AI in fire risk assessment reporting: what it can and can’t do
Published · Updated · 6 min read
AI is good at the mechanical half of fire risk assessment: turning rough site notes into consistent professional wording, transcribing dictation, suggesting captions and hazards from photos, and cross-checking a draft report for contradictions. It cannot and should not make the risk judgement — competence, inspection and the final rating remain the assessor’s. The productive pattern is AI as a drafting and QC assistant inside a structured methodology.
The write-up problem
Ask any fire risk assessor where their evenings go and the answer is report writing. The judgement work happens on site; the hours disappear afterwards, converting scribbled notes and hundreds of photos into wording fit for a client, an insurer or a court. Because the write-up is repetitive and pattern-heavy, it is exactly the kind of work large language models handle well — under supervision.
What AI does well today
Inside a structured assessment workflow, AI reliably helps with:
- Observation rewrites — turning "ext prop open fd kitchen corridor" into a clear, professional observation with a recommended action.
- Section narratives — drafting the commentary for each checklist section from the recorded answers and observations.
- Voice transcription — dictated notes on site become text attached to the right observation.
- Photo analysis — suggesting what a deficiency photo shows and drafting its caption.
- Document intelligence — extracting site details from a previous FRA or fire strategy to pre-fill a new assessment.
- Quality control — checking a draft for contradictions, empty sections, rating/finding mismatches and missing dates before issue.
What must stay human
The fire risk rating, the judgement of whether a deficiency is tolerable, the decision to escalate, and the sign-off are professional acts that carry legal weight. An AI system has not walked the building, cannot assess the credibility of what it was told on site, and does not carry professional accountability. Competent-person requirements in UK fire safety law are unaffected by the tooling used to produce the report.
Grounding matters too. Generic AI wording drifts; the useful systems constrain generation with the assessor’s own findings and recognised guidance so that drafts start from the right frame of reference. In AssessHub, generation is grounded in the relevant HM Government fire safety guide for the premises type and reviewed against FSO compliance themes — and every AI draft remains editable, with the assessor’s final text clearly separated from rough notes.
The practical payoff
Teams using AI-assisted drafting inside a structured PAS 79 workflow typically report write-up time falling by half or more, with the quality gain of consistent wording across every assessor in the company. The report reads as one voice, the evidence chain from checklist to observation to action plan stays intact, and the assessor spends their time on judgement rather than typing.
Frequently asked questions
Can AI write a fire risk assessment on its own?
No. A fire risk assessment is a professional judgement made by a competent person who has inspected the premises. AI can draft wording, transcribe notes and check consistency, but the findings, rating and sign-off must be the assessor’s own.
Is client data safe when using AI drafting tools?
It depends on the tool. Look for suppliers that process data under UK GDPR, keep each customer’s data segregated, and do not train models on your reports. AssessHub isolates every organisation’s data and photos per tenant.
Write your next FRA in half the time
AssessHub structures the checklist, drafts the wording and assembles the report — you keep the judgement.