Technology continues to evolve from spreadsheets to ERPs to process automation to the latest in generative AI; Each promises to solve problems more complex than the last and to reduce costs. Modern end-to-end technology and forms of artificial intelligence have been with us for decades and made their way through various industries until they hit the consumer market in 2023; That changed the conversation. As new solutions hit the market and talk moves from process automation to the cloud, to comprehensive automation and generative AI, the alternatives and options melded. It begs the question, what do I need when? 

While talk of artificial intelligence (AI), and especially generative AI, is everywhere, CFOs seek to understand where it fits in their toolkit. The technologies are merging and what was standalone just a few years ago is now leveraging technology from across the spectrum to be smarter and process faster. 

Automation fuels transformation. Finance transformation leverages automation to accelerate and streamline processes such as automating the invoice, payroll, payables, and billing process, streamlining reconciliation processes, enhancing financial reporting, and managing AP and AR.

What is RPA and how is the finance function using it?

Robotic Process Automation (RPA) is a form of business process automation that uses software robots or artificial intelligence agents to help accelerate business processes. RPA uses some similar technologies as artificial intelligence but is different because it uses technology to follow a predefined workflow rather than solve complex, unstructured problems. RPA can be a standalone process enhancement or used with AI or machine learning (ML) technology providing insights and intelligent automation. Regardless of the underlying technology, RPA has emerged as a key component in enhancing finance transformation initiatives.

Finance leaders are reaping the benefits of what RPA can do and provide including: 

• Enhanced efficiency, reduced costs, and improved productivity by automating their processes around transaction and invoice processing, vendor management, reconciliations, and reporting. 

• Automating the extraction of data from various sources, compare transactions, flag discrepancies for human speed up traditional processes, facilitate collections automatically, and generate timely, accurate reports, payroll, invoices, expenses.

• Responding to finance-related customer inquiries to provide faster, more consistent service. 

Finance departments can use RPA to address many of the challenges associated with manual processes, leading to more efficient, accurate, and timely financial operations. According to Automation Anywhere’s report, Now & Next: The State of RPA, businesses typically achieve an average ROI of 250%, recouping their initial investment within six to nine months after RPA deployment. Looking at the higher side, this ROI percentage has the potential to go up to 380%. Impact varies across function, but reduced cycle times in AP and AR benefits typically include:

• Reduced cost: We have seen a significant reduction in the cost per invoice with process redesign and RPA.

• Saving time: Significantly reducing time to process invoices, speeding up the payment cycle and cash flow.

• Minimizing errors: Automating data entry and validation using RPA can eliminate most manual processing mistakes, enhancing accuracy and compliance. 

8 common RPA challenges

However, embarking on an RPA journey without careful planning and consideration can lead to obstacles that hinder the realization of its full potential in your modernization journey.

To help ensure a smooth and successful RPA implementation, it is crucial to identify and address common challenges that can arise along the way.

1. Inadequate process selection and prioritization

Not all processes are suitable for automation. A critical step in the RPA journey is to thoroughly assess and prioritize processes that stand to benefit most from automation. Start by analyzing process characteristics, identifying repetitive and rule-based tasks, and evaluating the potential for automation to deliver significant improvements. Failing to conduct this assessment can lead to misallocation of resources and automation efforts that fail to yield the desired outcomes. 

For example, selecting overly intricate tasks can lead to delayed projects, increased costs, and a higher likelihood of failure. A more effective approach is to begin with simpler, well-defined processes that demonstrate the value of RPA and build confidence in its capabilities. As experience grows and expertise deepens, more complex automations can be tackled with a higher degree of success. 

2. Standardization and documentation

RPA bots rely on structured and consistent processes to function effectively. Without proper documentation and standardization, automation efforts can be hindered by ambiguity or inconsistencies. It is essential to ensure that processes are well-defined, documented, and standardized before embarking on automation. This involves mapping out the process steps, identifying decision points, and establishing clear guidelines for execution.

3. Neglecting continuous improvement 

RPA should not be viewed as a one-time project, but rather as an ongoing journey of continuous improvement. Automated processes should be regularly reviewed, evaluated, and refined to ensure they are operating at peak efficiency and adapting to changing business needs. This ongoing optimization process is essential for maximizing the long-term value of RPA investments.

4. Lack of performance metrics

Measuring the success of RPA initiatives is crucial for demonstrating its value and justifying further investments. Without clear key performance indicators (KPIs), it is difficult to assess the impact of automation on productivity, cost savings, and overall business outcomes. Defining and tracking relevant metrics provides valuable insights into the effectiveness of RPA implementations and guides future decision-making.

5. Inadequate training

The success of RPA hinges on the ability of employees, developers, and RPA users to effectively manage and utilize the technology. Comprehensive training programs are essential to ensure that all stakeholders have the necessary knowledge and skills to maximize the potential of RPA. This includes training on RPA principles, bot development, process analysis, and change management.

6. Ignoring change management

RPA can disrupt established workflows and introduce new ways of working, which can lead to resistance and adoption challenges. Effective change management is crucial to ensure that employees and stakeholders embrace RPA and adapt to the new processes. This involves clear communication, training, and support to address concerns and facilitate a smooth transition.

7. Lack of governance

Establishing clear governance and control mechanisms is essential for managing RPA implementations effectively. This includes defining roles and responsibilities, establishing approval processes, and implementing security measures to protect sensitive data and prevent unauthorized access. A well-defined governance framework helps ensure that RPA is implemented in a consistent, controlled, and secure manner.

8. Vendor considerations

While RPA vendors offer valuable expertise and solutions, over-reliance on a single vendor can limit flexibility and adaptability. It is important to maintain a balanced approach, ensuring that the chosen vendor aligns with the organization's needs while keeping an eye on emerging technologies and potential new solutions.

By following the guidance above, and mitigating these common challenges prior to embarking on your modernization journey, finance leaders can better navigate the automation and RPA landscape successfully, reaping the benefits of enhanced efficiency, reduced costs, and improved productivity while minimizing the risks and challenges associated with automation.