Other parts of this series:
Intelligent Automation (IA) is essentially the automation of a repeatable process, but through an intelligent system. It’s also the logical next step in making manual, time-consuming, and cost-heavy activities more streamlined, cost-efficient, and productive.
As I mentioned in my previous post, in the early days of enterprise transformation, firms applied methodology-based business process improvements to achieve greater efficiency. While these efforts were a good first step and offered tangible benefits, they often suffered from expensive, time-consuming, and inconsistent deployment. With the introduction of business process improvement technology, enterprises have increasingly adopted automated solutions to achieve greater efficiency—and with good results.
“82% of executives we surveyed agree that organizations are being increasingly pressed to reinvent themselves and evolve their business before they are disrupted from the outside or by their competitors.”1
The beauty of IA is that it takes those automated solutions to the next level, extending well beyond simply driving greater efficiency to truly transforming the business environment.
Smarter, better, more sophisticated processes
With IA, it’s not just about automating your processes; it’s literally about making them smarter. It’s not just about streamlining the workflow; it’s about making better business decisions. And it’s not just about conserving human resources; it’s about humans and machines interacting on an even more sophisticated level.
Before deciding how to make your firm’s automated processes more intelligent, it’s good to have a basic understanding of the IA technology spectrum.
The IA technology spectrum
Robotic process automation (RPA) is at the lower end of the intelligent automation spectrum, acting as a substitution for repetitive manual human tasks. An RPA tool can be triggered manually or automatically to:
- Move or populate data between prescribed locations
- Document audit trails
- Conduct calculations
- Perform actions
- Trigger downstream activities
RPA is a good solution for aggregating data, performing rudimentary analysis, and then visualizing the data (for example, in a dashboard as a chart or graph).
Advanced natural language generation (Advanced NLG) is at the higher end of the intelligence spectrum—acting as an automated analyst that analyzes and interprets structured data, then communicates relevant insights in narrative form. In addition to automating sophisticated analysis, Advanced NLG capabilities free up human analysts to work on more strategic tasks—such as decision-making. Because of its robust data analysis, insight derivation, and communication capabilities, Advanced NLG is particularly useful in engaging customers and accelerating time to market, as well as providing clear and accurate narratives regarding business performance.
RPA and Advanced NLG are complementary technologies that can often be used together for enhanced benefit―with RPA as the data aggregator and Advanced NLG as the power behind the analysis and communication. When firms combine Advanced NLG with RPA, they can scale IA for a larger end-to-end transformation. For instance, asset managers have used Advanced NLG successfully to automate portfolio commentary.
Taking the next step in positioning your firm for success
The writing is on the wall that IA solutions will increasingly be partnered with human counterparts to create the workforce and workplace of the future. The question is, how will you position your firm for success in this next phase of the enterprise business process transformation?
In my next post, I’ll answer that question by sharing some practical tips on how to make IA work for your firm.
For more information on applying IA in your firm, please see Accenture’s report: Intelligent Automation: The next step in your business transformation journey
1 “Intelligent Automation: The essential new co-worker for the digital age,” Accenture, 2016. Access at: https://www.accenture.com/us-en/insight-technology-trends-2016