Utilizing Robotic Process Automation (RPA) and Artificial Intelligence
By Kevin Buckley, Business Automation Executive, Technologist
Today more and more terms like Robotic Process Automation (RPA) and Artificial Intelligence (AI) are used together when it comes to business automation. But are they the same? How do you work together? How are they different? When should I use one instead of the other? More importantly, should I use both? That’s a lot to think about when executives are trying to place their bets on this emerging and promising technology.
Business executives today have heard of Robotic Process Automation and understand the importance of streamlining and automating many of their day-to-day business processes in order to lower their operational costs. They also know that by automating these processes they can gain a competitive advantage by increasing productivity, improving accuracy, streamlining growing compliance requirements, improving employee satisfaction, and of course, providing a better customer experience. Executives have heard the stories of how ROI can be returned in less than a year. PwC estimates that 45% of all work activities, regardless of industry, can be automated, saving US $ 2 trillion in global labor costs (1). And business people want to participate.
In addition to creating operational efficiencies and reducing internal costs, companies are also looking for competitive advantages to improve return on sales and gain market share. They often compete with a number of “born in the cloud” companies like Amazon who have created increased expectations for a true customer-centric experience while trying to enter new industries like pharmaceuticals, food services, healthcare and others.
So there are two areas in which companies can see real added value with business automation. One of them is to achieve cost savings and efficiency gains by automating business processes to reduce the bottom line (RPA). On the other hand, the data included in the various company applications is released in order to guarantee timely access and better visibility for both the company and the customers. In this way, companies can offer a better customer experience and identify and validate new business offerings (AI).
RPA should be used for what we normally think of when it comes to the generally accepted role of RPA, and that is as visited bots. The bots visited deal with highly repetitive, keystroke-oriented, and error-prone tasks that are normally performed on demand but still require human intervention. These visited bot workflows can be found in all business areas of a company, regardless of whether it is finance, human resources, legal, supply chain, IT, customer service or others. And while not all processes within a BU are suitable for automating robotic processes, 50% to 60% usually do. That’s still more than enough for a company to have a compelling ROI on RPA.
Of course, there’s a lot to do to identify the best workflow candidates, set up an in-house center of excellence (CoE), and do a business analysis on each workflow to see if you can improve it before automating it. RPA is built on a solid and strategic foundation of business automation disciplines and can expand company-wide, resulting in higher levels of efficiency and savings.
Regardless of the industry, most large companies today were not “born in the cloud”. These companies often have large and diverse business systems built over time on top of infrastructure and software stacks that they do business with around the world. And these different legacy systems are often in silos, each of which contains important business data. These apps are not designed to communicate with other older or modern apps. In those cases where an API has been built to provide some level of integration, the level of interaction is often rudimentary and it has proven prohibitive to expand functionality between these apps. Most of the data mining and analysis is still done manually using spreadsheets and other documents to organize, merge, and analyze the data.
With all the talk of going to the cloud, most companies have to use the cloud for applications like Office 365, archiving or some file sharing solutions. All of these are important to IT and offer some cost savings and a reduction in technical debt, but they don’t move the business in terms of growth, competitiveness, and profitability opportunities.
The reason little else is done in the cloud is that reformatting the critical legacy applications to make them cloud-ready is extremely costly and disruptive for an organization. Supply chain, healthcare, and manufacturing cannot pause as companies spend tens of millions of dollars and years of time and effort re-platforming to compete against many cloud-enabled competitors.
Enter artificial intelligence with an emphasis on business automation, which can also include machine learning (ML) and process mining. These AI use cases are known as unattended bots because they can perform tasks and interact with different applications regardless of human involvement. Once the workflow is established, which is usually manual or partially automated, the unattended bots perform the tasks, collect data, perform the analysis based on the business needs and present these results in 15 minutes instead of the typical 20-40 hour process. And they do this job 24 hours a day with no mistakes.
The growth is due not only to the collection of information from mobile devices, web traffic, industry data, and social media, but also to the combination of that data with the unique data the company has stored within and between its corporate applications. This unprecedented access and insight into business information enables companies to identify new opportunities, improve current initiatives, and target a market segment that other companies may have ignored or neglected to identify as new markets.
All of this can be done using the company’s current investments in infrastructure, security, governance, risk, and compliance. AI-driven automation promises to improve the playing field for more agile companies to compete, innovate and expand into new markets.
The journey to RPA, AI, and hyper-automation should be well thought out and planned. A successful RPA journey requires a CoE that brings together business and process experts, enterprise architects, and innovation leaders who are responsible for designing, building, and maintaining a company’s process robots. Businesses need to make sure they analyze the risks and variables that can cause costs to rise and results to fall.
Most companies embarking on this journey, whether they are brand new to RPA or have already taken some initiatives that may stall, use a hybrid model that uses the resources, knowledge, and experience of a third party who has the business expertise and in-house staff to help you get the best fundamentals and ROI for your business.
In summary, the combination of RPA and AI, as well as machine learning (also known as hyper-automation) is central to driving new business strategies and operating models based on the customer and employee experience. By using automation intelligently in your company, you will see improvements in the key business drivers in your company.
Kevin Buckley has over 25 years of experience as a technical and business consultant with a proven track record in providing technical and business value in all of the industries listed above. He has knowledge of business workflow processes, robot process automation, IT, business transformation, cloud activation and digital transformation.
Technologent is a global provider of Edge-to-EdgeTM information technology solutions and services for Fortune 1000 companies. They help companies outperform the new digital economy by creating IT environments that are fast, flexible, efficient, transparent and secure. Without these characteristics, companies will miss out on the opportunity to scale optimally. Technologent mobilizes the power of technology to turn visions into reality and enables it to drive innovation, increase productivity and outperform the market. Visit www.technologent.com