PMGT5887 Chap.10 Automation and Generative AI in Project Management
Automation and Generative AI in Project Management
Week 9 asks how automation and AI are changing the project manager's job, and this chapter answers with the unit's own distinctions. It separates manual work, the rules-based automation layer of notifications and scripted data pulls, and the AI layer of prediction, decision support and content generation, and lists the five areas where the slides say AI has most impact.
It draws on PMI's AI Essentials guide for the three elements of AI, perception, prediction and planning, the difference between narrow AI and general AI, and the contrast between predictive and generative AI. It maps AI uses and example tools across the life cycle, pairs each use of large language models with its limit, and teaches the four prompt engineering principles through a re-prompted plan after a budget cut.
It finishes with the ethical and quality lenses, the red flags and mitigations on the slides, the four sign-off questions a manager should ask, and the University's rules on generative AI in assessment. A re-prompted plan shows the four principles working together on one task.
What this chapter covers
- 01
From manual work to automation and AI
- 02
Where AI has the biggest impact
- 03
Narrow AI, predictive AI and generative AI
- 04
AI across the project life cycle
- 05
Large language models: uses and limits
- 06
Four principles of prompt engineering
- 07
Ethical and quality checks
Worked example · free
Improving a weak prompt step by step
- +1Be specific: "List the top five risks for a three-day product expo."
- +1Provide context: add the venue, the 40 exhibitors, the two-person crew and the fixed opening date.
- +1Set constraints: "Give each risk a likelihood and impact from 1 to 5 and one mitigation, as a table."
- +1Iterate and check: re-prompt if a key vendor drops out, then verify every figure and claim against your own plan before use.
Key terms
- Rules-Based Automation
- Software that performs a fixed action when a set trigger occurs, without prediction or judgement.
- Generative AI
- AI that creates new content, such as plans, emails or risk logs, from a prompt.
- Predictive AI
- AI that uses historical data to forecast or classify events, such as a likely schedule delay.
- Large Language Model
- A model trained on vast amounts of text to predict and generate human-like language.
- Prompt Engineering
- Crafting the inputs to an AI tool so that it produces precise, relevant outputs.
- Human in the Loop
- A control in which a person reviews and signs off AI output before it is used.
Automation and Generative AI in Project Management FAQ
Is a Jira automation rule a form of AI?
No. A rule that acts when a trigger fires, such as notifying the team when a card moves, is rules-based automation. AI predicts, recommends or generates content rather than following a fixed rule.
What are the main risks of using generative AI on a project?
Invented facts and dates, leaking sensitive data to public models, hidden bias from training data, inconsistent answers between runs, and over-reliance on output that nobody has checked.
Can I use generative AI in the in-semester test?
The official Semester 2 table prohibits AI in the in-semester test. It limits AI use for the early team charter according to Canvas and allows it for the listed practical tasks, presentation and participation. Follow the live brief, protect private information and acknowledge any use where the task requires it.
Which AI tools does the unit mention?
ChatGPT, Microsoft Copilot, Google Gemini, Meta AI and PMI Infinity for generative work, and examples such as ClickUp AI, Wrike Work Intelligence, Asana AI, Jasper and Notion AI across the life cycle.
What does narrow AI mean?
Narrow AI is built to perform specific tasks, such as chatbots or predictive analytics. PMI's guide contrasts it with artificial general intelligence, which would generalise across domains and which it says has not yet been built.
Assessment move
Keep three short lists: the five impact areas, the four prompt principles and the four sign-off questions. Practise classifying examples as automation, predictive AI or generative AI until it is instant. Then take a real task from your group work, write a weak prompt and an improved one, run both, and note what you had to correct; that note is your best revision for the ethics and quality questions.
Remember the boundary for your own work: the current table prohibits AI in the in-semester test, limits it for the early charter and allows it for the listed practical tasks. Practising without it before the test is the honest way to check what you know.
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