Maximize ROI from AI in Finance: A 2025 Guide
How to Get ROI from AI in the Finance Function
As of June 2025, the integration of Artificial Intelligence (AI) into finance functions is no longer a novelty but a necessity. CFOs and finance leaders are increasingly looking to AI and Generative AI (GenAI) to transform their operations, but the path to tangible returns on investment (ROI) remains challenging. Despite high expectations, with about 30% of executives believing AI will deliver transformative value by the end of 2025, only 45% can quantify ROI from their AI initiatives, with a significant portion reporting underwhelming returns[1]. So, how can finance leaders unlock the full potential of AI and ensure a substantial ROI?
Historical Context: Evolution of AI in Finance
Historically, AI in finance has evolved from basic automation tasks to complex analytics and decision-making processes. The journey began with simple machine learning algorithms for fraud detection and risk assessment, gradually moving towards more sophisticated applications like predictive analytics and portfolio management. Today, AI is being used in statutory and transaction accounting, treasury, controlling, and M&A support[1]. This shift reflects the growing confidence in AI's transformative capabilities, with many viewing it as more impactful than earlier technologies like robotic process automation[1].
Current Developments: Challenges and Opportunities
Currently, one of the major hurdles in achieving ROI from AI is the complexity of implementation. Finance teams face unique challenges related to compliance, regulation, and auditability[1]. To overcome these, many organizations are turning to AI and GenAI agents—custom-built applications designed to harness AI within complex financial environments[1]. Additionally, data quality and technical debt remain significant obstacles, requiring careful management to ensure effective AI deployment[4].
Future Implications: Breakthroughs and Expectations
Looking ahead, there is a strong anticipation of breakthrough results. Half of surveyed executives expect significant advancements within the next three years[1]. Moreover, 67% of business leaders believe AI will fundamentally reshape their organizations within two years, with substantial investments planned in generative AI[4]. As AI continues to evolve, it's crucial for finance leaders to adapt their strategies to capture the multifaceted benefits of AI, which extend beyond traditional profitability metrics to include productivity and innovation capacity[4].
Real-World Applications and Examples
Real-world applications of AI in finance are diverse and impactful. For instance, AI is being used to enhance accounts payable processes, reducing manual errors and increasing efficiency[5]. In treasury management, AI helps predict cash flow and optimize financial planning. Companies like Centage offer practical frameworks for calculating AI ROI, providing finance leaders with actionable insights to maximize their investments[2].
Strategies for Achieving ROI from AI
To achieve substantial ROI from AI, finance leaders should focus on several key strategies:
Data Quality and Management: Ensuring high-quality data is crucial for AI effectiveness. This involves continuous data cleansing and integration processes.
Customized Solutions: Instead of off-the-shelf products, organizations should develop tailored AI applications that address specific financial challenges.
Adaptive ROI Metrics: Moving beyond traditional profitability metrics, organizations should consider productivity, efficiency, and long-term innovation capacity as key ROI indicators[4].
Collaborative Approach: Encourage cross-functional collaboration between finance, IT, and other departments to ensure a cohesive AI strategy.
Comparison of AI Implementation Approaches
| Approach | Description | Benefits | Challenges |
|---|---|---|---|
| Off-the-Shelf AI | Pre-developed AI solutions | Quick deployment, cost-effective | Limited customization, potential for inefficiency |
| Custom AI Solutions | Tailored AI applications | High effectiveness, addresses specific needs | Higher development costs, requires specialized expertise |
Conclusion
Achieving ROI from AI in the finance function requires a nuanced understanding of both the potential benefits and the implementation challenges. As AI continues to evolve, finance leaders must adapt their strategies to capture its multifaceted value, focusing on customized solutions, data quality, and broader ROI metrics. With the right approach, AI can transform the finance landscape, offering unprecedented opportunities in automation, analytics, and risk management.
EXCERPT:
Maximizing ROI from AI in finance requires strategic planning, focusing on customized solutions, data management, and adaptive ROI metrics.
TAGS:
finance-ai, business-ai, ai-roi, generative-ai, ai-innovation
CATEGORY:
finance-ai