Move Beyond RPA to Automate More Back-Office Functions

Move-Beyond-RPA-to-Automate-More-Back-Office-FunctionsRobotic Process Automation (RPA) was a welcome advancement for IT and finance professionals because it easily automated routine tasks. But like all technical advances, this solution was an improvement, but not a panacea. Recent advances in Artificial Intelligence (AI) address RPA’s limitations and have the potential to make processes far more efficient than RPA alone. 

Finance professionals—your back office “To Do” list is long and growing daily. Check the data, update compliance rules, pay bills, and generate reports. You and your staff seem to spend your time treading water rather than advancing the business’s financial position, and initial RPA tools provided by IT are no longer checking the box. 

You are not alone: most finance pros have stated that repetitive follow-ups and a lack of responsiveness are their biggest challenges. Advanced technology has the potential to change that dynamic.

First-Generation and Second-Generation Back Office Automation 

RPA emerged as a first-generation solution. The preconfigured software uses business rules and predefined activities to choreograph and automate the execution of repetitive processes, transactions, and tasks involving one or more software systems. Such solutions deliver results with little to no human intervention, according to IEEE Standards Association (SA). 

The technology provided a welcome level of automation across business systems. These systems are good at inputting and consolidating information entered on generic forms. This advancement was a step in the journey to streamline functions but not the endpoint. 

Better tech emerged. AI has the power to combine cognitive automation, machine learning, reasoning; hypothesis generation and analysis; and natural language processing to produce insights that lead to actions automatically, according to IEEE SA.

Artificial-intelligenceExplicit and Implicit Instruction 

The two foundations operate distinctly. RPA is very regimented: every activity needs to be explicitly scripted. A bot has to be told precisely where to extract relevant information from each invoice. Because the software lacks intelligence, it does not learn by itself. If a minor item is overlooked, the solution is usually not smart enough to adjust. 

AI systems are intelligent. They often do not have to be told what step to take. They draw deductions and make decisions themselves. 

Structured Data and Unstructured Data

The type of information that computer systems create and track has also morphed through the years. Initially, enterprises relied on Database Management Systems that housed textual information in tables. RPA typically relies on such structured data.

With the move to images and video, data has become more freeform and unstructured. AI is flexible enough to work with structured and unstructured information. 

Installation and Maintenance 

Maintenance can be an ongoing challenge with software applications. With RPA, enterprises create a bespoke, unique solution, one suited to the business. As a result, you become responsible for ensuring that the solution runs the latest version of the software, so any security holes have been patched. Besides, you need to ensure that its connections to other applications are maintained whenever an update is released. Such work can be tedious and time-consuming. Most businesses lack the skills to do that work themselves, so they often offload it to (expensive) systems integrators.  

When you rely on a SaaS-based AI solution, you offload software maintenance to your supplier. They ensure that your application – and all others – is up to date. Not only does SaaS limit the need for professional services necessary, but it also speeds up the development process – and nowadays, speed and agility have become top business drivers. 

RPA’s Niche 

That said, RPA has appropriate uses. Some companies have relied on legacy on-premises systems, and for those systems, RPA works well. 

In comparison, AI systems address specific industry challenges. For example, Auditoria is designed for corporate finance. Organizations do not have to invest in teaching the system about particular tax laws or finance terms. Features, like Internal Revenue Service rule changes, are baked into the program.

AI and RPA are not antithetical. An AI system can complement an RPA system, especially if a purpose-built solution is the best choice for a specific department or function to streamline activities. In contrast, the RPA system may help the business overall. 

What You Need in a Financial Automation Solution

But not all AI-based SaaS systems are created equally. When evaluating them, you need to look for a simple, intelligent system of engagement that: 

  • Is purpose-built for your needs (vendor onboarding, audit, etc.) 
  • Works with your existing systems
  • Inherently understands specific industry challenges 
  • Provides sophisticated decisions support, including cash forecasting recommendations 

RPA solutions were a welcome addition to business processes several years ago. Recent advances led to the creation of AI solutions, which are smarter and require less maintenance than RPA systems. When examining your financial automation options, look for one with deep financial depth. With it, you and your staff will spend more time maximizing your firm’s fiscal resources and less completing routine transactions. 

To learn how Auditoria can accelerate your finance transformation, request a free demo of Auditoria SmartFlow Skills here