AI has become an important part of our research. We have explored practitioners’ expectations about its role in project work [1] and the implications of recent advances for engineering practice [2]. Within our group, we have also been collecting prompts and ideas to test in our own work.

We increasingly share these ideas with students and professional colleagues. PM Along AI gives them a public home where others can find them, try them, and adapt them to their own work.

A useful prompt does more than request an answer. It helps us define the task, examine the available inputs, make assumptions visible, and describe how a good result will be checked. Documenting why we use a prompt also helps others decide where it fits their work.

Prompting Idea

Map your work before deciding what to learn

Growing alongside AI means developing the expertise to direct its contribution and judge its results. That starts with understanding the work itself.

AI will not affect every task in the same way. A scheduler may use it to organize inputs or test construction logic. A risk analyst may use it to identify assumptions or compare scenarios. In both cases, the person still needs enough domain knowledge to notice missing evidence, weak reasoning, and project conditions that the AI does not understand.

Our work on project risk has highlighted the need to teach young engineers to challenge AI outputs [3]. Verification should be part of learning rather than a final administrative check. When we test an AI answer against evidence and professional knowledge, we also deepen our understanding of the task.

The following two prompts use that principle. The first creates a confirmed map of your work. The second uses that map to choose expertise and a practical experiment.

Prompt 1: understand my work

Interview me to understand how my work is done and where AI might help.

Ask one question at a time. Focus on the purpose of each task, how work
flows from inputs to outputs, where human judgment matters, how quality is
checked, and what repeatedly causes difficulty. Separate facts from my
assumptions.

Summarize what you learn as:
Task | Workflow | Human judgment and quality checks | Current difficulty

Ask me to correct the map before suggesting any use of AI.

Prompt 2: decide what expertise to build

Using my confirmed workflow map, identify where AI can assist now, what may
plausibly change over the next few years, and what human expertise should
grow alongside it.

Separate demonstrated capabilities from predictions and state the main
uncertainties. Recommend three learning priorities and one small experiment
I can try this week. For each recommendation, explain how I should verify
that it improves the work rather than merely making it faster.

Turn the result into practice

Choose one recurring task rather than trying to redesign an entire role. Correct the workflow map before asking for recommendations because a mistaken map will produce the wrong learning priorities.

Treat capability forecasts as scenarios, not facts. Tools, costs, organizational policies, and regulatory expectations can change. The useful question is which expertise remains valuable across several plausible futures.

Then run the proposed experiment on a low-consequence task. Compare the result with your current method. Check the quality, time required, errors, assumptions, and effort needed for review. Keep the practice only if it improves the work without weakening accountability.

This approach turns AI adoption into deliberate learning. The aim is not to predict every capability. It is to build enough understanding to make better choices as the technology and the work develop.

Notes

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References

1. Zangeneh, P., & Ghorab, K. (2025, March 12). Transformative technologies in construction and project management: 2025 outlook and survey results. EPM Research.

2. Zangeneh, P. (2026, September 11). AI takes shape: What recent AI advances mean for architecture, engineering, and construction. EPM Research.

3. Zangeneh, P. (2025, November 27). Industry should brace for the first wave of AI-driven risk management failures. EPM Research.