Have top performing finance functions reached terminal value in the age of AI?

Ask almost any finance leader, and they’ll say transformation usually focuses mostly on cost reduction — improving processes, automating transactions, reducing headcount and so on. That’s understandable. Over many decades, finance has implemented multiple layers of efficiency initiatives, from automating transactional processes to leveraging global business services (GBS) models.

While these measures have helped reduce costs, they’ve also led to a potential “terminal value,” with diminishing returns for many companies. This applies not only to traditional cost-cutting efforts but to finance’s value to its business partners as well.

For leading companies, the definition of finance transformation has expanded to focus on generating business insights, enabling business decisions and driving enterprise strategy. PwC benchmarking data from 2014 through 2023 shows this shift with top quartile finance functions spending more time on generating insights than automatable tasks for the first time in 2020. While this validates work to increase efficiency, it poses important questions: Have leading finance functions reached their terminal value and, if so, how can finance best add value to the organization?

It’s true that traditional cost-reduction measures have generally driven down the cost of finance as a percentage of revenue, with leading organizations now spending less than 0.8% of revenue on finance. But cost reduction has stalled as many companies have implemented the most available cost-reduction levers.

_{Source: PwC Finance Effectiveness Benchmarking Study}

In fact, PwC benchmarking data shows that incremental cost reductions from these measures have begun to flatten for top-performing finance functions, which raises critical questions:

• Where can we best spend our efforts to help drive enterprise value and be a better business partner?
• Where is the next cost pool to address?
• What new capabilities do we require and how do we get them?
• How do artificial intelligence (AI), machine learning (ML) and other new technologies fit into our overall strategy?

To enable more advanced capabilities and establish its value within your organization, finance should move beyond its traditional methods of transformation. This will likely require investing in new capabilities, driving a data strategy, upskilling people, and embracing nontraditional and new types of resources.

An “agentic organization” is now being discussed in which organizations can use intelligent, autonomous software agents to perform tasks and suggest outcomes traditionally handled by people — often orchestrating entire workflows across the enterprise. This enables humans to focus on strategy and creativity, serving as specialists in specific areas. It breaks down borders and boundaries with a hyper-distributed workforce, allowing finance teams to focus on insights and action.

As leading finance organizations approach their terminal value, the focus should shift to creating value through insights and driving the business strategy. This reflects the evolving role of finance as a strategic advisor to the business, providing real-time, data-driven insights that influence key decisions.

Finance functions are shifting toward a “finance for business” model, where time and effort are spent on internal and external business drivers that influence decision-making. Market trends, geopolitical factors, economic shifts and the like now have a role in CFO decisions, which also can uncover potential profit pools for the business.

To do this, finance teams are embracing advanced analytics, predictive models, AI — including ML and generative AI (GenAI) — and AI agents to:

• Deliver insights that enable faster, more informed decision-making
• Provide forward-looking perspectives on risks, opportunities and future performance
• Drive growth by aligning financial strategy with business strategy

This transition requires significant investment in technology, data and talent, with dollars once earmarked for efficiency initiatives now redirected to building capabilities that can generate insights. These investments also should help equip your finance workforce with the knowledge and skillsets to leverage these advanced tools, resulting in more proactive and strategic discussions with your stakeholders.

As automation, AI and AI agents take over repetitive processes, finance teams are freed from time-consuming manual tasks and can focus on higher-value activities. These can include forecasting future financial outcomes based on predictive models, analyzing market trends to inform investment decisions and assessing the impact of strategic initiatives through scenario modeling. As these models create multipliers for the human workforce, CFOs should shift their mindset from incremental change to helping the business bend the revenue and cost curves at a faster pace.

Automation can go beyond simple process improvement. By leveraging AI and ML, finance functions can continuously learn from data patterns and refine predictions, leading to better forecasting accuracy and a deeper understanding of business drivers.

Being able to automate transactional tasks while leveraging advanced analytics helps position the finance function as a proactive contributor to business strategy rather than a reactive processor of financial data.

While tech investments can enable the shift to insight-driven finance, they also come with a cost. For leading companies, the curve of cost reduction has flattened because investments in data infrastructure, AI/ML tools and advanced analytics are now required to generate value through insights. This reallocation of resources is essential for finance to evolve, but it also affects the finance function’s overall cost structure. In many organizations, the digital core that serves as the transaction engine still has technology and process debt that isn’t fit for purpose in changing times and should be addressed to leverage the benefits of data and AI.

For companies not yet at the forefront of adopting AI and other technologies, these innovations can help accelerate bending the cost curve. Organizations can unlock new efficiencies and insights, ultimately transforming their financial operations and gaining a competitive edge.

One paradox of reaching terminal value is that even as the cost of finance as a percentage of revenue has plateaued, further tech investments may temporarily offset some of the savings generated through traditional efficiency levers. Still, the ROI from new insights gained may very well exceed the cost savings from transactional process improvements.

The future-ready finance organization integrates real-time data, advanced analytics, AI/ML and scenario modeling into daily operations, allowing teams to not only monitor performance but predict and influence it. By embedding finance deeply within business processes, equipped with the right data and disruptive technologies, your company can be better positioned to ensure that financial insights are aligned with broader enterprise goals.

To transition your finance function from a transactional, cost-focused role to one that can drive strategic decisions, invest in these key areas.

• Capital allocation: As leaders in their companies’ AI strategy, CFOs will need to allocate capital to help accelerate transformation. They also should evolve the mindsets of the finance function and organization overall to be bolder and more proactive in bending the revenue and cost curves.
  
• Data and technology: Data warehouse and analytics systems, integrated financial systems and AI-driven tools are essential for creating a unified view that can be accessed and acted on at any time. Predictive models should be continually refined with high-quality data, and AI can be trained to evolve over time.
• Upskilling finance teams: The future finance professional should have experience not only in traditional accounting and financial analysis but also in data science, AI and scenario modeling. Upskilling your finance teams to work with advanced analytics tools and to communicate insights effectively will be critical. This shift can enable finance professionals to add value beyond reporting.
• Agile processes for dynamic decision-making: Adopting agile finance processes helps organizations respond quickly to changing market conditions. Static budgeting and forecasting processes are no longer sufficient. Instead, finance should be able to run continuous, iterative forecasts and model various scenarios in real time. This agility ensures that finance can provide actionable insights when they’re needed most, helping your business adapt and thrive amid uncertainty.