Microcredential
Practical AI Literacy for IT Professionals
This microcredential equips IT professionals with a clear, practical understanding of how AI systems work, where they are embedded in enterprise environments, and how to evaluate, use, and govern them safely.
It is AI capability literacy for IT practitioners.
- Duration: 20 hours
- Delivery Mode: Blended (live + guided labs)
- Level: Foundation (Level 1)
- Course Fee: $1,200 per participant (including GST)
Microcredential Overview
This microcredential equips IT professionals with a clear, practical understanding of how AI systems work, where they are embedded in enterprise environments, and how to evaluate, use, and govern them safely.
This is not a data science course.
It is AI capability literacy for IT practitioners.
Learning Outcomes
By the end of this microcredential, participants will be able to:
- Explain core AI and machine learning concepts in practical IT terms
- Distinguish between traditional automation and AI-driven systems
- Identify where AI is embedded within enterprise IT environments
- Evaluate strengths and limitations of large language models (LLMs)
- Apply safe and effective prompt engineering techniques
- Identify risks associated with AI systems, including hallucination, bias, and security exposure
- Assess readiness of an IT environment for AI adoption
Prerequisite
Basic IT knowledge (networks, systems, or software).
No AI background required.
Target Audience:
- Network engineers
- Systems administrators
- Developers
- SOC analysts
- Cloud engineers
- IT managers
- IT and computer science graduates
Modules and Topics
Module 1: AI Demystified for IT Professionals
Topics and activities:
- What AI actually is (vs hype)
- Machine learning basics explained simply
- Supervised vs unsupervised learning
- Neural networks at a conceptual level
- Automation vs AI
- map examples of AI already embedded in your organisation.
- Developing a short AI footprint summary.
Module 2: AI in Enterprise IT Environments (4–5 hrs)
Topics and activities:
- AI in cloud platforms
- AI in monitoring and observability tools
- AI in DevOps pipelines
- AI in security tools
- AI APIs and integrations
- Hands-on work on exploring AI capabilities inside major cloud or security platforms (simulated environment).
- Develop an enterprise AI systems map.
Module 3: Large Language Models & Generative AI
Topics and activities:
- How LLMs work at a high level
- Tokens, training data, model limits
- Hallucination and reliability
- Prompt engineering fundamentals
- Safe enterprise use of generative AI
- Write structured prompts
- Compare prompt variations
- Analyse failure cases
- Prompt design exercise with reflection.
Module 4: AI Risk, Governance & Security Basics
Topics and activities:
- Data privacy and AI
- Model bias
- Adversarial risks
- Shadow AI in organisations
- Governance principles
- Human-in-the-loop systems
- Conduct a simple AI risk scan for workplace.
- Develop an AI risk checklist.
Module 5: AI Readiness for IT Teams (2–3 hrs)
Topics and activities:
- Infrastructure considerations
- Data maturity
- Skill gaps
- Build vs buy decisions
- Evaluating AI vendors
- AI readiness and adoption self-assessment
- Develop readiness scorecard + improvement priorities.
Assessments
Assessments are practical, short, and applied.
- Assessment 1 – AI Systems Identification Exercise (25%)
- Assessment 2 – Prompt Engineering & Risk Reflection (25%)
- Assessment 3 – AI Readiness & Governance Brief (50%)
Have Questions About This Course?
We’re here to help! If you have any questions about the course content, admission requirements, or enrollment process, feel free to reach out to us. Our admissions team will be happy to assist you.
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