How to Calculate AI ROI for Your Business

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 comparing the cost of implementation against the benefits provided by AI technology.

You can use the following simplified formula to calculate it:

ROI = Net gain from Investment/Investment Cost X 100

To calculate AI ROI is accurately as possible, you should consider several different costs and potential benefits. We'll discuss them in more depth below.

For now, let's see which exact steps you need to take to calculate the ROI of your AI initiatives.

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Step 1: Identify Key Metrics

The first step is to identify the key metrics you'll use to track the performance and business impact of your AI. Ideally, you would identify them long before you implement AI in your business.

Key metrics can pertain to anything that the AI can help reduce, minimize, improve, or increase.

For example, they can include:

• Cost savings - e.g., reduced labor hours, decreased error rates
• Efficiency gains - e.g., reduced time-to-decision
• Revenue increase - e.g., deriving from the ability to offer more services, enhance existing services, improve decision-making processes, or boost customer satisfaction
• Error reduction - e.g., reducing errors in compliance reports can provide additional cost savings through avoidance of penalties

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

Step 2: Make Baseline Assessments

Making a baseline assessment is all about understanding your current state of operations without AI implementation. This should help you understand:

• the time you spend doing knowledge work,
• the resources you use, and
• the cost associated with your currently manual processes.

This is crucial for comparing your results before and after AI implementation and calculating the ROI.

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 you've identified in the previous step. This data will act as your reference point. For example, a banking company could've previously needed several days to approve a single loan. This information will be vital.

2. Document current processes. 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 when performing the required tasks manually.

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 additionally compare your current “numbers” against industry benchmarks.

This will give you an idea of where your business stands relative to competitors and best practices. It will also help you better assess your performance post-AI implementation.

Step 3: Research Implementation Costs

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

Here are some you should likely consider:

• Research and development costs
• Data preparation costs - e.g., data cleaning, labeling, etc.
• Model training costs
• Integration costs
• Consultancy and professional service fees
• Maintenance and upgrade-related costs
• Ongoing improvement costs

You may also want to consider potential downtime costs during the transition.

Note that implementation costs will highly depend on the size of your company, the complexity of your use case, and whether you're outsourcing AI development or performing it internally.

Outsourcing AI development is typically more cost-effective, especially for companies that lack the necessary in-house AI expertise.

Step 4: Gather Data

There are two ways to go about this step:

• If you have already implemented AI into your workflow, simply start gathering real data to calculate the actual ROI.
• If you have not yet implemented AI, use your target data to calculate the potential ROI. We suggest considering both best-case and worst-case scenario metrics to get a more reliable estimate.

In either case, you will want to start collecting data for the same KPIs you've identified previously. For example, if your main KPI was revenue increase, gather data that tells you how much your revenue has actually increased since integrating AI.

For more clarity, let's imagine the following scenario:

• A mortgage company used to spend 15 hours reviewing a single mortgage application.
• After they've integrated AI, they need just 3 hours to approve or deny it.

Based on this data, we can conclude the following that the time-to-approval and labor hours have decreased by 80%.

If we image that employees are paid $30/hour, this would mean the company is saving about $360 per application just in labor costs.

Additionally, we should also consider that decreased time-to-approval also helps the company serve more customers in less time. This can result in an additional revenue increase, which should be accounted for as well.

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 comes after the automation of multiple tasks or, 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

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

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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.

Get Help Calculating Your AI ROI

Interested in working with us on your next AI project? Let us help you calculate the potential ROI of automating your workflow with our AI Agents. Schedule a 30-minute call with our experts to get started.

FAQs

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.