Equinet Academy
Programme Outcomes
Skills You’ll Take Away
Completing this programme gives you the ability to make sound, defensible decisions about AI adoption, deployment, and oversight. You will be able to:
- Evaluate AI and ML use cases using a structured feasibility framework covering data readiness, outcome clarity, and the consequences of error
- Build and interpret supervised regression models and unsupervised clustering analysis using no-code tools on real data
- Use generative AI tools with consistent, professional results across writing, analysis, research, ideation, and visual content generation
- Apply advanced prompt engineering techniques, including Chain-of-Thought, Template Pattern, Root
- Prompts, and Semantic Filters, to produce reliable, guardrail AI outputs
- Configure and deploy AI agents that automate multi-step business workflows with appropriate human oversight and error handling
- Monitor and govern AI systems across their lifecycle for performance, safety, and responsible use
- Communicate AI capabilities, limitations, and outputs clearly to technical and non-technical stakeholders
- Recommend AI adoption strategies grounded in feasibility, ROI, and organisational readiness
The outcome is decision-making capability, applied tool competency, and operational control over AI systems: not surface-level familiarity.
Target Audience
Who This Programme Is For
This programme is designed for professionals who are responsible for applying, evaluating, or governing AI initiatives, and who need to do so without a technical background.
Business owners and SME leaders who need to evaluate AI investments, brief technical teams, and make informed decisions about AI adoption
Product managers overseeing AI-enabled features or workflows who need a structured framework for feasibility and risk evaluation
Operations and transformation managers deploying AI in business processes who need to assess use case fit and manage output quality
Consultants and advisors who guide clients on AI adoption and want stronger frameworks for evaluation, governance, and practical application
Marketing and content professionals who use generative AI tools daily and want to develop more systematic, high-performance prompting and workflow automation capabilities
Mid-career professionals moving into AI-enabled roles who want structured, credential-backed evidence of applied AI competency
Innovation leads and digital transformation professionals who need to understand the full AI application stack: from ML fundamentals to agentic deployment, without a technical background
Differentiators
What Makes CAIP 2.0 Different
Most AI training available to business professionals falls into one of two categories. The first is technical: programmes designed for developers, data engineers, or ML practitioners who can write code, interpret model architectures, and work with training pipelines. These serve the people they are designed for well, but they assume a technical foundation that most business professionals do not have and do not need.
The second is introductory: short workshops and one-day tool demonstrations that show learners how to write basic prompts, walk through a few use cases, and end with a certificate. These are accessible, but they stop exactly where the hard questions start. How do you reliably control what a model produces? How do you evaluate whether AI is worth deploying in a specific business context? How do you build automated workflows that run without constant supervision?
CAIP 2.0 occupies a position that neither category addresses: a full, structured AI capability pathway built for non-technical business professionals who need to apply, evaluate, and govern AI at a practitioner level. Not a tool demonstration. Not an engineering course. A deliberate progression from AI and machine learning fundamentals through to generative AI application, advanced prompt engineering, and agentic workflow deployment, with governance and responsible AI integrated throughout every module.
Get Certified
Certification Pathway
Participants who complete all four modules and pass the required assessments in each will be awarded the Certified AI Practitioner (Business and Work Applications) 2.0 certification issued by Equinet Academy.
To attain the full CAIP 2.0 certification:
- Complete Module 1: AI Essentials (1 day, 8 classroom hours) and pass the module assessment to receive the Certificate of Completion
- Complete Module 2: Generative AI Applications (ChatGPT, Claude, and Mainstream AI Tools) (2 days, 16 classroom hours) and pass the module assessment to receive the Certificate of Completion
- Complete Module 3: Prompt Engineering (1 day, 8 classroom hours) and pass the module assessment to receive the Certificate of Completion
- Complete Module 4: Agentic AI (2 days, 16 classroom hours) and pass the module assessment to receive the Certificate of Completion
- Pass all module assessments across the four modules: Case Study Written Assessment and Individual Project Presentation in each module
Total Duration: 4 Modules / 6 Days / 48 Classroom Hours
Each module can also be completed independently and carries its own Certificate of Completion by Equinet Academy. Learners who have already completed one or more modules of the original Certified AI Practitioner programme may count those completions toward the CAIP 2.0 certification by completing the remaining modules under the new structure.
Candidates who complete each module with at least 75% attendance and demonstrate competency in all assessments will receive the corresponding award for that module. Candidates who complete all three modules will receive the Certified AI-Practitioner (Business & Work Applications) certificate from Equinet Academy.
Programme Modules
Structured Lineup
The programme comprises four modules. Modules 1 and 3 run for 1 day each (8 hours). Modules 2 and 4 run for 2 days each (16 hours). Each module combines interactive instruction, hands-on applied practice, a written case study assessment, and an individual project presentation. Modules can be taken individually or as a full certification track.
Focus:
Practical introduction to AI and machine learning for non-technical professionals. Learners work with 181,262 real Singapore HDB transactions using the free Orange Data Mining tool to build, evaluate, and interpret supervised and unsupervised ML models without any code. Covers AI use case evaluation, EDA, regression modelling, clustering, responsible AI, and communicating data-driven insights. Carries a Certificate of Completion by Equinet Academy.
- AI, ML, and Data Analytics Foundations: what AI and ML are, supervised vs unsupervised learning, the 3-question feasibility test, the no-code tools landscape, and Orange Data Mining setup with the HDB dataset
- Exploratory Data Analysis and Supervised Machine Learning: Price Detective (EDA), Feature Face-Off (correlation and feature selection), Beat the Model (Linear Regression vs Random Forest, RMSE), and interpreting ML outputs for business decisions
- Unsupervised Learning, Responsible AI, and Solution Adoption: Name That Cluster (k-Means clustering), responsible AI evaluation framework, bias and data privacy, and communicating data-driven insights to stakeholders
- Assessment: Case Study Written Assessment and Individual Project Presentation
Focus:
Hands-on capability across the leading generative AI tools, including ChatGPT, Claude, Gemini, Perplexity, and Microsoft Copilot. Covers how LLMs work, structured prompting, output evaluation, business writing and summarisation, visual content generation, tool selection, hallucination detection, and responsible AI use in professional contexts. Carries a Certificate of Completion by Equinet Academy.
- How Generative AI Actually Works: tokens, probability, training data, why outputs vary, model differences between ChatGPT, Claude, and Gemini, and common failure modes including hallucination and sycophancy
- Getting Consistently Good Outputs: prompt anatomy, bad vs good prompts, role prompting, iterative refinement, chain-of-thought prompting, and hands-on practice across tools
- Applying GenAI Across Business Functions: business writing, summarisation, research with Perplexity, ideation, visual content generation with DALL-E and Midjourney, and hallucination detection in written outputs
- Evaluating, Selecting, and Using AI Responsibly: tool comparison and task-tool fit, data privacy and confidentiality, copyright and IP, communicating AI-generated work, and building AI into professional workflows
- Assessment: Case Study Written Assessment and Individual Project Presentation
Focus:
Systematic prompt design for professionals who need consistent, high-quality AI outputs. Covers prompt types, structural frameworks, the Cognitive Verifier, Chain-of-Thought, Template Pattern, Root Prompts, Context Injection, Semantic Filters, and integrated prompt system design. Trainer: Tat Yuen. Carries a Certificate of Completion by Equinet Academy.
- Foundations of Prompting and Structure: prompt types (zero-shot, few-shot, Persona Pattern, conditional), the 4-component structural framework, the Cognitive Verifier, wicked problems and complex use case framing, and the Flipped Interaction technique
- Advanced Strategies, Optimisation, and Evaluation: Chain-of-Thought and Zero-Shot CoT, Task Decomposition, Outline Expansion, Template Pattern with structured blueprints, Meta Language Creation, Alternate Approach Pattern, LLM Grading, Prompt Critique, and Fact Check List integration
- Model Adaptability, Guardrails, and Applied Prompt Design: model parameters (temperature, top-p, tokens), cross-model performance comparison, Root Prompts, Context Injection, Semantic Filter, responsible AI guardrails, layered multi-step prompts, and a final integrated prompt engineering session
- Assessment: Case Study Written Assessment and Individual Project Presentation
Focus:
Build-first course covering how AI agents reason, plan, and act. Learners deploy single-tool agents, build multi-step automated workflows, design human-in-the-loop checkpoints, handle errors, and deploy and monitor agents in live business environments. Trainer: Dale Peh. Carries a Certificate of Completion by Equinet Academy.
- How Agentic AI Works: what makes an AI agent different from a chatbot, the think-act-observe loop, types of agents (single-agent, multi-agent, tool-augmented), the agentic AI landscape, and real business applications across functions
- Setting Up Your First AI Agent: platform selection, writing effective agent instructions, connecting tools (web search, documents, APIs), testing against real tasks, and diagnosing and fixing common first-agent failures
- Building Multi-Step Agentic Workflows: workflow design principles, chaining actions, tool integration (email, calendar, CRM, files), human-in-the-loop checkpoints, and handling errors and unexpected agent behaviour
- Deploying and Managing Agents in Business Contexts: moving from test to live, monitoring agent performance, maintaining and improving agents over time, practical guardrails, scaling to multiple workflows, and a final build presentation
- Assessment: Case Study Written Assessment and Individual Project Presentation
Trainers
Meet Your Educators
Fees Breakdown
Programme Fees
| Module | Full Fee | After Bundle Discount | After Max Modular Funding |
|---|---|---|---|
| M1: AI Essentials | S$239.20 | S$239.20 | |
| M2: Generative AI Applications (ChatGPT, Claude, and Mainstream AI Tools) | S$399.20 | S$399.20 | |
| M3: Prompt Engineering | S$239.20 | S$239.20 | |
| M4: Agentic AI | S$399.20 | S$399.20 | |
| Full Certified AI Practitioner (Business and Work Applications) 2.0 Programme After Max Modular Funding | Total Nett Fee: | From S$1,276.80 excl. GST (GST Absorbed) | |
*Funding eligibility varies by module. Select modules currently qualify for SkillsFuture Credit and SSG subsidies when taken individually. Speak to a course consultant to confirm eligibility for specific modules and to understand the right enrolment path for your situation.
Frequently Asked Questions (FAQs)
The Need to Know
Everything you need to know about the programme. Can’t find the answer you’re looking for? Please contact our friendly team.
Yes. Each module can be taken as a standalone course and carries its own Certificate of Completion by Equinet Academy. The full CAIP 2.0 certification requires completion of all four modules. The recommended sequence is Module 1, then Module 2, then Module 3, then Module 4, as each module builds on the capabilities developed in the previous one.
The recommended sequence is Module 1 (AI Essentials), then Module 2 (Generative AI Applications), then Module 3 (Prompt Engineering), then Module 4 (Agentic AI). Each module builds directly on the knowledge and capability developed in the previous one: Module 2 builds on AI literacy from Module 1; Module 3 builds on the prompting foundations from Module 2; Module 4 requires comfortable, regular use of AI tools and builds on the prompting and workflow skills from Modules 2 and 3.
No. The CAIP 2.0 is designed specifically for non-technical business professionals. No programming, coding, or mathematical background is required across any of the four modules. Module 1 uses Orange Data Mining, a free no-code visual tool. Modules 2, 3, and 4 work with web-based AI tools accessible to any professional with a laptop and internet access.
Each module includes two standard assessment components: a Case Study Written Assessment (open-book, 45 minutes) and an Individual Project Presentation (approximately 15 minutes). Assessments are performance-based and test applied competency through realistic professional scenarios, not theoretical recall. Learners must pass both components in each module and achieve at least 75% attendance to receive the module Certificate of Completion.
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