Why Outcome Delivery Isn't Enough

Outcome delivery is necessary. It is not sufficient.

When decisions are reviewed—by an investment committee, a risk officer, or a regulator—delivery alone doesn't answer the real question:

How did you get that number, and can you prove it?

That gap shows up in three places: defensibility, drift, and silent failure.

1) The Defensibility Problem

The most common moment of failure isn't when an outcome is delivered. It's when someone asks how to explain data discrepancies after the fact.

Typical scenarios:

• An IC meeting asks for a decision audit trail: What did we know at the time, and why?
• A regulator or client requests audit data lineage requirements: show the sources, transformations, and methodology.
• A risk committee asks whether a specific number can be recreated months later.

If the outcome is delivered without evidence, those questions lead to manual reconstruction, argument, or—worse—no answer at all.

Defensibility isn't about being right forever. It's about being able to explain and reproduce what was believed when it mattered.

2) The Drift Problem

Outcomes can be correct and still degrade over time.

Inputs change. Assumptions shift. Filings get amended. Models get updated. A result that was accurate last week can be misleading today.

Without clear data drift detection, teams keep operating on stale truth:

• Outputs diverge from reality without a visible signal.
• Two systems disagree, and no one knows which one is authoritative.
• The same “correct” number shifts between runs with no traceable reason.

Delivery doesn't prevent drift. Verification does.

3) The Silent Failure Problem

The most expensive problems are the ones you discover late.

Silent failures happen when:

• A data source changes format and pipelines keep running.
• A model degrades but still returns plausible outputs.
• A dependency fails and results default to last-known values.

By the time someone asks how to defend model outputs, you're already in reactive mode. Retrospective discovery costs far more than early detection.

The goal isn't perfect prediction. It's early visibility into when outputs are at risk.

Why Outcome Delivery Alone Breaks Under Pressure

Delivery answers: What happened?

Defensibility answers:

• Why was this believed?
• What evidence supports it?
• What changed since then?
• Can another team reproduce it?

When pressure arrives—audits, disputes, or automated decisioning—delivery-only systems fail because they don't carry the provenance and verification needed for explanation.

A Better Standard: Outcomes With Evidence

A defensible outcome should include:

• A named result with a clear scope
• The evidence behind it (sources, timestamps, methodology)
• A reference execution that can be compared and validated
• A path to explain deltas when numbers disagree

That combination transforms “delivery” into a position you can stand behind.

If you want a reference for how we approach this, see how we build outcomes with evidence.

---

See it in action:

• Browse all Outcomes — Named results with evidence attached
• Example Evidence Pack — What defensibility looks like in practice