With two years of rapid changes behind them, CFOs are looking to secure agile and efficient financial processes in their organizations. They are also reevaluating and changing their investment priorities, so that instead of merely adapting to every new change as they come, they are fully prepared and capable of driving the digital transformation forward in their financial organizations.
A report released by Management Events this year details the results of an executive trend survey and interviews with 318 European CFOs. Overall, the results showed that the greatest focus area for CFO investment was Finance solutions, including reporting optimization and invoice management, chosen by 58% of finance leaders. The next focus area, chosen by 11% of finance leaders, was Big Data and Analytics, followed by IT Management, chosen by 10% of finance leaders.
Source: 2022 report by Management Events: “The Resilient CFO: Optimizing Technology in Finance”
Finance solutions is seen taking an obvious lead among the CFO focus areas for investments. Within this focus area, the survey showed that out of the finance leaders, 75.4% prioritized finance-related digital transformation and automation solutions, 52.6% prioritized financial planning and forecasting, and 42.1% prioritized optimization of financial reporting.
The focus on these areas means investing in systems and tools like Robotic Process Automation (RPA), Enterprise Resource Planning (ERP), Business Intelligence (BI), and Artificial Intelligence (AI).
Technology-leveraged growth is at Staria’s core
At Staria, we wholeheartedly agree with the findings of this report. The three focus areas and new systems and tools that CFOs are looking into reflect crucial features that finance executives need when preparing for the future.
Technology and technology-leveraged growth has always been at the core of Staria’s services, and with our one-stop delivery model we strive to offer all the tools needed to meet the current and future needs of CFOs and their financial organizations. We believe that RPA, AI, and BI tools will certainly be needed in the future, and with our wide service offering, we are more than happy to assist your organization in making the transition towards more automated operations.
RPA and AI increase efficiency and add business value
According to CFOs interviewed in the report, the choice to invest in Automation technologies like RPA and AI was driven by goals to increase efficiency in the financial organization, and to remove the risk of human error by automating manual processes related to financial operations. The goals were also for these technologies to add business value and help the company stay frontrunners in their industry.
It’s clear that the needs of financial management organizations and businesses are getting both more diverse and more detailed at the same time, with an increasing workload in running high quality financial operations. Using automation technologies like RPA and AI will give your financial operations more transparency, efficiency, and quality. Meanwhile, your financial professionals can challenge themselves with more versatile and meaningful tasks creating more value for your customers while basic tasks are run seamlessly with automation in the background.
Staria has solid and wide experience in automating different processes across different industries with RPA and AI, tapping into the first of the three focus areas highlighted in the report. We provide RPA as the Nordic partner of RPA software vendor UiPath, and Staria AI, an easy-to-use AI-solution that frees up time used to handle purchase invoices. Our automations are built to meet each customer’s individual needs to ensure that the automated process meets all the requirements and controls needed. RPA is the right tool when it comes to automating rule-based processes, but for processes where the variety of rules is usually too complex to be created manually, like AI-based invoicing, we leverage machine learning to create AI that can build the rules itself by using historical invoice posting data.