The nightmare scenario is well known enough that it shapes decision-making: a go-live weekend, a system that doesn’t behave as expected, and a plant that can’t ship for two weeks. It happens rarely, but it happens often enough that plenty of manufacturers delay improvements they need out of entirely rational fear.
The risk is manageable. It’s mostly a function of how the rollout is sequenced.

Never Run a Hard Cutover on Planning

Transactional systems sometimes require a clean switch. Planning systems almost never do, and treating them as though they do creates most of the risk.
Run parallel instead. The new system produces its recommendations, your existing process produces its own, and you compare. For a period, you act on the old process and simply observe where the two diverge. Divergence is not a problem — it’s your test data. Every difference is either the new system finding something, or the new system missing a constraint nobody told it about. Both are worth knowing before you depend on it.

Sequence By Consequence

Start where being wrong is cheap and reversible. Analytics and visibility first — the system reads data and reports, and if it’s wrong you’ve lost nothing. Then forecasting, where output informs human decisions rather than driving them. Then procurement recommendations, which a buyer reviews before acting. Production scheduling last, because that’s where an error stops a line.
Within that, pick one product family or one site rather than everything. A narrow first scope means problems surface in a contained environment and the team learns on low stakes.

Fix Data Before go-live, Not During

Most implementation failures presented as software problems are data problems. BOMs that don’t match reality, lead times that were accurate three years ago, stock records that disagree with the floor, phantom items nobody cleared out.
Audit and clean these first. It’s tedious, it has no visible payoff, and skipping it is the single most reliable way to derail a rollout — because a system fed bad data produces bad recommendations, the team stops trusting it in the first month, and no amount of later correction recovers that.

Keep The Fallback Genuinely Available

Keep the old process operable until the new one has proven itself through a full cycle including a month-end and a peak period. Not documented — operable. If reverting requires a week of reconstruction, you don’t have a fallback, you have a plan for one.

Let The Floor Shape it

Involve the people who’ll use the system in configuration, early and genuinely. They hold constraints that exist nowhere in your data: this machine can’t run that material reliably, this operator is the only one certified for that process, this customer must never be late regardless of what the priority field says.
Systems that ignore this knowledge get overridden until they’re ignored entirely. Systems that capture it get used. That difference is decided during configuration, not after.
ticktick.ai supports parallel running by module, so recommendations can be evaluated against existing process before anything depends on them.