25 January 2023
By reducing absolute costs and redistributing effort toward more value-added activities, effective finance teams serve as guardians of corporate value development.
Finance leaders will probably need to change their goals in order to reach the next level of effectiveness and efficiency in the financial sector. In offering higher real-time insights, reducing biases and human error, and accelerating workflows and decision-making, these two actions are extremely important.
It Is Time To Look Into More Than Simply Transactional Actions
The efficiency of transactional tasks, particularly those related to accounts payable, accounts receivable, and other key accounting areas, has significantly increased in many top firms. Although the majority of businesses have space for improvement, further efforts to increase efficiency will almost certainly result in decreasing returns as the cost base for these activities continues to decline. The more critical areas of finance, such as FP&A, optimizing capital structures, tax planning, controllership, internal audit, and financial-risk management, on the other hand, have actually seen fewer efficiency benefits. However, this seems to be about to change.
Machine learning and artificial intelligence (AI) are becoming more and more applicable to complicated jobs as computing power increases, building on the success of robotic process automation (RPA) and related technologies that automate transactional processes. Machine-learning algorithms and analytics are currently being used by one high-tech producer to assess financial and business continuity concerns. With the aid of these technologies, audits may now concentrate on the riskiest units while taking up less staff time overall.
The management discussion and analysis for a monthly operational review was also provided by a worldwide consumer packaged goods firm using natural language generation (NLG). With the help of this technology, structured data is transformed into insightful financial language that synthesizes and summarizes information. Highly skilled finance employees have more time to address concerns and pursue opportunities thanks to the automation of certain of the reports. The demand for professionals with analytical skills, such as data scientists and machine-learning engineers, has surged as a result of the growth of big data. The pool of talent is growing despite the fact that demand continues to outpace supply. This is due to higher salary, improvements in university computer science curricula, a rise in the number of free online AI resources, and private sector training efforts.
Advanced procedures and a competent workforce are combining forces to create an environment that will unlock efficiency in the value-adding sectors of finance. Following this directive, CFOs can:
- Focus on high-end automation instead of low-end automation. Few industry leaders are utilizing machine learning and related cutting-edge technology in “second-wave” automation use cases in capital allocation, financial planning, and audit rather than merely concentrating on mature, first-wave automation approaches like RPA. However, it is important to recognize the intricacy of these technologies. Many businesses have had trouble using AI. To find the best use cases for new technologies, CFOs must heavily invest in their pilot programs and be ready to pivot if their initial efforts are unsuccessful.
- Utilize staff time spent on value-added tasks more effectively. The optimum use of the time of the finance team should be to conduct analysis that influence actual business performance. By ensuring that requests for further information are based on a clear comprehension of a set of agreed-upon drivers of firm financial success, leaders can support their staff. CFOs might also establish detailed rules for how finance staff members should allocate their time. Consider imposing a rule that at least 80% of analyses should concentrate on prescribing future courses of action, as opposed to undertaking reactive analysis of historical data to explain previous performance.
- Sync up with the rest of the company’s use of AI and machine learning. Over the past few years, the technical environment has altered, with some platforms gaining prominence while others losing customers. A company-wide strategy on which technologies to utilize not only enables more targeted investments but also promotes more cooperation between the finance and other areas.
- Give employees in key positions the experience, mindset, and power they need to have an impact on the company. Even if cost reduction is a continuing priority, staff members still require ongoing skill development to effectively play the roles of advisors and counterbalances to top executives in directing the financial course of the company. For employees in senior FP&A and finance business-partnering roles, skill development is crucial.
Find Ways To Incorporate Additional Capabilities In The Finance Operational Model
In order to focus on the more urgent issues affecting their business, finance organizations are moving toward a new operating model that enables employees to change their job swiftly and dynamically. This necessitates not only a different method of task organization but also a different kind of financial specialist.
The new financial operating model starts from a leaner core, with tighter data standards, new data-management techniques, enhanced automation, and integration with a wide range of associated digital technologies, to reduce the work involved in operational operations. Several adjustments must be made in order to implement this paradigm. Create flat networks of teams instead of traditional hierarchies. The network model enables business partners in finance to access a pooled pool of analysts who are allocated to particular work items in accordance with clearly stated and accepted business priorities.
To provide deeper insights into company difficulties, mobilize temporary teams. The establishment of sprints to discover, create, and implement financial analyses that offer insights into business difficulties is one example of how agile working concepts are applied when building this capacity. Integrate digital capabilities throughout the finance department. Developing bot algorithms, leveraging analytics software, or understanding how to transform company data into useful insights are a few examples of these competencies.
Create a core of financially aware business leaders with the authority to interact with firm executives on a peer basis. Strengthening job rotations within finance as well as between finance and the business creates a pool of qualified individuals with easy access to other areas of the company. One automaker mandates that executives rotate through a minimum of two divisions, two financial departments, and two countries before moving up to senior positions. Senior finance leaders must rotate through non-financial positions at another automaker. The key distinction between the two scenarios is the focus on developing operational, leadership, and technical financial capabilities.
By creating a strict, open competency matrix, you may strengthen your financial skills. This thorough collection of standards enables managers and individuals to select between clear capability-building measures to support career advancement, helping to anchor talks about finance talent in objective criteria. For instance, advanced practitioners in a certain skill set would be obliged to devote at least 10% of their time to enhancing the abilities of other employees.
Create both formal and informal rewards for developing your skills. Examples include openly defining targets for internal promotions to pay for leadership roles, connecting incentives to knowledge and capability development, and publicly rewarding managers who grow their teams’ skills through coaching.
At the forefront of effectiveness, finance leaders provide much more than just the fundamentals of finance; their work daily directs how the entire organization operates. The next-generation finance function can develop the insights, performance, and planning capabilities leaders will need to support dynamic decision-making over the coming ten years by focusing on four key imperatives.