Three Skills Finance Professionals Need in the Age of AI

Few sectors stand to be as fundamentally reshaped by artificial intelligence as finance. From automating routine reconciliations to generating real-time forecasting insights, AI has already redefined how finance teams operate, and the pace is only accelerating.

Staying competitive as a finance professional requires more than simply adopting AI as a tool. Finance professionals must seize this moment to develop new, strategic skills to carry their expertise into the next era of autonomous finance. Specifically, finance needs people who can translate intelligence into forward-looking decisions – who understand how AI models work and where they fall short, who can architect data to make insights reliable, and who can use those insights to stress-test assumptions and navigate uncertainty.

In this shifting landscape, organizations need to build talent capable of shaping the future of finance. Three capabilities, in particular, are becoming essential.

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1. AI Literacy and Oversight 

AI is fundamentally transforming the finance industry, with 87% of CFOs believing artificial intelligence will be extremely or very important to their finance department’s operations in 2026, according to the Deloitte Q4 2025 CFO Signals Survey. As AI becomes more central to finance operations, professionals who lack fluency in how these systems work cannot engage meaningfully in the conversations and decisions that matter most today. Beyond fluency, finance professionals must also be equipped to oversee and govern the AI systems increasingly embedded in their workflows.

In a 2025 Economist Impact report sponsored by SAP, CFOs see AI’s greatest value in fraud detection and regulatory compliance, two high-stakes areas where errors are costly and accountability is paramount. With generative AI now used to parse complex legal language, track rapidly changing rules, and automate compliance updates, finance professionals need the oversight skills to understand not just how these tools work, but also who owns the decisions they produce, how errors are flagged, and what guardrails ensure outputs meet audit and compliance standards.

To effectively upskill AI literacy and oversight, finance professionals must develop the ability to: 

• Evaluate and validate AI outputs, not just consume them
• Understand potential AI-related risks and establish guardrails
• Define governance frameworks and accountability structures for AI-driven decisions

AI literacy and oversight are no longer differentiators in finance; they are quickly becoming basic expectations. As AI takes over foundational finance tasks, the professionals who thrive will be those who know how to direct, question, and govern what AI produces.

2. Data Architecture Strategy 

AI is only as good as the data that fuels it, yet many organizations operate with fragmented systems and limited real-time data access. In the 2025 Economist Impact report, more than half of CFOs cite these same barriers as major obstacles to AI adoption.

Solving these barriers is now a top priority. When asked about finance transformation, the highest-ranked responses from CFOs were integrating AI agents (54%) and improving data quality, access, and usability (52%), according to the Deloitte Q4 2025 CFO Signals Survey. This pairing is no coincidence; AI agents are only as effective as the data architecture supporting them. As organizations invest in AI agents across finance, professionals who understand the data infrastructure behind those agents, and can actively shape it, will have a significant advantage over those who only consume what AI produces.

This is not about replacing IT, as data architecture remains firmly within IT’s domain. But as AI’s effectiveness increasingly depends on data quality, completeness, and real-time accessibility, finance professionals who cannot engage with data strategy are ceding control of their most vital tool to another function. The Economist Impact report notes that CFOs are already addressing data gaps by creating bespoke datasets from various internal and external market signals. As one CFO states: “No one has perfect data — but it’s about building the data.”

To be effective partners in data architecture strategy, finance professionals must be able to:

• Identify data gaps and determine the business requirements to fill them
• Integrate data from various internal and external sources into a clear view
• Support real-time monitoring and decision-making with an appropriate data infrastructure
• Recognize how data quality, completeness, and accessibility directly affect AI performance
• Work with IT to turn business needs into data solutions

Data is the foundation of every AI insight, forecast, and output. Finance professionals who are stewards of that foundation will only become more essential.

3. Scenario Planning and Risk Intelligence

The first two skills, AI oversight and data architecture strategy, are most valuable when they come together to enable the third skill: scenario planning and risk intelligence. This is where the distinctly human work begins: synthesizing models into insight and translating insight into decisions that matter.

The 2025 Economist Impact report reveals a critical vulnerability: while nearly 90% of CFOs remain confident in revenue and profit goals, only 37% feel the same about hitting liquidity targets – a gap traditional backward-looking reporting cannot close, and one that leaves organizations exposed to liquidity shocks in real time. In an environment defined by macroeconomic volatility, geopolitical tensions, and rapidly shifting trade policies, finance professionals need the ability to model scenarios by reading what the data signals and acting before uncertainty becomes a crisis.

CFOs are already adapting. The same report shows finance leaders embedding risk management into daily operations and using AI-powered scenario planning to stay ahead of disruption. Rather than waiting for uncertainty to resolve, they are drawing on micro-operational signals, like transaction volumes, supplier behavior, and spending patterns, and synthesizing them into forward-looking risk indicators that guide the broader business.

With 84% of CFOs reporting they are more involved in risk management and compliance than three years ago, the demand for this skill is accelerating. Strategic scenario planning and risk intelligence are less about running models and more about knowing what to do with them. Finance professionals should be able to:

• Translate AI-generated outputs into actionable, forward-looking business decisions
• Design and stress-test scenarios that account for macroeconomic, geopolitical, and operational risk
• Identify and interpret micro-operational signals as leading indicators of broader risk
• Clearly communicate uncertainty and trade-offs to executive and cross-functional stakeholders
• Decipher when to trust the model and when to override it

In an environment where uncertainty is constant, the finance professionals who can turn complexity into a strategic advantage will be the ones organizations cannot afford to lose.

From Disruption to Differentiation

As AI takes on more routine work, the opportunity for finance professionals to step into more strategic, higher-value work has never been greater. In fact, the same Deloitte survey reveals that when asked to name their top priorities for finance talent in 2026, nearly half (49%) of CFOs cite automating processes to free employees to do higher-value work, making it the most popular response. Those who build expertise across these three, higher-value capabilities will not only survive this moment but will lead it.

The future of finance belongs to professionals who can do what AI cannot: exercise judgment, navigate ambiguity, and turn insight into decisions that move organizations forward. We are entering the age of agentic finance, and the finance professionals who grow alongside this transformation will be those who shape what comes next.

About SAP

SAP is a global leader in enterprise applications and business AI, helping organizations run end-to-end operations on a unified cloud ERP platform. With SAP S/4HANA Cloud, SAP Business AI, and SAP Business Technology Platform (BTP), SAP enables companies to connect data, automate processes, and make real-time, intelligent decisions across the enterprise.

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