This article is part of VentureBeat’s special issue, “The Real Cost of AI: Performance, Efficiency, and ROI at Scale.” Read more from this special issue.
As we step into 2025, **CFOs are shifting from eager experimentation with AI** to demanding impactful results. Recent **surveys and discussions at VentureBeat** reveal a growing urgency for **quantifiable returns** on AI investments, marking the end of the honeymoon phase.
Heightened Expectations for AI ROI
<pAccording to a KPMG survey, 90% of financial executives now consider demonstrating ROI on generative AI critical, up from 68% a year prior. This escalating demand signifies a palpable shift in focus—CFOs are no longer merely intrigued by AI demos; they are increasingly beholden to stakeholders who expect tangible metrics to justify these investments.
From a **Bain Capital Ventures survey** of 50 CFOs, **79% plan to boost AI budgets**, with **94%** recognizing generative AI’s potential to enhance finance functions. The trend points to an evolution in measuring AI’s value; companies leveraging generative AI are primarily experiencing efficiency gains, a hint of what’s to come.
“We automated vendor identification, slashing month-end close time from 20 hours to just 2 hours,” remarked Andrea Ellis, CFO of Fanatics Betting and Gaming.
Similarly, Jason Whiting, CFO at Mercury Financial, reinforced this sentiment, stating, “The primary perk has been the **speed of analysis**.” However, the landscape is now shifting as CFOs seek to move beyond rudimentary time savings to **strategic implementations**.
The First Wave of AI Value: Efficiency Metrics
The initial wave of AI implementation has prioritized **efficiency metrics**. CFOs are focused on the **time and cost savings** that promise immediate returns—the **low-hanging fruit** of AI adoption. Take for example Drip Capital, which has achieved a staggering **30-fold increase in capacity** by automating trade finance operations. **Karl Boog**, the company’s chief business officer, noted, “We’ve seen a **70% productivity boost** while keeping human oversight where it counts.”
KPMG’s research echoes this sentiment: CFOs across sectors consistently seek tangible productivity improvements that translate directly into financial gains. Yet, as they measure these efficiency gains, many recognize that this is just the **beginning of AI’s potential value**.
Developing Sophisticated Efficiency Metrics
As CFOs gain confidence in their ROI calculations, they are **evolving their efficiency metrics** to encapsulate more than just time and cost savings. Advanced metrics include:
- Time-to-completion ratios: Comparing project durations before and after AI implementation.
- Cost-per-transaction analyses: Tracking the resources used for each transaction.
- Labor reallocation metrics: Measuring how team roles transition from manual tasks to analytical responsibilities.
These metrics form the foundation upon which more comprehensive evaluations can be built, as top CFOs understand the need for robust frameworks that capture AI’s full strategic impact.
Beyond Efficiency: New Financial Metrics
As they move past initial efficiency gains, CFOs are refining financial metrics to ensure a comprehensive understanding of AI’s business impact. A notable **shift in priorities** sees productivity metrics eclipsing pure profit measures as the focal point for AI projects in 2025. This change reflects a maturing perspective, emphasizing AI’s role in amplifying human capabilities rather than merely driving costs down.
**Time to Value (TTV)** is emerging as a key metric in investment decisions; approximately **one-third of AI leaders** believe they can evaluate ROI within six months. Quick-win projects that deliver measurable returns are becoming increasingly attractive to CFOs, building confidence for larger AI initiatives.
Data Quality as a Metric
Additionally, **data quality metrics** are receiving more acknowledgment. Roughly **64% of leaders** see data quality as their **most significant AI challenge**, driving a need for thorough assessments of data readiness. Without high-quality data inputs, even the most innovative AI applications can falter.
Reassessing Amortization Timelines
A pivotal area of recalibration for CFOs is how they approach the **amortization of AI investments**. Unlike traditional IT systems, AI technologies often yield evolving returns that can significantly increase over time as they learn. This necessitates a critical evaluation of investment outlooks, pivoting away from simply asking “**How much will this save?**” to “**How will this transform our market position?**”
KPMG’s findings reveal that **61% of AI leaders** report returns exceeding expectations, urging CFOs to adopt advanced amortization models that anticipate returns as these technologies mature. Some leading finance teams are implementing **pilot-to-scale methodologies** to confirm ROI before fully launching AI systems.
Linking AI to Shareholder Value
CFOs are transforming how they integrate AI investments into broader frameworks aimed at creating **shareholder value**. This shift highlights AI not just as a cost-reduction tool but as a **strategic asset** driving enterprise growth. Metrics now assess AI’s impact across three critical dimensions:
- Revenue Acceleration: Enhancing customer acquisition, optimizing prices, and expanding market reach.
- Risk Reduction: Bolstering forecasting accuracy and optimizing capital allocation.
- Strategic Optionality: Opening doors to new business ventures and markets previously inaccessible.
By developing comprehensive reporting mechanisms, CFOs can clearly articulate both immediate returns and long-term strategic advantages to stakeholders. **Integrated executive dashboards** are becoming commonplace, aligning AI-related metrics with traditional financial KPIs to create a clearer picture of value.
Ultimately, **CFOs who master these evaluation frameworks** will catalyze the next wave of successful AI adoption, moving away from speculative measures to disciplined investments that deliver sustainable competitive advantages. As the landscape of AI continues to evolve, these financial frameworks will be vital for making informed decisions that support organizational success.
