Finance 2030: Four imperatives for the next decade

The trade-off between cost reduction and increased effectiveness of the finance function is a false choice. Leading finance departments are guardians of enterprise value creation, demonstrating stewardship of their own spend by lowering absolute costs and shifting work towards more value-added activities.

We have analyzed the finance functions of hundreds of companies to understand how cost and effectiveness have evolved over the past ten years. After controlling for differences in sector, scale, and geographic footprint, several findings emerged:

• Finance organizations have, on average, decreased their cost by 29 percent.
• The most efficient cohort of finance departments (“finance leaders”) achieved similar cost improvement to the level shown by average performers—an impressive feat given that the finance leaders started from a lower cost base.
• Finance leaders spent 19 percent more time on value-added (versus transaction-processing) activities than a typical finance department did.

What can companies do differently to join the finance leaders? The research points toward four imperatives. The first is to cast a wider net for new efficiency opportunities, reaching beyond the transactional activities that have long been the primary focus of attention. Second, boost finance’s role in managing data, whether consolidating, simplifying, or controlling the flood of information flowing across the organization. Third, strengthen decision-making through widespread adoption of data-visualization, advanced-analytics, and debiasing techniques. Finally, reimagine the finance operating model so that it fosters new skills and capabilities.

These steps are already enabling companies to join the finance-function elite—while cutting audit costs by double-digit percentages, improving data quality (and reducing wasteful data-cleaning efforts), upskilling finance teams, and enabling the function to guide better decisions throughout the enterprise.

A decade of efficiency gains

While the magnitude of improvement varied among sectors, ranging from 15 to 35 percent over the ten-year period, the decline in the cost of finance departments is consistent across industries (Exhibit 1).

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More significant, however, were the gains among the finance leaders, whose improvement rate was slightly higher (26 percent versus 25 percent) despite a leaner starting point—demonstrating the value of continuing to focus on finance-function efficiency regardless of previous gains.

Finance leaders further differentiate themselves by spending a greater portion of their time on value-added activities, such as financial planning and analysis (FP&A), strategic planning, treasury, operational-risk management, and policy setting. Today’s finance leaders spend 19 percent more of total finance-staff bandwidth on value-added activities than the average company did ten years ago (Exhibit 2). This prioritization enables finance leaders to build deeper capabilities in value-additive areas, creating a positive feedback loop that could result in even greater advantages in the future.

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The finance department of the future

Achieving the next frontier in finance efficiency and effectiveness will likely require finance executives to shift their thinking from the priorities of the past. Four moves are especially critical for delivering greater real-time insights, minimizing human error and biases, and driving speed in workflows and decision-making.

Imperative #1: Look beyond transactional activities

Many leading organizations have substantially increased efficiency in transactional functions—by 39 percent or more—including areas such as accounts payable, accounts receivable, and other core accounting areas. While most companies have room for further improvement, subsequent efficiency efforts will almost inevitably show diminishing returns as the cost base for these activities continues to shrink.

In contrast, our research found fewer efficiency improvements in the more strategic areas of finance, such as FP&A, optimizing capital structures, tax planning, controllership, internal audit, and financial-risk management. This appears likely to change.

With advances in computing power, machine learning and artificial intelligence (AI) can increasingly be applied to complex tasks, building on the lead started by robotic process automation (RPA) and similar technologies that are used to automate transactional activities. One high-tech manufacturer is now using machine-learning algorithms and analytics to monitor financial and business-continuity risks. These technologies have allowed audits to focus on the units that pose the greatest risks, and to reduce staff time needed to complete each audit. As a result, the manufacturer has reduced the total cost of internal audits by 15 to 20 percent.

Likewise, a global consumer-package-goods company used natural-language generation (NLG) to provide an initial draft of the management discussion and analysis for its monthly operational review. This technology converts structured data into meaningful financial prose that summarizes and synthesizes insights. Automating portions of the reports freed up the time of highly skilled finance staff allowing them more time to resolve risks and pursue opportunities.

The rise of big data has increased demand for workers with analytical skills, such as data scientists and machine-learning engineers. While demand continues to outpace supply, the pool of talent is increasing as a result of higher pay, upgrades in universities’ computer-science curricula, increased availability of free online AI resources, and private-sector investments in training.

These converging forces of advanced processes and a skilled work force are creating an environment to unlock efficiency in the value-adding areas of finance. To pursue this imperative, CFOs can:

• Shift focus from low-end to high-end automation. Instead of focusing solely on mature, first-wave automation approaches such as RPA, a few leading companies are using machine learning, generative AI, and similar advanced technologies in “second-wave” automation use cases in capital allocation, financial planning, and audit. The complexity of these technologies should not be underestimated, however. Several companies have stumbled in their use of AI. It is critical that CFOs overinvest in piloting these technologies to identify the right use cases, and be prepared to change direction if initial experiments fail.
• Make better use of staff time spent on value-added activities. Finance staff’s time is valuable, and best devoted to analyses that drive actual business performance. Leaders can help their teams by ensuring that requests for more information are grounded in a solid understanding of an agreed-upon set of drivers of business financial performance. CFOs can also set specific guidelines on where finance staff spend their time. For example, rather than performing reactive analyses of historical data to explain past performance, consider adopting a guideline that at least 80 percent of analyses focus on prescribing future courses of action.
• Align with the wider enterprise on AI and machine-learning technologies. The technical landscape has changed over the past few years, with some platforms growing in popularity while others have lost users. Having an enterprise-wide strategy on which of the myriad technologies to employ not only allows more focused investments, it also encourages further collaboration between finance and other functions.
• Equip staff in critical roles with the necessary level of experience, leadership mind-sets, and authority to influence the business. While pursuing cost efficiency is a constant imperative, staff nevertheless need continual capability building if they are to successfully perform their roles as advisors and counterweights to senior executives in steering the financial trajectory of the business. Skills development is particularly important for those staff in senior FP&A and finance business-partnering roles, as discussed in more detail below under imperatives 3 and 4.

Imperative #2: Help finance lead in data

The size, complexity, and importance of data is growing at a record pace. The amount of data in the world is anticipated to reach 175 zettabytes (175 billion terabytes) by 2025, for an annual growth rate of approximately 66 percent over 2018 levels. The data that the finance department will use to create competitive advantages and remain compliant is no exception. The exponential growth poses a significant challenge for finance teams as they seek to distill ever larger and more complex data sets into a single source of truth that provides actionable information and insights to the rest of the organization.

Finance departments need a clearly defined master data-management strategy to guide the collection, storage, and interrogation of the rising volume of data needed to perform the types of analytics the business requires. This has become even more important for making good use of emergent technologies such as generative AI, which requires highly trusted sources of data to use techniques such as retrieval-augmented generation to improve response accuracy. To support the business—whether through more nuanced financial-scenario planning, insight into how to better manage liquidity, or improved guidance on where to best deploy assets—finance must be able to quickly marshal high-quality, trusted data.

Owing to its central role, the finance function is uniquely positioned to help define the master data strategy for the enterprise. As part of the function’s responsibility to consolidate, simplify, and control company-wide data, finance leaders can:

• Prioritize data quality and consistency. The first step is setting high standards on data structure, entry, aggregation, storage, and protection across the company. CFOs can also take a larger role in driving enterprise-wide transformation—historically, less than half of CFOs have led those efforts. By steering the development of data governance and data operating-model decisions, CFOs can reinforce change-management practices not only within the finance organization, but also in adjacent functions that produce much of the data finance consumes.
• Help lead data-standard alignment across departments. Although in many organizations the chief data officer is part of the IT function, the CFO has a unique perspective as both a major consumer and provider of consistent information across the company. Indeed, the CFO is often well-positioned to marshal resources across departments to address the topic of data standards and raise it to the top of the corporate agenda. While finance cannot drive enterprise data efforts alone, it can promote collaboration among the leaders of the IT, digital, customer-facing, and operational functions—especially by developing robust business cases that articulate quantifiable returns on investment for the associated improvements in data quality.
• Invest in an agile, tech-enabled data backbone. CFOs can advocate for adopting a layered architecture, with a common data layer that is flexible enough to accommodate changing business needs while preserving a single source of truth.
• Allocate finance-staff capacity to clean data. Investing finance-staff capacity to validate and clean data at the point of entry is far more efficient than addressing data-quality issues after they have occurred. In parallel, finance staff will likely need additional capabilities to understand the limitations of data and how to resolve them.
• Deploy technology for better-quality data. Data cleaning has always been essential but until recently it involved tedious and time-consuming manual work. Today, machine-learning algorithms can help cross-reference and validate data, which can reduce errors and the time needed to ensure data are correct when ingested.

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Imperative #3: Improve decision-making

The use of advanced analytical techniques to solve pressing business problems is increasingly a requirement for finance departments. Two years ago, our colleagues found that over half of the CFOs they surveyed wanted to use advanced analytics to improve the accuracy of cash-flow forecasts.

These sorts of efforts are gathering pace. A North American consumer-goods company, for example, is building a forecasting tool that will gather and analyze data including macroeconomic conditions, geographic factors, demographics, and other variables. Armed with this information, business leaders hope to be able to alter pricing decisions on the fly, as needed.

Beyond providing analytical insights, the finance department is also responsible for framing discussions on company performance and the actions needed to improve it. To be effective, finance staff need to be able to provide:

• Clearer insights, by communicating performance unambiguously, highlighting shortfalls against expected outcomes, as well as the underlying factors and context that informs why these gaps occurred.
• Faster insights, so that management can understand recent performance and make decisions to change its trajectory quickly and decisively.
• Richer insights, based on more robust datasets from a wider variety of sources, providing broader perspectives than those based only on internal financial data. For example, profit projections can be contextualized against the overall sector’s performance. By basing insights on multifactor datasets that include both internal and external data sources, organizations gain a more realistic view of likely performance outcomes and reduce the chance that unexpected shocks will render projections inaccurate. Finance organizations that can simulate multiple scenarios are better equipped for black-swan events such as the COVID-19 pandemic.

Two further actions can help improve insight generation and the decisions it informs. The first centers on training, particularly in analytical, data-visualization, and debiasing techniques and technologies. In industries where these tools are new, the potential competitive advantage they offer is high—but so is the required commitment. Other sectors can provide inspiration on how best to upskill staff. For instance, a leading telecom player found that almost half of its back-office staff lacked the technical skills needed to achieve future goals, and 42 percent were in roles that were unlikely to exist in a decade. Rather than eliminate staff, the company encouraged employees to assess their own capability gaps and learn new skills. In its first year, the program enabled the company to fill 40 percent of new job openings with internal candidates.

The second action companies can take is to increase the quality and stature of senior finance business-partnering roles. The people in these positions need deep experience and perspective to drill into the causes of underperformance, and to push back against over-optimistic or unnecessarily conservative financial assumptions that may get baked into business plans. Matching true high performers to these critical roles is an essential component of translating analytic capabilities into realizable business outcomes.

Imperative #4: Reimagine the finance operating model with new capabilities

Finance organizations are moving toward a new operating model that allows staff to adjust their work quickly and dynamically so they can focus on the most pressing topics facing their organization. This requires not only a different way of organizing how work gets done, but also a different type of finance professional.

To reduce the effort involved in operational tasks, the new finance operating model starts from a leaner core, with tighter data standards, new data-management practices, enhanced automation, and integration with a wide range of related digital technologies. Implementing this model involves a series of changes.

• Break traditional hierarchies into flat networks of teams. The network model allows finance business partners to draw on a shared pool of analysts, who are assigned to specific work items based on well-defined and agreed-upon business priorities.
• Mobilize temporary teams to deliver deeper insights into business problems. Creating this capacity follows agile working principles, such as instituting sprints to identify, design, and implement financial analyses that provide insights into business challenges.
• Embed digital skills across the finance organization. These capabilities may include programing bot algorithms, using analytics software, or learning how to translate business data into actionable insights.
• Develop a core of business-savvy finance leaders with the stature to engage company leaders as peers. Strengthening job rotations, both within finance and between finance and the business, builds a cadre of skilled professionals who can move easily throughout the organization. For rotations within finance, one automotive company requires executives to have worked in a minimum of two divisions, two finance functions, and two countries before ascending to senior roles. Another automotive company requires senior finance leaders to rotate through non-finance roles. In both cases the emphasis on building operational and leadership—as well as technical finance—capabilities is the important differentiating factor.
• Bolster finance skills by developing a rigorous, transparent competency matrix. This detailed set of standards helps ground discussions about finance talent in objective criteria, and enables managers and individuals to choose among explicit capability-building actions to support career progression. For example, advanced practitioners in a given skillset may be required to spend at least ten percent of their time helping develop the skills of other staff.
• Create formal and informal incentives for skill development. Examples include tying incentives to knowledge and capability development, setting explicit targets for internal promotions to finance leadership positions, and explicitly recognizing the accomplishments of managers who foster skill development by coaching their teams.

These steps came together recently at a global consumer-goods manufacturer developing a new performance-management system. The effort built a set of standard key performance indicators (KPIs) linked to the organization’s overall value-creation roadmap and cascaded to each layer of the management structure. After harmonizing these measures across multiple business and geographic entities, the company then implemented a data lake to house all the metrics, populating the information in real time. In turn, the data lake supported real-time dashboards displaying KPI and financial-performance data from the general ledger, and allowed for drill-down capability. The initiative succeeded in reducing the time that the FP&A team spent on data capture, presentation, and manipulation by as much as 65 percent.

At the frontiers of effectiveness, finance leaders deliver far more than core financial skills: their work guides the functioning of the entire organization every day. Through four imperatives, the next-generation finance function can build the insights, performance, and planning capabilities leaders will need to support dynamic decision-making through the next decade.