6 Ways to Modernize the Finance Organization

Traditional finance organization designs and operating models are increasingly out of step with new, technology-augmented and automated processes. CFOs are battling a siloed finance department structure, complex roles and traditional ideas about which finance activities should be performed by which subgroup. All of these issues limit the impact and cohesiveness of digital initiatives. 

A new, more flexible operating model must replace these outdated approaches. The shared service models that finance has been embracing for several years will facilitate this transition. However, the original purpose of shared service organizations (SSOs) is changing. Instead of enabling the centralization and automation of high-volume, low-value activities, SSOs will transition to support the digitization of finance.

This will become possible as machines increasingly execute standard finance activities and technology improves processes, resulting in more SSO capacity to focus on other priorities. These priorities may include hyperautomation and other forms of digital transformation.

In an ideal state, SSOs will become digital transformation support centers. A finance-SSO digital program can facilitate that transition by:

• Defining a technology roadmap
• Identifying synergies
• Driving cross-departmental digital enablement

There are talent implications for this revised, shared service operating model. The unique insights that finance staff have into the individual business units they serve have decreased in recent years due to increased specialization, coupled with the democratization of business data. Yet finance still has a unique ability to see patterns across business units or “portfolio-level insights.” SSOs with more cohesive and cross-functional digital capabilities can amplify this value add.

To do that, forward-thinking finance leaders must continue to migrate common activities to shared service centers (SSCs) and centers of excellence (COEs) to free up human capacity to help business units make informed decisions. This approach harnesses finance’s cross-functional visibility and influence over the decisions that are common across the organization. Groups who introduced this model saw a 250% improvement in the financial soundness of operational decisions.

Finance organizations with traditional operating models have by necessity pursued digitization in a siloed way. This results in pockets of digital enablement that don’t translate into functionwide efficiencies or improvements for finance. Moving away from siloed digitization toward digital cohesion requires a finance organizational structure that embeds closeness between digital support staff and end users. Do this in two ways:

1. Invest in a finance IT team: This reduces reliance on enterprise IT and builds finance-related technology expertise. It also enables a more informed process for identifying and selecting digital investments for finance. These investments are a high priority. Finance IT is the largest area of growth in finance staffing growth, and 62% of finance organizations plan to increase finance IT headcount.

2. Create an indirect reporting line between finance IT and a digital champion:  A digital champion for finance is a person in the CFO’s reporting line who drives digital decision making and responsibility — including recruiting and leading a central digital transformation team. Establishing a reporting line between this person, the CFO and IT enables more transparency around digital investments. It also drives resources toward initiatives that address panfinance needs, which avoids privileging those that are strategically connected or influential.

The finance teams of the future must prioritize five digital competencies to drive organizational outcomes. Knowledge about and skills in digital technology, core finance, data analysis and social/creative feed into these competencies. Note that only one of these components is technology-based.

The five digital competencies in finance are:

Technological Literacy

Defined as the ability to exploit digital technology to drive better outcomes, technological literacy enables finance staff to leverage robot process automation (RPA), machine learning (ML) and natural language processing (NLP). Finance leaders with technological literacy will know:

• Which digital software solutions exist and how they help automate finance activities

• The basics of the underlying programming languages

• How new technologies fit into the existing technology landscape and into finance processes

The two abilities that support finance talent in their acquisition and practice of technical literacy are intellectual curiosity about how digital technologies enhance finance outputs and the knowledge of problem solving and experimentation to overcome challenges.

Digital Translation

Finance professionals need to explain — or translate — key concepts about digital technologies, processes and systems to stakeholders. The skills that build finance’s digital translation capabilities include:

• Increasing the quality and relevance of financial and nonfinancial data for decision making

• Strengthening the analytical output of data analyses, such as by assessing ML algorithms

• Data storytelling, or explaining data-driven insights and where they come from

Finance teams that excel at digital translation will productively challenge assumptions and increase buy-in.

Digital Learning and Development

This allows employees to keep pace with the rapid evolution of digital technologies and acquire new digital knowledge through guided learning, experimentation and technology application. A large share of digital learning happens by “doing” — for example, by identifying a challenge that is a poor fit for RPA and developing a pilot to address it using ML. The best digital learners have mental agility that enables them to actively apply new digital knowledge and skills.

Digital Bias Management

Digital bias management is the capability to understand and minimize bias in the use of ML. Finance staff with the knowledge and skills for digital bias management understand how ML technology works and how to manage against ML biases, while delivering business value. They can:

• Detect bias in data

• Validate algorithms before they go live

• Identify ML training data sources that reflect the right business context

Digital bias management is new terrain for most finance teams and requires intellectual curiosity and problem solving to figure out why an algorithm makes certain predictions.

Digital Ambition

The abilities that finance staff need to have digital ambition include:

• Trying new technologies and approaches when there is no clear mandate to do so

• Learning about a technology before it is rolled out to the broader team

• Overcoming obstacles with new technology or with stakeholders resisting using it

Finance teams with digital ambition have strong stakeholder management and change management skills to enable quicker buy-in from partners in finance and the business.

Today’s modern finance organizations have already adopted a cost optimization approach — as opposed to cost cutting — which involves structured improvements in efficiency and productivity. Looking forward, finance leaders will evolve toward value optimization driven by business-oriented analytics and reporting strategies.

Spending benchmarks are among the key analytics that inform value optimization. The top 10 Gartner benchmarks — reported here as cross-industry numbers — include:

1. Finance Spending as a Percentage of Revenue. This foundational metric helps finance leaders understand their costs relative to peers. It provides a baseline from which the function can explore reducing spend, shifting resources and/or investing in new capabilities. Gartner benchmarks overall finance spending at 1.32% of revenue.

2. Distribution of Finance Spending by Asset Category. Spending across personnel, technology and external experts reveals how the finance function prioritizes various inputs. Cross-industry analysis by Gartner shows that compensation for in-house staff and contractors accounts for more than 80% of finance spend.

3. Distribution of Finance Spending by Process Category. This data benchmarks the relative distribution of the finance budget across activities such as budgeting, forecasting, analytics and reporting. Gartner shows that the highest share of spend, 26%, goes to accounting and reporting.

4. Spend on External Experts and Support. Finance organizations often have some process outsourcing as well as consultant advisors who provide advice. Gartner benchmarks show that 22% of external spending goes to outsourcing and 78% to consultants.

5. Finance Headcount per Billion in Annual Revenue. This benchmark allows finance leaders to assess how much they invest in staffing compared to their peers. Benchmarked finance headcount is 97.5 FTEs per billion USD in annual revenue, according to Gartner analysis.

6. Distribution of Headcount by Role. Headcount distribution allows leaders to assess their organizational makeup relative to their peers. Organizations with higher proportions of senior staff often have higher personnel spend. In our cross-industry analysis, 8% of staff roles are in senior functional leadership.

7. Distribution of Headcount by Process Category. Similar to the distribution of spend by process category, evaluating headcount by process category can help identify inefficiencies. On average, the highest percentage of staff are in transactional finance at 31%.
   

8. Finance Compensation Spend per Staff Member. This metric compares staff pay and incentives to those of peer organizations. The median finance compensation per employee is approximately $115,000.

9. Emerging Finance Technology Investments. Tech spending benchmarks help finance leaders understand if their investment level aligns with corporate goals. The most common areas of finance function technology investment are cloud-based enterprise resource planning (ERP), in which 64% of organizations plan to invest, and advanced data analytics, which is a priority area for 57% of organizations.

10. Finance Technology Spend to Run, Grow and Transform the Business. In 2020, Gartner introduced run, grow and transform parameters for benchmarking IT spend. Gartner benchmarks show that finance technology spend is weighted toward run at 79%. Technology spending to grow and transform the business is 13% and 8%, respectively.