Artificial Intelligence
How to Maximize Your AI ROI
Top-performing companies are moving beyond efficiency gains to maximize AI’s return on investment (ROI).
Sydney Scott
Editorial Strategist, AI
Workday
AI adoption is accelerating, but the real question is whether AI is creating measurable business impact.
Productivity gains are one place the gap becomes visible. According to Workday research, 85% of employees save between one and seven hours a week using AI, yet 37% of that time savings is offset by rework like correcting, rewriting, or clarifying AI-generated output.
For leaders, AI ROI needs to capture the broader business value AI systems create. The strongest measures connect AI use to outcomes the business can see, quantify, and use to guide future action.
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What Is AI ROI?
AI ROI measures the business value companies generate from AI investments compared to the cost of implementing and operating those systems.
Financial return is part of the equation, but AI solutions can also create value through greater productivity, stronger output quality, reduced risk, increased workforce capacity, and more effective decision-making.
Depending on the use case, organizations may measure AI ROI through outcomes such as:
Lower operating costs or reduced manual effort
Faster cycle times for high-volume processes
Increased revenue, conversion, or customer retention
Fewer errors, exceptions, or compliance issues
Higher employee satisfaction and productivity
Better forecasting, planning, or decision quality
Some businesses are far more effective at maximizing returns than others. PwC found that the top 20% of organizations capture 74% of AI-driven returns. What are those organizations doing differently?
Organizations capturing more value tend to be more deliberate about their AI strategy, including where AI is applied, how long-term success is defined, and how quickly they learn from what is and isn’t working. Those disciplines are what turn AI investment into sustained returns.
While 85% of employees save one to seven hours a week using AI, yet 37% of that time savings is offset by rework.
Best Practices to Maximize AI ROI
Maximizing AI ROI requires a clear connection between where AI is applied, how success is measured, and what happens once value starts to emerge. The best practices to maximize AI ROI include:
1. Start With the Business Outcome
Before deploying AI, define the business result the use case is meant to improve and establish a baseline for current performance. In finance, that might mean documenting close time, reconciliation effort, and exception volume. In recruiting, it could mean tracking screening time or time-to-fill.
Once the baseline is clear, set a target for meaningful improvement over a specific timeframe. If the close currently takes eight days, for instance, the team should agree on the reduction that would justify the investment and use that target to evaluate performance.
A clear target also creates the foundation for measuring ROI more accurately once AI is in use. Without the proper business objectives in place, gauging the success of an AI project is impossible.
2. Measure AI’s Net Value
ROI should reflect AI’s effect on the full workflow, including any additional review, correction, or oversight it creates.
Workday’s 2026 Beyond Productivity research found that employees spend the equivalent of roughly 1.5 weeks each year fixing AI-generated output. If a task drops from six hours to two but requires another hour of review, that additional effort belongs in the calculation.
The same principle applies to other outcomes. A faster financial close should be considered alongside accuracy and exception volume, while shorter customer service resolution times should be evaluated alongside escalation and rework.
Measuring those effects together shows how much value the use case is actually creating, the costs saved, and where further improvements may increase the return.
ROI should reflect AI’s effect on the full workflow, including additional review, correction, or oversight it creates.
3. Put AI-Created Capacity to Better Use
Once AI begins reducing workload, organizations can extend the return by deciding where that newly available capacity should go.
A finance team spending less time on reconciliation might devote more attention to forecasting or scenario analysis. Recruiters could redirect time saved on initial screening toward engaging qualified candidates. Building those expectations into the rollout makes it easier to see whether efficiency gains are contributing to broader performance.
Workday research on AI outperformers found that a small group of organizations generated 5x more value from investing in AI than the average company, with annual savings averaging $16.3 million. Their results were supported by organizational changes that helped translate AI-enabled efficiency into sustained value.
As those gains become repeatable, the focus can move from proving value in one AI use case to preserving it at scale.
4. Build the Foundation to Scale AI Value
Scaling a successful use case requires clarity around the conditions that made it work. Companies should document the data sources it relies on, ownership and access requirements, quality thresholds, and where human review remains necessary.
Integration is equally important. If employees still have to move information manually between systems or recreate work outside the AI-enabled process, some of the value gained in the original use case can disappear as adoption expands.
Regularly comparing results with the original business target then gives leaders a consistent way to evaluate performance as AI scales. That evidence can guide where to expand investment, where the workflow needs refinement, and where another use case may offer greater potential.
AI outperformers generate 5x more value from AI than the average company.
Turn AI Investments into Lasting Value
As we move into the next era of enterprise AI, adoption is becoming the operating standard. What will set organizations apart is how effectively they can turn that adoption into measurable business value. Wanting to broadly improve efficiency isn’t concrete enough.
That makes measuring AI ROI increasingly important as a management discipline. Leaders need to understand when AI initiatives are generating meaningful returns and use those insights to guide future investment.
The more clearly organizations can connect AI investment to business performance, the better positioned they will be to direct both AI and human capacity where it has the greatest impact.
More than 8 in 10 employees waste significant time coordinating teams and tools. Download this report and learn how to free them for higher‑value work with AI.
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A gap between effort and impact is the real ROI problem. Discover the solution.