Process mining can optimize operations, but many initiatives fail due to avoidable pitfalls. Common issues include applying it to processes that don’t need it, incomplete or poorly formatted data, and excessive concept drift—where processes change during analysis, destabilizing results. Other failures stem from unclear business objectives or overlooking overall value, leading to wasted effort and weak adoption. Success requires selecting the right processes, ensuring data quality, managing drift, and defining goals upfront. With these steps, companies can gain reliable insights, reduce bottlenecks, and drive meaningful automation outcomes.

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