How to measure and generate ROI with AI: An actionable guide

Artificial Intelligence (AI) has been rapidly transforming businesses across every industry, doing everything from automating repetitive tasks to providing advanced data analytics and more personalized customer experiences. 

But as with any investment, there’s one key question: How do you measure and generate return on investment (ROI) with AI?

In this guide, we’ll break down what AI ROI means, the areas where AI can deliver the most significant value, and how to effectively measure and optimize what you’re getting out of AI.

What does ROI for AI mean?

In the context of AI, ROI measures the value or profit a company generates from implementing AI tools and solutions compared to the cost of those initiatives. 

This includes both tangible financial returns—such as cost savings or revenue growth—and less quantifiable gains like improved customer satisfaction or enhanced decision-making (or “hard” vs “soft” ROI, which we’ll get into later).

Calculating ROI involves understanding both the benefits AI delivers and the expenses associated with it, including development costs, software licensing, data acquisition, and the necessary hardware infrastructure.

Key areas where AI drives ROI 

AI has the potential to drive ROI across various business functions, especially in these key areas:

• Marketing and sales: AI-driven tools can help you optimize marketing strategies with personalized content, real-time customer segmentation, and predictive analytics. AI can also drive more sales by identifying high-value leads and optimizing sales workflows through automation.

• Product development: Some companies are using AI to accelerate product innovation by analyzing market trends, customer feedback, and even simulating product testing scenarios, reducing the time to market. This lets you bring products to market faster and more efficiently while ensuring those products align better with your customers’ needs.

• Customer service: AI-driven chatbots, virtual assistants, and sentiment analysis can drastically improve customer service experiences. The ROI for conversational AI can be wide-ranging—it can reduce costs by helping agents resolve inquiries more quickly and efficiently, reduce onboarding time and training costs for new agents, and more.

• Human resources: AI helps HR teams with talent acquisition by using algorithms to screen candidates, assess cultural fit, and in some cases, predict employee performance. It can also personalize employee development plans and optimize workforce management.

• Supply chain management: AI can optimize supply chain operations by helping teams forecast demand, improve logistics, and minimize delays through predictive maintenance and real-time inventory management. These applications can also lower operational costs by reducing waste and preventing costly downtimes.

Hard ROI vs soft ROI

When you’re calculating the ROI of AI, it’s important to differentiate between hard ROI and soft ROI.

Hard ROI

Hard ROI refers to quantifiable financial benefits, such as increased revenue, reduced costs, or productivity improvements. 

For instance, if AI automates a process that saves the company $500,000 annually, that’s a clear, measurable return.

If you want to invest in AI with the goal of seeing hard ROI, focus on specific, high-impact use cases where AI can directly improve financial performance, such as automating repetitive tasks, reducing operational costs, or increasing revenue through personalized marketing. 

You could start by identifying bottlenecks or inefficiencies where AI-driven automation or analytics can save time or resources, then set clear measurable goals like reducing processing times, improving accuracy, or increasing sales conversions.

Soft ROI

Soft ROI refers to less tangible benefits that may not directly translate into immediate financial gain, but still add significant value.

For example, this might include a more attractive brand, better customer experience, or more effective decision-making capabilities. Soft ROI is harder to measure, but still crucial for long-term growth and sustainability.

With soft ROI, you’d focus instead on your intangible assets such as customer experience, brand reputation, or employee satisfaction. Start with AI solutions that improve user interactions, such as chatbots for customer service or AI-driven insights for personalized marketing, to create a more engaging and responsive experience. 

You could also look at AI tools that empower your teams to innovate and come up with ideas more effectively, which would also improve productivity and morale. 

In many cases, you can set qualitative metrics such as written customer feedback, employee engagement levels, and brand perception to gauge the impact of AI on these intangible benefits. Although these gains are harder to quantify with numbers, they’ll still contribute significantly to long-term business success and give you a competitive edge.

How to measure the ROI of AI in 8 steps

Measuring the ROI of AI involves more than just comparing costs to benefits. Follow these eight steps to get a more comprehensive understanding of the value your AI solutions are providing:

1. Define your objectives and goals

The first step in measuring AI ROI is to clearly define the objectives and goals of your AI investment. What specific problems are you aiming to solve, or what opportunities are you looking to capitalize on? Establish measurable targets, such as reducing operational costs by a certain percentage, increasing sales conversion rates, or improving customer satisfaction scores.

At this stage, it’s best to avoid vague goals like “improve efficiency” without specifying how you’re defining “efficiency” or how much you want to improve it by. Clear, quantifiable goals are essential for tracking success.

2. Calculate your costs

Next, calculate all the costs associated with implementing the AI solution. This includes not just the initial investment in technology and infrastructure, but also ongoing expenses like maintenance, data acquisition, and training.

Many businesses underestimate hidden costs, such as the need for additional software or hardware upgrades, so make sure you find out these details from your vendor.

3. Measure financial benefits

Quantify the financial benefits that the AI solution is expected to deliver. To do this, first calculate the potential cost savings from automation, revenue increases from enhanced marketing efforts, or reductions in error rates that could lead to fewer costly mistakes.

Be careful not to overestimate benefits based on optimistic projections—instead, you could have a range of options showing the “best, average, worst” case scenarios using historical data to cover your bases and forecast potential financial gains more accurately.

4. Determine the time frame

Establish a clear time frame for evaluating the ROI of your AI investment. This involves deciding how long it will take to implement the solution and when you expect to start seeing measurable results.

Avoid setting unrealistic expectations for the time frame. Depending on the project, AI sometimes takes time to fully deploy and integrate into existing systems, so be prepared for potential delays and ensure your time frame is achievable.

Dialpad Ai Voice, for example, is a unified communications platform that’s designed to be easy to set up virtually—the Harrelson & Harrelson firm got up and running in under an hour.

5. Calculate ROI 

Now, you need to calculate the ROI. Use this formula:

ROI = Net profit / Investment cost×100

Here, your net profit should include all the financial benefits resulting from using the AI solution, while the investment cost should include The total cost of your AI investment.

6. Consider other benefits

Beyond financial metrics, your AI investment might lead to other advantages like higher customer satisfaction or better decision-making, which can have a significant positive impact on long-term business value.

Quantifying intangible benefits can be challenging, but it’s essential to include them in your ROI assessment. Use customer feedback surveys, employee satisfaction scores, and other qualitative metrics to provide a fuller picture of AI’s impact.

7. Adjust for risk and uncertainty

AI projects definitely come with inherent risks, such as potential technological issues, changing market conditions, or other unpredictable operational challenges. Adjust your ROI calculations to account for these risks and their potential impact on the investment.

Not accounting for risk can lead to overly optimistic ROI projections, so make sure to conduct a risk assessment and consider different scenarios to understand how various factors might affect your ROI.

8. Monitor and adapt

Finally, continuously monitor the performance of your AI solution and adapt or switch to another platform as needed. For most businesses, AI is relatively new and requires a period of testing and iterating to figure out which features are crucial and which ones aren’t. 

Regularly review your ROI metrics and be ready to adjust your strategy based on real-world results and feedback. Try not to treat ROI measurement as a one-time exercise—AI systems and market conditions are constantly changing, so ongoing monitoring is vital for sustaining and improving ROI.

How to optimize AI investments for maximum ROI

Maximizing AI’s ROI requires more than just a strong initial implementation. Here are some tips to ensure you’re getting the most out of your AI investment:

1. Set clear objectives 

Start by defining clear, measurable objectives that align with your business goals. At the beginning, you’ll want to prioritize projects with the highest potential impact and focus on areas where AI can solve significant problems or create substantial opportunities (like driving cost savings, revenue growth, or operational efficiency).

2. Invest in quality data and skilled teams

AI depends on data, so invest in your data processes to ensure that your AI systems are trained on accurate and relevant information. 

Depending on your AI solution, you may also need a team of data scientists, AI specialists, or domain experts—but a good AI product should be easy to use for any business, even if they don’t have a huge technical in-house team.

3. Leverage advanced AI tools and platforms

Avoid relying on generic or basic AI solutions that just tap into existing consumer-grade LLMs like ChatGPT. These tools aren’t tailored to business-specific needs and often need to be customized extensively to integrate with existing tech stacks. 

For example, Dialpad Ai has analyzed over six billion minutes of business conversations, meaning all the data it’s trained on is focused on business use cases like sales, marketing, and customer support. Compared to a consumer-grade LLM like Claude or ChatGPT, that means its outputs are a lot more focused. You also have the option of training it on your organization’s data, which can tailor its recommendations even more specifically for your business.

If you have different teams that will leverage AI, plan ahead to help them collaborate by sharing insights and feedback—this will help you maximize your AI solution’s impact and make sure it’s aligned with broader business strategies and needs.

4. Monitor performance to scale up successfully

Keep an eye on performance and evaluate your AI’s effectiveness and identify areas for improvement. This is where those metrics and objectives that you set earlier will come in handy. Based on these insights, you can then adjust your strategy and scale up successful projects to maximize their impact.

Further reading: Learn how Splunk, a global enterprise, scaled up headcount 10 to 20% with an AI communications platform.

5. Manage risks, partnerships, and ethical considerations

AI legislation is starting to ramp up, with the AI Act in Europe coming into force and other countries following suit. That means you need to have a plan that addresses potential risks such as data privacy issues, algorithmic biases, and technological challenges. Make sure to choose reputable vendors and stay informed about ethical standards and regulations related to AI.

Let Dialpad Ai drive your ROI

AI has proven to be a powerful tool for driving ROI across a range of industries and functions, from marketing and sales to customer service and supply chain management. 

By understanding both the hard and soft returns AI offers, and following a structured approach to measuring and optimizing ROI, businesses can ensure that their AI investments deliver lasting value. Implement AI strategically, monitor performance closely, and continually adjust for the best possible outcomes.