What is intelligent automation and how is it evolving within support services?

Barbara Hodge, Editor
Posted: 11/02/2017

Before we examine how IA is evolving, it’s important to clarify what is meant by the term. Given the relative newness of this market sector, at least for services support, there is a lot of confusion around terminology, with the tendency to sweep everything into the “RPA” moniker. Bringing some clarity to the jargon, IEEE Standard 2755 was approved in June 2017 and was published in September 2017. This first standard is a guide to terms, concepts and nomenclature. IEEE 2755.1 is under development and will provide a taxonomy for these new technologies in recognition of the extraordinary interest they are receiving, and explosion of solutions. This work will help practitioners compare offerings across providers as the industry falls into line behind the terminologies, and adopts common standards.

RDA or robotic desktop automation generally refers to an automation running on the desktop and working with the operator automating fragments of transactions, whereas RPA or robotic process automation reflects a server-based, unattended process execution. Both are using already available human interfaces for applications rather than the IT centric APIs. Moving along the maturity curve, we see the integration of cognitive and artificial intelligence (AI), as well as machine learning (ML) and data analytics. Intelligent Process Automation has emerged as an holistic description of everything from desktop scripting to artificial intelligence, as applied to process execution.

Despite the fact that the most lucrative option for most organizations right now lies in RDA/RPA, consultants say that clients spend far more time talking about AI and its potential applications, than robotics. This is, to use an old expression, putting the cart before the horse. As of this writing, there are just a couple dozen organizations in the world willing to publicize that they have sizable (400+ bots) RPA implementations already running. There are more that are quietly driving major automation programs, but the vast majority are just now exploring Intelligent Process Automation.

Like most transformative technologies, there are those on the leading edge, fast followers, mainstream adopters, and laggards. Most results are from leaders and fast followers, while the bulk of enterprises are just getting poised for mainstream adoption. However, practitioners are already recognizing that the data derived from process automation can fuel machine learning to drive a cognitive capability offering massive returns. There is a major effort underway from almost all Intelligent Process Automation providers to provide not just an execution and orchestration capability, but also an intelligence engine that delivers cognitive decision making and business process insights.

The evolving capabilities of intelligent automation tools are best described as moving from:

  • simple scripting
  • RDA and RPA
  • digitized RPA
  • machine learning
  • artificial intelligence.

Modern-day solutions incorporate some or all of these capabilities, whereby we see a distinct trend of ambitious providers aligning themselves with the end-to-end process solutions that drive proportionately greater change, and a server based model that has the advantage of being scalable, more easily governable, and more secure from an IT perspective.

A major and fundamental difference that must be stressed here is that most Intelligent Process Automation technologies rely on the already “published” or available human interface to enterprise applications. This means they can be driven by the business rather than traditional “heavy” IT.

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Previously, any business user wanting new capability needed to follow the long and expensive traditional IT processes. Today, in a matter of days or weeks, business users can configure process automation and get it into production.

Another way of categorizing the vast array of IA technologies is according to what kind of process they can help and what kind of information they can process. For example:

  • Robotic process automation can work with standard processes that are rules based, and structured and predictable data. Traditional RPA represents the first stage, for repetitive transactional type work. This applies to more than 60% of automated process activities and delivers significant cost savings. It is limited, however, in its capacity to manage unstructured data, leverage natural language processing, or embed judgment. For most organizations, this is where the key focus of robotics activities is. RPA is best used where process predictability and stability is high and a majority of processing can be performed in Straight Through Processing (STP).
  • Robotic desktop automation can dynamically “pause” its automation at points in the process where human judgment or decision making is required in order to move the process forward. RDA is best used in processes that are complex or have dynamic inputs that influence how a process should be executed.
  • Machine learning uses structured, semi-structured, and unstructured data to create high-confidence predictive and prescriptive analytics that can substitute for human decision making in process orchestration. Note however, that this emerging field has enormous dependency on data availability and curation.
  • When prescriptive analytics from machine learning are combined with process execution capabilities, they form a cognitive solution. The data created from automated process execution, combined with other data sources, enables dynamic context sensing and decision making that enables an entirely new level of STP. Where processes were fragmented to allow for human orchestration and judgment, a cognitive solution can provide both decision making and execution.
  • The use of intelligent chatbots supports user interaction, and improves the customer experience. Chatbots can be powered by a set of rules or machine learning, whereby the latter are referred to as intelligent chatbots and act primarily as an interface between humans and robotics.
  • Finally, and eventually, narrowartificial intelligence will become more mainstream. AI crosses the boundaries of what is likely to happen and what should be done about it to what might occur if… AI produces a deductive analytic that has been exclusively in the human domain. While there are a few limited examples of AI that get a lot of attention (and seem to dominate the press) AI, for most enterprises, is some time away. It is worth noting that for purposes of providing business value, cognitive solutions will be the workhorse of modern enterprise. Most things labeled “AI” are, in fact, limited cognitive solutions centered around a very narrowly defined knowledge domain.

It's conceivable that an organization could enter at any of these stages. However, many of the Intelligent Process Automation failures are due largely to entering at a stage that is too complex and where general data poverty exists. Again, it is important to emphasize that as one moves up the continuum, data dependency becomes ever more vital. A typical result of machine learning implementations is that necessary data is either discarded intentionally by legacy enterprise applications, or can only be found in the minds of operators. Starting with RPA and RDA creates a foundation of data on which more advanced solutions can be built. In essence, each generation builds on the preceding one, without the need to replace each other entirely. In this way a more advanced stage can leverage and build upon the successes achieved at an earlier stage.

EY IA chart

Source: EY Advisory

EY IA chart 2

Source: EY Advisory

As organizations move up the IA continuum, solutions become more sophisticated, but the complexity and cost of projects increases. Despite the obvious attraction of the more evolved approach, the majority of efficiencies or savings are still derived from basic RPA and RDA. However, for those organizations with the foresight to build cognitive and AI-based processing capabilities into their plans, benefits will be exponentially higher. While the tip of the evolutionary pyramid is narrower and more specialized, the inherent value-adds of these activities are greatly multiplied via IA – though frequently more qualitative than quantitative or financial in nature.

At the very top level is where the enterprise experiences a step change in its modus operandi, which can propel it ahead of its competition. However, in the initial phase, the financial benefits of basic RPA can be used to demonstrate the value of intelligent automation and thereby gain support for moving along the curve.

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This article is extracted from SSON’s Intelligent Automation Global Market Report 2017 H1. Download the full report here.

The H2 version of this report will be published in December 2017.

 

Barbara Hodge, Editor
Posted: 11/02/2017