During the past century, finance has grown in sophistication, precision, and importance – however, the operating model of the finance function has changed surprisingly little. Now is the time for finance, like many enterprise functions, to adopt a more agile mindset focused on decision-support, growth, and profitability. By leveraging today’s technologies such as artificial intelligence, automation, and machine learning, finance is in a position to drive digital change, lead enterprise value creation, and improve overall performance.
According to research conducted by the McKinsey Global Institute, 40% of core finance activities – including revenue management, cash disbursement, and accounting and operations – can be fully automated, and another 17% can be partially automated. Automating functions like these will enable finance teams to spend more time on high-value tasks, such as generating insights, managing liquidity and expenses, and tracking investments. These all require expertise and the human touch as well as the right set of tools, approaches, and capabilities such as dynamic planning and forecasting, cross-functional collaborations, low latency dashboards, and KPI monitoring.
More automation plus more use of AI and machine learning will generate speed and flexibility for finance teams, accelerating organizational decision-making and enhancing business resilience.
The Evolution of Finance
The recent evolution of the finance function has been driven by the need for more transparency, visibility, and accuracy of data – and the global pandemic has served as a catalyst for the increased adoption of digital tools by finance teams and throughout the enterprise.
Cutting-edge finance teams are shifting to more AI-based data-visualization tools paired with automation capabilities to generate clear, timely, and actionable business reports. This generates faster insights for end-users, improves productivity, reduces time spent on data collection, and promotes more focused (and faster) business discussions. AI-powered finance drives business performance.
The Building Blocks Transforming Finance
The future of finance will be more digital and much more highly automated. It requires two things: First, finance leaders should create a culture in which teams spend more time analyzing data and facilitating decision-making rather than just collecting data. Second, they should consider deploying an integrated AI solution that leverages data aggregation, data visualization, workflow automation, KPI reporting, scenario simulation, and advanced analytics.
An AI-based solution can enable real-time monitoring capabilities for financial processes such as budgeting, forecasting, and working capital management – and automatically generate insights-driven warning signs. Both the cultural and technology changes should reflect the strategic vision and long-term goals of the enterprise.
Budgeting and forecasting: These processes are complex and ingrained with legacy business practices that answer questions like “How are we doing year to date?” or “What is our expected cash flow from operations for the next three years?” In a more digital, more automated finance function, budgeting and forecasting should be reimagined to incorporate flexibility and intelligence. This inevitably requires breaking down the barriers that exist between finance, operations, and strategy. And it will enable the enterprise to generate relevant insights and answer questions such as “How are we doing today?” and “What metrics can we use to track our investments now?”
The finance team should be the enabler that assists in holistic and continuous planning, which leads to an increase in visibility and an improvement in decision-making. To realize these outcomes, this type of planning must also include modeling capabilities that can test various assumptions encompassing changes in revenue or costs of goods sold from a regional, product portfolio, customer, and/or sales channel perspective.
Here’s an example of automation in action in finance: A $5+ billion global apparel company wanted a microscopic view of cash flows to effectively plan and measure the performance of one of its business units. Working with this industry leader, Wipro created a revenue-shaping solution that accurately forecast revenue four quarters in advance while providing unbiased, evidence-backed inferences and suggesting interventions – with simulations to illustrate how the outcomes would change. This enhanced the organization’s decision-making so much that the company’s financial planning and analysis end-users adopted the model within the first six months. The project generated trust by stakeholders that an AI model could forecast revenue with more accuracy than finance professionals who had deep knowledge of the apparel industry.
Working capital management: Managing the cash a business requires for day-to-day operations is intricate and inefficient due to local nuances and misaligned teams. Working capital analysis should factor in receivables, payables, and inventory. Next-generation working-capital models need a more-efficient cash-management process powered by advanced digital solutions that leverage machine learning and automation. Wipro helps companies create AI-enhanced working capital frameworks with a hybrid approach that combines methodologies, advanced analytics, and process mining to offer faster and more-granular insights with less effort.
For instance, a European automation and robotics manufacturer experienced a fragmented invoice and collection process that was proving to be an obstacle to future growth. To address this challenge, Wipro helped design a working-capital roadmap and adopt industry-leading practices for the order-to-cash process. Accounts receivable collections improved substantially, with the use of machine-learning algorithms to recommend the best collection strategies at the customer/transaction level. Moreover, automated reminders, more accurate invoices, and faster dispute management helped the manufacturer reduce working capital.
In another case, one of the world’s largest consumer-care companies struggled to efficiently navigate a convoluted source-to-pay landscape, characterized by a large supplier base, isolated contracts, ambiguous payment terms, and unstructured payments. In turn, Wipro helped co-design and scale the global procurement processes to synchronize contracts, procurement agreements, and payments terms. The quality of decision-making improved through clearer, richer, and faster insights from organized data sets, leading to timely and efficient payments to partners.
AI in the Finance Transformation Journey
So how should finance leaders start implementing AI as part of the finance transformation journey? It can feel daunting: The effort requires an assimilated AI solution that combines cognitive, big data, machine learning, and automation capabilities under a single roof. A step-by-step approach can help.
Data consolidation is the first step to leverage AI to transform the finance function and set it up for long-term success. Most organizations have at least one enterprise resource planning (ERP) system that tracks orders and transactions, invoices, payments, warehouse details, cost center information, and more. Typically, enterprises use multiple independent systems to track specific details (such as product line scrap) for which ERP systems are not provisioned to account. Consolidating and harmonizing data sources make it easier to gather significant intelligence that is otherwise hard to leverage for financial planning and analysis. While consolidating data can be painful, slow, and labor-intensive, it delivers real benefits.
The next step in adopting an AI solution is to train the algorithm to identify relationships between the data sets created during data consolidation. A trained algorithm gives finance leaders the ability to model different scenarios via predictive modeling and stress testing, and it helps them to understand the impact of externalities on the business from a financial and operational perspective.
Additionally, companies must define clear, granular, and measurable key performance indicators (KPIs) to test the algorithm’s results and predictive powers. Ultimately, KPIs make the algorithm more transparent and possibly warrant cross-functional collaboration depending on the insights generated.
An organization’s finance leadership has the right and responsibility to inquire how the day-to-day operations of a firm are generating value. With the implementation of AI, finance leaders can not only synthesize such information on a quicker basis, but also assist in strategic decisions by utilizing AI-based predictive models to unlock their potential.
Artificial Intelligence – Real Insights
The pressure to deliver top-line growth, cost optimization, and alignment with business strategy will only get more intense – and demands that the finance function considers a technologically advanced, cutting-edge approach. That approach is here today in the form of automation and artificial intelligence. By leveraging AI as part of the finance transformation journey, organizations can develop more real-time and accurate reporting, increase forecast accuracy, optimize the use of resources, and minimize manual interventions. Through the adoption and execution of an AI-enhanced vision, supported by the creation of a new insights-driven culture, organizations can set themselves on the path to flourish and thrive for years to come.
The authors wish to thank Amit Jain, a Principal Consultant in Wipro Digital’s Intelligent Business Re-Imagination practice, for his contributions to this article.