Obviously, if your data science process was set up by an external consulting team, you don't have much of a choice other than to bring them back in. If your data science process is the result of an automated ML/AI service, you may be able to re-engage that service, but especially in the case of the change in business dynamics, you should expect to be involved quite a bit - similar to the first time this project was run.
One side note here: Be skeptical when someone is trying to push for super cool new methods. In many cases, this is not needed, but one should rather focus on carefully revisiting the assumptions and data used for the previous data science process. Only in very few cases is this really a "data 0" problem where one tries to learn a new model from very few data points. Even then, one should also explore the option of building on top of the previous models and keeping them involved in some weighted way. Very often, new behaviour can well be represented as a mix of previous models with a sprinkle of new data.
But if your data science development is done in-house, now is the time where an integrative and uniform environment that's 100% backwards compatible comes in very handy: The assumptions are all modelled and documented in one environment, and well-informed changes and adjustments can be made. It's even better if you can at the end validate, test and deploy this into production from that same environment without the need for manual translation/interaction.
About the author:
Michael Berthold is CEO and co-founder at KNIME, an open source data analytics company - www.knime.com