AI in Finance: Moving Beyond Cost-Savings to Real ROI

Every time you check the news, it seems like Artificial Intelligence is remaking industries, from marketing to manufacturing to healthcare. But one of the most exciting (and under-talked) frontiers is finance. 

The world of ledgers, forecasts, and closely held numbers is quietly undergoing a transformation. Suddenly, finance teams aren’t just closing books-they’re asking: “How much value did AI really add?”

Here’s the honest truth: amid the buzz around AI in finance, trailing indicators like cost-savings and speed gains are easier to track, but what we often miss is the full ROI story. The people who will win this shift are those who measure, understand, and communicate AI’s value rather than simply deploy it.

So let’s pull the curtain back a little and explore: what does ROI look like in a finance context? How do baseline metrics, time-to-value, and productivity capture really work? And how can finance leaders craft a narrative that connects AI to business outcomes, not just automation?

Why Finance Is the Perfect (But Cautious) Playground for AI

On first pass, it may sound odd: finance, of all functions, being the “early adopter” of AI. Yet it makes sense when you dig in. The finance function is data-rich, process-driven, rule-governed, and loaded with repetitive work. That sounds like a perfect match for AI’s capabilities.

• Finance teams handle huge volumes of transactions, reconciliations, forecasts, and many of these tasks are rule-based and repetitive.
  
• That means the potential gain from automation and intelligence is large. For example, a recent survey by KPMG International found that 71 % of organisations are using AI in their finance operations, and among the subset identified as ‘AI Leaders’, 57 % say ROI is exceeding expectations. (KPMG)‍
• At the same time, finance leaders aren’t exactly leaping blindly-they worry about accuracy, auditability, governance, data quality, and explainability. So there’s a natural caution built in.
  

But that tension, between high potential and high accountability, is what makes finance such an interesting testing ground for AI. The conversation isn’t just “Can we do AI?” but “How do we do AI well, so it’s measurable, auditable, and scalable?” 

Finance teams are evolving from reactive to predictive: moving from “we close the books to see what happened” to “we forecast and influence what happens next.” AI is the enabler of that shift, transforming finance from a reporting function into a strategic decision partner.

Defining ROI in AI-Powered Finance: It’s More Than Cost Savings 

When finance functions talk about the ROI of AI, the first image that comes to mind is often “saved hours” or “fewer manual tasks.” Those matters, certainly, but they’re just the tip of a much deeper value iceberg. In the world of finance, an ROI framework for AI must reflect decision quality, speed, transparency, and strategic forward momentum, not just lower costs.

Here are the key dimensions:

• Cost/efficiency savings: AI meaningfully reduces time and manual effort in high-volume finance workflows like reconciliations, invoice processing, payouts, and forecasting cycles. Instead of treating these as isolated use cases, think of them as measurable ROI drivers. Recent surveys back this: Emburse reports that 74% of finance teams using AI say the return is as expected or better, largely because automation frees hours otherwise spent on repetitive, rules-based tasks. Gartner also notes a 21-point year-over-year jump in AI adoption in finance, driven by clear efficiency gains rather than experimentation. (Emburse)
  
• Improved forecasting & planning accuracy: The function is shifting from reacting to what happened to anticipating what will happen. Accurate forecasts using AI’s predictive modelling reduce variance, free up capital, and improve resource allocation.
  
• Faster decision-making & velocity of insight: AI can accelerate cycle times (e.g., month-end close, planning iterations) so finance teams spend more time on strategy rather than data collection. A recent study found that 58% of finance functions are now using AI in 2024, up from 37% in 2023. Gartner
  
• Transparency, auditability, and governance: AI tools give easier trails to audit and govern systems. Part of the reason they’re perfect for finance is that they can easily answer questions about small complexities in large financial systems and data. As the Organisation for Economic Co‑operation and Development (OECD) notes, AI in finance isn’t just about capabilities; it must also factor in model explainability, data quality, and risk management. OECD
  
• Strategic value creation: The ultimate ROI is enabling finance to move from “number-keepers” to “narrative-builders”, providing insight, advising business units, and influencing outcomes.
  

Therefore, when measuring ROI in an AI-finance programme, it’s wise to ask:

What was our “before” state? What is our “after” state?
How quickly did the change take effect?
Are the gains incremental or compounding?

By reframing ROI this way, finance leaders move beyond championing automation and position AI as a business-enabling engine.

The 3 Dimensions of Measuring AI ROI in Finance

Baseline ROI: Quantify Your “Before State” 

Before any meaningful change can be measured, the pre-AI environment must be understood. Baseline metrics serve as the yardstick against which future gains are measured.

• Identify the existing process: For instance, how many hours does the team spend closing the books each month? What is the variance in forecast vs actual? How many manual reconciliations occur? Ask the teams for these metrics themselves. They know it best.
  
• Set quantifiable benchmarks: A study notes that without a baseline, an AI deployment is often classified simply as “innovation”, not an investment.
  
• Document costs and risks: Include not only labour hours but also error rates, time spent on risk mitigation, audit adjustments, and governance overhead.
  
• Ensure alignment with business goals: If the finance team’s mandate is to reduce close cycle by 20% or reduce forecasting variance by 15%, then baseline numbers must reflect those targets.

Tip: Create a simple dashboard or “before AI” snapshot. e.g., Close cycle = 9 days, Forecast variance = ±12%, Manual approvals = 1,200 per month. This becomes the reference point for tracking improvements.

Time-to-Value: When Does AI Start Paying Off? 

An AI in finance initiative rarely delivers its full effect overnight. There is a time-to-value curve: implementation, learning, adoption, optimization, scaling. Understanding this timeline is critical to ROI.

• Early wins often come from automation of rule-based workflows (e.g., invoice matching, expense auditing). These wins may surface within weeks.
  
• Strategic wins, improved forecasting, scenario modelling, and decision support may take months of live data, adoption, refinement, and trust-building. According to BCG’s 2025 study of finance functions, the median ROI from AI/GenAI initiatives is just 10%, underscoring that without rigorous value-tracking, deployment across multiple use-cases, and change management, strategic wins, such as enhanced forecasting, scenario modelling, and decision-support, typically require months of live data, adoption cycles, and trust-building. Boston Consulting Group
  
• A practical approach: Define milestones such as “pilot complete”, “first month of live results”, “first full financial period impacted”.
  
• Monitor adoption metrics: How many users regularly use the AI tool? What proportion of decisions reference AI-insights? Adoption leads to value.
  
  

Tip: Create a “Time to Value plan” alongside your baseline. Set expectations: e.g., Month 1–3: automate manual workflows → Month 4–6: roll out forecasting copilot → Month 7+: integrate into business partner workflows.

Productivity Capture: Turning Efficiency into Business Value 

After establishing a baseline and mapping a realistic time-to-value, the next essential step is capturing productivity gains in a way finance teams can translate into tangible business impact. Efficiency isn’t simply about “doing tasks faster”; it’s about freeing human capacity for strategic work and making better, sooner decisions.

Key productivity capture levers include:

• Cycle time reduction: Streamlining month-end closes, audit adjustments, or manual reconciliations. For instance, when 71% of organisations report using AI in finance operations, the top leaders among them (57%) say ROI is exceeding expectations, suggesting these tasks are already being affected. (KPMG)
  
• Variance improvement and forecasting agility: With better forecasting models, companies reduce “surprise costs” and make proactive decisions. According to research by the Boston Consulting Group (BCG), only 45% of finance leaders could properly quantify ROI, and many reported returns under 5% unless frameworks were applied. Boston Consulting Group
  
• Decision-support uplift: The shift from “reporting what happened” to “advising on what to do next” is critical. The ROI benefit comes not just from fewer human hours but from better human-machine collaboration.
  
• Scalable automation + skill re-deployment: When automation handles routine work, finance teams can focus on value-add tasks, scenario modelling, strategic business partnering, and insights generation. This multiplier effect is often more valuable than the raw efficiency metric.
  
  

Case Example:
In a recent global survey by KPMG of 2,900 organisations across 23 countries, the most advanced AI users (classified as “leaders”) had developed, on average, 6 AI use cases, almost double the number among less-mature peers. Moreover, 57 % of these leaders said their AI-in-finance initiatives were exceeding ROI expectations. KPMG: This suggests that productivity gains accrue fastest when multiple processes are automated and insights are embedded, rather than just in a single isolated workflow.‍

Tip for finance teams:
Build a simple “productivity capture ledger” that tracks not only hours saved, but also hours re-allocated to strategic tasks, variance improvement achieved, and decision turnaround time. These metrics are the bridge from “we saved time” to “we influenced business outcomes”.

Here’s a quick framework to visualise how finance teams can measure ROI across three dimensions:

Balancing Automation with Human Judgment

As financial organisations accelerate AI adoption, one recurring theme emerges: automation isn’t enough without human judgment. Especially in a domain as high-stakes and regulated as finance, human + machine collaboration is the optimal model.

Why this balance matters:

• Explainability & trust: The Organisation for Economic Co‑operation and Development (OECD) emphasises that AI in finance must incorporate governance, transparency, and model-explainability, not just raw performance.
  
• Context-rich interpretation: For example, an AI model might flag a cost increase, but human insight may know it’s tied to a one-time investment in a go-to-market launch, not an operational inefficiency.
  
• Ethics & risk management: In credit decisions, fraud detection, treasury risk, blind automation can lead to regulatory issues or brand risk. This is why productivity capture and ROI tracking must include governance metrics.
  
• Skill evolution: Finance staff aren’t replaced; they evolve. CFOs become “narrative builders” rather than just number-keepers. AI provides the data and models; humans provide the story and the strategic judgement.
  

Practical approach:

• Deploy AI for rule-based, high-volume tasks first (expense processing, invoice matching).
  
• Simultaneously invest in governance frameworks and skills training.
  
• Start tracking automation adoption and enhancements to human decision-making (e.g., the number of decisions aided by AI, the variance in decision outcomes).
  
• Reinforce the narrative: AI doesn’t replace judgment, it scales it.

Before measuring ROI, it helps to understand which kinds of AI are actually shaping finance today, and how they’re redefining value creation and capture.

What Kind of AI Is Used in Finance, And How It Shapes ROI 

The phrase “AI in finance” once referred mostly to automated scripts or robotic process automation (RPA) bots. Today, it represents a broad ecosystem of intelligent systems that can learn, predict, communicate, and even reason. Understanding the types of AI driving this shift is essential to interpreting ROI.

1. Machine Learning & Predictive Analytics
Studies (for example, McKinsey’s work on AI-driven forecasting) show that AI/ML can reduce forecasting errors by 20–50 % in data-intensive functions such as operations, although the finance-function specific figure varies.”

2. Natural Language Processing (NLP)
NLP powers finance copilots that understand plain-English queries (“Why did our Q3 forecast drop?”). It turns data analysis into conversation, improving accessibility and adoption across non-technical finance users.

3. Generative AI and Agentic AI Systems
Generative models summarise reports, draft variance explanations, and create scenario narratives; agentic systems go further by executing multi-step workflows across ERP, CRM, and commission systems. 

4. Computer Vision & Document Automation
Receipt scanning, invoice recognition, and audit trail digitisation, all powered by visual-AI. These applications deliver some of the fastest time-to-value wins in ROI measurement.

Together, these layers explain why ROI has evolved. Finance isn’t just about automating; it’s about augmenting human intelligence. The more integrated and contextualised the AI stack, the greater the measurable return.

Explore how Visdum’s AI copilots simplify payout analytics.

What Can Benefit Most from AI in Finance: The High-Impact Areas

Not every corner of finance gains equal benefit from AI. High-impact areas share three traits: volume, repeatability, and business influence.

1. Forecasting & Planning
AI-driven FP&A tools like Pigment and Cube improve forecast accuracy, allowing real-time re-forecasting as CRM or ERP data shifts. According to BCG (2025), companies embedding predictive models can achieve forecast accuracy improvements of 20–40%.

2. Expense & Audit Automation

Expense & audit automation platforms detect duplicates and reconcile entries, enabling rapid ROI for many teams within months.

3. Risk & Fraud Detection
Major banks like JPMorgan Chase and HSBC use AI-driven transaction-monitoring systems that can scan high-volume flows and spot anomalies faster than traditional systems. (IBM Think 2024). Reducing fraud loss directly translates into ROI. 

4. Commission & Payout Analysis
In revenue operations, AI copilots explain payout anomalies in plain language, saving hours of analysis and improving trust between finance and sales teams.

5. Working-Capital & Treasury Optimisation
AI agents model cash-flow scenarios, anticipate liquidity gaps, and recommend portfolio actions. That means finance leaders move from reactive reporting to predictive decisioning.

These areas share measurable ROI markers, time saved, variance reduced, errors prevented, and confidence gained. By tracking those metrics, finance teams can show how AI translates efficiency into enterprise value.

The Future of ROI: From Reporting to Reasoning

Finance is on the cusp of its next transformation: autonomous and reasoning-based finance. The frontier isn’t just AI doing tasks faster; it’s AI thinking with context.

• Continuous Forecasting: Static budgets will give way to rolling forecasts updated instantly as CRM or ERP data changes.
  
• Agentic AI Networks: Multi-agent systems that not only analyse financial data but also take autonomous actions, from updating forecasts to triggering workflow steps, turning insights into execution.

• Narrative-Driven Finance: CFOs become storytellers of the business. AI turns data into clarity, but human judgment translates it into strategy.
  
• ROI as a Living Metric: Instead of a post-implementation report, ROI will be a continuous dashboard that tracks accuracy uplift, decision speed, and strategic impact in real time.
  

Key Takeaways: Measuring ROI of AI in Finance

AI’s impact in finance isn’t defined by automation alone; it’s measured by clarity, confidence, and the quality of decisions that follow.

Here’s what finance leaders should remember as they track ROI across their AI initiatives:

• Start with the baseline. Quantify your “before AI” state, from close-cycle times to forecasting variance, so improvements are real, not anecdotal.
  
• Map your time-to-value. ROI compounds over time. Early automation wins come fast; strategic forecasting and decision-support gains take longer but drive deeper impact.
  
• Capture productivity where it matters. Don’t just count hours saved, measure how human capacity is redeployed toward analysis, foresight, and strategic partnering.
  
• Balance automation with judgment. AI scales insight, not intuition. The best outcomes come from pairing machine intelligence with human context.
  
• Think of ROI as a living metric. It’s not a one-time report; it’s an evolving measure of how well finance teams turn intelligence into influence.
  

AI doesn’t just optimize processes; it elevates the finance function itself, from reporting what happened to shaping what happens next.

Wrapping Up: ROI Isn’t a Report. It’s a Reflection. 

AI has moved finance beyond automation into augmentation. The real ROI is not merely time saved or cost cut, it’s the new capacity to think ahead, to act faster, and to see around corners. Finance leaders who measure ROI through that lens move from fire-fighting to foresight. They build teams that use AI not to replace judgment, but to amplify it. And they start treating ROI as a living, evolving metric, a story of continuous improvement.

Curious how leading finance teams turn AI insights into real impact?

Explore more stories, frameworks, and ideas that decode what “ROI from AI” really means for modern finance.

→ Read more insights on AI and the future of finance at Visdum.

FAQ 

1. How is AI used in finance?

AI is used to automate reconciliations, accelerate month-end close, enhance forecasting, detect anomalies, streamline audits, and generate real-time insights. It reduces manual work and helps finance teams move from reporting to proactive decision-making.

2. What are the benefits of AI in finance?

AI improves accuracy, reduces cycle times, cuts manual effort, enhances forecasting, and increases visibility across financial data. It enables faster decisions, stronger governance, and more strategic analysis.

3. What is the ROI of AI in finance?

ROI comes from three areas: efficiency gains (hours saved), accuracy improvements (lower variance), and strategic impact (better decisions, faster insights). Teams see measurable value once automation and predictive models are adopted across workflows.

4. How does AI improve financial forecasting and planning?

AI analyzes historical and real-time data, reducing forecast variance and enabling continuous, scenario-based planning. It makes forecasting more accurate, responsive, and aligned with shifting business inputs.

5. What are the risks of using AI in finance?

Key risks include data quality issues, model explainability, governance gaps, and regulatory compliance. Finance teams must pair AI with strong controls, auditability, and human oversight.

6. What types of AI are used in finance?

Finance teams use machine learning for prediction, NLP for explanations and querying, generative and agentic AI for analysis and workflows, and computer vision for document automation.

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