How to succeed with Intelligent Automation: what our surveys tell us
Posted by firstname.lastname@example.org on July 19, 2018
It's a question that is sowing a lot of confusion these days. Firstly, there is simple terminology. Weren’t we all just talking about "RPA" –Robotic Process Engineering? And now that we have our mind around that, and understand what we think it can do, we’re suddenly reading about intelligent automation (IA), robotic desktop automation (RDA), and machine learning (ML).
So let's start with the definition. Recognizing the immense potential for confusion in terms of what is being offered by providers and what is being bought by enterprise customers, the IEEE global standards body – under the chairmanship of Lee Coulter, SVP of Ascension and CEO of Ascension Services Center, and Chief Intelligent Automation Officer at SSON – is leading the way in establishing, defining, and setting-in-stone much needed standards to guide the emerging and fast developing Intelligent Process Automation sector.
In brief, the correct designation is "Intelligent Automation", which encompasses simple desktop robotic automation all the way through to artificial intelligence, and whatever is yet to come. That still leaves you with trying to figure out which of the acronyms is right for you, however. While the true answer – “it depends” – won't win us any friends, the truth is you won't know which tool or solution is right until you are able to define your desired output or end result. The banking industry was one of the first to embrace robotic process automation for its ability to provide a [temporary] 'Band-Aid' between its legacy systems and more advanced customer facing, digital capabilities. In plain English, that meant that RPA could provide a highly effective temporary fix to a solution that would require ERP updates that might take years. Such RPA solutions were necessarily limited, discrete, and not necessarily scalable
“Scalability” has emerged as the buzzword of today. Whether that means vertically, horizontally, across regions or operations… the idea is that once a given solution has been tested and piloted, the benefits could be rolled out across a predefined sector. The challenge is that scaling later requires planning before. Too many enterprises have been caught short because they didn't ask the right questions up front. And while the tendency is to blame the providers, this simply isn't [always] fair.
The other surprise has been “data”. Similar to the scaling issue, data needs to be considered upfront. The problem is that you often don't know which opportunities will present themselves until you have actually lived through the implementation. At that stage in the lack of easy access to one source of data, real-time data, true data, or even sufficient quantities of data, will severely impact your ability to proceed.
So, while SSON Analytics’ recently released workbook – What are the Benefits of IA and How is IA Evolving Within Support Services? – lists a myriad of drivers for adopting IA, including talent retention, compliance, security, and agility, there are still plenty of impediments to overcome.
Nevertheless, nearly 6 out of 10 respondents have identified intelligent automation as an enabler of more knowledge-centric work, and the vast majority say they are implementing initial IA solutions within significantly less than three months. In addition, nearly half the respondents to a recent SSON event poll indicated they believed up to 40% of processes under their control could be automated.
The SSON Analytics report helpfully lists various intelligent automation 'tools', and identifies the data type required for the solution to work – i.e. structured, unstructured, etc. Of course, today, there are plenty of solutions that take unstructured data and turn it into structured, so the industry is certainly reacting to customers’ needs.
A key consideration from a customer perspective is ‘operating model’. Again, the report lists the pros and cons of the three major options: in-house, outsourced, or hybrid.
The multiple case studies at conferences, on SSON Analytics’ site, and SSON’s website prove IA is a winning solution. However, like any large- or small-scale transformation it requires upfront planning and careful execution. One of the leading causes of failure, the workbook highlights, is targeting the wrong solution:
“Highly complex processes are appealing but that is the wrong approach. Even if these are more painful for human employees, their complexity may delay the big cost-savings that result from low hanging fruit.”
Read the workbook Starting on Your IA Journey: What You Should Know
Note: This visual analytics workbook blends data from SSON Analytics' most recent surveys and Global Intelligent Automation Market Reports to answer 6 key questions to support a successful Intelligent Automation (IA) journey.
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