Reduce administration. Protect data. Keep people in control.

Yes, subject to appropriate governance, safeguarding, privacy, accuracy, procurement and human oversight. Each proposed use should be assessed on its merits.

Do not assume an AI product or school is DfE-accredited. DfE guidance and product safety standards are not equivalent to a universal certification scheme.

Lower-risk examples include routing enquiries, collating non-sensitive operational data and drafting routine internal reports with human review.

Not without an approved lawful basis, appropriate contracts, security assessment and trust policy. Personal and sensitive information needs particular protection.

Where processing is likely to be high risk to individuals, a DPIA is required before processing. The trust should involve its data protection lead.

Approved tools, prohibited data, verification, transparency, human decisions, safeguarding escalation, records, intellectual property and incident reporting.

Assign ownership, maintain an AI use-case register, require proportionate approvals and monitor privacy, safeguarding, bias, accuracy and supplier risk.

AI outputs should not replace professional safeguarding judgement. Human accountability and established escalation routes remain essential.

Measure staff time, accuracy, error rates, user experience, cost and risk before and after the pilot, with realistic baselines.

Data handling, retention, model training, subprocessors, security, accessibility, service resilience, contractual terms and exit arrangements.

Require source checking, human review, constrained use cases, documented limitations and training on unreliable outputs.

Choose a repetitive, measurable, low-risk administrative process with a clear owner and a defined pilot evaluation.