Beyond the Pilot: A CFO's Guide to Measuring Enterprise AI ROI

Launching an AI pilot is easy. But is the boardroom convinced of how worthy the pilot is? 

That’s a big question lingering in every CFO’s mind. In other words, the AI ROI of ambitious artificial intelligence projects. What was once considered a futuristic vision is now a present-day technological phenomenon we see today in almost every industry. And beyond experimental pilot projects, innovation, and promising business transformation lies the critical role of CFOs in guiding this shift.

In the tech space, a CFO guides investments, ensures financial discipline, and translates technical innovation into measurable value on the bottom line. When a pilot project is introduced, its role is to justify investments in the project and determine the value it can bring to the organization. And when it comes to enterprise AI initiatives, AI ROI is a key parameter that can’t be ignored. This blog will give you the insights you need to know on measuring enterprise-wide AI value realization as a CFO.

What is AI ROI? Defining Return on Investment in Artificial Intelligence

In traditional finance, Return on Investment (ROI) is typically defined as the ratio of net profit to the cost of investment, expressed as a percentage. Add AI to the mix, and there’s a slight difference in definition. ROI in this measure the financial and operational benefits from investing in artificial intelligence solutions compared to the costs incurred. 

But what makes AI ROI unique compared to traditional ROI? 

• For starters, when traditional ROI primarily focuses on direct revenue generation, the other focuses on real value. By this, we’re not just talking about revenue growth, but also cost savings, increased efficiency, operational improvements, and better customer experiences. 
• It also emphasizes mitigation of AI adoption risks such as skill shortages, fragmented data, internal resistance, and transparency. 

Here’s another question: Why do AI projects fail? 

It’s not always on the CFO. According to estimates published by Rand, more than 80% of AI projects fail, which is twice the rate for IT projects that don’t involve AI. (Source) The root causes have been linked to a lack of concrete data to train AI models, misalignment on problems to solve with AI, and a lack of infrastructure, disrupting the potential for scalability. 

Therefore, with massive upfront investments and considerations for AI projects, a CFO establishes the framework where AI ROI measures the hard and soft benefits, balanced with a rigorous account for costs.

Step-by-Step Guide to Calculating ROI: Formulas, Tools, and Examples

Calculating the ROI for enterprise AI projects starts at the core - defining project objectives and KPIs. Labor cost reduction, stakeholder satisfaction, and processing times are commonly tracked KPIs. For every CFO, here’s the classic ROI formula with an AI tweak:

Now, let’s look at the step-by-step approach to AI ROI calculation:

Step 1 - Identifying AI investment costs

Before the commencement and deployment of an AI project, there are four costs CFOs must identify:

• Development (Data acquisition, software, & tools)
• Personnel (Vendors & data scientists)
• Infrastructure (Cloud computing & storage)
• Maintenance and retraining

Step 2 - Project and calculate net benefits

Estimate tangible financial gains expected from AI and use the formula: 

Step 3 - Compute ROI and Payback Period

Apply the ROI formula and calculate how long it will take to recover the investment. You can either use spreadsheets to set up basic ROI calculations with error-free formulas. Microsoft Excel and Google Sheets can help you track multiple investments and analyze ROI with conditional formatting. 

Not familiar or well-versed with Excel? Specialized ROI calculators come with advanced features like automated forecasting, risk adjustments, visual reports, and payback time calculations that evaluate the effectiveness of AI investments. 

Step 4 - Monitor and Reassess

Measuring AI ROI is not a one-time process. You consistently refine it with the times. Keep tracking results over time and update ROI calculations for better accuracy. 

Key Metrics That Drive AI ROI: Cost savings, Revenue uplift, and productivity gains

Three key metrics drive ROI calculation for AI projects. Here’s how you baseline and track them:

Why AI ROI Differs from Traditional ROI: Soft vs Hard Benefits Explained

Think about this. When you evaluate a new investment, are you just looking at hard numbers? Maybe you do that with traditional ROI, as it focuses mostly on the numbers. However, it’s not the same with AI ROI analysis as you need to consider long-term value and strategic positioning too. That’s where the distinction lies between the hard and soft benefits of calculating ROI in AI projects. Let’s dig deeper:

 The end goal of a CFO is not just realizing either of the benefits. It’s blending immediate hard returns with ongoing soft returns that compound over time. In short, it’s to achieve Real AI ROI. For instance, an AI project may deliver significant cost savings within 12 months. But it should also drive customer/employee satisfaction along with the company’s long-term growth. 

Comparing Generative AI ROI and Conversational AI ROI: Use Case Benchmarks

Here’s a detailed comparison between Generative AI and Conversational AI ROIs through use case benchmarks:

Generative AI ROI

• Enterprises investing in generative AI have seen strong returns on investment, with an average of 3.7x return for every $1 spent. (Source).
• Given that GenAI is widely used in content creation, financial services, and media, these sectors have been able to achieve significant ROI and operational efficiency.
• GenAI’s value also spans other segments like customer support, marketing, and R&D, with the potential to add billions to the global economy. 

Conversational AI ROI

• Conversational AI doesn’t particularly emphasize numbers. What matters is customer satisfaction and brand loyalty. 
• This technology’s built to make conversations with chatbots and virtual assistants feel natural—like talking to a friend over coffee.
• ROI benchmarks for this typically include improvements in customer engagement, reduced wait times, and higher customer satisfaction. 

Industry-specific AI ROI Use Cases: Retail, Healthcare, Finance, and Beyond

While the method of calculating ROI in enterprise AI is generally consistent across most industries, valuations are tailored to sector-specific metrics. Let’s see how it applies to the following:

Retail

AI has some great applications in the retail segment, from inventory optimization to offering tailored recommendations for customers. Machine learning algorithms also adjust prices in real-time based on demand, maximizing the potential for profits.  

• Sample metrics - Average Order Value (AOV), inventory turnover, conversion rates
• Calculation - Sales, costs, and stock metrics are calculated before and after AI implementation.

Finance

In a cut-throat industry like finance, AI is the staple technology that facilitates fraud detection, reduction of manual workloads, algorithmic trading, credit risk assessment, and more. It’s also the key to faster transaction processing, loans, and insurance claims.

• Sample metrics - Process automation rate, cost per transaction, fraud detection accuracy, Customer Lifetime Value (CLV), loan approval rate
• Calculation - The formula applied will be:

Healthcare

In healthcare, the functions of human doctors and AI go hand-in-hand. With AI-powered diagnostics, doctors can recommend the right treatment plans. This technology also automates analysis of MRIs, CT scans, and X-rays, enabling higher throughput and more accurate results. 

• Sample metrics - Readmission rates, patient satisfaction scores, diagnostic accuracy, cost per patient encounter, claim denial reduction, wait time reduction
• Calculation - The formula applied will be:

Note: Variable ROI depending on hospital setting and workflow integrations is also accounted for. And non-financial metrics like higher patient satisfaction and reduced readmissions are also factored in. 

Thus, AI ROI represents a significant blend of quantifiable improvements with strategic, sometimes intangible benefits. Sometimes, organizations, even beyond healthcare, finance, and retail, tailor their calculations to their operational context and strategic goals. 

Top Benefits of Measuring AI ROI: Data-Driven Decisions & Stakeholder Buy-In

Even with typical ROI calculations, organizations aim to maximize value from investments made. The same applies to ROI for AI initiatives as well, the focus being on data-driven decision-making and long-term stakeholder engagement. The key benefits of measuring AI ROI in this case include:

Supports scalable investment

As a CFO, you may ask yourself: Which AI projects will deliver 25% more revenue next year, and how do I allocate funds and resources accordingly?

But with AI ROI, you’re no longer guessing. Instead, you’re scaling successful pilots and reallocating AI spend from lagging projects. And by doing so, your operations can grow without a commensurate rise in costs, while you keep your CEO and stakeholders happy. By quantifying both the financial and operational benefits of ROI measurement, you can make informed decisions about scaling AI across business lines. 

Builds executive and boardroom consensus 

Let’s be real, not every executive or boardroom member might welcome AI initiatives with open arms. This is where you come in as the CFO to demonstrate successful AI ROI with real-world use cases and evidence. You not only help align leadership around AI investments, but also win over reluctant stakeholders who may not realize the value this technology can bring to an organization. 

In short, you don’t just receive ROI insights for planning. With rich, data-driven storytelling, you offer clear, quantifiable business cases that bring executives and board members on the same page.

Enhances transparency across business-IT teams

Wouldn’t it be easier if both your finance and tech teams spoke the same language? With shared ROI KPIs, every member of your finance and IT teams can seamlessly collaborate, seeing the direct link between AI algorithms and their impact on revenue and compliance. This is where AI ROI enhances transparency, ensuring every project is aligned, measurable, and bridges the knowledge gap between cross-functional teams. 

How to Integrate AI ROI Tracking Into Your Tech Stack

Integrating ROI tracking for enterprise AI initiatives goes beyond simple calculations and KPIs. It requires a technical and strategic mindset to understand the value driven by such projects and how AI investments align with expected business outcomes. And as a CFO, your list of priorities for integrating AI ROI tracking includes:

Step 1 - Establish an integrated and transparent tech stack

Set up a robust finance technology stack that can seamlessly integrate with AI tools without disrupting core financial systems. Emphasize scalability, interoperability, and audit readiness so AI outputs can be tracked, validated, and reconciled within existing financial reporting structures. 

Step 2 - Implement systematic ROI tracking and governance 

For systematic ROI tracking and governance, GenAI performance dashboards can do the trick as they monitor budget usage and adherence in real-time. Cross-functional AI committees that include the CIO and CTO should also be in the loop to review investments, assess risks, and prevent non-compliance breaches. 

Step 3 - Use AI-driven financial analytics

This step is for you to monitor and refine critical forecasting and risk management processes. One of the best ways to go about this is to integrate Agentic AI to fast-track finance tasks and elevate your role to a strategic business leader. Aside from a simple AI ROI calculator, the following ROI tracking tools can help you get there:

Overcoming Challenges in AI ROI Measurement

Running a race to embrace artificial intelligence, organizations often face a big challenge despite the hard and soft benefits - measuring the actual ROI. For instance, according to a study from Dataiku, 65% of organizations globally report positive returns from generative AI investments. (Source) However, the overall ROI from data, analytics, and AI initiatives remain static. Now, why is that the case? Here are some common causes of that:

Dealing with certain or lagging outcomes

Measuring AI ROI can sometimes be a slippery slope due to initial lags or uncertainties derived from AI benefits. For example, insights from financial forecasting may take several months to manifest real, tangible value. 

Siloed reporting and poor data quality

Poor data quality and fragmented data are additional problems CFOs face that can impair the measurement of ROI. Working with legacy systems aggravates the problem further, leading to misconstrued outputs and inconsistent data standards. 

Change management and training for finance teams

When it comes to AI adoption, CFOs must foster a cultural shift that focuses on upskilling, redefining roles, and embracing change. It all starts from the leadership phase, where executives and boardroom members adapt and accept AI tools, facilitating smooth AI ROI evaluations for real value. 

In short, here’s a summarized ROI checklist for CFOs to help overcome the challenges in measuring AI returns:

• Set realistic expectations by distinguishing short-term gains from long-term benefits 
• Avoid isolated pilots and integrate AI into broader finance transformation
• Invest in change management and training
• Look for hidden AI value from new strategic insights to decision-making agility. 
• Continuously audit AI value delivery 

Why Choose Tredence to Maximize Your AI ROI

At Tredence, we don’t just implement AI; we ensure it directly funnels measurable and favorable outcomes to your business. With a proven track record of translating AI innovation into tangible ROI, we bridge the gap between data strategy and real-world impact, enabling you to realize the full potential of your AI initiatives. Whether it's process maps, design workshops, or defining goals and KPIs, we assist you with all. 

And if you are a CFO looking for value realization and effective AI ROI calculations of your company’s AI investments, contact us today! We will help you turn your vision into action and insights for long-term competitive advantage.

FAQs

1] What are the most reliable industry benchmarks for measuring generative AI ROI?

Quality of AI outputs, impact of innovation, revenue growth, and customer engagement are considered the most reliable industry benchmarks for measuring generative AI ROI.

2] How does data quality and availability impact the accuracy of AI ROI measurements?

It’s quite simple. High-quality and availability of data offer valuable insights for better AI-based decisions. In contrast, poor or inaccessible data skews results, undermining ROI measurement. 

3] Can agentic AI deliver faster ROI than traditional AI models?

Thanks to its autonomous decision-making capability, Agentic AI can easily deliver faster ROI than traditional ROI models, reducing the need for or time for human intervention.

AUTHOR - FOLLOW
Editorial Team
Tredence