Why Most S/4HANA Migrations Miss Their Budget and Timeline, and How to Improve the Odds

The value here has nothing to do with prediction for its own sake. It comes down to lead time. A risk signal that surfaces six weeks before a status report would have caught the same problem gives a program real options: reallocating resources, adjusting scope, resequencing work. None of that is available once a milestone has already been missed.

of S/4HANA transformations experience significant budget deviations. Most of that risk is visible in program data well before it shows up in a status report. (Horváth)

A 70%+ projected shortfall against original business case objectives is not a minority of poorly run programs pulling down an otherwise healthy average. That's the expected outcome for a majority of ERP initiatives under current practice. Predictive risk scoring won't change that baseline by itself. What it changes is how early a program learns it's tracking toward that outcome, and that's the one variable a steering committee actually controls.

Risk-scoring models are only as good as the program data feeding them. A program with poor data hygiene in its test-management or project-tracking tools will get an unreliable score, no matter how sophisticated the underlying model is. The models are also backward-looking in their training even when the output looks forward: one trained mainly on brownfield conversions won't necessarily transfer cleanly to a greenfield redesign with a very different risk profile. Used well, predictive risk scoring is an early-warning system that buys a steering committee more time to act. It doesn't, on its own, replace the committee actually acting on what it's told.