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

In part one of our point of view, we addressed three of the top myths CFOs encounter when implementing intelligent automation (IA) in their operations:

  • Myth one: intelligent automation is all about efficiency gains for the finance organization – it cannot really drive bigger business outcomes
  • Myth two: IA in finance is simply about deploying robotic process automation and automating manual activities
  • Myth three: you must choose between intelligent automation or finance and accounting (F&A) platform enhancements (for example, S/4HANA)

Here are two more myths surrounding IA in the finance function that must be busted so organizations can deploy IA effectively.

Myth four: your data strategy can run separately from your IA initiative

Reality: by combining your data and IA strategies, you can fully leverage internal and external data sources to make decisions and drive key outcomes

Finance teams have started to provide businesses with data-driven predictions, but the approach is largely unstructured and ad hoc. In addition, most functions only generate these insights for a few purposes. To deliver genuine value for the business at a time when there's a pressing need for real-time data, finance leaders must acknowledge their important role as data guardians building insight engines. Then, they must make the case for greater investment in data analytics skills, capabilities, and infrastructure.

Finance has traditionally provided analysis on past performance. However, with artificial intelligence (AI) and advanced analytics, it is now possible to provide more accurate and faster predictive, or forward-looking, insights. The challenge today is that most functions only provide predictive insights when requested. Very few functions have a structured approach to providing predictive insights and none have a structure that caters to the varying needs of different levels of the business.

Encouragingly, F&A functions that generate predictive insights do so to improve cash flow and customer analytics, which is exactly what business leaders request of them (see figure 1).

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Figure 1: Where finance teams are making an impact with predictive analytics

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

One of the things that we have learned during the COVID-19 crisis is that expectations for speed have risen. Organizations are much more willing to get quick wins from IA while staying consistent with their central strategy.

Another challenge that organizations face in their automation journey is the lack of structured data. This is where AI comes in to help. In our study, AI 360: Hold, fold, or double down, an improved ability to use data and analytics is the second most significant benefit of AI according to 500 senior executives. You can use intelligent document processing to read and extract information, conversational AI to understand customer intent, and machine learning to handle data variations – all of which free up your employees to work on more strategic tasks.

Myth five: either the CFO or the CIO of organization should govern IA in finance programs – there is no need for business involvement

Reality: IA initiatives in F&A need a multidisciplinary team comprising finance, IT, and business partners to govern them

At first glance, running IA projects as small, independent initiatives may seem a faster approach. However, finance or IT organizations that jump into automation without consulting with each other or with other parts of the business often face resistance, low adoption, and significantly higher investments than necessary.

Before applying automation to finance processes, it is important to rethink the approach. IA in support of suboptimal processes as is may achieve modest savings but will in many cases miss out on opportunities to dramatically improve process outcomes, quality, costs, and cycle times.

Combining the capabilities of IA with a Lean Six Sigma process improvement approach allows enterprises to automate processes at a speed and cost point better than that of simple automation.

For instance, take two companies in the same industry that used different approaches when deploying IA. The first reengineered, standardized, and harmonized finance processes across legal entities prior to automation and saw significant automation benefits. The second company, with higher internal resistance for process standardization ahead of automation, underachieved automation benefits vis-à-vis expectation.

Focused execution of identified IA projects starts with having multiskilled teams including experts from the business, process domain, digital, and operations.

  • Finance cannot meet business' goals with traditional skills and capabilities alone. Digital technology and data science capabilities must supplement them. In addition, negotiation, change management, and business skills are crucial for the function to get the most from automation, analytics, and AI technologies
  • Having in place a strong stakeholder governance framework is also critical to ensuring the success of IA. It is important to create stakeholder buy-in with early program success and create sponsorship for future program wave

The path to truly intelligent automation

Overcoming these five myths is the first step in designing a comprehensive execution roadmap and maximizing outcome realization from IA. We recommend that you implement the following six steps, also laid out in part one of this post, to get IA done right:

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

The COVID-19 pandemic has made the imperative of digital transformation of the finance function even more evident. This gives CFOs the opportunity to prioritize key initiatives – from cloud journeys to data and insight – and drive digitization of value chains end to end using a well-planned and well-executed IA approach.

Case study

Intelligent automation helps F&A step up to the plate

Pre-COVID-19, a large manufacturer of medical devices focused on growing revenue and getting ahead of the competition. The finance organization decided to fast-track and scale IA projects from transactional processes such as accounts payable and accounts receivable to non-transactional ones like record to report and financial planning and analysis.

But during the COVID-19 crisis, organizational priorities shifted to managing cash flow. The finance organization had to play a significant role in providing insights to the business on segmenting their customer base and prioritizing shipments. Fortunately, it had already automated many of its processes before the COVID-19 pandemic hit, and this allowed the team to derive customer insights effortlessly.

IA also made the business more responsive in the delivery of its products – to rush the equipment to where it was needed and customize its response to the needs of specific locations. It could also quickly benchmark against external data to define and validate its pricing strategy in an agile fashion. This enabled the business to stay resilient and competitive at a particularly challenging time.

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