Outcomes against pre-defined metrics
The success criteria are fixed before judgement, not after, so performance is measured against what the deployment was actually approved to deliver, in numbers agreed by the people who approved it.
Make the AI answer for itself.
A framework for after go-live that checks whether your AI is actually delivering what it was built to deliver, and names who’s responsible when it isn’t.
Pre-defined metricsPerformance driftNamed accountability
Set up a success ledger →
“Most deployed AI is never measured. The business case is approved, the system goes live, and within six months no one can say with confidence whether it’s working, because no one defined what working meant.”
What the framework tracks
Three accountabilities, held in view from day one.
Three things we hold in view from day one. It’s an ongoing position, each one has to be tracked continuously, not reviewed once a quarter when everyone’s forgotten the details.
Performance drift
Models decay. Inputs shift, behaviour changes, and a system that performed at launch can erode without anyone noticing. The framework watches for that erosion and surfaces it before it costs you.
Accountability when results fall short
A gap with no owner is a gap that stays open. Each outcome carries a named owner and an agreed response, so when results fall short, there’s a person and a path, not a post-mortem.
A system that performed at launch can quietly erode, the framework keeps the number, the owner, and the obligation in view for as long as the system runs.
What you receive
The Success Ledger, a standing account of whether the AI is earning its place.
Metric Charter
The agreed definition of success, signed off by the people accountable for it before deployment begins.
Performance Ledger
Outcomes tracked against target on an agreed cadence, with drift flagged as it occurs.
Drift Alerts
Threshold breaches surfaced when they happen, not at the next quarterly review when the window has closed.
Ownership Map
Every outcome tied to a named owner and an agreed response path. Accountability without ambiguity.
How it works
Fix the meaning, then hold the line.
Stage 01Define
Pin down “worked”
We recover or set the success metrics the deployment should be held to, and get them agreed before any measurement begins.
Stage 02Instrument
Wire the measurement
We establish how each metric is captured and how drift is detected, built into the workflow, not bolted on as an afterthought.
Stage 03Reconcile
Run the ledger
Outcomes are reconciled against target on a set cadence, with drift and breaches surfaced as they occur.
Stage 04Answer
Close the gap
When results fall short, the named owner and agreed response are triggered, accountability without a witch-hunt.
Who it’s for
Anyone who has to answer for what the AI was supposed to do.
Sponsors who approved an AI build and now have to report on whether it paid off.
Teams running deployed systems with no agreed definition of whether they’re still working.
Risk and governance owners who need drift and accountability documented, not assumed.
Compliance note. Success monitoring is conducted under the Digital Personal Data Protection Act, 2023 and applicable sectoral and AI-governance guidance current at the time of engagement. Performance and drift measurement processes only the data agreed for the purpose, under a defined retention basis. The framework establishes accountability and reporting; it does not warrant any specific business outcome, and named ownership is assigned by you, not by Purposive Intelligence.