The argument that repeats every month
The model forecasts one number, the sales team expects another, and the meeting resolves it by whoever is more senior or more persistent. Next month the same argument happens with different numbers.
Nothing accumulates because nobody records who was right. The same disagreement is relitigated indefinitely on the basis of recent memory, which favours whoever remembers their wins most clearly.
Record both, then review
- Capture the model's number and the human's number separately, before the outcome is known.
- Record a short reason for any override - a new contract, a known problem, an unusual event.
- When the outcome arrives, record it against both.
- Review the accumulated set quarterly, broken down by reason and by product or account.
After a couple of quarters the answer is no longer a matter of opinion. It is usually more interesting than either side expected.
What the review normally shows
The common pattern is that neither is uniformly better. The model is more consistent across the routine majority; people are better where they hold information the model cannot see.
| Case type | Usually better | Why |
|---|---|---|
| Routine, high volume | Model | Consistency, no fatigue or optimism |
| A known upcoming event | Human | Information not in the data |
| A new or unusual account | Human | Model has no basis |
| Under pressure to hit a target | Model | No incentive to lean |
That last row is worth naming plainly. Where forecasts feed targets, human numbers acquire a direction, and it is not dishonesty - it is what incentives do.
Design the process around the finding
Once you know where each is stronger, the process can reflect it. The model produces the baseline everywhere; overrides are permitted with a recorded reason; reasons that have historically improved accuracy are accepted routinely, and ones that have not are challenged.
That is a considerably better arrangement than either 'the model decides' or 'the team decides', and it is reached by evidence rather than by argument.
Feed the good overrides back
Where human adjustments consistently improve accuracy, the information behind them should become a model input. A planner who knows about an upcoming contract is using data that exists somewhere - a CRM opportunity, a signed order - and could be fed in.
That turns a recurring override into a feature, and it is one of the more reliable ways to improve a deployed model. It also demonstrates that the team's knowledge was taken seriously, which does more for adoption than any amount of consultation.
Write down both numbers before the outcome. After two quarters, nobody needs to argue.