Incident and Risk Reviews With AI
Run blameless incident reviews and risk registers with AI help, verified against the facts
A taste of a lesson
The AI drafted our timeline and says the outage started because of a config change at 14:02. Can I put that in the report?
Not yet. That sentence contains two claims: that a change happened at 14:02, and that it caused the outage. Check the first against the change log, including its time zone. The second is a causal claim, so look for evidence like errors starting right after the change and stopping after rollback. If you cannot show that, write it as a contributing factor under investigation. What record would confirm the time of that change?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Run a blameless review that surfaces contributing factors rather than a culprit
- Build an AI assisted timeline in which every entry cites a source record
- Use AI to generate alternative explanations and test your own story
- Write risk register entries with clear cause, event, consequence and controls
- Review incidents involving AI systems with the right evidence captured
Lesson plan
- 1 What a Blameless Review Is For Explain the purpose of blameless reviews and how they differ from disciplinary processes. Start
- 2 Building a Verified Timeline Assemble a timeline from exported records with AI help, citing a source for every entry. Start
- 3 Contributing Factors, Not One Cause Identify several conditions that combined to allow the incident. Start
- 4 Actions That Actually Close Write actions with owners, dates and a way to verify they had the intended effect. Start
- 5 From Incidents to the Risk Register Turn patterns across incidents into risk entries with ratings, controls and owners. Start
- 6 Reviewing Incidents That Involve AI Capture the evidence needed to understand failures in AI assisted processes. Start
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About this tutor
For operations managers, team leads and risk owners who run reviews after things go wrong: late shipments, payment errors, outages, data mistakes or an AI feature that misbehaved. The lessons cover blameless review principles, building a verified timeline from logs and chat exports, finding contributing factors instead of a single culprit, using AI to challenge your own explanation, writing actions that actually close, turning incidents into risk register entries, and reviewing incidents that involve AI systems. You practise on a past incident of your own with sensitive details removed. You finish with a review template and a habit of checking every AI drafted claim against source records.
Reviews
4.3
4 ratingsSample
- Kwame O.Sample
Good on blame language and on action tracking. The risk register lesson was a bit quick for me since we had no register at all, but the cause, event, consequence format was easy to adopt.
- Mei L.Sample
Our first incident involving an AI summariser was hard to review because nobody saved the prompt version. The checklist for AI incidents is now part of our template.
- Hannah B.Sample
The rule that every timeline line cites a record saved us. Our AI draft had merged two time zones and put a fix before the alert. We would have published that.
- Pavel K.Sample
Solid method, but I expected more on using AI to analyse a large incident history. It spent most of the time on running single reviews well, which was useful but not what I came for.
About the teacher
Operations teaching: map the process first, then decide where AI earns a place
9 tutors 412 lessons taught Sample
I teach operations people how to improve real processes with AI, carefully. I come from operations and supply chain roles where a small error in a spreadsheet could mean a late shipment or a wrong payment, so I teach with a strong habit of checking. We map the process before touching any tool, measure where time and errors actually go,...
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