Call it digital transformation, digital disruption, digital operations or digital process automation. No matter what term an organization uses to describe automation technology, the goal the same everywhere: to harness the power of digital to elevate the customer journey and drive end-to-end business outcomes.
It all comes down to how an organization understands and uses its information cornerstone for success in creating a smart digital business. Software robots, often referred to as robotic process automation (RPA) systems, have gained considerable ground on this front, welcomed by C-suite executives who are keen to cut costs, improve productivity and gain a competitive advantage.
Laying the Groundwork for AI
RPA technology is in a relatively early stage, laying the groundwork for not only digital transformation, but also a new generation of intelligent applications that go beyond automating repetitive tasks, such as robots powered by artificial intelligence (AI). Research from Gartner suggests that the state of the market is such that CIOs shouldn’t hesitate to begin experimenting with AI technologies.
The research firm predicts that by 2020, AI will be a top five investment priority for more than 30 percent of CIOs. In the interim, CIOs will be scrambling to make sense of machine learning and other AI technologies, to figure out what roles those systems can play in digital business, and to launch the internal pilots that will test that knowledge and insight. At the same time, CIOs will have to sift through competing vendor claims and promises to identify and assess the genuineness of AI capabilities.
Today, there is confusion when it comes to which use case is best, where to start and what technologies to apply. The question is which technologies work together or complement one another when it comes to AI. It doesn’t help that much of the messaging today tends to be bandwagon cheerleading and hype. Because of this, there are often misconceptions about how these technologies work, and which technologies and approaches are truly best suited for different business processing situations. These messages will become clearer as more real-world use cases prove AI to be effective.
The key is for users to define tangible problems they are trying to solve and not worry so much about the technology terms, such as cognitive computing, natural language processing and machine learning. It’s critical to note that Gartner also suggests that CIOs take the simplest approach that will do the job, rather than jumping headfirst into cutting-edge AI techniques.
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Getting to Know RPA and AI
The relationship between RPA and AI is often misunderstood, exacerbated by the wide array of technologies that span the spectrum of intelligent automation. Numerous technology terms are commonly used to describe AI or “cognitive automation,” including adaptive alteration, machine learning, neural networks, predictive analytics and more. Differentiating the core capabilities of these various intelligent automation tools can bring clarity to discussions, helping identify both the opportunity and solution requirements.
Because of their flexibility and depth, AI and RPA can work together or separately, and do not necessarily represent a maturity curve where automation progresses from one technology to another. There are cases where RPA is deployed and then no longer warranted as the process is later redefined, but RPA and AI are not necessarily the two elements a “stair-step” approach.
Deciding which technology to implement depends on the specific operational challenges to be solved. Ideally, you should address obvious process challenges first and embark on a discussion of AI’s potential to solve both current and future business problems.
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Think Digital Transformation
Enterprises are now approaching opportunities with a clearer picture of what RPA can do well — handle repetitive, structured processes that drain worker productivity. When combined with advanced technologies used for understanding context, content and sentiment, robots can perform much more sophisticated work and organizations can enable employees to spend more time on higher value work (e.g., exceptions and decisions).
Because RPA is comparatively mature and has a proven ability to enable compliant, repeatable processes, CIOs who embrace it can get early wins and fast time to value, and never touch advanced learning technology. For example, a multistage energy provider is using RPA for simple automated tasks, such as starting or stopping service via requests initiated by the consumer. In this case, the company’s chat feature relies on live chatbots rather than humans, which is an example of enterprises experimenting with where automation fits into operations. Online conversations happen with the consumer, while RPA bots work behind the scenes to execute the service request. If the bot is unable to help the customer, the customer might need to complete a more detailed request or the conversation may be handed over to a call center rep.
At the same time, the discussion around artificial intelligence will continue to evolve as organizations and CIOs experiment with AI and start to plan how to successfully pilot the technology in well-understood uses. These efforts will begin to close the gap between the hype in the marketplace and real enterprise implementations.
Focus on Solving Problems
There is some urgency to bridge the gaps between systems, silos, processes and people — yet the transformation approach most often taken has focused on which technology to apply, rather than how to best solve the problem. CIOs who approach the customer journey in a more holistic way will be the automation trailblazers, proving the value of RPA and AI with real-world applications.