Celonis popularized the concept of 'happy path'. Essentially how a process (and the software) is supposed to work. In reality, the processes look something like picture below. It is all over the place with lots of exceptions.

Most of the steps in the less happy path are a result of policy guardrails with require manual research and summarization or upstream data quality issues. This intermediate research step creates bottlenecks and results in lot of non value added work in the enterprise.

The promise of GenAI models is that eventually we will see more AI enabled workflows that a) automate the research step more effectively than traditional RPA bots b) Suggest the right guardrails and calibrate the system performance and c) nudge to correct the upstream data challenges. For instance ineating a sales order,there is a guardrail of credit check in certain instances which creates friction in the process and needs manual research and intervention.

Alternatively, and this is where things get more interesting; you can think of the process itself as dynamic / adaptive. The workflow is in fact a series of decisions and depending on the type of customer or order, you will take different paths than optimize for process outcome metric. Not all orders should need standard sales order generation process. Maybe there is 'click to buy' equivalent of Amazon buying experience for certain types of orders or customers. The probabilistic approach to process execution will be the foundation of new breed of AI native software.

Every enterprise software company is going to incorporate these models in the workflow but the challenge will be how to think about AI enabled vs AI native approaches and what is easier for enterprise to adopt and what is the willingness to pay for it.