The blog post talks about range forecasts and probabilistic forecasts. Also they talk about relatively small number of scenarios ("at least 5-10...") so it brings the question of accuracy with such small resolution.
Feasible decisions ranking and economic drivers are absent while they are essential components for choosing decisions with highest return on investment.
Level of details is underwhelming so far.
Though this is great news. They will definitely accelerate the adoption of the term.

Acknowledgement from that the probabilistic approach is superior, or at least will offer more resilient decisions. How long to get to market in a core product that actually works? 10 years?

10 years is a good ballpark assessment to produce a good software product - assuming there are people who will stick around for a decade to see it through. See https://www.joelonsoftware.com/2001/07/21/good-software-takes-ten-years-get-used-to-it/ Written 20 years ago, but the points are still largely valid.

We strongly believe that probabilistic methods play a key role in the future of supply chain planning. They represent the right way to go for areas that contain uncertainty, including for all types of forecasting and the subsequent planning situations.
Traditional deterministic planning methods base their decisions on the mistaken assumption that uncertain values can be approximated by a single average number. As a direct consequence of this assumption, plans are often infeasible at the time they are created and manual interventions are continuously needed.

Better late than never! However, let's immediately point out that the SAP IBP architecture is very much hostile to probabilistic modeling. More specifically, the high memory consumption of HANA is going to add some massive overhead on top of methods that not exactly lightweight in the first place.