As part of the Future of Finance survey, KPMG professionals asked executives both what initiatives they are most focused upon and how successful they expect to be with them. Consistent with a desire to increase the value provided by the finance function by enabling better business decision-making, the most commonly cited high-priority initiatives focus on utilising data and analytics (D&A) and automation to increase the quality of insights and improving planning and forecasting accuracy. Yet these initiatives are precisely the ones with the lowest success rates, while lower priority ones such as deploying blockchain show much greater success. Becoming a leading finance organisation will require the CFO to invert these results by devoting outsized resources to these highest-priority, hardest-to-execute activities.
A majority of organisations, in fact, struggle to successfully implement most of their initiatives, with less than a third of respondents calling any a ‘great success’. And, while Finance organisations have increasingly been called upon to improve the quality of insights they provide, they continue to struggle in this area, with the largest gap between ambition and success lying in using intelligent automation (IA) to improve analysis quality.
A number of challenges stand in the way of implementing advanced D&A and automation:
Organisations have proven somewhat more successful with cost-focused initiatives, including those involving automation. Technologies such as Robotic Process Automation (RPA) in core finance processes are more mature, and generally easier to implement, with cloud and managed services offerings often presenting an easy implementation path. Deploying blockchain to support core financial applications enjoys the highest success rate, though it carries the lowest priority of any initiative.
Finance functions do relatively well at the traditional core imperative of complying with regulatory changes. While this capability is an important part of the finance function’s basic responsibilities, in the age of technology-led disruption, it is increasingly viewed as low-value ‘table stakes’. The finance organisation of the future will be measured on its ability to respond to market disruption and drive innovation both within finance and enterprise-wide. And, the survey results reveal that most finance functions do a relatively poor job in these areas.
Given its expertise, finance has a natural lead role to play in capital allocation decisions, a critical component of innovation. In order to succeed at enabling innovation, however, finance must move away from its traditional ‘control’ mentality and think more like a venture capitalist, leading agile new funding approaches and spreading bets across riskier investments, quickly discontinuing projects that do not succeed and doubling down on those that do.
Utilise an agile, dynamic funding model. The annual budgeting process, one of the most commonly used mechanism for capital allocation decisions, is often inadequate to accommodate a rapidly changing business environment. A more dynamic model, governed outside this process, allows for small, quick, ongoing decisions that can be revised as needed.
Have a portfolio view, balancing ongoing investments in core areas with riskier, cutting-edge ones. At many organisations, there is a strong bias to build upon legacy initiatives rather than deploying new investments that may be viewed as risky or unproven. Overcoming this bias requires a new approach to risk mitigation and new investment criteria such as urgency, competitiveness, feasibility, and strategic fit.
Enable a ‘test-and-learn’ approach. A test-and-learn approach involves experimenting with new ideas in a limited fashion and observing the results of those experiments, then quickly scaling up if successful or discontinuing if not.
Look beyond ROI to measure success. Simply looking at how much a project increases revenue or decreases cost will often be inadequate to measure the benefits of many technology implementations. In addition to ‘hard’ measures, focus on more difficult to quantify benefits such as increased customer satisfaction or more timely, higher quality analysis.