One of the most important recent changes is the acceleration of the digital transformation trend that was already underway. Digital transformation means using technology to make step-change improvements to core finance department processes in a way that enables the entire organization to improve performance and competitiveness.
Here are a few examples: First, using digital innovation to achieve greater efficiency, accuracy and timeliness in reporting on a global scale and being able to do so while still respecting differences in local accounting standards, regulatory requirements and customs. Second, technology allows organizations to gain agility and resilience by using predictive analytics to provide tailored alerts when conditions deviate from plans. And third, organizations can use software to give them the ability to routinely run rapid, high-participation planning cycles that integrate operational and financial elements to enable the entire organization to quickly adapt to changes in markets, the economy, taxes or regulations.
I believe the FP&A mission is still basically the same, but there will be a change in emphasis as technology enables finance professionals to have more time for analysis so they can take a more advisory role with the rest of the organization. They will be able to focus on these activities because they spend less time on acquiring and preparing data. Moreover, technology will enable FP&A to address three critical requirements that many organizations confront in dealing with uncertainty, particularly in their markets, economies and supply chains.
One is the fundamental need to have precise answers to what-if questions in an actionable time scale. Another is having the ability to quantify the financial impact of a range of operating scenarios by combining operational and financial plans. And a third requirement is to substantially cut the time it takes to complete a company-wide planning cycle that combines financial and operational planning. This type of planning and budgeting incorporates operating forecasts (including headcount, sales, and materials used), the line-item budget derived from the operational plan, as well as the pro-forma cash flow and balance sheets for that scenario.
These aren’t new requirements—people like me have been talking about them for decades—but there’s now a willingness to make the investments in technology and fundamentally change planning processes to meet the needs.
AI won’t put robots in charge, but it will take the robotic work out of the department so professionals can focus their energy on tasks that make best use of their training, experience and judgement. And it won’t happen all of a sudden because it will take the better part of this decade to achieve the kind of whiz-bang benefits futurists have been forecasting. Meanwhile, AI will be an inexorable force shaping how finance departments operate going forward. Innovations using AI in finance applications will be accelerating.
AI is already being used in a supervisory role that highlights outliers to spot errors, automatically processes forms to perform data entry, and analyzes written or spoken input to answer questions and make suggestions. Increasingly AI will act as a planning assistant for managers, coming up with an initial forecast that the manager—using AI—can quickly refine to create a set of expected outcomes based on specific scenarios. Rather than spending days on this, they will be able to create or update their outlook in less than an hour.
It’s not so much a matter of innovations as it is technology making it far easier to incorporate more sophisticated analytics in forecasting, planning and budgeting. Today, a majority of organizations mainly use descriptive and diagnostic analytics based on historical data to explain the past. But the ultimate purpose of planning is to make the right decisions about the future.
Our research shows that only a minority are using predictive analytics in a systematic fashion. These techniques improve the decision-making process by assessing a wide set of data to find useful correlations that have a causal relationship with outcomes. Predictive analytics use statistical and modeling techniques that comb through historical data to identify the relevant conditions that influence outcomes.
Predictive analytics can identify more complex relationships such as multiple factors that drive desired outcomes and relationships that are non-linear or otherwise not easy to spot. Having the ability to continuously analyze data to spot relationships and identify the factors driving outcomes enables decision-makers to quickly respond to events with greater intelligence.
One of the most important is having a platform that serves as a single, authoritative source of data for analysis, planning, budgeting and reporting. To be effective, this must flexibly serve the needs of the finance department as well as executives and operating managers. As a platform, it should be able to integrate with financial and operational data sources to automate data movements from source systems to ensure that all information is immediately available and easily accessible.
The software should offer interactive dashboards and a full range of reporting capabilities, including automated and self-service reporting to keep executives and managers in touch with conditions and performance. I recommend a cloud-first approach because of the flexibility and connectivity this provides and a full set of mobile capabilities for monitoring, reviewing and approvals. And the software must demonstrate that it can scale to the organization’s requirements.