Measuring ROI of AI Projects: A Practical Approach

The growing investment in AI projects is proof of the trust that organizations have shown in the potential of AI. However, without measuring the ROI of AI projects, you will be walking in the dark, expecting to achieve your goals by some miracle. Did you know that more than 80% of AI projects fail and do not move beyond pilot stages? (Source) Every business leader will have questions about chances of success of AI projects and ROI provides an effective indicator of the same.

AI brings promises of intelligent automation, new ways of working and better decision-making. On the other hand, it is important to acknowledge the challenge of transforming the potential of AI into tangible business impact. Therefore, every organization looking for opportunities in AI must have a clear roadmap for measuring ROI of their AI projects. You should understand the significance of ROI in artificial intelligence projects, key metrics for measuring ROI and important steps in determining ROI.

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Unraveling the Meaning of ROI in AI Projects

The first thing that you must know before learning steps to measure the ROI of AI projects is the meaning of ROI in context of AI. ROI or Return on Investment is a standard term in traditional finance that represents the ratio of net profit to the cost of investment. The ROI for AI projects or Return on AI represents a measurable value of profit on investments in artificial intelligence. Return on AI or ROAI provides a clear impression of the net benefits against costs of deploying and maintaining AI systems.

If you break down ROI of AI, then you will get two distinct categories of hard returns and soft returns. 

• Hard ROI

Hard returns in AI projects point at the cost savings you achieve from automation of routine tasks with AI. The hard returns also include the improvement in revenue from personalized marketing campaigns or fraud detection systems.

• Soft ROI

The soft ROI in artificial intelligence projects focuses on the satisfaction employees get from being relieved of repetitive tasks. In addition, organizations can see soft returns on AI from the happiness customers get when their issues are resolved quickly. 

What is the Formula for Measuring ROI of AI Project?

You might be wondering why this discussion has not shifted towards the mathematics of calculating ROI in AI projects. It is important to understand that finding the ROI in enterprise AI projects begins with definition of project objectives and KPIs. The answers for “How to measure ROI of an AI project” will depend significantly on what you want to achieve with the project. The commonly used KPIs for artificial intelligence projects include,

• Reduction in labor costs
• Processing times
• Stakeholder satisfaction

Once you know your project objectives, it is easier to follow a step-by-step method to calculate ROI of AI projects.

1. Start by Outlining Cost of AI Investment

The first step to calculate your AI project ROI requires identification of major costs for the project. You must identify the costs for,

• Development, including costs for data acquisition, tools and software
• Infrastructure, primarily focused on cloud computing and data storage
• Personnel, including the remunerations for data scientists and vendors
• Maintenance and retraining

2. Use the Formula to Find Net Benefits

You can determine a tangible estimate for financial benefits expected from the AI project with the following formula,

Net Benefits = Total Benefits – Total Cost

3. Determine the ROI and Payback Period

Once you have found the net benefits expected from the project, you can calculate the ROI by using the formula,

ROI = (Net Benefits – Cost of AI investment) / Cost of AI investment

You should also use the formula to determine the payback period or the time required to recover the investment. Microsoft Excel and Google Sheets serve as valuable resources for tracking multiple investments and analyzing ROI across different projects.

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Roadmap to Measure ROI and Showcase Business Impact

Many business executives believe that they can post good numbers for generative AI ROI by looking at successful examples. However, it is also important to recognize that measuring AI ROI requires a journey in which you have to define AI readiness and the relation between business goals and AI use cases. Without a disciplined roadmap to measure ROI, your AI initiative may stop way before it delivers any meaningful business impact.

• Alignment between AI Projects and Business Goals

The foremost step in every successful AI project begins with identification and assessment of business objectives. Artificial intelligence is valuable only when it helps in achieving something your business really cares about. It is important to know why you are investing your money because AI projects that start with clear goals have more chances of succeeding. In addition, your goals also help you justify the investment and secure buy-in for the project.

• Calculating ROI with Use Case Modeling

The next crucial aspect in measuring ROI of AI project focuses on identifying an appealing business use case of AI. With a specific use case, you can quantify the potential impact of artificial intelligence and move beyond general assumptions of efficiency. You can look for the following categories of business value from your AI project.

• Revenue growth
• Cost optimization and enhancing efficiency
• Decision quality and accuracy
• Risk mitigation and compliance

Integration of AI in customer experience and ERP systems will help you achieve accurate use case modeling. You must also remember to check whether the value benefits of AI are one-time or will compound as time passes. This step is the most critical one in developing a strong business case for executives and measuring AI performance over time.

• Use Baseline Comparisons for Quantifying Value

The most critical requirement for measuring meaningful business impact from AI projects revolves around the definition of clear performance benchmarks. You should note down the current KPIs, including customer satisfaction scores, processing times, revenue per transaction and error rates. Subsequently, you must also predict how artificial intelligence will change these numbers. As a result, you can establish a realistic break-even point and payback period for your AI investment. 

It is also important to understand that the true value of AI projects is not limited to the traditional formula. Think of the benefits that can be obtained from teams focusing on strategic initiatives and boost for innovation. The cascading effect of automation with AI projects will have a far more positive impact on ROI than the initial efficiency improvements. When you present AI as a strategic investment and not just a discretionary expenditure, you have a strong business case.

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• Tracking Real-world Metrics

You may have assumed that measuring ROI is an exercise limited to the initial stages of AI projects. On the contrary, you have to focus on real-world metrics to track ROI for AI projects after deployment. The performance data and metrics that show whether your project is delivering the desired results are integral to measuring ROI.

You should measure the KPIs before and after deploying the AI project alongside monitoring adoption and usage metrics to check whether the solution is being used effectively. Measuring the downstream impacts will also help you find out whether improvements in one department led to improvements in another. You can leverage these insights for performance optimization, clearly communicating results and justify ongoing or future investments.

• Do Not Forget the Qualitative and Strategic Returns

Business leaders and CFOs must also know that all returns from AI projects don’t show up on financial reports. You should also focus on the qualitative and strategic returns when building a business case for AI projects. Some organizations have achieved significant improvement in everyday productivity through automation of repetitive tasks. 

On top of it, sales teams have also reported improvements in productivity with better access to customer data and streamlined workflows. It is also important to know that AI projects can lead to significant reduction in operational costs by reducing the need for complex integrations and simplifying operations. Most important of all, AI could also have significant impact on customer experience and retention, which ultimately bring positive returns.

Final Thoughts

The task of measuring ROI of AI project may seem like an insignificant one from the outset. However, business leaders must recognize the fact that many AI projects fail due to the lack of a strong business case. Without measuring ROI, you cannot present an AI initiative as a strategic investment. You should follow a clearly defined roadmap to evaluate ROI of artificial intelligence projects and also consider qualitative and strategic returns as critical benefits.

FAQs

How do I calculate the ROI of an AI implementation in my business?

You can calculate the ROI of an AI implementation in your business by dividing the net benefits by the total costs. It is important to focus on tangible metrics, such as reduced operational costs, fewer labor hours and increased revenue. The important steps in measuring ROI also involve identification of implementation costs and determining the payback period on AI investment. 

What are key performance indicators for measuring AI project success?

The key performance indicators or KPIs for measuring AI project success can be divided into two categories, financial and operational metrics. The financial metrics measure the business impact of the AI project, such as cost savings, efficiency gains and revenue growth. On the other hand, operational metrics focus on adoption rate, customer experience and system reliability. 

How do you calculate the return on investment for a machine learning initiative?

The process to calculate the return on investment for a machine learning project is the same as that for AI projects. It involves comparing the net financial benefits against the total cost of investment, including development and ongoing maintenance costs. You should track the ROI of machine learning projects periodically to bring performance improvements and model training into account.

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