This means business users with minimal development skills can automate many types of work processes in mere weeks. Teams of "virtual RPA workers" can be scaled up or down instantaneously or, even better, autonomously, as task volumes ebb and flow.
Getting RPA right for most companies means understanding the automation vendor landscape, reviewing and prioritizing processes, launching pilots and proofs of concept and, finally, determining the ideal model that will best support them in the long term.
- Systems that "think" so you can make decisions autonomously
Software that can operate more dynamically, even in situations with variances
This next level of automation (systems that think) is able to execute processes much more dynamically than the first horizon of automation technologies, removing most complexities in dynamic decision-making. The magic ingredient here lies in the introduction of logic, which allows these programs to make decisions independently when they encounter exceptions or other variances in the processes they execute.
For example, IT service automation can analyse a user-generated request, trouble ticket for keywords or other triggers, and then based on embedded algorithms and logic, make decisions about prioritising and addressing each case. As they develop comprehensive histories of resolution data, their performance and ability to make the right decisions accurately can improve over time.
These thinking systems deal far more effectively with less defined processes and unstructured data. In this way, they differ from RPA and other systems that "do," which operate best with defined, rules-based processes.
Natural language processing (NLP) is another example of an automation technology that "thinks". NLP is a fast-evolving form of software automation that can interpret spoken or written communications and translate it into executable actions. Smartphones increasingly rely on NLP for hands-free use. Call centers increasingly deploy NLP-based automated agents to help them handle more calls with greater efficiency, scale and consistency.
- Systems that "learn" to make optimal adjustments when variables change
Software that can adapt, enabling a rich partnership between humans and software bots
There is a range of fast-evolving technologies that are characterised by their ability to analyse vast amounts of dynamic and unstructured input, as well as execute advanced processes. These learning systems are also quick learners, in that they can apply one set of rules in one situation and then make optimal adjustments when variables change.
For example, online retailers are now able to create highly-individualized catalogs based on user behaviour and preferences. Software companies can leverage these technologies to test for security vulnerabilities and detect anomalies.
Their impact on everything from financial trading systems, to real-time pricing engines, to patient care, to completely individualised insurance programs - is enormous, and is just beginning to be recognised by the front runners.
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