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Turning big data into big content: business process management is resurging with robotics process automation

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Operational transparency 

The degree of transparency facilitated by any aspect of AI is pivotal to risk management and its overall sustainability. The difference between tempered automation and full-fledged autonomy is fine, particularly when digital agents are creating action—and issuing judgments—that affect customers or potential customers. IPA options and intelligent systems converging digital agents with cognitive computing deploy numerous measures to ensure there is strict accountability for bots so there’s “no black box,” Bellini remarked. Dashboards dedicated to virtual agent activity reveal “the data on what problems, what alerts [the agent] addressed, did he decide to take action or not and why,” Bellini revealed. “If he’s saving money, up in the right-hand corner there’s a running tally of how much cost he’s avoided or revenues he’s generated by the decisions he’s making.” 

A fundamental means of ensuring individual bots don’t go rogue is to simply limit their scope to a particular sphere of a business process. Instead of deploying a single digital agent to perform the hundreds of steps in an insurance claims process, it’s more beneficial to ensure “it has its own level of autonomy within the boundaries of what it is actually supposed to do,” offered Mahalingam“A data acceptance bot can’t decide claims controls; that’s not its job.”  Competitive solutions in this space have specific orchestration layers for virtual agents that issue what Kakhandiki termed “operational insight,” which involves identifying if bots are running smoothly and, if a bot has failed, the possible reasons for that failure and what can be done to prevent it from happening in the future. 

Modernizing business processes 

Whether in standalone solutions, RPA, or IPA offerings, digital software agents are becoming increasingly entrenched in the simplest to the most complex of business processes. Their capacity to facilitate the latter is largely based on the burgeoning accessibility of AI to the enterprise, a trend which the very deployment of bots directly impacts. The effects of this development are measured at the micro and macro levels. 

At the former, BPM is gaining renewed interest, becoming much more efficient than before, and broadening its business value within the data landscape as a whole. At the macro level, it’s responsible for enabling organizations to derive meaningful content from the mounds of big data with which they’re inundated. And though data is not always treated as an enterprise asset, the content derived from data—and its demonstrable business value—almost always is.

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