Practical AI for CFOs

Finance leaders have become the gatekeepers for companywide adoption of AI-enabled technologies. CFOs are tasked with their company’s financial health and strategy, including weighing the best place for internal investments. AI adds a new challenge: making the best choices to avoid being left behind. 

In recent years, the role of the CFO has evolved dramatically, positioning them as a key driver of transformation, charged with linking operational functions with strategic initiatives. Among corporate leaders, the CFO plays a significant role as a champion and critical sponsor behind enterprise-wide AI efforts.  

While all C-suite leaders share responsibility for the AI strategy, the CFO is uniquely positioned to drive both the strategic initiatives that cut across the organization as well as those that benefit the finance function.  

CFOs and other corporate leaders are right to be wary about AI adoption. The sheer speed of development and surrounding hype have led many vendors to oversell the benefits and underplay the complexity. CFOs are trained to understand and trust the underlying data used for financial reports. Introducing AI tools and their data adds a ‘black box’ and a trust issue that can slow their adoption in the finance department. 

When it comes to leading AI implementation within their own finance teams, CFOs need choices and solutions. These should be tailored to their organization’s broader position in adopting the evolving technologies. Such customization will lead to the practical implementation of AI to drive improvements within the core finance function.  

An AlixPartners survey of executives found the finance function generally ranked in the middle of the pack as the major focus of AI investments, with customer service ranked first. However, implementing AI in finance assumed greater importance for the top-ranked companies by profitability in the survey. 

Before embarking on the journey to transform the finance function, aligning with the three core principles outlined in our Practical Guide to AI for CEOs is an essential first step: 

• Strategy – ensure that AI deployments focus on solving the right business problems 
• Execution – turn the strategic plan into operational success that enhances innovation, financial performance, and competitive positioning 
• Foundational pillars – provide the necessary infrastructure and environment to support the strategy and execution of value-creating use cases  

With these core principles in mind, this CFO guide is intended to support you and your team in assessing a broad array of competing stakeholder demands, as well as developing your own understanding of the possibilities and capabilities of this game-changing technology. 

1. The value roadmap and selective adoption 

The rollout of AI across finance functions has been metered by concerns about its impact on complexity and risk, as well as the task of identifying immediate ROI gains. AI applied carefully in the finance department can make the function a more effective strategic partner, able to monitor activity in real time to enhance forecasting and reduce risk. 

CFOs can secure actionable insights by identifying high-impact areas such as planning, budgeting, and forecasting to drive better outcomes. 

Successful pilots can be used as building blocks to provide internal credibility and confidence that compliance and regulatory requirements are maintained. 

2. The right solutions 

The central role of the CFO makes them the bridge between operations and technology, with AI-enabled tools as an additional lever to effect change and pursue efficiency.  

This may require bespoke implementations aligned with a company’s unique processes and goals, leveraging internal experience, and adjusting for the limitations of legacy infrastructure. Less critical functions may benefit from more standardized applications that can be developed in-house.  

AlixPartners provides end-to-end support from initial assessment through pilot programs, expansion, and scaling. 

3. Data central 

AI can process vast amounts of financial data in real time and provide executives with timely and actionable insights. Enhanced visibility leads to more informed decision-making. 

AI-driven analytics can provide real-time updates on cash flow, enabling companies to make strategic adjustments on the fly. Models can be trained to issue early warnings of issues such as late customer payments or incomplete supplier shipments.  

More accurate forecasting can enhance risk mitigation by analyzing historical data, identifying patterns, and predicting future trends. Tools that cross-check financial reports against industry standards and regulatory guidelines can also enhance the accuracy of public financial statements. 

4. Change management and adoption 

Introduction of AI solutions transforms established processes and practices and needs to be coupled with a change management strategy. At the current stage, AI typically augments rather than replaces the existing workforce.  

However, the lure of automation can generate employee resistance that has to be overcome with trust in the shift from transactional tasks towards pursuing more strategic priorities.  

The AlixPartners difference 

Barriers abound in AI adoption, emphasizing the need to pick the right initial targets. Internal culture, legacy infrastructure, and available resources all have to be weighed by the CFO, whether they are making their first steps in AI or sprinting for the finish line. 

CFOs and their teams have proven adept as early adopters of new technologies. AlixPartners can partner to provide a pragmatic approach to selecting and implementing AI tools where they can make a measurable impact. 

Assessing readiness 

Assessing the readiness of the finance function is crucial. AlixPartners’ experience can identify the initial steps required. 

1. Do you know where to start? Speed-to-insight is essential. Does your company take longer than eight business days to close month-end financials? If the finance team often faces delays in finalizing its reports due to manual data reconciliation, AI can streamline the process by providing faster access to critical financial insights.  

2. Is your data unstructured and difficult to access? Forecasting and consolidation accuracy are paramount. Do financial forecasts often miss the mark, in part because of difficulties in consolidating financial data from internal and external sources? AI-driven tools can help refine revenue predictions by analyzing more variables and identifying patterns. 

3. Excel has its place, but how widespread are routine tasks such as manual invoice processing and data entry? If your finance department spends a significant amount of time processing invoices manually, AI can help automate the workflow and reduce errors. 

4. Have you had a bad experience with accuracy and compliance when preparing financial statements? AI tools can cross-check financial data against regulatory requirements and previous relevant case studies to reduce the risk of costly mistakes.  

5. What are your productivity goals?  What are the challenges in pinpointing cost and process inefficiencies and streamlining opportunities? AI can analyze spending patterns and process flows to highlight inefficiencies. 

6. How effectively can your organization foresee potential issues such as cash flow shortages or delinquent payers before they escalate? AI can shift your finance function from reactive to proactive, predicting customer ordering patterns and detecting when expected orders are not being placed on time.