Is Record-to-Report Automation Threatening 20% of Finance Roles?

With AI delivering results 3x faster, RPAs reducing R2R journal entries cost by 38%, and achieving 25% quicker financial closes, the record-to-report (R2R process) of finance is undergoing a transformative journey.

Don't be catfished by the buzz that surrounds AI and Automation. Because the true tale is written in the adoption rates, which are directly tied to how readily the new changes are embraced.

While it's true that the statistics are compelling, there's a mysterious reluctance among many companies. Data from a study conducted by IBM in 2023 reveals that only 35% of companies are harnessing the advantages of AI and Automation in the Record-to-Report (R2R process), which represents 20% of total roles in the finance function.

The study explores the adoption rates of these exponential technologies in the R2R processes. The study surveyed 544 finance managers across 25 countries, representing 18 different industries.

So brace yourself—this is not another AI puff piece!

We’ll explore the practical aspects, challenges, and clear benefits of integrating AI and automation in R2R, providing examples of successful case studies.

Key Operational Challenges with R2R Today

The R2R process, encompassing recording, consolidating, reporting, and closing on financial data, is fraught with several challenges.

Most challenges occur during R2R journal entries and reconciliation processes.

Journal entries face challenges including numerous follow-ups, manual interventions, slow financial closing, and delayed decision-making due to limited real-time insights. Reconciliation processes struggle with repetitive manual tasks, lack of transparency, limited insights, and poor user experience due to siloed operations.

Considering these challenges in the R2R process, we need a big change! This could come from using technologies like AI and automation.

Yes, these could make many finance (R2R) jobs obsolete, but they also offer a future where we work more efficiently, spend less, and focus on important tasks like analysis and actions.

20% of Finance’s R2R Roles May Become Obsolete, But Not Immediately!

The potential for improvement is immense.

Improving the efficiency of the R2R cycle can lead to lower process costs. This is significant as the R2R process makes up about 20% of all FTE roles in finance.

Indeed, data availability, automation, and AI can enhance efficiency, but there isn’t a one-size-fits-all solution.

According to IBM, the significance of the aforementioned processes in bringing down financial close time varies for companies based on their annual volume of R2R journal entries.

These companies fall into two categories and each should prioritize different processes:

1. For organizations processing over 1.5 million R2R journal entries annually, AI significantly reduces financial close time by handling large data, making quick decisions, and providing insights, enhancing the efficiency of the R2R process.
2. Organizations with fewer than 1.5 million yearly R2R journal entries should prioritize data access and management to reduce financial close time. Using AI and Robotic Process Automation (RPA) can also enhance their R2R cycle efficiency.

Source: IBM Institute for Business Value

The potential redundancy of some finance roles could be a cause for concern for some, but it’s necessary to view this shift not as a loss, but as an evolution towards more efficient, insightful, and strategic roles within finance.

Also read: Will AI Kill 300 Mn Jobs, Is DSO Dead, and More. (Learn more)

At the same time, the adoption of Cloud technologies is a vital step in this transformation.

The Cloud can help businesses manage and maintain data, which is crucial for AI and automation. This will set the stage for the future of Record-to-Report processes.

Only 35% Use the Cloud to Improve Data Collection Methods for Efficient R2R

In the R2R process, data is king!

61% of organizations optimizing for data availability and analysis are in the process of adopting cloud technology for general accounting and reporting. However, out of those, only 35% of respondents have reported successfully doing so, indicating an untapped opportunity for many finance organizations in cloud computing.

But, why is there such a huge drop off?

Collecting, Structuring, and Validating data isn’t always straightforward

Today’s organizations typically face three major challenges:

1. Collecting and handling organized and unorganized data from various sources
2. Gathering and organizing a large volume of data
3. Validating and reconciling data

And, the adoption of Cloud technology offers three major benefits:

1. On-demand scalability and compatibility with existing workflows
2. Efficient sharing and control over data
3. Easy integration of financial and operational data

Adopting Cloud Tech: A Single Source of Truth

When it comes to dealing with the challenging tasks of data collection, structuring, and validation in the R2R process, today’s exponential technologies are needed.

Generali Hong Kong, part of Italy’s Assicurazioni Generali, tackled this issue by adopting a cloud-hosted ERP solution, which shifted their focus from mundane tasks like reporting and reconciliation to more analytical roles.

The result? A smoother operation with real-time data flow, better financial data control, and efficient tasks like vendor payment and bank reconciliation. The staff are now free to focus on analytics and insights, showcasing how cloud tech can benefit the R2R process.

But the transformation doesn’t stop there. The next big leap towards efficiency in the R2R process involves the implementation of Robotic Process Automation (RPA).

This technology, when used in conjunction with cloud-based systems, can provide a dramatic boost in productivity and accuracy.

Only 10% Have Optimized RPAs for R2R Processes

R2R management involves a series of steps taken by accountants during their monthly financial close process. Finance leaders, leveraging RPA, are seeking to reduce costs and capture operational efficiencies.

In reconciliations, adopting RPA can lead to a 60-75% reduction in journal entry time. However, only 10% of respondents have optimized RPA for their general accounting process.

RPA can Reduce Manual Work by 75% and Potentially Lead to a 30% Reduction in Staff

Implementing RPA in R2R processes, as evidenced by the experiences of two companies in IBM’s report (which were left unnamed), can result in significant efficiency gains.

1. The first company’s automation efforts led to a two-day decrease in the close cycle, a 30% headcount reduction, and $25 million in cost savings. This was achieved through the standardization, centralization, and optimization of various processes, allowing finance teams to shift their focus to performance insights.
2. In the second case, the company’s implementation of RPA to automate its daily reconciliation process drastically improved their inventory planning. This resulted in a 39% financial close time reduction and 75% decrease in manual work.

Artificial Intelligence (AI) is another piece of the puzzle which can unlock the potential for smarter and more efficient decision-making. This integration allows for the processing of vast amounts of data and the extraction of valuable insights in real-time, thus enabling businesses to make informed decisions more quickly and effectively.

Despite AI Offering 3x Faster Results, Only 40% Have Fully Implemented It

AI, with its ability to process large volumes of data, make real-time decisions, and provide valuable business insights, can significantly improve the efficiency and effectiveness of the R2R process. But, despite these many advantages, only 40% have reported fully implementing AI for general accounting and Record to Report (R2R) processes.

Source: IBM Institute for Business Value

Organizations fully using AI for finance operations and mature in data availability and analysis are top performers in record-to-report, processing journal entries 66% faster than others. On average, such organizations take only 1 hour per journal entry compared to 3 hours for others.

Do You (Really) Need to Modernize Your R2R Process?

Quick Answer: ‘YES’!

When underpinned by proper data management, the combination of automation and AI can revolutionize the R2R process.

These new technologies can lead to real business improvements. They can cut more than two days from closing times, increase efficiency by over 70% and cut costs by 40% – 60%. Organizations can expect financial insights to improve by more than 50%, helping them understand opportunities and risks better.

So, the question stands. How to get started? Let’s answer that as well with a 6-step action plan:

Modernizing the R2R Process: A 6-Step Action Plan

Step 1: Leverage Design Thinking: Utilize design thinking to identify and understand current experiences and pain points in your R2R process.
Step 2: Prioritize Potential Solutions: Concentrate on possible methods to update the R2R process.
Step 3: Develop a Business Case: Formulate a business case for the potential solutions, including estimated costs and expected benefits.
Step 4: Create an Implementation Plan: Develop a plan for modifying the R2R process, including a roadmap, business goals, key steps, and costs.
Step 5: Continuously Monitor Performance: After implementation, continuously monitor performance to assess the success and make necessary improvements.
Step 6: Scale Up the Execution: Improve execution and regularly review the progress with business stakeholders to understand roadblocks and value realization, and adjust the plan as needed.

Embracing A Transformation Journey in Record-to-Report Processes

We stand at the cusp of a paradigm shift in the finance sector. The Record-to-Report process is becoming simpler and faster, changing from a slow and repetitive process into an efficient and insightful one—giving way to a vibrant era of real-time actions and strategic thinking.

The R2R process is no longer just about managing R2R journal entries; it’s about harnessing automation and AI to transform the entire R2R cycle into a powerhouse of efficiency and insight.

But, this transformation is not without its challenges—and with AI, Automation, and data collection, we are more than equipped to face them.

The road to modernization may still be under construction, with only 10% optimizing RPAs and a mere 35% utilizing the cloud, but we are on the right track.

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