why.
- Support participants in distinguishing agent-enabled workflows from standard chat interactions using live tool examples.
Activities/Case Study
- Exercise: map three business scenarios to AI suitability.
- Case study: AI misuse and its business consequences.
- Simulation: identify AI-ready versus AI-risky tasks.
Structured prompt frameworks and components
Prompt chaining for multi-step tasks
Context-setting and persona instructions
Saving, scheduling, and sharing prompts
Managing conversation history and continuity
AI Integration
- Analyze prompt drafts submitted by participants and identify structural weaknesses that reduce output quality.
- Support iterative prompt refinement by generating comparative outputs from structured versus unstructured prompt versions.
Activities/Case Study
- Hands-on: build a structured prompt for a recurring task.
- Case study: inconsistent output caused by weak prompting.
- Simulation: prompt chaining for a multi-step business request.
Designing role-specific prompt templates
Organising a shared prompt library
Configuring agents from pre-built templates
Adding knowledge and instructions to agents
Sharing and governing prompt assets across teams
AI Integration
- Analyze existing business task descriptions to recommend prompt template structures suited to each recurring workflow.
- Support agent configuration by generating draft knowledge-base instructions participants can test and refine in real time.
Activities/Case Study
- Hands-on: create a five-prompt library for your role.
- Case study: team-wide prompt standardisation in consulting.
- Simulation: configure and test a pre- built agent for a workflow.
Drafting emails, reports, and presentations
Summarising meetings and unstructured inputs
Adapting tone and format for stakeholders
Cleaning and analysing data with natural language
Creating visualisations from AI-generated insights
AI Integration
- Analyze participant-submitted draft documents and generate structured revision suggestions aligned to the target stakeholder audience.
- Support data analysis tasks by converting natural-language business questions into structured queries and visualisation recommendations.
Activities/Case Study
- Hands-on: draft a stakeholder report using AI tools.
- Case study: AI-assisted executive briefing in financial services.
- Simulation: reformat a dense report for three audience types.
AI-readiness criteria and feasibility scoring
Mapping current-state workflows for AI gaps
Prioritising workflows by value and risk
Designing AI integration points in a process
Measuring productivity and quality improvement
AI Integration
- Analyze submitted workflow descriptions to identify high-value AI integration points and flag feasibility risks automatically.
- Support process mapping by generating draft future-state workflow diagrams from participant-provided current-state descriptions.
Activities/Case Study
- Hands-on: score three workflows using an AI-readiness matrix.
- Case study: workflow redesign in a retail operations team.
- Simulation: redesign a selected workflow end-to-end with AI.
Human-in-the-loop verification techniques
Detecting hallucinations and factual errors
Prompt injection and data-leakage risks
Responsible AI principles in daily practice
Regulatory and data-protection considerations
AI Integration
- Analyze AI-generated outputs submitted by participants to flag potential hallucinations, factual inconsistencies, and bias indicators.
- Support governance practice by generating risk classification summaries for participant-described AI use cases across their functions.
Activities/Case Study
- Hands-on: audit an AI-generated report for errors and bias.
- Case study: governance failure and its regulatory consequences.
- Simulation: apply a verification checklist to a live AI output.
Use-case evaluation and prioritisation criteria
Build, buy, or partner: decision framework
Defining KPIs and baseline success metrics
Quantifying productivity and cost benefits
Presenting the business case to stakeholders
AI Integration
- Analyze participant-drafted business cases to identify gaps in KPI definition, cost assumptions, and risk disclosure sections.
- Support ROI modelling by generating scenario-based benefit and cost projections from participant-provided baseline data.
Activities/Case Study
- Hands-on: complete a business case template for one AI use case.
- Case study: ROI measurement for an AI initiative in banking.
- Simulation: defend an AI investment proposal under stakeholder questioning.
Assessing team AI readiness and maturity
Addressing resistance and uneven adoption
Building an AI adoption and communication plan
Scaling from pilot to team-wide deployment
Capstone: AI-first workflow redesign and pitch
AI Integration
- Analyze participant-submitted adoption plans to identify change-management gaps and recommend targeted communication strategies.
- Support capstone preparation by generating structured feedback on workflow redesign logic, governance choices, and ROI claims.
Activities/Case Study
- Hands-on: build a one-page AI adoption plan for your team.
- Case study: scaling AI adoption across a government department.
- Role-play: present your capstone workflow redesign to a panel.
Participants are placed into cross-functional teams representing four industry contexts: Professional Services and Consulting, Banking and Financial Services, Retail and FMCG, and Government and Public Sector.
Each team is assigned a realistic business scenario in which their organisation has committed to an AI-first transformation initiative but faces the full range of challenges identified in the programme: inconsistent prompting, unverified outputs, unclear governance, and a leadership team demanding a credible ROI case within a defined timeline.
Teams must act as AI-Enabled Business Professionals and AI Adoption Champions, making decisions under realistic constraints without access to technical or developer support.
Working through a structured simulation arc, each team maps their assigned workflows, scores them for AI readiness, designs prompt libraries for the highest-priority tasks, applies responsible-AI and governance checks, and builds a business case with defined KPIs and an adoption plan.
At the close of the simulation, each team presents their redesigned workflow, governance framework, and investment case to a simulated leadership panel.
Facilitators provide structured feedback on prompt quality, verification rigour, governance completeness, and the credibility of the ROI argument, giving every participant a realistic rehearsal of the professional outcomes the programme is designed to produce.
Participants enter a facilitated online sandbox environment where they work with the AI tools introduced throughout the programme, including generative AI chat interfaces, pre-built agent templates, and prompt management workspaces.
The sandbox presents a set of recurring business tasks drawn from the four industry domains covered in the programme: drafting stakeholder communications, summarising research inputs, cleaning and analysing operational data, and routing approval workflows.
Participants are challenged to automate each task using only the prompting, agent configuration, and workflow redesign skills developed in the training, with no coding or technical support available.
Using generative AI and agent-enabled automation, participants design prompt sequences that trigger consistent, reusable outputs across each task type.
They apply governance checkpoints at each automation step, verifying outputs for hallucinations, bias, and data-leakage risk before the automated result is treated as final.
Each participant produces a documented automation blueprint covering the prompt library, the agent configuration, the verification checklist, and the expected business outcome.
Facilitators review each blueprint against the responsible-AI and ROI criteria established in the programme, ensuring that every automated workflow is both productive and governance-ready before participants take it back to their organisations.