Measuring AI ROI is one of the biggest challenges faced by today’s finance leaders.
Financial planning and analysis (FP&A) teams are pouring time and resources into AI, yet fewer than half can put a concrete number on what they’re getting back. The median ROI today hovers around 10% – far below most organizations’ targets – while only the best-performing quintile are seeing returns north of 20%.
Research shows just how wide that gap can be. According to Forrester, some finance teams are delivering ROI above 300%, with AI investments paying for themselves in under six months.
The difference comes down to how ROI is measured and managed. High-performing FP&A teams establish baselines early, choose use cases with a direct line to impact, and apply proven implementation tactics. They measure not only cost savings, but also gains in strategic decision-making, agility, and business alignment.
This article will walk you through how to build that discipline from day one. We'll back up those insights with a measurement scenario based on high-impact use cases, plus real-world ROI benchmarks.
Determine your baseline before beginning
The most expensive mistake in AI measurement happens before implementation even begins: failing to establish your baseline. Without clear before-state metrics, you can't prove impact, justify continued investment, or learn from any failures.
A practical way to set your foundation is with a four-pillar ROI framework that captures efficiency, savings, revenue, and strategy:
- Efficiency & productivity – like time saved per process, throughput, and automation rates
- Cost savings – like labor reduction, external spend cuts, and error minimization
- Revenue impact – like forecasting accuracy, client engagement, and top-line acceleration
- Strategic decisioning enhancement – like decision velocity, risk reduction, and agility
Record baselines with timestamps, data sources, and calculation methods. Even six months later, these details become invaluable when demonstrating impact.
Account for data and infrastructure readiness
Your AI ROI depends in part on the strength of your data foundations. Clean, connected data and stable infrastructure are what enable AI to deliver accurate forecasts, fast scenario runs, and reliable insights.
Still, readiness doesn’t mean perfection. High-performing FP&A teams build structures to improve data quality over time – auditing where data lives, how it flows between systems, and whether the architecture can handle real-time updates. That way, AI can add value quickly while foundations continue to strengthen.
Choose high-impact use cases
FP&A leaders who see above-average ROI focus on use cases with a direct line to measurable value. Consider starting with one of the following high-impact use cases:
To show how these metrics translate to actual ROI, we’ll use an example scenario:
An FP&A team of 10 currently spends significant time on manual data consolidation, forecasting, and scenario planning. Here’s how this team can determine the ROI of an AI initiative designed to boost productivity:
Step 1: Calculate the gain
Start with time saved on core processes. If each analyst saves 130 hours annually through automated data integration (a conservative estimate for eliminating manual consolidation), that's 1,300 hours total. At $87 per hour, you're looking at $113,100 in annual productivity gains from this single improvement.
Step 2: Add secondary benefits
Now, layer in additional use cases. Add 20 hours saved per analyst per forecast cycle (12 cycles annually = 240 hours), plus 12 hours saved per quarterly scenario planning session (48 hours annually). Factor in reduced errors and rework. Across your team, these efficiency gains compound to approximately $450,000 in first-year value.
Step 3: Calculate total investment
When calculating total investment, consider both direct and indirect costs, including:
- Platform licensing and user access fees
- Initial implementation and configuration costs
- Training and change management expenses
- Ongoing support and maintenance requirements
The specific investment will vary based on your organization's size, chosen capabilities, and implementation approach. Work with vendors to develop a detailed cost analysis aligned with your use cases and scale. Review industry case studies and ROI analyses to benchmark expected returns against typical investment levels for organizations similar to yours.
Step 4: Determine ROI
Calculate your first-year ROI using the standard formula: (Gains - Investment) / Investment. As teams become proficient and you expand to additional use cases like headcount planning and revenue forecasting, benefits typically compound while platform costs remain relatively stable. This multiplier effect across processes often leads to accelerating returns in subsequent years.
Key factors that influence ROI growth include:
- Increased user adoption and efficiency
- Expansion to additional use cases
- Process automation compounding effects
- Reduced manual effort across workflows
- Enhanced decision-making capabilities
Get detailed formulas and benchmarks across all 21 essential FP&A metrics
Measure early and track compounding impact
The biggest returns don’t appear overnight; they emerge as AI becomes embedded in daily workflows. Measuring early is essential to prove value and keep leadership engaged, but the largest gains usually appear later, as use cases become more ingrained and efficiencies begin to compound across processes. Treat ROI measurement as a rhythm that evolves with your AI adoption.
- At early milestones: Capture quick wins by checking results after your first forecast or planning cycle. The goal here isn’t perfection, but directional proof that the system is working and saving time.
- As adoption deepens: Once use cases expand beyond pilots, track broader KPIs like forecast accuracy or cycle times. This shows how AI is becoming part of the fabric of planning, with measurable improvements visible across teams.
- Over time: ROI strengthens as adoption matures. Automation frees up more hours, data flows improve, and benefits compound across multiple workflows. Embedding ROI reviews into quarterly or annual planning makes progress visible and keeps the value story fresh.
Not all returns will show up in hours saved or dollars earned. Qualitative feedback matters, too. For example, is AI improving how your team feels about their work? Are employees spending more time on analysis instead of manual prep? Does being seen as an AI-forward company make you a more attractive place to work? These cultural and talent impacts can be just as valuable as hard metrics – and, together, they provide the fullest picture of AI’s long-term return.
Apply proven implementation tactics
Even with the right baselines, clean data, and carefully chosen use cases, attempts to prove ROI can stall if execution falters. The best finance teams approach AI implementation and ROI measurement as disciplines that are sequenced, value-driven, and collaborative.
BCG identifies four strategies that separate high-performing finance teams from the rest:
- Focus relentlessly on value. Instead of experimenting endlessly, successful teams prioritize quick wins with clear ROI metrics. This includes establishing dedicated AI budgets with rigorous value demonstration requirements and systematic tracking using proxy metrics when direct measurement proves challenging. Teams following this principle achieve 6% higher success rates.
- Embed AI within broader finance transformation. High performers don’t treat AI as a standalone project. They connect related use cases to maximize infrastructure investments – a “string-of-pearls” approach that drives compounding benefits. Organizations taking this perspective see 7% higher success rates.
- Collaborate with IT and external partners. Finance teams that work closely with IT functions are 5% more successful. Leading organizations also lean on vendor expertise rather than building everything internally, accelerating implementation while reducing risk.
- Execute in targeted, scalable steps. Instead of launching across dozens of use cases, successful finance teams start small, prove value, and scale thoughtfully. This incremental approach delivers 6% higher success rates while ensuring that pilot projects grow into sustainable programs.
Underpinning all of this is process mapping. When you map end-to-end workflows, inefficiencies and bottlenecks come into focus. Documenting your workflows creates the baseline for measuring change and helps prioritize which processes are ripe for AI. That clarity makes it easier to prove early ROI, show compounding benefits over time, and sustain momentum as adoption expands. Plus, it ensures you’re proving value in ways leadership can trust.
A real-world ROI example: Pigment’s impact on FP&A
One way to ground ROI conversations in reality is to look at independent benchmarks. Forrester’s The Total Economic Impact of Pigment report examined the results four enterprises achieved after moving their FP&A and planning workflows into Pigment. The study created a composite customer – a global retailer with $1.3 billion in revenue and 7,000 employees – to help model impact.
Here’s what they found:
- Financial planning & analysis: 130 hours saved per analyst annually, plus 480 hours per payroll FTE and 84 hours per executive, producing a three-year present value of $1.83M in FP&A productivity gains
- Sales planning: 619 hours saved per analyst and 201 hours per sales planner, freeing up capacity for more strategic work
- Forecasting: 20 hours saved per analyst and 16 hours per executive per forecast cycle, worth $858K in three-year present value
- Scenario planning: 12 hours saved per analyst and 6 per executive at each quarterly review, adding $583K in value over three years
All told, the composite organization realized 306% ROI, a $6.13M net present value, and a payback period of under six months. For FP&A teams, this shows that AI investments deliver measurable, near-term impact when tied to core planning workflows – and that adopting agentic AI, which can adapt and act on data autonomously, is the most effective path to sustaining those gains.
Explore Forrester’s Total Economic Impact of Pigment report to learn more about these results.
Is your finance team ready for AI?
Maximizing ROI from AI starts with a clear picture of where you are today. Pigment’s AI Readiness Assessment benchmarks your team’s data quality, planning processes, and governance practices – then maps out the most direct route to measurable returns.
Start your free AI Readiness Assessment here!
See how Pigment can maximize your ROI
The fastest paybacks and highest returns come from AI that’s embedded in the right workflows. Pigment’s team can show you where the biggest gains lie for your FP&A function and how to capture them.
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