BPM will become a key driving force behind further development of AI. Machine learning in business applications is blind without guiding role of BPM, similarly as a company is blind without its management.
Despite impressive success, AI is still in its infancy in respect to its literal involvement into high level business processes. Concurrently with the increase of the role of AI in principal business contexts, there will grow demand for BPM in coordination and structuring of AI for business purposes.
As in any other business domain, BPM is suitable and responsible for proper AI positioning in business. Without precise BPM governance, AI bears excessive risks for a business as any important but poorly managed and not transparent part of ongoing operations in an organization.
There always exists a discrepancy between a model of business process, however well designed and accurate, and real execution of this process in a business environment. The reason for this gap is an unforeseen depth and hidden details inherent to any real process. Real business model of organization is ultimately unlimited in its depth. Going from highest management levels, it descends to individual departments, client relations, production units, technical code of equipment and controllers etc. In vast majority of cases, it is impossible and senseless to build a complete model covering all and every fine detail of the business. Omitted lower layers of the model create (pseudo) random fluctuations during execution of the model. Real execution paths of a process never follow its model exactly. However, in case of the correct model, we can expect to see that an ensemble of execution paths statistically converges to the model as to its average path over a significant set of observation...
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