$2.1M in automation savings the team didn't know existed

Operations leaders rarely lack for process data; they lack a way to turn it into a prioritised, defensible list of what to automate. For this logistics organisation, the delivery and operational processes generated plenty of event logs, but the question "where exactly are we losing money to manual work?" had no rigorous answer, just intuitions and anecdotes. Stride's Optimize module mines those event logs to reconstruct how work actually flows, not how the process diagram says it should. Bottleneck heatmaps surface where time pools; the AI then proposes automation opportunities ranked by impact, each tied back to the evidence in the log. The output isn't a vague "you could automate more". It's a roadmap with numbers attached. That roadmap is where the $2.1M figure came from: automation savings the team, in their own words, didn't know existed. The value of process mining here was less about discovering that automation helps (everyone assumes that) and more about quantifying and prioritising it well enough to act. The 3.2x return on the platform investment is the same story from the finance side: the savings the roadmap identified dwarfed the cost of the tool that found them. Crucially, the Head of Operations called out that the ROI roadmap made executive buy-in effortless. That's the underrated part. Most automation initiatives stall not because the savings aren't real but because nobody can show them in a form a CFO will sign off on. A roadmap that ties each opportunity to logged evidence and an expected return converts a hunch into an approval. The engagement is anonymised to role and sector by agreement. The mechanism (mine the real process, rank the opportunities, attach the numbers) is what travels.