AI and the future of Engineering and Skilled Trades: what changes, what stays human

Key facts

• The ILO puts building electricians (0.19), plumbers (0.18), motor mechanics (0.18) and welders (0.13) in its lowest band, "not exposed", on a 0 to 1 scale. Global generative-AI task-exposure estimates; not a Malaysian forecast; not a job-security rating. The index does not cover robots^[1].
• A US model by Anthropic (September 2026) estimates robots could technically do about 74% of US physical tasks, mostly in limited settings, but are cost-competitive for only 0.3% of job tasks today. US model data, not Malaysian demand^[2].
• Electronics employers in the World Economic Forum (WEF) survey said people mainly do 40% of tasks today and expected 25% by 2030; 87% plan to automate more processes. Global employer expectation, published 2025^[3].
• Malaysia's National Semiconductor Strategy, launched in May 2024, set a target to train and upskill 60,000 high-skilled engineers. A target, not an outcome^[4].

In engineering and the trades, "AI" means two different things. Generative AI, which writes, calculates and drafts, touches the paperwork and design side. Robots and factory automation touch the physical side, and they move at the speed of cost and safety rules. Keep the two apart when you read any forecast.

For routes in, SPM subjects, TVET options and the licences some trades need, start with Engineering & Skilled Trades after SPM.

How to read this page

Observed means someone measured it. Projected means someone estimated the future, so we say who, when and for where. Projections are often wrong.

The scores come from the International Labour Organization (ILO). They estimate how much of an occupation's current tasks generative AI (tools that write, summarise or make images from a prompt) could potentially do, from 0 to 1^[1]. "Not exposed" is a technical label: most of the occupation's tasks scored consistently low for what generative AI can do today^[1]. It does not mean AI or machines will never change the work, and it says nothing about robots, cost or Malaysian employers. Exposure is not job loss either: the ILO says that depends on adoption and on whether workers get the chance to learn the tools^[1].

What is already happening (observed)

• Malaysian factories are automating step by step. The Federation of Malaysian Manufacturers (FMM) reported in July 2026 that Industry 4.0 adoption among manufacturers in its previous survey had risen to 38%, and that their workforce plans focus on upskilling, automation, internships and stronger TVET recruitment^[5]. This is a survey of FMM members, not of all firms.
• One usage dataset. In Anthropic's February 2025 data (Claude.ai conversations sorted into US job categories), jobs needing a lot of manual skill showed very low use of its AI^[6]. That is usage data for one AI product. It does not show Malaysian demand or future job security.

What people project, and who says so

• Robots, a model for the US. Anthropic's September 2026 model estimates robots could technically do about 74% of US physical tasks, mostly in limited or purpose-built settings such as factories, but are cost-competitive for only 0.3% of job tasks. Robots that could weld on their own cost about five times as much as a human welder. The authors say the model, built with their own AI's ratings, cannot confirm future outcomes^[2]. US model data, not Malaysian demand.
• Electronics employers worldwide. They said people mainly do 40% of tasks today, technology 21% and the two together 40%, and expected 25%, 35% and 39% by 2030; 87% planned to speed up automation. Growing roles included AI and machine learning specialists, robotics engineers and electrotechnology engineers. Global employer expectation, published 2025^[3].
• Manual skill plus tech. The WEF says growing jobs that need high manual skill, such as drafters, engineering technicians, electrotechnology engineers, mechanics, machinery repairers and solar installation engineers, increasingly combine manual ability with technological literacy and problem-solving. Global employer expectation, published 2025^[3].
• Malaysia. The National Semiconductor Strategy (May 2024) aims to train and upskill 60,000 high-skilled engineers^[4]. TalentCorp's study expects about 37,000 electrical and electronics jobs to be significantly impacted within three to five years, the Human Resources Minister said in August 2025^[7]. Both are plans and projections, not outcomes.

We did not find a current Malaysia-specific study that measures how AI changes trade and technician jobs here.

Task by task

These cards are our reading of the sources for a classroom exercise, not a forecast. A practitioner may disagree, and their reasons are worth hearing.

Install and test wiring in a building

• What a person does: the work in the ceiling, the testing, the safety call.
• What AI might assist: looking up rules and drafting the test report.
• What this source does not show: licensing rules or demand for electricians in Malaysia.

Find and fix a fault in a car or machine

• What a person does: confirms the fault and repairs it.
• What AI might assist: suggesting likely faults from codes and manuals.
• What this source does not show: how electric vehicles change this work in Malaysia.

Weld or fabricate a steel frame

• What a person does: varied and on-site welding with skilled hands.
• What AI might assist: little; robot welders exist, but cost far more in the US model.
• What this source does not show: robot costs in Malaysian workshops.

Check site work and run a safety briefing

• What a person does: sees the site, leads the crew, stops unsafe work.
• What AI might assist: daily reports and schedules.
• What this source does not show: how construction firms here adopt these tools.

Design a part and produce its drawings

• What a person does: checks the design and signs it off.
• What AI might assist: calculations and draft drawings.
• What this source does not show: who is liable when an AI calculation is wrong.

Assemble electronic boards on a production line

• What a person does: sets up, supervises and fixes the line.
• What AI might assist: machines more than AI; employers plan more automation.
• What this source does not show: how many Malaysian line jobs change, or when.

New tasks that are appearing

• Setting up, programming and repairing robots and automated lines.
• Reading machine data to predict breakdowns before they happen.
• Checking AI-produced calculations, quotes and drawings against standards before anyone builds from them.
• Keeping connected machines secure.

This list is our reading of the sources above, not an official one.

What stays human: hypotheses to test

These are practitioner hypotheses to test, not findings. The sources do not show that any of these tasks will stay unchanged.

• Does skilled work in unique, messy places (a 40-year-old house, a muddy site) still need a person?
• Who takes responsibility for safety?
• Who diagnoses a machine that is failing in a new way?
• Who explains the problem to a customer and earns their trust?

Ask a technician what has been automated where they work, and what has not.

What this means for you

• TVET routes are a strong start here. Employers say growing manual roles need technological literacy as well as hands-on skill (global employer expectation, published 2025)^[3]. Choose the route for the work, not for a low AI score.
• Learn the tech that runs the machines. Sensors, programmable controllers and basic coding make a technician more useful as factories automate.
• Collect qualifications and licences step by step. For routes into solar, semiconductors and electrical work, see Green and semiconductor jobs in Malaysia.
• Check every AI calculation. A wrong cable size or load figure is a safety risk, not a typo.
• Watch the National Semiconductor Strategy's progress, FMM's surveys on automation, and whether robots get cheaper faster than the US model assumes.

Your next step

1. On Day 2, choose the Engineering and Trades taster (a bridge that carries the load) or, if your cohort runs four labs, the Build and Engineer lab. Read the Build and Engineer lab brief first.
2. Ask the practitioner: "What has been automated in your workplace in the last five years, and who maintains it now?"

About the evidence

ILO scores (a global generative-AI task-exposure estimate; not a Malaysian forecast; not a job-security rating), on a 0 to 1 scale with the ILO's band^[1]:

• Electronics engineering technicians 0.38, minimal exposure
• Draughtspersons 0.35, minimal exposure
• Mechanical engineers 0.32, not exposed
• Civil engineering technicians 0.28, not exposed
• Construction supervisors 0.28, not exposed
• Electrical and electronic equipment assemblers 0.28, minimal exposure
• Air-conditioning and refrigeration mechanics 0.21, not exposed
• Building electricians 0.19, not exposed
• Plumbers and pipe fitters 0.18, not exposed
• Motor vehicle mechanics 0.18, not exposed
• Machinery mechanics 0.17, not exposed
• Welders and flame cutters 0.13, not exposed

The bands run from "not exposed" and "minimal exposure" up to gradients 1 to 4, the most exposed^[1]. In the ILO's expert review, one structured money task, developing a household budget, was rated highly automatable (0.64), and scores for physical tasks such as welding were lowered^[1]. The ILO built its scores from a survey of 1,640 workers in Poland, rounds of discussion with international experts, and an AI assistant built on that input. Every task counts equally, and the ILO says local factors its global study does not cover can speed up or limit the change^[1]. ILO occupation groups do not map one-to-one onto Malaysian job titles, and we do not claim a direct mapping.

WEF figures are expectations from a survey of more than 1,000 employers across 55 economies, published January 2025^[3]. Anthropic figures are its own usage data or a US model, not Malaysian demand. The method behind the task cards is in Tasks, not job titles.

For facilitators

Use the task cards as a starting list for the Day 2 AI task X-ray and ask the practitioner to correct them before learners use them. Each learner supports one card with a source and challenges one assumption. Stop any conclusion that a job is "safe" or "dead": the evidence here does not support either.