Capitalizing on GenAI: How CFOs Can Turn Real-Time Insights into Proactive Decisions

Real-time predictions, insights, and decision-making are now essential for proactive finance organizations. As CFOs and senior finance leaders, we no longer need to wait for a manual, month-end close to identify exceptions, anomalies, and operational shortfalls. The most advanced SaaS native ERP platforms now integrate data and automation technology with embedded AI and generative AI. These platforms can help minimize manual effort and accelerate decision-making, providing real-time information that enables us to proactively seize opportunities and address problems before they escalate. In this article, we will focus on how real-time, data-driven insights empower us to enhance decision-making and become more effective operationally and strategically.

Predictions: Data Powers AI

GenAI capabilities provide transformative potential across finance. AI and process automation enable near real-time transaction and data processing, helping reduce unnecessary manual tasks and providing integrated, high-quality data for rapid predictions and insights. The resulting efficiency and productivity gains free up vital resources for more value-added analytical activities (for more, see Navigating the GenAI Future: How CFOs Can Maximize Productivity).

Having access to complete and accurate data from a single source provides rocket fuel for AI-driven automation, predictions, and insights. The most advanced SaaS native ERP platforms on the market today have long integrated AI with data, software, and infrastructure. These platforms also natively integrate AI/GenAI and large language models (LLMs). Doing so can result in powerful benefits, including:

• AI capabilities seamlessly embedded in application process flows, allowing for new and fast-changing AI capabilities and other advanced technologies to be easily introduced and adopted without the significant manual effort or rework inherent in a fragmented platform approach
• The highest level of AI quality, relevance, and reliability, since the AI is being applied to your specific data; disjointed or siloed systems are not suited for this
• Localization of data that delivers a strong level of security, avoiding the exposure of sensitive financial or management information to public LLMs
• Data that is more easily staged to take advantage of AI-driven automation to accomplish the goal of touchless processing

Bottom line: CFOs must require the use of the most advanced integrated SaaS native ERP platform and mandate the AI-driven touchless processing that these platforms allow. Such an ERP platform provides the high-quality data necessary for real-time AI-driven predictions and insights. The data generated allows for immediate AI-driven insights into exceptions and anomalies, and, critically, offers predictions of operating results and cashflow versus forecasts with narrative analyses. This capability gives CFOs the information they need to effectively allocate capital to opportunities and to preemptively address issues.

The role of the ERP platform extends beyond internal financial data aggregation; it integrates data from all relevant sources to enhance prediction accuracy and reliability. For instance, incorporating external, forward-looking data on commodity prices, weather, global supply chains, distribution channels, and banking information can help make AI-driven predictions more reliable and resultant actions more relevant and timely. For example, consumer goods businesses can gain continuous visibility through the touchless integration of data from trade promotions and channel inventories, and manufacturers can drive better decisions with the incorporation of commodity prices and supply chain data into financial prediction models.

AI in the Field

Organizations are already leveraging AI creatively to provide insights and predictions that enhance business planning, processes, and operations. For example:

• A large defense contractor uses AI-driven insights for variance analysis and anomaly detection and categorization, driving exception-based management and actions.
• A global fintech company generates weekly AI-driven predictions for payment volumes, which inform both revenue and expense predictions.
• A transportation service provider uses AI-driven monthly volume predictions of repairs by vehicle type to plan and accrue maintenance expenses, one of their largest expense line items.

AI in its current form can offer numerous ways to simplify or eliminate processes, increase accuracy, and improve efficiency. As organizations continue to leverage data, traditional AI and new GenAI capabilities will be introduced that can dramatically change our traditional finance approach.

Insights: What’s Next

GenAI helps significantly accelerate thought to action, substantially reducing the manual effort involved in deriving insights. We believe that GenAI will continue to be seamlessly integrated with traditional AI to further enhance insights and predictions in innovative ways. Finance organizations will use GenAI to provide easy-to-understand contextual narratives that explain exceptions, anomalies, and variances in predictions versus forecasts, delivering deep analyses, observations, and recommended actions to address issues or capitalize on opportunities. These contextual narratives will enable broader sharing of insights up and down the organizational chain beyond the FP&A team. We believe that GenAI will also provide explanations of the specific factors that influence a given prediction. This explainability of prediction models is critical for building confidence and trust from the always-skeptical finance users.

Steering Finance Toward a Strategically Focused Future

As finance leaders, we can now define data-driven outcomes powered by the synergy of traditional AI and GenAI, aligning with our operational and strategic goals. Numerous use cases exist for obtaining insights, predictions, and recommended actions for managing revenue, costs, collections, cash, and capital; improving operations through KPI analysis; executing strategic projects, negotiations, and financing transactions, and more. With the addition of GenAI, our teams can gain intelligent insights and direction, enabling proactive and timely decisions to achieve strategic objectives.

It is important to note that not all existing processes will be immediately augmented by AI, as there is a period of training AI and refining data to offer quality, reliability, and confidence in the technology. Realizing the benefits of AI also requires data-driven processes, challenging us to rethink organizational roles and responsibilities, even cross-functionally.

CFOs need to make AI adoption a strategic priority now. Expect a new normal to be proactive, real-time, fact-based decision-making driven by data and AI. Finance teams must embrace these capabilities immediately to establish a strong AI foundation for data-driven outcomes, learn and master the skills necessary to achieve the highest value, and be prepared to ingest the newest capabilities as they are introduced.

Waiting is not an option.