← UK Occupations
🇬🇧 UKSOC 3313· Protective Service Occupations

Fire service officers (watch manager and below)

Fire service officers (watch manager and below) co-ordinate and participate in firefighting activities, provide emergency services in the event of accidents or bomb alerts, and advise on fire prevention.

Fire engineerFire safety officerFirefighterWatch manager (fire service)
£40,775
median salary
£30,655 – £48,367
47,900
currently employed
47,202
projected 2035
Strong growth
9 tasks scored3 AI Assists6 Human-Led

Task Bundle Breakdown

How AI interacts with this occupation's task bundle

33%
67%
0%
AI automates
33%
AI assists
67%
Human-led

Task-by-Task Breakdown

Every task scored individually — sorted highest to lowest AI exposure

67

advises on fire safety measures in new buildings

AI Assists

AI tools like generative design software and fire safety compliance platforms (e.g., Revit with AI plugins) can analyse building plans and flag non-compliance, significantly supporting advisory work.

44

inspects premises to identify potential fire hazards and to check that firefighting equipment is available and in working order and that statutory fire safety regulations are met

AI Assists

AI tools like building inspection software and risk assessment platforms can flag hazards from floor plans or historical data, but a human officer must physically verify conditions on-site.

44

supervises a watch

AI Assists

AI scheduling and workforce management tools can assist with rota planning and performance tracking, but effective watch supervision relies on interpersonal leadership and situational judgment.

33

arranges fire drills and tests alarm systems and equipment

Human-Led

AI can help schedule drills and log test results digitally, but the physical execution of drills and hands-on equipment testing requires human presence and judgment.

22

removes goods from fire damaged premises, clears excess water, makes safe any structural hazards and takes any other necessary steps to reduce damage to property

Human-Led

Post-incident clearance and structural hazard assessment is a hands-on physical task, though AI structural analysis tools could theoretically assist with hazard identification from sensor data.

22

attends and deals with bomb alerts and accidents involving spillage of hazardous substances

Human-Led

Responding to bomb alerts and hazardous spills demands real-time human decision-making and physical action, though AI-powered chemical identification apps can assist in identifying substances.

11

travels to fire or other emergency by vehicle and locates water mains if necessary

Human-Led

AI has negligible impact on the physical act of driving to emergencies and locating water mains, though GPS and mapping tools like Google Maps already assist with routing.

11

operates hose pipes, ladders, chemical, foam, gas or powder fire extinguishing appliances

Human-Led

Operating hoses, ladders, and extinguishing equipment is a physically demanding, real-time task that AI cannot perform or meaningfully assist with in the field.

11

rescues people or animals trapped by fire or other emergency situations such as flooding and administers first aid

Human-Led

Rescuing people and administering first aid requires physical dexterity, real-time human judgment, and emotional intelligence that AI cannot replicate in emergency conditions.

ROI Calculator

Fire service officers (watch manager and below)

6.3hrs
saved per week
300hrs
per year ≈ 8 weeks
£6,796
salary-equivalent capacity freed per year

This is not a pay rise — it's 300 hours of productive capacity that AI could return to you, valued at your current rate.

£13,592
value at risk — 33% of role exposed to AI

The portion of your current salary tied to tasks AI can automate or augment. Not a prediction of job loss — a signal of where your role is changing fastest.

How is this calculated?▾

Task weights — each task is weighted by its O*NET frequency rating. Where frequency data is absent, equal weighting is applied.

AI time reduction — tasks scored as "AI Automates" assume 80% time saving; "AI Assists" assumes 40%; "Human-Led" assumes 5%.

Salary equivalent — annual hours saved × (salary ÷ 1,800 working hours/year).

Value at risk — salary × share of role time in AI-automate or AI-assist tasks.

Figures are indicative estimates for planning purposes, not guarantees of productivity gain or job loss risk.