Series
AI in Institutions
How institutions adopt AI responsibly: inventories, policies, assistants and automation that pays for itself.
05guides, in reading order
The series, guide by guide
- 01 What to define before automating a workflow
- 02 Build an AI inventory with owners, evidence and a review cycle
- 03 From an AI-use policy to decisions staff can actually follow
- 04 How to evaluate an internal knowledge assistant before release
- 05 Calculate automation value after exception handling and running costs
- Related practices
- Management & institutional strengtheningTraining & capacity buildingAI governance & assurance
Issues in this series.
GuideWhat to define before automating a workflowAutomation projects often disappoint because they were never defined properly. Before anyone builds, pin down the task, the volume, the exceptions and who is accountable.
GuideBuild an AI inventory with owners, evidence and a review cycleYou cannot govern AI you have not listed. An AI inventory with owners, data, effect on people and review dates is the first control every organisation needs.
GuideFrom an AI-use policy to decisions staff can actually followAn AI policy that nobody applies is worse than none. Build it in a workshop, around the decisions staff actually face, and leave nothing important undecided.
GuideHow to evaluate an internal knowledge assistant before releaseAn internal AI assistant is only as good as its documents and its tests. Evaluate it against real questions, including the ones it must refuse, before anyone relies on it.
GuideCalculate automation value after exception handling and running costsAutomation business cases count the hours saved and forget the hours still spent. Subtract review, exceptions, maintenance and running costs before you believe the saving.
What needs to move forward?
Tell us the decision, the challenge or the opportunity. We reply with a scoped approach, a named principal and a fee before any work begins.
No charge to submit an enquiry.