Build and Engineer lab: how AI is changing the tasks

Key facts

• The ILO's 2025 index rates building electricians, welders and machine tool setters "not exposed" to generative AI, with scores from 0.13 to 0.19 out of 1.^[1]
• Software developers score 0.53, in the ILO's second-highest exposure group.^[1]
• In April 2025, 79% of conversations on Anthropic's coding tool were people handing a task to AI rather than working alongside it.^[2]
• Employers in the WEF's 2025 survey listed solar energy installation engineers, mechanics and machinery repairers among growing jobs that need high manual skill.^[3]
• Malaysia's semiconductor strategy aims for 60,000 highly skilled engineers by 2030; 22,849 people had been trained by the third quarter of 2026.^[4]

In the Build and Engineer lab you work on things that have to stand up, switch on, run or flow: wiring, machines, sites, water, chips, solar panels and software. This brief shows how AI is changing some of the tasks in that work. It uses the "tasks, not job titles" method: look at what people do, then ask which parts AI can help with.

How to read the evidence

Most of the scores below come from the International Labour Organization's 2025 index. It rates each occupation from 0 to 1 by how much of its current tasks generative AI could potentially do, then groups occupations from "not exposed" up to gradient 4, the most exposed.^[1] Three warnings:

• It measures potential, not what has happened. The ILO stresses that exposure does not mean a whole occupation will be automated.^[1]
• It covers generative AI only. "Not exposed" means the ILO currently sees no such potential; robots and other machines are outside it, so it is not a promise that a job will stay the same.^[1]
• Matching a lab task to an ILO occupation is our choice and could be argued. The "how sure" column is our judgement too.

Example tasks and how AI is changing them

None of these tasks is "safe" and none is doomed. Robots, new rules or a change in costs could shift any of them, and the studies above do not model Malaysian workplaces.

Business, enterprise and AI run through every task

• Costing a job: pricing a solar installation, a repair or a filter that must be cleaned all year. The ILO's experts judged structured money estimates, such as drawing up a household budget, to be a task GenAI can do much of.^[1] AI can draft the quote; you still check the prices, the margin and what it costs to run.
• Pitching: explaining to a client why your design is worth the cost, and what it cannot do. AI can help you prepare; trust is built face to face.
• Using AI tools safely: check every number and safety rule an AI gives you against the official standard or your own test, and never paste a client's private details into a public tool. UNESCO's AI framework for students puts a human-centred mindset and the ethics of AI alongside AI techniques.^[5]

In the taster

In the Build and Engineer taster you build a water filter, ask AI for a layer order and test whether your results agree. That is the same move an engineer makes with any AI suggestion: treat it as a draft, test it, and decide. Making something work is not the same as making it safe, so say what your test did not check.

For facilitators

Ask the lab practitioner to correct the table before learners use it, and to swap in tasks they actually do. In the Job X-ray, learners can use the software developer row (new tasks: reviewing AI-written code, writing specifications and tests), the site supervision and drawings rows, and the solar and water rows.

What this means for you

• Hands-on technical work is among the least exposed to generative AI, on the ILO's measure. That is a reason to take TVET and technician routes seriously, not a guarantee.
• Software is changing fastest. If you like code, expect to spend more time checking, testing and explaining what AI writes.
• The money and people side matters in every job here. Costing, quoting and explaining a design to a client are skills you can start practising now.
• Your labels are guesses until a practitioner checks them. Ask, and change them when the evidence says so.

Your next step

1. Choose one task from the table and ask a practitioner in the lab: "What part of this has already changed in your work, and what has not?"
2. Read how AI may change your career area: engineering and trades, technology and AI or environment and sustainability. Then X-ray one job with the Tasks, not job titles method.