AI for Decision Support

Summary

AI for Decision Support is for managers and analysts who prepare or make recommendations that other people rely on. It teaches AI as a way to strengthen judgement widening the options considered, stress-testing a recommendation before it goes forward, and keeping a clear record of what the tool contributed versus what the decision-maker owns.

Who Should Attend

• Managers and analysts who prepare the case for a decision, or who make decisions others act on
• Teams facing recurring judgement calls prioritisation, resourcing, risk trade-offs
• Anyone who currently writes a ‘the case for X’ document and wants a sharper method for building it

Course Description

The course treats a decision differently from a report: a report describes what happened, a decision has consequences and an owner. It opens with framing defining what is actually being decided, what is known, and what is genuinely uncertain because a well-framed decision gets far more useful help from AI than a vague one.

The middle of the day is spent widening and then narrowing: using AI to surface options a first pass would have missed, then deliberately asking it to argue against your own preferred option, find the weakest assumption, and raise the strongest objection. This turns the tool from an agreeable assistant into something closer to a sparring partner, which is where most of the value in this course sits.

The final module addresses what AI cannot do: absorb accountability. Participants build a simple decision log that records what the AI contributed, what was verified, and what judgement the decision-maker made a record that holds up when the decision is questioned later.

Duration and Format

• Length: One full day, instructor-led and case-based
• Format: A mix of discussion and applied exercises, worked against a real decision each participant brings

Pre-Requisite

Participants should:

• Have completed AI Foundations, or be comfortable with the four-part prompt structure and basic verification
• Bring a live or recent decision they are working on, with enough detail to work from

Course Outline

      01: Framing the decision before you frame the prompt
      02: Generating and widening the options
      03: Stress-testing a recommendation
      04: Data-informed judgement, not data-replaced judgement
      05: The decision log

What You Will Be Able to Do

• Frame a decision clearly enough that AI’s help is aimed at the right question
• Generate a wider, better-considered set of options before narrowing
• Stress-test a recommendation through structured challenge, not just review
• Distinguish AI-supplied analysis that needs verification from your own judgement
• Keep a decision log that would hold up if the decision were questioned later

You Leave With

• A structured options analysis for one live decision from your own work
• A stress-tested version of your own recommendation

A decision log template ready to reuse