While today organizations are investing much time and effort into understanding and applying “systems that do,” such as RPA, the real excitement is around what’s coming next, as systems that “think and learn” become more prevalent. Whereas RPA systems can work only with structured inputs and hard-coded business rules, the next level of automation – Systems that Think – are able to execute processes much more dynamically than the first horizon of automation technologies. The big advantage with automation technologies that think is the introduction of logic which allows these programs to make decisions autonomously, or on their own, when they encounter exceptions or other variances in the processes they execute.
Systems That Think
If you look at IT service automation as an example, those automation systems are able to analyze a user generated request or trouble ticket for key words or other triggers, then based on embedded algorithms and logic, think and make decisions as to how to best prioritize and address each case. Even better, over time their performance improves as they develop comprehensive histories of resolution data, which they are able to access to improve future decision making. These thinking systems deal far more effectively with less defined processes and unstructured data, and in this way differ from RPA or other systems that ‘do,’ which operate best in defined, rules-based processes.
Natural language processing (NLP) is another example of an automation technology that thinks. Natural language processing is a fast evolving form of software automation which can interpret spoken or written communication and translate it into executable actions to be taken by the computer system. Smartphones increasingly rely on NLP for ‘hands free’ use and call centers increasing deploy NLP-based automated agents to help them handle more calls with greater efficiency, scale and consistency.
Systems That Learn
Looking at the third horizon in our Intelligent Automation Continuum, Systems that Learn, we see a range of fast evolving technologies characterized by their ability to analyze vast amounts of dynamic and unstructured input and execute processes that are highly dynamic and non-rules based. As an example, machine learning is the technology that is improving the diagnostic capabilities of medical imaging systems, allowing online retailers to create highly individualized catalogs and improving the way software companies test for security vulnerabilities in future application releases.
These learning systems are also adaptive, in the sense that they can apply one set of rules in one situation but when variables change – such as location, resource availability, or suspicious activity is present, they can make adjustments optimized for the new situation. In the enterprise world, imagine these Systems that Learn running in tandem with your research and development teams, sales organizations, manufacturing and logistics operations or customer service departments. Data intensive processes and decisions predicated on understanding several complex variables and large volumes of information could move at machine speed and produce far more accurate, reliable and timely results. The impact here, from financial trading systems, to real time pricing engines, to patient care to completely individualized insurance programs is enormous and is just beginning to be recognized by early adopters.
What is most important to understand in terms of the Intelligent Automation Continuum of 2016, is that there exists in every organization vast opportunities to apply all the technologies of Do, Think and Learn to improve business processes, accelerate outcomes, increase data quality and enable powerful and predictive analytics. Even more powerful than this digitization of work these technologies provide is the improvement in the human role of organizational operations. People are now more empowered than ever to do what we do well – which is to think creatively, problem solve, prioritize and interact with our clients, partners and coworkers in smarter, more productive ways than ever before possible.
Coming Soon… Systems That Adapt
As time goes on, we expect embedded intelligence to become tablestakes, even in consumer technologies. Imagine your set top box without DVR or smartphone without a voice activated personal assistant. Expect the same transition to occur in automation as today’s “systems that do” vendors build or buy their way to smarter technologies. This change will make implementations faster and easier, extend applicability to more dynamic processes and improve outcomes by creating fewer exceptions, improving output data and further compressing cycle times.
We also expect the automation continuum to take on a new dimension, which we call “systems that adapt.” As the technologies that enable intelligence become more pervasive across the ecosystem, the “systems that do” horizon will become narrower and less useful. “Systems that think” will become the entry tier as learning systems become mainstream. By mid-2017, the do-think-learn model will shift to think-learn-adapt, as the current systems that learn evolve to include the self-awareness to decide even more autonomously how to apply that learning to provide smarter, more effective outcomes. These systems that adapt will be characterized by their ability to modify themselves or optimize performance depending on changes to their environment, to divert from or defend themselves from security threats and to interact more seamlessly with other systems and the people they serve and support. In this adaptive realm we will see an even greater degree of interaction and partnership between humans and these software ‘robots’ at our side throughout both our work and personal lives.
As was suggested earlier, the promise of intelligent automation is real and it’s here now. Business leaders should be taking steps and building plans now to understand the present and future opportunities and begin charting the path forward for their organizations. Keep in mind that the full continuum of Do, Think and Learn technologies will have a role in your organization’s new digital delivery and operational models and be prepared to adjust and evolve as this automation ecosystem is sure to continue doing so as well. Most important, now is the time for starting your organization on the automation journey so you too can begin experiencing the benefits of process acceleration, greater efficiency, quality gains and people and work teams unleased from rote rule books to begin collaborating, creating and improving results like never before.