Use this when

You want real-world evidence about an AI workflow before it takes any real action.

The pattern

REAL CASE
   ├── normal process runs and acts (in control)
   └── AI runs in parallel and records its result (no action)
                    ↓
          COMPARE AI vs reality
                    ↓
        few, understood disagreements? → consider going live

The AI sees real, live work but changes nothing. Its output sits beside what actually happened, for comparison.

Why it works

Shadow mode gives you the realism of live operation with the safety of a test. The AI meets genuine, unpredictable inputs, but because it does not act, a wrong result costs nothing. The disagreements between AI and human become a precise map of where the AI needs work. See how to run a shadow-mode pilot.

How to apply it

  1. Run the AI alongside the real process on live cases.
  2. Record its output without applying it.
  3. Compare against what people actually did.
  4. Review disagreements and feed real cases into your golden test set.
  5. Set a clear bar, few and understood disagreements, before going live.

Good fit

  • Consequential workflows where a wrong first action would be costly.
  • Decisions and classifications that can be compared against human choices.

Poor fit

  • Cases where there is no human baseline to compare against.
  • Very low volume, where shadow mode would take too long to see enough cases. A closer human review may serve better.

What this does not solve

Shadow mode builds evidence for the decision to deploy; it does not prove the acting side works, and it ends the moment you switch to live action. Pair it with careful go-live testing and monitoring after launch.