INSIGHTS
Practical thinking on operational AI.
Research, evidence and field lessons on applying AI to safety, security and resilience work where traceability, control and human judgement matter.
START HERE
Three useful places to begin.
For teams deciding where AI fits, how to test it, and how much control consequential work requires.
How to run a safe first AI pilot in security, safety or resilience
Start with one bounded workflow, define the evidence and review points, then test performance on real operational tasks before wider deployment.
Read the guideChoose a controlled operational workflow
Why the operational task, evidence base and success measures matter more than a generic AI demonstration.
Why System 3 thinking needs an operational layer
Why useful AI support still needs explicit review, traceability and human accountability.
BUYER QUESTIONS
Explore the question you are trying to answer.
Find practical guidance organised around operational decisions, not technology labels.
Where should we use AI first?
Choosing bounded, consequential workflows where improvement can be measured.
Safe first pilotHow much judgement should AI carry?
Keeping people responsible for interpreting evidence and approving consequential actions.
System 3 thinkingHow should outputs be evidenced?
Approved sources, traceable findings and outputs that can be checked.
Governed AI agentsHow do we retain what the team learns?
Turning decisions, actions and outcomes into reusable operational memory.
Operational memoryCan AI help with RAMS and document review?
Evaluating content against expected standards without substituting competent review.
RAMS review checklistWhat changes when reports become usable intelligence?
Moving from stored records towards patterns, timelines and actionable findings.
From DOBs to operational AIEVIDENCE & RESEARCH
Claims should be testable.
Separate measured operational findings from estimates and research, so readers can assess the basis for each claim.
Anonymised customer sprint · Unauthorised access investigation
Case-identification results from a customer sprint. Time comparisons are estimates based on average durations, not independently verified production benchmarks. The 20-minute estimate includes human CCTV review and final verification.
See the case-study evidenceBalancing responsiveness and reliability in high-risk operations
Evidence-based outputs, governance and auditability in consequential operational work.
How AI can help investigate an unauthorised access incident
How access-control records, CCTV and human investigation fit together.
Operational AI case studies
Real operational problems, outcomes and the basis for reported results.
CURRENT THEMES
Operational AI is as much an operating-model question as a technology question.
Build, buy or adapt?
A consequential workflow needs to be designed, tested, governed and maintained. Where should that responsibility sit?
Evaluating AI workflowsHuman review
Define what the reviewer checks, when escalation happens and who approves the result.
Human judgementApproved sources
Ground outputs in the organisation’s procedures, evidence and agreed standards.
Evidence and governanceOperational memory
Retain actions, lessons and decisions so previous learning informs future work.
Why operational memory mattersALL INSIGHTS
Latest writing and research.
Browse practical guidance, governance analysis, research and lessons from operational work. The latest articles appear below.
