When should companies implement Robotic Process Automation and Artificial Intelligence to Improve the bottom line (and the top line)?
Today we are seeing more and more of the terms Robotic Process Automation (RPA) and Artificial Intelligence (AI) used together when discussing Business Automation. But are they the same thing? How do they work together? How are they different? When should I use one instead of the other? And more importantly, should I use them both? That’s a lot to ponder as Executives look to place their bets around this emerging and promising technology.
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Executive leadership within organizations today have heard of Robotic Process Automation and they understand the intrinsic value of streamlining and automating many of their daily business processes in order to lower their operational costs. They also know that by automating these processes they can gain a competitive edge by increasing productivity, improving accuracy, streamlining the increasing compliance requirements, improving employee satisfaction and of course, delivering a better customer experience. Executives have heard the stories of how ROI can be returned on in less than a year. PwC estimates that 45% of all work activities, regardless of vertical, can be automated, saving $2 trillion in global workforce cost1. And business executives want in on it.
In addition to creating operational efficiencies and driving down internal costs, companies are also looking for competitive edges to improve the top line and gain market share. They often find themselves competing with any number of “born in the cloud” companies such as Amazon that have created a heightened expectation of a true customer centered experience while at the same time trying to enter new verticals such as pharma, food services, healthcare and others.
So, there are two areas where organizations can see real value with Business Automation. One is creating cost savings and efficiencies through business workflow automation in order to shrink the bottom line (RPA). The other is unleashing the data trapped within their disparate enterprise applications to create timely access and increased visibility into both their business and their customers. By doing this companies can provide a better customer experience and they can identify and validate new business offerings (AI).
RPA should be leveraged for what we typically think of when it comes to the commonly accepted role of RPA, and that is as attended bots. Attended bots address highly repetitive, keystroke centric, and error prone tasks that are usually executed on demand but still require human intervention. These attended bot workflows can be found across all business units within an organization, whether it is Finance, HR, Legal, Supply Chain, IT, Customer Service and others. And while not all processes within a BU lend themselves to robotic process automation, typically 50% to 60% of them do. Which is still more than enough for a company to realize a compelling ROI for RPA.
Of course, there is much work to be done around identifying the best workflow candidates, establishing an internal Center of Excellence (CoE) and doing a business analysis of each workflow to see if you can improve it before you automate it. Built on a solid and strategic foundation of business automation disciplines, RPA can expand across an Enterprise generating a greater scale of efficiency and savings.
AI is a broad topic addressing many aspects of the digital transformation revolution in business. So where exactly does AI fit into the business automation scenario?
Regardless of the vertical, most large Enterprises today were not “born in the cloud.” These companies often have large and disparate enterprise legacy systems that have been built over time on infrastructure and software stacks by which they conduct business on a global scale. And these disparate legacy systems often stand in silos, each with critical business data within them. These apps were not designed to talk with other legacy or modern apps. And 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 cost 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, coalesce and analyze the data.
As a result, with all the talk of “going to the cloud,” most companies are relegated to leveraging the cloud for applications like Office 365, archiving or some file sharing solutions. All of those are important to IT and do provide some cost savings and reduction of technical debt, but they don’t move the business in terms of growth, competitiveness and opportunities for profitability.
And The reason that little else is done in the cloud is that the re-platforming of the critical legacy applications to make them cloud enabled is extremely costly and disruptive to a business. Supply chain, healthcare and manufacturing can’t pause while organizations spend $10’s of millions of dollars and years of time and effort to re-platform so they can compete with many cloud-enabled competitors.
Enter Artificial Intelligence with a business automation focus which can also include Machine Learning (ML) and Process Mining. These AI use cases are described as unattended bots because they can execute tasks and interact with disparate applications independent of human involvement. Once the workflow that is typically manual or partially automated is mapped out, the unattended bots perform the tasks, collect data, do the analysis based on business requirements and present those findings in 15 minutes instead of the typical process of 20- 40 hours. And they do this work 24 hours a day with zero errors.
The growth comes not only from gathering intelligence from mobile connected devices, web traffic, industry data, and social media but also pairing that with the unique data the company has siloed within and across its enterprise applications. This unprecedented access and insight into business information enables companies to identify new opportunities, improve on current initiatives, and address a market segment that other companies may have ignored or neglected to identify as new markets.
All of this can be done leveraging the Enterprise’s current investment in infrastructure, security, governance, risk and compliance. So, AI driven automation holds the promise of leveling the playing field with more agile businesses 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 combines business and process experts, enterprise architects, and innovation leaders responsible for designing, building and maintaining a company’s process robots. Companies need to make sure they analyze the risks and variables that can cause costs to spiral and results to diminish.
For most companies embarking on this journey, whether they are brand new to RPA or already have some initiatives in place that may be stalled, using a hybrid model that leverages the resources, knowledge and experience of a third-party provider who has the business expertise and in-house staff, will help deliver the strongest foundations and best ROI for your company.
In summary, the combination of RPA and AI as well as Machine Learning (aka hyper-automation), has become central to driving new business strategies and operating models based on customer and employee experience. By intelligently applying automation in your organization, you’ll see improvements across the key business drivers in your business.