This is exactly the kind of “real-world” test that exposes whether an AI workflow is…

Imagine a sales team using AI to qualify inbound leads, summarize discovery calls, and draft follow-up emails. If their entire workflow depends on one AI provider, an outage doesn’t just mean “the chatbot is down.” Leads can pile up, follow-ups get delayed, and the sales process starts backing up.

But if that same workflow has a second model already connected, cached context, or a simple human/manual fallback, the work keeps moving.

AI resilience isn’t about assuming outages won’t happen. It’s about designing workflows so that when one piece fails, the entire process doesn’t.

The outage is the design review.

The workflow is the test.

And the fallback is the strategy.

If your workflow stopped today, do you know exactly where it stopped — and how you would keep it moving?

Reposted with commentary. View the reposted item ↗