Enterprise AI
March 19, 2024

How to Calculate AI ROI for Your Business

Should you invest in AI? Will it pay off? Decide by calculating the ROI of AI for your business. Here’s a 5-step formula you can use.
How to Calculate AI ROI for Your Business

Artificial intelligence is rising and transforming industries by bringing unparalleled opportunities in various sectors. With the help of predictive analytics, natural language processing, and AI technologies, companies can revolutionize operations in industries such as healthcare, finance, and insurance.

Implementing AI technology correctly is an investment that can help save time, improve efficiency, and make more money. Calculating the AI ROI can get complicated but down below, we’ll use a simple formula that can give you an accurate estimate of the ROI you can expect after implementing AI, so let’s break it down!

Key Takeaways

  • Calculating AI ROI is challenging due to many variable factors, as well as data quality.
  • AI implementation can lead to huge cost savings and efficiency improvements.
  • AI also has the potential to drive revenue growth and increase customer satisfaction.
  • ROI of AI can be calculated using a simple formula, but getting the key metrics and data right is still a challenge.

How to Calculate AI ROI

Calculating AI ROI includes factoring in the cost of implementation and the benefits provided by the AI technology. While the following method is ideal for any type of AI ROI, it is very accurate in calculating the AI ROI of Multimodal’s AI SuperAutomation platform because of the transparent investment cost.

The simplified formula: ROI = Net gain from Investment/Investment Cost X 100

AI ROI calculation formula

Step 1: Identify Key Metrics

Key metrics include anything that the AI can help reduce, minimize, improve, or increase. The key metrics to identify include:

  • Cost savings
  • Efficiency gains
  • Revenue Increase
  • Error reduction

The best examples of cost-saving metrics include reduced labor hours, decreased error rates, and even operational efficiency gains.

When it comes down to efficiency gains, the best way to calculate this key metric is to measure the time you save in knowledge work processes due to AI automation.

Revenue increase is a straightforward metric, and if it qualifies, calculate the increase in revenue. Such an increase can come from a variety of factors, such as the ability to offer more services, enhancement of existing services, better decision-making processes, or even better customer satisfaction.

AI investments provide much better accuracy than humans in many operations. Calculating the error reduction is also important. Reducing errors in compliance reports can provide additional cost savings by avoidance of penalties, for example.

We highly recommend working closely with the CFO and relevant stakeholders to identify these key metrics for the company.

Step 2: Make Baseline Assessments

Making a baseline assessment is all about understanding your current state of operations without AI implementation. This can tell you a lot more about your workflow, but also help you gather information on the time you spend doing knowledge work processes, resources you use, and cost associated with your workflow.

This is important because you get to compare it with results after AI implementation to understand the improvement in the workflow and also calculate the ROI after the implementation costs.

Here’s our advice on how to go about it: 

1. Gather historical data. First, collect past data – for example, from the previous quarter or year – for each key metric. This data will act as your reference point. 

2. Document current processes. Document how your workflows and processes stand without AI. This can include noting down the steps involved and how much time they take, resources they require, and errors or issues your staff commonly encounters.

3. Measure current performance. Use your defined measurement methods to assess current performance for each metric. These methods can be as simple as tracking time, calculating current error rates, or measuring productivity by counting the number of units produced per hour.

4. Analyze resource usage. Determine current resource usage, such as labor hours and costs associated with executing tasks manually or with existing non-AI systems.

5. Consider qualitative factors. Finally, make sure not to overlook qualitative factors such as employee satisfaction or customer feedback. These are also great indicators of baseline performance levels.

Once you make this initial assessment, you can compare your current “numbers” against industry benchmarks. This will give you an idea of where your business stands relative to competitors and best practices.

Step 3: Research Implementation Costs

AI implementation costs include anything from licensing and training to necessary infrastructure changes.

The most common research implementation costs include:

  • Research and development
  • Data preparation
  • Training
  • Integration
  • Consultancy and professional service fees
  • Maintenance and upgrades
  • Ongoing improvement costs

You can even include downtime costs during the transition to the implemented AI model. You can include almost anything that will cost you before, during, and even after the implementation.

Depending on the size of the company and its complexity, implementation costs can range from thousands of thousands to millions of dollars.

Step 4: Gather Data

When you implement the AI into your workflow, collect data on the performance using key performance indicators, similar to the ones in step number one. This will help show the benefits of integrating AI, as well as providing data for comparison that can help calculate AI ROI.

Step 5: Calculate ROI

The formula: ROI = Net gain from Investment/Investment Cost X 100

Using the simplified formula, input the estimated net gain after the implementation of AI, divide it by the investment cost of implementing AI in your company, and multiply it by 100. Calculating ROI estimates will depend on the accuracy of your net gain estimates.

It’s important to keep in mind that ROI is bigger when you automate repetitive tasks. However, the biggest ROI is seen after the automation of multiple tasks and even better yet, multiple workflows. Automating multiple workflows compounds the gains. It also helps different AI agents exchange data, improve the decision-making process, provide better performance, and decrease manual labor or provide help to staff.

After you calculate ROI, continue monitoring the performance of the implemented AI. This will also help you notice if further adjustments are needed to improve the efficiency and overall benefits.

Need help calculating the potential ROI of AI for your business? We can help. Learn more about our AI Strategy Services.

Metrics That Matter the Most

Metrics for AI ROI calculation

The whole ROI of AI calculation starts with the identification of the key metrics. Even two similar companies can have very different key metrics that matter to them.

When considering investments in AI, estimated AI ROI can later help confirm the ROI and justify the investment cost afterward.

With every company having different goals, we highly recommend identifying key metrics after working closely with the CFO and stakeholders.

However, we’ve seen some metrics that matter the most and these include:

  • Operational efficiency
  • Cost savings
  • Revenue increase
  • Customer satisfaction

Operational Efficiency

In AI projects, operational efficiency is one of the key metrics that refers to the ability of the company to utilize resources efficiently to maximize input and minimize input.

AI helps improve operational efficiency by:

  • Streamlining processes
  • Optimizing resource utilization
  • Automating routine tasks
  • Enhancing decision-making processes
  • Continuously improving the workflow

AI implementation is all about leveraging technology to optimize processes, help employees, and improve customer satisfaction.

Cost Savings

Cost saving is a critical factor in the AI ROI calculation process because AI can help companies save money across business processes. The good news is that most businesses need less than a year to start decreasing their expenses.

  • Labor cost reduction
  • Efficiency improvements
  • Inventory management optimization
  • Energy efficiency
  • Fraud detection/prevention
  • Better customer service efficiency

Revenue Increase

The revenue increase is another crucial factor for measuring AI ROI. While cost savings are all about reducing expenses, revenue increases focus on generating additional income with the help of AI.

AI can help increase revenue by:

  • Personalizing marketing and sales
  • Dynamic pricing optimization
  • Identifying cross-selling and up-selling opportunities
  • Improving customer retention
  • Optimizing product development
  • Optimizing lead generation (and sales)

Even the expansion to a new market is a way to increase revenue and it’s something AI can help with by streamlining new and additional tasks and improving the company’s workflow.

Customer Satisfaction

AI can play a significant role in improving customer satisfaction through personalized experiences, streamlined interactions, and quicker resolution of queries.

Depending on how you communicate with your customers, AI can help and fit in many different ways, where some of the ways include:

  • Predictive customer service
  • Personalized recommendations
  • 24/7 support with AI agents
  • Emotion recognition (with the help of natural language processing)
  • Feedback analysis with generative AI

Challenges in Measuring AI ROI

Challenges in measuring AI ROI

Even though we have mentioned a very simple formula, identifying key metrics and making baseline assessments can be difficult for some companies that struggle with poor data quality, data inconsistency, and even disparate data sources.

With multiple factors influencing the performance of the AI, it can be tricky to determine the outcome of the specific contribution of the AI to a company. There’s also a need for longer timeframes to complete all 5 steps to accurately measure the AI ROI.

Understanding the implementation cost is also difficult at times, depending on the AI model you choose. However, with MultiModal’s AI model, this isn’t an issue and it’s the easiest factor to input in the AI ROI formula.

Lastly, risk and uncertainty of business value after implementation of AI is another challenge. Negative outcomes are possible if the AI isn’t implemented correctly, which can be a step back when calculating the ROI. But most importantly, overcoming any of these challenges is possible to maximize the value and efficiency of the AI investment.

Meet Your AI ROI

Measuring the return on investment can showcase both opportunities and challenges when trying to leverage AI technology. Artificial intelligence is already advanced enough to improve operational efficiency, reduce costs, increase revenue, enhance customer experience, and more.

Calculating the AI ROI requires careful consideration of several factors.

Even though the implementation and calculation process can be challenging, AI ROI is much more than just financial metrics. We like to think of it as an approach and impact of AI on business performance, organizational capabilities, and even customer experience.

This alone can tell you how investing in AI can drive tangible ROI, allow for a competitive advantage, and secure long-term success in the digital age.

Maximize Your AI ROI with Us

If you’re looking for a quality AI to implement – and a very positive ROI – please schedule a 30-minute call with our experts. We can discuss your needs and best use cases for your company, as well as demo how our AI Agents work live.


What is the average ROI for AI?

According to the IDC’s research, the average ROI for AI is $3.5 for every $1 invested.

How do you maximize AI benefits?

To maximize AI benefits, you should invest in infrastructure, align AI technology with business objectives, and continuously optimize AI models for optimal performance.

What is a good ROI?

A good ROI is about 50% on average, according to McKinsey.

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