It synthesise this into 16 different research-backed approaches - from “circuit breaker protocols” for irreversible decisions to “feedback learning” for low-stakes automation. Each designed around giving humans the authority, time, and understanding they need to be genuinely effective - but also very cognizant of AI’s potential to automate routine tasks and perform consistently with the right guardrails in place.

The goal isn’t perfect categorisation but moving beyond generic “human in the loop” to build the systems we actually intend, not the ones we accidentally create.

E-commerce: email marketing Goal: Send personalised emails to millions (optimising for speed/volume and quality). Risks: Poor copy, spam risk (recoverable setbacks).

→ _Batch processing + Spot checking: _AI generates emails, marketing team reviews random samples pre-sending, and monitors overall engagement metrics, intervening when unusual patterns seen

Recruitment: applicant screening Goal: Support recruitment/HR team by processing hundreds of applications efficiently (volume, quality, compliance) Risks: Systemic bias, missing great candidates or hiring poor fits (high-impact failures).

→ Monitored automation + regular expert review + approval workflows 
 AI screens applications for requirements and fit indicators, flagging top candidates and clear rejections. Recruiters review candidates and can override any AI decision. Hiring managers get AI analysis alongside resumes for final interviews.