Debunking five myths about automation in finance: part one
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Debunking five myths about intelligent automation in finance: part one

CFOs have been embracing and getting value from automation for some time. They've improved revenue, customer service, efficiency, predictive insights, risk management, and more.

But in most automation journeys there are still exceptions – or gaps – that organizations continue to handle manually. Finance organizations can close these gaps with intelligent automation (IA), which combines the strengths of robotic process automation (RPA), data analytics, artificial intelligence (AI), and human intelligence. Achieving this requires companies to use multiple automation approaches and tools, from integrating basic bots into tasks to fully digitizing processes and systems and using data intelligently.

Despite the opportunities, misconceptions persist, leading to a shortsighted view of what digital can do for finance and what the right use cases are. And inevitably, this leads to disappointment in the outcomes and makes it hard to build the case for greater investment in digital transformation.

In the first of this two-part series, we decode three of the top five myths about intelligent automation in finance. And we explore how CFO organizations can overcome them to drive IA adoption and deliver value to the business.

Myth one: Intelligent automation is all about efficiency gains for the finance organization – it cannot really drive bigger business outcomes

Reality: An integrated approach to IA improves effectiveness, insights, and customer experience

The first wave of automation generally focuses on reducing a team's headcount. In many cases, finance organizations approach RPA as they do outsourcing projects: for productivity gains. After getting limited value from this approach, companies realize that before they can realize the benefits of IA, they must align the finance function's goals with business objectives.

For example, for a large healthcare manufacturer, its priority before COVID-19 was revenue growth and leapfrogging the competition. As it faced the crisis, organizational priorities shifted to liquidity and cash management. With a clear view of these priorities and strong alignment with the business, the finance team could make the most of its investments in IA to guide strategic decisions, such as where to ship equipment and how to minimize default risk from key customers.

Organizations that have approached intelligent automation holistically report significantly higher value. It all starts with defining the desired business outcomes. Understanding why and what to automate is more important than how to automate. For example, companies can create and lose significant value at process intersections. To improve the customer experience on the receivables side, an IA project must consider order management, invoice to cash, and supply chain processes.

Before identifying which processes to automate, companies need to define the metrics that will measure the outcomes. Specific metrics will be measures of a reimagined process vision. For example, for accounts receivable (AR), a process vision can be straight-through processing, and the company could measure it using an invoice-efficiency index. Other examples of metrics include the percentage of invoices paid on time with touchless processes in accounts payable (AP), the customer-experience score within an order-management process, and achieving a real-time close in record to report.

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Myth two: IA in finance is simply about deploying RPA and automating manual activities

Reality: IA is a journey that integrates multiple technologies to ensure you orchestrate automation across the end-to-end process

RPA received a great deal of attention when it first appeared on the market, partly because it's easy to deploy. And partly because it's a cost-effective option and a great alternative to big IT spend.

When finance organizations applied it to simple, rules-based processes, they got immediate results. But many used RPA in a piecemeal fashion to automate parts of a process. So, the ROI of the first wave of RPA implementations was limited.

Adopting automation, analytics, and AI (AAA) technologies simultaneously is vital for finance teams to meet business leaders' evolving expectations of business priorities. Though the rate of adoption varies within the function and across companies, most F&A teams still only use them to a limited extent for a small number of applications.

Figure 1: AAA technologies have been implemented narrowly to date

Related graphic 1 debunking five myths about intelligent automation in finance part one

IA combines a range of technologies – such as process mining, RPA, orchestration, and AI – to create seamless end-to-end processes and experiences. Though each technology brings a different set of capabilities to the table, the key is to tie them together in a holistic approach (figure 2).

It's also important to understand and define how different technology levers coexist to improve different parts of a process.

For example, to transform an AP process with intelligent automation, start by automating routine, repetitive processes using robotics. Then use analytics or machine learning to automate exceptions, then bring in predictive and prescriptive analytics to rethink key metrics, such as invoices paid on time. Then apply natural language understanding to drive intelligent document extraction. Combining these technologies delivers better process improvements than implementing them individually.

Figure 2: A holistic approach to intelligent automation

Related graphic 2 debunking five myths about intelligent automation in finance part one

Myth three: You must choose between intelligent automation or F&A platform enhancements (for example, S/4 HANA)

Reality: Work on enhancing F&A systems of record and adopting IA concurrently to drive the highest ROI

Finance organizations with limited transformation budgets often face this conundrum: should I enhance my central finance platform or automate portions of a process?

The companies that choose to wait for their new platform to be in place spend significant time on platform enhancements and lose out on the value that automation can add to their legacy applications.

Probably the biggest challenge finance organizations face when implementing IA is the complexity of connecting legacy systems to digital initiatives. Successful organizations build a thin integration layer between new and existing technology to create a system of engagement (SoE) between people and machines.

Think of this SoE as a single pane of glass that has people on one side and machines and data on the other. It manages multiple processes, disparate data, and legacy systems to avoid the need for a large-scale implementation.

An SoE delivers easier integration across systems of record, full visibility and control over end-to-end processes, a single place for managing process rules, and the ability to unlock new data-driven insights.

Myths debunked. What's next?

By dismissing these IA myths you can design a comprehensive execution roadmap and realize your expected outcomes. As you build your execution roadmap, we recommend following six key steps:

  • Define shared organizational goals and priorities
  • Identify the metrics to measure your desired outcome
  • Scope, prioritize, and reengineer processes before applying intelligent automation
  • Take a holistic approach to IA technologies
  • Maintain strong program management and stakeholder governance
  • Maintain and continuously improve digital implementation

Intelligent automation brings a level of flexibility to finance that delivers a competitive advantage to the company. By seeing through the myths, finance organizations can guide strategic decision-making, build a resilient business, and improve the customer experience.

Case study

Using IA at scale

To stay competitive, a leading food distributor knew that it had to make fundamental shifts to streamline finance operations and provide an exceptional experience for customers and other internal and external stakeholders.

Due to its rapid growth and acquisitions, the company had an array of fragmented and inconsistent finance systems and processes across its organization. The finance team used intelligent automation to update its global financial management platform, increase centralization and standardization of end-to-end processes, embed operational excellence, and deliver greater visibility and insights from data.

It deployed a common technology layer, which acted as a system of engagement across AP, AR, and record to report, allowing the finance team to interact with multiple legacy systems through a single user interface.

Transforming the GBS organization

A global retailer had a mix of disconnected legacy systems and manual processes, which made it difficult for employees to manage high volumes of supplier and customer invoices. This led to millions of dollars in cash leakage and write-offs.

With a focus on improving the customer, supplier, and employee experience, the retailer introduced intelligent automation to power a single, scalable platform that streamlined invoice management and allowed full visibility of payment status. This has reduced cash leakage and cut processing time from weeks to hours.

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