PLAYBOOK · CHF 49
The AI Delivery Playbook
Eight short chapters pair the decision you need to make with the artifact that makes it testable. Use the PDF to guide the work and the editable templates to run it.
Eight chapters, one path from idea to production
- 01
Use-case canvas
Define who it helps, what hurts today, what the AI will do, where it stops, and the value you expect.
- 02
Prioritization scorecard
Compare user pain, measurable value, a feasible first slice, strategic fit, controllable risk, and adoption readiness.
- 03
AI stories and acceptance criteria
Write down what the system must visibly do, how well, and what happens when it falls short.
- 04
Model-risk and data checklist
Assign controls for privacy, quality, security, auditability, human review, and lifecycle.
- 05
Testing and golden sets
Build a fixed set of example cases with expected results and the score a release must reach.
- 06
Rollout and adoption plan
Sequence shadow use, assisted use, training, champions, feedback, and support.
- 07
Benefits KPI tree
Connect model behavior to process performance and business value without double counting.
- 08
Cadence and definition of done
Set the meeting rhythm, the evidence each decision needs, owners, and rollback conditions.
Artifacts made to be used
For the person coordinating the whole system
Who it is for
- Delivery and transformation leads with an AI ambition and no common standard.
- Product owners turning business problems into a clear, controlled plan of work.
- Risk, data, engineering, and operations partners who need one evidence trail.
- Teams in insurance, financial services, and other regulated settings.
What it is not
- Not a catalog of model techniques.
- Not a generic innovation framework.
- Not a substitute for legal, risk, security, architecture, or domain specialists.
- Not a promise that every use case should reach production.
Questions before you buy
Do I need an engineering background?
No. The playbook is written for the person coordinating business, product, technical, risk, and operations work. Technical evidence still needs the relevant specialist to produce and approve it.
Can I use this with an existing agile method?
Yes. The templates define decisions and evidence, not new meetings. They fit the routines and tools your team already uses.
Is this specific to underwriting?
No. Underwriting intake is the worked example. The templates apply to any bounded AI use case with users, data, risk, a target process, and measurable outcomes.
Make the next AI project easier to approve and deliver.
Keep the decisions, evidence, owners, and failure responses in one visible delivery system.