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Artificial intelligence (AI) has rapidly transformed 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, the inevitable question arises: How do you measure and generate AI’s return on investment (ROI)? Use this guide for best practices.
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 more efficient decision-making.
Calculating ROI involves understanding both the benefits that AI delivers and the expenses associated with it, including development costs, software licensing, data acquisition and the necessary hardware infrastructure.
Key ROI Drivers for AI
AI has the potential to drive ROI across various business functions, particularly in these four 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 and customer feedback, and even simulating product testing scenarios, greatly reducing their time to market.
- 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, including cutting costs by helping agents resolve inquiries more quickly and efficiently, reducing onboarding time and training costs for new agents and more.
- 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 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 the less tangible benefits that may not directly translate into immediate financial gain, but still add significant value. This might elevate your brand, increase customer delight or result in more effective decision-making capabilities. Soft ROI is harder to measure, but still crucial for long-term growth and sustainability.
With soft ROI, focus instead on your intangible assets like 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 more engaging and responsive experiences. Also, consider AI tools that empower your teams to ideate and innovate more effectively, which can 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 provide:
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. Assess your costs
Next, identify the costs associated with implementing your AI solution. This includes not just the initial investment in technology and infrastructure, but also ongoing expenses like maintenance, data acquisition and training.
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Many businesses underestimate hidden costs, such as the need for additional software or hardware upgrades, so be sure to include these details.
3. Measure your financial benefits
Quantify the financial benefits that your 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, include a range of options that demonstrate the “best, average and worst” case scenarios using historical data to cover your bases and forecast potential financial gains more accurately.
4. Identify your 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. 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 goals are achievable.
For example, Dialpad, a cloud-based AI-powered customer communications platform, enabled the Harrelson & Harrelson firm to get up and running in under an hour.
5. Calculate your ROI
Now you can calculate your ROI using this formula: 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 more complete picture of the impact AI can have on your business.
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 your 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 are not.
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.
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 that AI offers, and following a structured approach to measuring and optimizing ROI, you can better understand your AI investment and ensure that it delivers lasting value. Implement AI strategically, monitor performance closely, and continually adjust for the best possible outcomes.
Let Dialpad drive your ROI. Get started at dialpad.com.
