Practice applied intelligence

Finance leaders have an opportunity to profoundly change how their organizations operate. Systems that Do, Learn and Reason allow finance professionals to spend less time doing mundane, repetitive tasks and more time planning, analyzing and advising the business.

Each type of system delivers value, but finance leaders who combine all three can prepare their companies to practice what Accenture calls Applied Intelligence. This is the rapid implementation of technology and human ingenuity across the business to achieve previously untapped sources of insight and innovation.

The digital journey

The three tiers of systems: those that do, learn and reason.
  1. Do: Technologies such as Robotic Process Automation (RPA) are used to automate discrete rules-based tasks

    Organizations often implement these systems to automate specific business functions with the objective of reducing data entry errors, improving turnaround times and reducing the number of people required to perform simple tasks. For example, posting of journal entries, checking for duplicate payments or processing split POs can all be handed over to bots.

    While systems that Do allow organizations to rapidly digitize their high-volume tasks, they do not fundamentally change the business process. The tools enabling this capability are mature and can be implemented either in-house or on the cloud. They deliver value relatively quickly.

    As the use of these systems increases, the finance organization needs to:

    • Optimize the “bot” workforce by improving utilization and/or accuracy
    • Manage the infrastructure, including hardware and software, that supports bot deployment
    • Upskill their current workforce and redeploy to drive greater value for the organization
  2. Learn: Learning systems use structured and unstructured data to gain analytic-based insights

    It is estimated that over 75 percent of the data an organization generates is never used and is referred to as dark data.1 Learning systems can help organizations unlock the value of dark data. For example, 82 percent of companies surveyed by Accenture found that machine learning-enabled processes help them find solutions to unsolved problems through data they had not previously been able to tap.2

    Learn systems overlay external data to enrich the analytics and provide greater insights. This enables the CFO to look not only at the impact of actions in the finance function, but also across the organization and at external drivers such as macro-economic trends, weather, or reviews on social media platforms. Learning systems usually use two or more technology enablers to deliver the results, i.e., artificial intelligence (AI), analytics and RPA. As an example, analytics can prescribe when an invoice should be paid to minimize impact to cash flow. The payables analyst may make some adjustments to the proposed payment plan. The system records the manual adjustments and uses them to make future recommendations. It self-learns how to better handle a particular type of invoice.

    To enable these learning systems, organizations need to manage the data supply chain including:

    • Data governance
    • Master data management
    • Data consumption patterns
  3. Reason: By coupling RPA and analytics with machine learning and natural language processing (NLP), these systems can learn and begin to make recommendations.

    To truly benefit from systems that Reason, it is important to think about business outcomes and the end-user experience. Often, this requires looking at processes differently to:

    Natural language generators are an example of tools that enable systems to reason. They can analyze data and produce narrative text that can be consumed by end-users. A North American telecommunication company used natural language generators to deploy standard reports to store managers in over 5,000 stores with no training required.

Turning on the turbo boosters

An Accenture survey found that 45 percent of companies have deployed successful AI programs within their organization or in market offerings. An additional 41 percent are in the early stages of implementation.3

For the finance leaders who have implemented systems that can Do and/or Learn, you have a head start at delivering value to the organization and to end users. Continue to scale these systems, looking for opportunities to work across business units and functions. The value of these systems has been demonstrated, now use these early wins to think big. With a third of companies expecting AI to be a top strategic priority within a year, and another third saying it already is, the probability that your company is going to invest in AI this year is high.4 Seize the opportunity and position yourself to lead the use of AI in the back office.

For finance leaders just getting started on the journey, find out about your organization’s plans to invest in AI. Think about the most pressing business outcomes. Be bold. Start with a pilot that brings the combinatorial power of systems that Reason to deliver exceptional results. Bring along another business unit; work with IT and/or a preferred vendor in designing and implementing a pilot. Do not be afraid of failing and do not obsess about getting it right the first time around. Use an agile mindset. Be comfortable with a minimal lovable product and iterate to scale it.

1IDG Connect, “What awaits discovery within ‘dark data’?”, April 2017.

2Accenture, “Process reimagined: AI at work”, 2018.

3Accenture Strategy 2017 Tech-led change for artificial intelligence research.

4 Ibid.

Dhruv Jain

Senior Principal – Accenture Strategy, CFO & Enterprise Value

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