Intelligent automation, or robotics process automation (RPA), is set to be one of the biggest game-changers for organizations in expediting business processes, reducing errors, and cutting operational costs.
It is true that RPA saw its share of hype, with claims it would cut delivery costs in half, leave workers without jobs and kill off the sourcing industry altogether. Little wonder then, that many business leaders expected this war between ‘man and machine’ to signal the end of the workforce as we know it.
However, while it has not completely transformed the industry, the fact remains that business leaders are increasingly starting to embrace intelligent automation. These forward-thinking businesses that are now taking advantage of RPA will ultimately see results in their performance, agility and competitive capabilities.
Intelligent automation is not about entirely replacing the human element, but about elevating the role people play in operations and putting businesses on the fast track to success. It is also not about replacing underlying IT systems. On the contrary, it offers a non-invasive and cost-effective approach to making rote and repetitive processes digital, instrumented, analyzed and intelligent. That makes intelligent automation all the more relevant in the face of legacy systems that entail extremely high remediation or change costs.
In the digital era, speed is the new currency in business. Organizations need to address the fast-arriving enabling technologies, techniques and tools that will allow them to digitize their processes — and do so quickly.
Embracing RPA may not be a one-size-fits-all solution for businesses. For example, tasks that rely on a significant amount of creativity or intelligence cannot easily be undertaken by a piece of software. Instead, business leaders should take the time to evaluate their business strategy and build plans to integrate RPA in ways that will help understand current and future opportunities to move forward.
So how should businesses navigate the tricky ‘first steps’ in adopting intelligent automation? RPA can be broken down into three simple areas for businesses to understand the real opportunities and chart the best path forward: I Do, I Think, I Learn. The key to intelligent automation lies in an agile framework. Be well-prepared and kick-start your automation journey with a ‘do, think, learn’ framework.
Systems that “do” so you don’t have to
Software that can replicate repetitive, rules-based human actions
Automating processes with RPA tools can be likened to creating traditional flowcharts. Once built and tested, libraries of automated tasks can easily be reused or quickly customized to make future automations go faster. Essentially, any rules-based activity that can be applied to different processes and situations is likely to be a viable RPA candidate. Such tasks include loan application processes, claims adjudication, accounts payable and receivable, invoice reconciliation, data entry/extraction and report generation.
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 analyze a user-generated request, trouble ticket for keywords or other triggers, and then based on embedded algorithms and logic, make decisions about prioritizing 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 characterized by their ability to analyze 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 behavior 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 individualized insurance programs — is enormous, and is just beginning to be recognized by the front runners.
Now, imagine if machines could ‘learn’ from their human-counterparts, the ability for businesses to improve accuracy and make significantly fewer errors could easily offset the cost of adopting such technologies.
Every business has a vast opportunity to apply all the “do, think and learn” technologies to improve business processes, accelerate outcomes, increase data quality, and enable powerful and predictive analytics. With intelligent automation, neither man nor machine can claim the sole prize in this battle. Instead, people who adopt RPA will be more empowered to do what they do well: think creatively, problem-solve, prioritize and interact with clients, partners and co-workers in smarter and more productive ways.
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