Automation Doesn’t Always Mean RPA

For many businesses, automation can be a confusing concept. With so many types of automation technologies available, it can be difficult to discern the differences between them—and to identify which to invest in. Organizations know that automation is supposed to make them more efficient, but they don’t know how to make that vision a reality.

In fact, a recent Capgemini study indicates that only 16% of organizations are using automation for multiple use cases at scale. Why is scaling automation such a challenge? And how can businesses overcome this to reap the benefits of wide-scale automation?

What is automation?

Generally, when we talk about automation in a business context, we mean business process automation (BPA). BPA entails technology-driven automation of complex business processes and functions that go beyond conventional data manipulation.

There are lots of tools on the market that label themselves as business process automation tools—but they can be markedly different from each other in terms of approach and functionality. On a high level, BPA platforms can broadly be broken down into two types:

Rules-based automation

Rules-based automation relies solely on logical parameters to execute tasks. According to Capgemini, “rule-based technologies automate high-volume, repeatable tasks and mimic human actions and include both IT process automation and robotic process automation (RPA).”

Traditional rules-based automation approaches—like IT process automation—rely on APIs or custom scripts to interact with different systems in order to complete tasks. Working with these tools can be difficult and expensive, because they often require adequate developer resources or third-party System Integrators to code automations that will work across your apps.

Recently, robotic process automation has emerged as a less resource-intensive alternative. Though it is still a rules-based form of automation, it generally interacts with programs on the surface by scraping the user interface (UI) to collect and transport data —instead of connecting to your apps on the backend. This approach is particularly fragile as it needs to change whenever a program’s UI changes or the target fields change—something that happens more often than you would expect.

Cognitive or intelligent automation

Like other forms of business process automation, intelligent automation (IPA) to execute business tasks. But it works differently: it leverages “smart” technologies—like speech recognition, artificial intelligence (AI), and machine learning (ML)—along with APIs to execute workflows.

These cognitive technologies can also help businesses create automations based on best practices. Intelligent process automation generally:

  • Applies machine learning technology to a rich corpus of popular business workflows being used by thousands of other innovative companies. It can automatically create customizable workflows that are smarter and more well-thought-out than you would get by starting from scratch.
  • Weaves in rich, third-party AI technologies like Watson, Cortona, and Einstein to create smarter workflows than before. When a customer support request comes in, for example, having Watson analyze the tone, urgency, and emotion of the message can help you dictate smarter automations surrounding how that request is handled.
  • Helps simplify B2B and business process outsourcing. Today, business workflows must extend to engage thousands of companies and individuals in your customer and partner ecosystems. Brute force, one-at-a-time workflow execution is expensive and error-prone, and it lacks digital engagement. Intelligent workflows-as-a-service can drastically simplify operations, reduce costs, and improve engagement.

With so many options, what’s the problem?

Today, rules-based automation platforms are fairly popular—RPA in particular. In fact, a recent APQC study indicates that RPA is the core of about 69% of digital strategies.

At the same time, businesses still haven’t been able to scale their automations, despite investing more in rules-based tools than ever before. So what’s the problem?

There are a few aspects of rules-based tools that make them challenging to scale when used alone. These tools:

  • Require significant talent and resources to implement and scale. 57% of businesses say a lack of automation talent keeps them from deploying it at scale, according to Capgemini Research.
  • Increase aversion to change within the organization. Roughly 40% of organizations say that a lack of technological awareness among mid-level management is hampering their attempts to scale their automations. Given how difficult rules-based tools can be to use, it’s not surprising that many key stakeholders in automation projects aren’t familiar enough with the technology to make informed decisions that push the business towards its automation goals.
  • Make automation an “IT-only” project. Automation is a team sport, but rules-based tools require technical know-how. If they want to scale their automations, businesses can’t simply rely on IT to choose which processes to automate and then build those automations. Instead, they need to encourage cross-functional participation where lines-of-business users can help ideate, create, and implement automated workflows.

Automating processes end-to-end and wall-to-wall (that is, across the organization) can be a demanding project, and rules-based tools are not making it any easier. So although it’s incredibly useful in some scenarios, rules-based automation isn’t the only type of automation that most organizations need over the long-term.

How does intelligent automation resolve these challenges?

At Workato, these are exactly the challenges we set out to solve. We wanted to create an intelligent automation platform that can empower both IT and lines-of-business users to create scalable, governable, agile automations.

To do this, we allow users to:

  • Draw upon a wealth of previously-created automations, so it takes less time, talent, and resources to build all the automations you need
  • Build automations using logic and everyday English, as well as intelligent suggestions—so there’s less of a barrier to automating your work.
  • Create automations across your entire organization, not just the back office.

This intelligent automation approach is crucial to empowering businesses to scale smarter processes more quickly. You don’t have to re-invent the wheel every time you try to solve a business problem or streamline a workflow. It’s also more suited to creating the wall-to-wall, end-to-end automations that modern digital businesses demand.

This makes it much easier for both IT and business teams to collaborate. Employees who are closest to business-critical processes often have the best ideas about how to streamline them, and intelligent automation gives them the tools!

Can you use intelligent automation and RPA together?

Of course, this doesn’t mean there’s not a place for rules-based tools like RPA. In fact, both rules-based automation tools and intelligent automation can be used together. That’s actually one reason why more and more companies are choosing intelligent automation: it gives them the freedom to use RPA in conjunction with other technologies, without struggling to scale their automations.

Let’s look at how a global company uses intelligent automation to coordinate between its RPA tool and other apps for an agile, end-to-end workflow.

See It In Action: How a Global Food and Nutrition Company Streamlines Order Fulfillment and Shipping

A global food and nutrition company that ships more than 650 by-products to food manufacturers, pharmaceutical companies, and cosmetics brands in over 100 countries wanted to streamline its order fulfillment and shipping processes. Every order has several documents that must be packed with it when it ships. These include documents like the order invoice and shipping label. If even one item is missing or incorrect, the box cannot be shipped. Additionally, document data must be entered into the company’s mainframe system, which isn’t accessible via API. It’s a time-sensitive and, without automation, a labor-intensive process.

Like many logistics providers, the company chose to use Workato to tie together its RPA tool with best-of-breed apps and intelligent technologies. When documents are uploaded to the company’s enterprise cloud storage app—such as OneDrive, Box, or Dropbox —Workato picks them up and sends them through Docparser, which determines whether each document is an invoice, a shipping label, or something else.

Workato then sends the files to ConvertAPI, which associates the correct invoice with the other documents for that order. Once the documents are bundled correctly, Workato sends the bundles to the printer so they can be shipped with the right boxes. Throughout the process, employees can track the status of a particular document via Google Sheets. The document data still needs to be entered into the mainframe system, so Workato can interface with a surface automation tool that enters the data.

The workflow saves the company roughly four hours of labor every day and processes about 30,000 pages of documents per month. Not only can the company design a streamlined shipment workflow, but they can use the Workato platform to create a wide range of automations for every business process imaginable. Another benefit is that the company can choose which best-of-breed apps it wants to use for document parsing and storage—and change those apps as their business needs evolve. They can change their cloud storage app, for example, without requiring a developer’s help to tweak the workflow.

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