What Is Finance Transformation — And What Does It Achieve?

When we examined how CFOs rank their most critical priorities, five themes emerged.

Theme 1: Data, metrics and analytics

Data, metrics and analytics is the top theme because of its outsized impact on all CFOs’ top priorities, including AI, and accuracy in budgeting and forecasting. In fact, 30% of finance leaders say data quality is a key inhibitor for low AI adoption in finance. CFOs must enhance data quality and governance, bridging financial and nonfinancial data to drive smarter decisions across the organization.

Theme 2: Efficient growth

CFOs are aiming for efficient growth by balancing cost objectives with investment in digital initiatives. Achieving sustainable top-line and bottom-line improvements is rare but rewarding, offering a shareholder return premium. CFOs must foster a cost-conscious culture while driving growth.

Theme 3: AI adoption in finance

AI usage in finance is becoming mainstream, doubling in the past year. CFOs are leveraging AI for automation and insights but struggle with quantifying investment and ROI. Intelligent process automation shows productivity gains, yet real costs are often underestimated significantly.

Theme 4: Time allocation and leadership capacity

CFOs face expanding responsibilities beyond finance, requiring better team leverage. Optimal time trade-offs can enhance organizational effectiveness. Delegation is challenging due to leadership gaps, complicating succession planning as CFOs scale into larger roles.

Many finance transformation initiatives focus heavily on technology or organizational design, underinvesting in other operating model decisions. Prepare your finance leadership team to make decisions about the whole operating model for autonomous finance.

Because no two companies are alike, there is no single “right” operating model for autonomous finance. However, most firms should incorporate the following common general principles to move successfully toward autonomous finance. 

Ways of working include more collaborative and transparent approaches to work, behaviors, informal workflows, team customs and the adoption of methods such as agile and lean.

Decision rights (held by key stakeholders) address who makes decisions, how decisions are made and how disputes are resolved.

Talent considerations include the competencies, skills and profiles needed to execute work in the operating model.

Organizational structure defines key roles, reporting relationships and collaborative networks involved in the finance transformation.

Tools include the assets, applications and functionalities that should (or should not) be used for teams to perform their responsibilities.

Financials include how finance transformation will be funded; how budgets will be allocated, planned and monitored; how costs will be recovered or priced; and how investment aligns to strategy.

Performance refers to the approach to performance management that will ensure the effective execution of strategy and ongoing operations.

Sourcing defines the approach to outsourcing and partnership with third-party service providers.

Alliances define the necessary partnerships and how to ensure they create value.

Places define the global locations of key people and assets.

Finance needs more digital talent than ever, but today’s digital talent composition does not meet tomorrow’s demand. Currently, less than 15% of finance talent have digital capabilities, but soon, nearly half of the function will need those capabilities for finance to succeed.

Why the urgency? Finance teams that lack critical skills stand to miss out on seven key benefits of digitalization. The following list explains these benefits in order of importance and the skills that finance employees need to achieve them:

1. As digital technology helps streamline financial data, simplify core processes and reduce resource expenditures, productivity and efficiency improve. To reap this benefit, finance employees need an in-depth knowledge of internal finance processes, the ability to recognize opportunities to increase productivity and the ability to process mining skills.

2. Finance attains a high level of technical sophistication when dedicated digital talent and advanced technology users can accomplish complex tasks with a moderate to high level of technical acumen. To get there, finance talent must be able to analyze data and its nuances, learn new analytical approaches and tech skills, understand and troubleshoot finance technology systems and have knowledge of a programming language.

3. As efficiency, productivity and technical sophistication increase, finance employees gain increased capacity to take on more high-value analysis work, engage in strategic business partnering and customize systems. To do so, employees must be able to establish and maintain more advanced technological platforms and automate complex processes.

4. Digital technology helps finance identify problems and address them through tailored solutions — like modifying or constructing products in a way that aligns with stakeholders’ specific needs. To deliver these solutions, finance employees need advanced data analysis, management and interpretation skills and the ability to customize technology platforms.

The AI technology adoption gap in finance is closing. Fifty-eight percent of finance functions are at least piloting AI tools, and 66% of finance leaders feel more optimistic about AI’s impact. 

Four use cases continue to stand out for using AI in finance transformation.  

• Intelligent process automation: Leveraging the AI capabilities of existing automation tools (such as RPA) to enhance information processing.

• Anomaly and error detection: AI-enabled identification and reporting of errors and outliers in large datasets such as internal claims, expenses and invoices.

• Analytics: The creation of better financial forecasts and results analysis that can lead to improved decision making.

• Operational assistance and augmentation: Emulation of human-judgment-based decisions in operations through AI and generative AI.

Although finance AI adoption is increasing, almost half of finance leaders currently using AI report that AI adoption went slower than anticipated. Only 11% report that the adoption went faster than anticipated.