Among the hundreds of processes that keep companies ticking, many — including financial reporting and invoice handling — are based on structured data and involve a series of manual, repetitive and often paper-based tasks to complete. That makes them ideal candidates for robotic process automation (RPA), which organizations have been using to put more efficient, accurate and faster workflows into place.
But on their own, these “dumb robots” aren’t enough to help companies keep pace with increasing competition, to fight the “business as usual” inertia and to achieve their ambitions for full automation. For that, they need a digital workforce of the future, comprising “smart robots” — an AI-based toolset featuring capabilities such as cognitive document automation, which includes machine learning and natural language processing, all key capabilities of intelligent automation.
The truth is that the ability to maximize and extend the scope and degree to which processes can be optimized is redefining how organizations work. The ability to scale is essential if organizations intend to optimize and connect business areas and operations. RPA at scale means applying RPA enterprise-wide, on processes ranging from simple to complex — unstructured data, managing work between RPA bots and employees, and so forth.
Dumb robots are ‘business as usual’
RPA is the first step toward automation, and it is adept at automating simple human tasks based on structured data. Within structured workflow sequences, RPA bots are capable of making simple decisions, and they’re masters of working across multiple systems to search, aggregate or update structured information. This includes the ability to automate business intelligence insights by collecting data from multiple external sources and augmenting basic internal forecast reports, providing a deeper understanding of the business situation.
“Dumb” robots manage such tasks faster and without error, and they give human talent time to focus on “brain” work, rather than mundane, repetitive tasks. However, while working faster, more efficiently and more accurately is a step forward, it’s still “business as usual.”
The real obstacle to working like tomorrow is unstructured information, which is projected to increase significantly in volume. According to a survey of global leaders by AIIM, respondents said they anticipate massive data growth over the next five years — including as much as a fivefold increase over the next two years alone — and that as much as 62% of it will be unstructured. This is a problem, especially as 70% of global leaders also claim unstructured data is their Achilles’ heel as they embark on RPA implementations.
Another obstacle companies face is scaling automation. RPA point technologies or “dumb” robots won’t allow organizations to scale automation across use cases.
Smart robots that work like tomorrow
That means businesses have an imperative to find a way to automate processes involving unstructured information, such as documents, records, emails and text messages. Just 13% of companies said they’ve fully automated unstructured text interpretation, according to a Forbes Insight survey. And even though 59% said they conduct some automation to handle keyword extraction or sentiment analysis, there’s still quite a bit of room for improvement.
What’s needed is an RPA toolset that can recognize, classify, read and manage semi-structured documents, like invoices, or unstructured documents, like contracts, emails or texts. Organizations also need the flexibility to automate functions across multiple business systems — without tying up their IT teams and resources.
The next generation of RPA or intelligent automation brings together RPA and extended AI capabilities that enable companies to automate many processes, and capture and include structured and unstructured data to provide greater business insights. This empowers enterprises to achieve goals such as using data to improve products and services; provide comprehensive data integration across all apps and cloud environments; and optimize operations and processes that include increasingly complex business data through greater levels of automation.
Smart robots use AI to make sense of unstructured and semi-structured data, processing information using a collection of tools such as machine learning, natural language processing and sentiment analysis. These next-generation RPA robots can also use advanced analytics and collection techniques that not only pull rich, insightful data into business processes, but also analyze, report and provide advanced metrics to refine business processes and achieve defined benchmarks or strategic goals. As a result, they’re capable of much more than routine tasks or simple decisions.
The digital workforce of the future
Next-generation robotic process automation includes or is integrated with process orchestration, cognitive document automation, advanced analytics, and mobility and engagement technologies to deliver a truly intelligent automation system. Intelligent automation is the digital workforce that uses AI to manage much of the routine cognitive workload, such as reading physical documents, analyzing text for sentiment and tone, and synthesizing with other information. When this digital workforce is well-aligned with an enterprise’s human talent, the human talent can add context, initiative, strategic insight, creativity and intuition to the process. Thus, smart robots provide business insights that support high-level human decision-making, adding significant value to the organization. According to a report by Accenture, this alignment of human talent and a digital workforce powered by intelligent automation can increase an enterprise’s revenue by 38%.
With both RPA and AI, businesses are able to automate simple role-based actions to conduct work on existing systems while also deploying smart robots to solve next-generation problems today. This digital workforce of the future enables companies to offload yet another layer of human involvement in task management in a way that lets them work like tomorrow.
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