Despite artificial intelligence (AI) continuing to build momentum and reshape how businesses operate — especially in finance — adoption in the middle market remains in the early stages. In 2024, fewer than 10% of finance organizations were using generative AI in production, but 80% of large enterprise finance teams are projected to use internal AI platforms by 2026.
Many mid-sized organizations have yet to fully embrace AI, often due to limited resources, uncertainty around return on investment (ROI) or lack of clarity on where to begin. However, understanding AI’s potential impact is no longer optional. As automation tools become embedded in everyday applications and workflows, finance leaders must be equipped to evaluate where AI technologies can drive value, reduce risk and support long-term growth.
This practical guide will help you unlock value from AI in your finance organization, as we break down common terminology, highlight actionable finance use cases and provide guidance on measuring ROI.
The Jargon: What Finance Leaders Need To Know
AI may feel overwhelming for finance leaders who are already navigating complex systems, tight deadlines and evolving compliance requirements. Even in the daily news and on social media, AI terms are used in misleading and general ways. Understanding the terminology is key to getting started.
- Machine Learning (ML): Refers to algorithms that learn patterns from historical data to make predictions or decisions without being explicitly programmed for each scenario. In finance, an ML model might analyze three years of invoice and payment data to predict the likelihood of late payments, enabling proactive collections.
- Generative AI: A subset of machine learning that generates new content (text, images, videos or data) based on learned patterns. We’ve all heard of ChatGPT or Copilot, both of which fall into this category. A financial planning and analysis (FP&A) analyst might use a generative AI tool to draft variance analysis commentary in seconds by interpreting deviations between budget and actuals.
- Intelligent Automation: Refers to the combination of AI with robotic process automation (RPA) to automate end-to-end processes. In finance, this may look like using AI for accounts payable workflows, with the technology reading invoices and extracting key fields, while RPA posts transactions in the enterprise resource planning (ERP). These systems follow predefined logic and workflows, offering speed, accuracy, and cost savings while reducing human error. However, their capabilities are largely reactive — they execute tasks based on structured inputs and rules without autonomous decision-making
- Agentic AI: Following an initial prompt or instruction, autonomous AI “agents” can make decisions, carry out multistep tasks and adapt based on outcomes independently. In the finance team, an AI agent could monitor daily cash balances, predict shortfalls and initiate intercompany transfers to optimize liquidity, all with minimal human intervention. Unlike intelligent automation, these agents are capable of multi-step planning, learning from outcomes and adapting to changing conditions.
Finance Use Cases Already Delivering Value
1. Automating Core Finance Processes for Operational Efficiency
A combination of AI and RPA can automate repetitive processes, such as accounts payable and accounts receivable (AP/AR), reconciliation and recurring reporting. By reducing manual work, finance teams are freed to focus on strategic analysis. For example, in a recent PYMNTS survey, 85% of small and mid-size businesses cited increased accuracy, efficiency and streamlined processes as key benefits of their AP automations.
To measure the impact of this kind of intelligent automation, look at metrics like invoice processing time, Days Sales Outstanding (DSO) or Days Payable Outstanding (DPO).
2. AI-enhanced FP&A and Scenario Modelling
Generative AI and machine learning can enhance decision-making by generating rolling forecasts and building predictive models based on historic financials and external data (e.g., commodity prices, foreign exchange rates). This kind of enhanced predictive forecasting can strengthen business continuity, equipping leadership to adapt with agility.
Tracking percentage reduction in forecast variance, hours saved per forecasting cycle and scores for stakeholder satisfaction can help to measure ROI for FP&A-AI initiatives. A 2024 FP&A Trends survey found that FP&A teams using AI achieve 25% higher forecast accuracy and are 18% better optimized in comparison with non-AI teams.
3. Predictive Risk Management and Compliance Monitoring
AI models can detect anomalies, compliance breaches and potential fraud in real-time by flagging unusual transactions or behaviors. By embedding compliance rules into automated workflows, finance teams can reduce risk exposure and improve audit readiness.
Key performance indicators (KPIs) to measure success in this area may include the number of anomalies detected versus false positives, the average time to resolution of flagged items or a reduction in financial losses due to fraud or compliance violations.
4. AI as Your Finance Team’s Assistant
With major workspace technology providers such as Microsoft and Google embedding generative AI across their suites, finance professionals, like the rest of the business, can benefit from leveraging AI for everyday tasks.
In a 2024 McKinsey survey, improved productivity was the most often cited impact area for CFOs whose teams have already adopted AI, mentioned by 71% of respondents, while 83% said AI would have demonstrable effects on employee productivity in the next five years. From writing complex Excel formulas to drafting stakeholder emails, AI can dramatically reduce the time spent on simple but laborious tasks, allowing staff to dedicate more hours to high-value work.
A Strong Foundation: Data Governance, Quality and Security Pre-requisites
Without clean, well-structured and properly governed data, AI initiatives will not have the same impact. Establishing this foundation is critical to ROI and managing risk. Finance leaders should keep a close eye on:
- Data Governance: Define ownership, quality standards and lineage for all key financial data. The auditability of actions taken on financial data is essential.
- Data Standardization: Consider centralizing and standardizing datasets to facilitate seamless integration across systems and platforms, and to ensure pilot projects are replicable and scalable across teams.
- Security Protocols: Ensure your teams are trained on cyber risks and best practices, and that your information technology (IT) team carries out regular security audits to protect sensitive financial and personal data. Agents must operate within tightly defined roles and responsibilities to prevent misuse or unintended actions.
Next Steps To Achieve AI ROI for Mid-market Finance Leaders
There are clear areas where mid-market finance teams can draw value from AI today, from automating core processes to enhancing decision-making with better forecasting and risk oversight. Measuring ROI through targeted KPIs, from process cycle times to forecast accuracy, anomaly detection rates and productivity metrics, will enable initiatives to stay on track.
Get in touch with Cherry Bekaert to assess your AI readiness and, as part of your finance modernization efforts, build a phased roadmap that delivers measurable returns. As AI continues to mature, early movers will secure a lasting competitive advantage. Don’t let uncertainty hold you back.
Related Insights
