The business process automation market is the victim of false marketing promises, resulting in widespread confusion when it comes to making purchasing decisions. The first wave of solutions was overhyped, which got things off to a bad start. Then there’s the fact that every automation product is unique, making comparisons difficult. In addition, there are few, if any, insightful case studies to go by because enterprise operations are still in the early phase of adopting automation on a large scale.
Despite the challenges, the world’s biggest and smartest enterprise operations are cutting through the noise and rapidly adopting. Read on for a more in-depth diagnosis of the obstacles and how buyers are overcoming them.
The overselling of RPA
Truly useful product marketing gives prospects a list of features and benefits, along with insights learned from customers, overlaid with a mercifully thin layer of varnish to catch the eye of prospective customers. Bad marketing is just the varnish, and that’s what has covered much of the automation market. Robotic Process Automation (RPA) vendors have oversold the capabilities of their products with shiny, bold marketing; and some even market their products as artificial intelligence (AI) solutions when their technology provides strictly rules-based robotics.
It has taken a number of years for rules-based automation software to find its place in the enterprise software stack, but at last it has achieved mainstream awareness as RPA. It is an excellent solution for automating routine, binary tasks that human workers perform on legacy applications — like entering passwords into and operating the user interfaces of SAP and Oracle — and moving structured data from one system to another.
Given the waning benefits of labor arbitrage and mounting pressure to reduce costs, the BFSI industry in particular blazed a trail into RPA, focusing on common horizontal business processes like eInvoice processing, Procure to Pay (P2P), Record to Report (R2R) and vertical challenges like KYC, AML, settlements and claims processing — in other words, the labor-intensive middle and back-office work that has for decades been mostly offshored.
After initiating a proof of concept (POC) or even a full deployment, customers measure their results and wonder, “Where is the 90 percent cost reduction I was promised? What about the suffocating volume of manual, unstructured data work in the rest of the business process? How do I affordably handle the exceptions in the process that need human action? How do I automate the rest of the process?” This is a bit of a paraphrase, but it’s a fair synthesis of how end users react after using RPA-only products. The problem with RPA as a category is not the technology. It’s how vendors have marketed their RPA products.
It is true that RPA automates the operation of desktop application user interfaces, and one “bot” can deliver about 1.5x the productivity of one worker, provided the work only involves structured data. It’s “hand” work — tasks that a human can perform without thinking, performed in accordance with a strict and rigid set of rules. RPA does not process unstructured data (PDFs, docs, email messages, news feeds, web content, etc.), nor does it have human-in-the-loop exceptions processing for when the rules governing a “bot” change.
Additionally, some RPA solutions are not deployed at a server level, which, for customers who value security, means occupying a desktop and exposing passwords and other sensitive data to human workers.
So, half the hype is true; RPA is indeed a powerful feature that delivers great results when applied to structured, rules-based tasks. However, it belongs as a feature in a complete suite of automation capabilities, and it must be paired with machine learning to automate a complex process from end to end.
Vendors are racing for the top
The various players in the market started with different theses, built different products based on their origins and marketed to different sizes and types of businesses. These differences are architectural, functional, horizontal and vertical.
The automation field now finds itself in the competition phase. Some of the early players have already cashed out, other players are marketing capabilities they do not have and still others are only just now coming to market. Buyers find it hard to compare them because there isn’t a standard set of features to shop for, and some buyers don’t yet know what they need. After all, only a few years ago, offshoring was state of the art and business process automation was an expensive, uncertain scripting endeavor imposed upon data science and IT teams.
Little hard data
Many consumers rely on word of mouth or online reviews to help them choose from among a huge selection of retailers and service providers. In the same way, clear, relatable, quantitative case studies from seasoned customers make it easy for enterprise software buyers to select a technology. These case studies are only just now surfacing, and that has made it more challenging for buyers to make decisions.
The reasons for this dearth of evidence are several. First, business process automation is still new. Second, there’s still confusion between the features and benefits of robotics, cognitive automation and AI. Most buyers are only a year or two into their business process automation journey, few of them will continue to use the platforms they started with, and many companies have only just begun their due diligence. Making meaningful bets is difficult without hard data.
Recommendations for effective decision making
Following are three recommended steps to cut through the confusion. The first step is centralizing control of the buying process in centers of excellence (COEs). The fastest and most efficient purchase and deployment efforts have leveraged this model, which brings together operational requirements from user groups across different divisions, product knowledge and decision-making liberties. These COEs typically begin with reports and briefings on smart automation and digital operations from Everest, Gartner, Forrester, HfS and other leading analyst firms that are shedding light on the market. The COEs create a long list that becomes a short list that becomes a pragmatic, informed product selection.
Next, work with one or more vendors to create a list of business processes for a meaningful proof of concept; a good vendor will be able and willing to help you. These processes should represent the way the business operates. If you’re a global banking or insurance operation, thousands of people ingest and process a combination of both structured and unstructured data and leverage and feed dozens of systems. You have some legacy technology that you do not wish to disrupt, and you have some point tools that you wish to rationalize out. You have both internal and external demands to accelerate transaction times and reduce manual work while improving accuracy. You are under the gun to cut costs immediately, and any solution you consider must pay for itself in under a year. So, a good set of processes for a POC will take these factors and challenges into account.
Third, conduct the actual POC. The CIO of one of the largest European banks once declared that if a technology could not demonstrate results in one quarter, it had no place in the operation. This is an excellent rule for automation.
Make sure you can afford the POC by securing executive sponsorship. POCs are often affordable enough for divisions within companies to execute without executive support, but full deployments across an enterprise are not. There’s no point to doing a POC if your organization is not committed to modernization or transformation at the executive level. Get buy-in early.
The POC is not only a chance to see how a product performs, but it’s a chance to see how a product would deploy. Does the vendor deploy directly? Through partners? As an on-premises solution? Cloud? Desktop or server, or both? POCs are your opportunity to not just kick the tires but to drive the car, and you should drive the car hard and fast in many conditions before you buy it.
Covering all the bases
The right technology can turn the operations department from what is perceived as a cost center into a cost savings center. However, care must be taken to steer clear of grand marketing promises. Automation software prospects need clear communication regarding real features and benefits, not inflated talk of AI where none exists. The limited experience thus far has demonstrated that RPA, while great in itself, is not sufficient on its own; instead, organizations need a full suite that includes business process management (BPM), RPA and AI-powered cognitive automation.
Adam Devine is vice president of marketing at WorkFusion, where he leads market development, product and brand marketing and strategic partnerships. He began his career in management consulting in the financial institutions group at BearingPoint and has spent the past 14 years in tech product marketing and advertising. He was most recently director of strategy at 360i.