AI ROI: Unlocking the True Value of Artificial Intelligence for Your Business

Artificial Intelligence (AI) is no longer a buzzword – it’s a competitive necessity. It is revolutionizing industries worldwide, transforming how businesses function and people engage with technology. From financial algorithms that refine stock trades to healthcare innovations that diagnose illnesses rapidly, AI optimizes things in ways we could never imagine.

According to research, 77 percent of companies are either using or exploring the use of AI in their businesses, whereas 83 percent of the companies claim that AI is a top priority in their business plans.

But with significant AI investment comes an important question: Is AI a good investment or an expense?

Understanding AI return on investment (ROI) is essential for CTOs and other business leaders. It will help them justify their AI spending, refine business strategies, and maximize their technological usage.

This article will explain what exactly is AI ROI (Return On Investment)? Why it matters, and how leaders can effectively measure and enhance their return on investment with AI to achieve long-term success. 

What is AI ROI and why does it matter?

AI ROI (return on investment) refers to the process of calculating the financial and operational benefits gained from investing in AI projects compared to the costs incurred. Simply put, it’s a way to measure whether your AI initiatives bring in more value than they cost you.

Benefits of measuring AI ROI

Evaluating AI ROI is essential for several reasons. Here are a few to note:

Helps justify AI expenditure: 

AI projects require substantial investments. By measuring ROI, leaders can determine if AI deployments generate good value that outweighs the costs, including technology, talent, and infrastructure. 

Ensures AI initiative is aligned with business goals:

AI investments must align with broader business goals, such as increasing operational efficiency, enhancing customer experience, or driving new revenue streams. Measuring AI ROI ensures that the needs are met and that AI adoption is intentional and strategically sound. 

Helps identify areas for improvement:

A clear understanding of AI ROI helps leaders know which projects deliver strong ROI and which are underperforming. This ensures that the right tasks are allotted time, money, resources, and effort to drive positive outcomes. It also helps ensure that AI investments contribute meaningfully to business growth.

Helps gain stakeholders’ confidence:

Before committing significant resources to AI projects, stakeholders want to see precise, tangible results. By measuring AI ROI, leaders can prove to their stakeholders that the AI investment gives the desired financial returns, such as cost savings or increased revenue.

The ambiguity surrounding ROI is not just a financial concern; it fundamentally affects strategic decision-making across every level of the organization.

When leaders can’t clearly quantify or predict the benefits of AI initiatives, they tend to lack direction, potentially leading to missed opportunities.

Conversely, a lack of clear ROI may drive some leaders to impulsively invest in AI technologies without a solid strategic foundation. This leads to misallocated resources and use cases that fail to align with the business’s core objectives or deliver compelling financial returns.

Factors that need to be understood before investing in AI

Here are a few factors that need to be understood before investing in AI:

Have a clear or realistic understanding of what AI can cost

AI isn’t free. The cost of building and implementing AI systems can vary significantly depending on the following scenarios:

Are you enhancing or working on the existing AI platform/tool to power your system’s functionality or are you building something from the ground level?

These factors lead to a better understanding of the initial costs of buying /building AI applications and the long-term support costs. 

Organizations need to start compiling all costs related to the development and deployment of AI systems. This includes upfront expenses like software acquisition, hardware investments, and costs of integrating AI into existing systems.

Likewise, organizations should consider ongoing costs such as maintenance, upgrades, training employees to use AI tools, and potential increases in operational costs due to new technology.

The way you define value is critical to the investment justification of AI

This is different for all organizations. Some greatly value customer satisfaction, others value agility more, while others prioritize innovation above everything else.

What’s important here is to define the organization’s value drivers before looking at what AI can do to empower them.

Organizations must focus on what contributes to business value in using AI or the degree to which they want to leverage this technology in existing or new systems. Most of the time, value weight is rarely considered in AI decisions.

As a result, organizations often end up with investments that bring little value back to the business or, indirectly, more expenses.

Envision the future – Foresee how AI values will change over time

It’s perfectly justifiable to make an investment that won’t return value in the first few years but will provide other benefits later.

The trick is to be farsighted enough to understand this technology’s potential over time, which means predicting the future of markets and customer personas.

Key metrics that help measure AI ROI

These metrics should cover various aspects of business operations, from operational efficiency to customer satisfaction and revenue growth.

Here’s a breakdown of essential KPIs to help measure the efficacy of AI projects across these critical areas.

Productivity time:

Leaders can analyze the time taken to complete specific operations or processes before and after AI integration. A reduction in process times can indicate increased efficiency due to AI automation.

Error rates:

AI is often implemented to reduce frequent errors. Tracking error rates before and after AI implementation can highlight improvements in accuracy.

Cost savings:

Compare the cost of a task, pre- and post-AI integration. If something that used to cost thousands of dollars a month is now done faster and at a lower cost, then the AI initiative is worth the investment.

Customer satisfaction:

To measure the impact of AI investments on customer satisfaction, organizations should track key metrics like CSAT (Customer Satisfaction) scores, NPS (Net Promoter Score), and customer feedback analysis. Monitoring customer behavior data allows leaders to anticipate churn and deliver personalized offers to retain customers and drive upsells.

Employee capacity and satisfaction:

Track employee capacity. If AI is working, the team should be able to do more with less, leading to swift operations, greater output, and more substantial profit margins. It’s not just about faster results; it’s about multiplying the impact without multiplying headcount. 

Next, to understand how AI impacts the team, conduct employee surveys with retention data analysis. Retention data reveals patterns of turnover and disengagement, while surveys provide insights into employee sentiment and satisfaction.

Adaptive AI

Another aspect that needs to be checked is how adaptive your AI is.

Check whether it has the capability to learn, adapt, and improve as it encounters changes, both in data and the environment. Adaptive AI lets organizations become more flexible, resilient, and effective when faced with complex challenges in daily business.

It helps avoid unexpected loss or hurdles in the future.

Real-world example of excellent AI ROI

Bank of America’s (BAC) crown jewel is Erica, an AI-powered virtual assistant.

Launched in 2018, Erica handles around 2.4 billion interactions annually with a 98 percent containment rate, serving 20 million active users as of today.

The Real ROI: –  

This tool, used by 90 percent of BAC’s employees, has reduced IT support calls by over 50 percent since 2020.

Plans to integrate generative AI by 2025 will expand its role in product knowledge and compliance, further boosting productivity and growth.

Common challenges in measuring AI ROI

Difficulty in quantifying intangible benefits

Many of AI’s benefits—like improved customer satisfaction, better decision-making, or enhanced employee morale—are hard to quantify in monetary terms.

Developing indirect measures, like customer satisfaction scores or employee feedback, can help estimate these intangible returns, but they may not capture the full value.

Longer time horizons

Unlike traditional investments, which may yield faster results, AI projects often take time to deliver significant value. It could take months or even years to see the full benefits, making it difficult to maintain stakeholders’ interest and to justify continued investment in the short term.

The long-term nature of AI can complicate ROI measurement.

Evolving AI technologies

AI tools and techniques are constantly evolving. Hence, what’s popular and sounds cutting-edge today might be outdated tomorrow. You might face business failure if you do not grow with the advancements.

Leaders need to stay agile and adapt to new developments, which can make it difficult to set consistent, long-term ROI benchmarks.

Likewise, staying abreast up with these advancements also requires ongoing funds, with further complicates the measurement of returns.

Data availability and quality

Measuring AI ROI requires accurate and relevant data. However, many businesses struggle with data quality or availability issues. If the data used to train AI models is incomplete, outdated, or biased, the results may be unreliable, affecting both the performance of the AI and the accuracy of ROI measurements. Ensuring a steady flow of clean, high-quality data is crucial for successfully evaluating AI’s impact.

Things to keep in mind: Paving the way for future AI investments

Maximizing AI ROI isn’t just about implementing the technology; it’s about being strategic at every stage. Successful AI projects require careful planning, execution, and continuous evaluation. By following best practices, leaders can ensure that the AI investments deliver the highest possible returns and long-term value.

Moreover, measuring AI ROI is not just a one-time task. It’s an ongoing process that provides valuable insights and drives continuous improvement.

Lastly, don’t just get distracted by the shiny new AI keys that are dangling in front of you. To fully leverage the benefits of AI, analyze the business’s present and future states and then determine how AI can add value. This is the most common-sense and successful approach to justify the investment in AI, or in any technology for that matter.

AI has incredible potential. Still, it’s wise to be cautious and to make sure the investment will pay off for your business in the long run.

In brief:

Measuring AI ROI is worth the effort for several reasons. When you measure AI ROI, you rely on concrete results to evaluate the success of the AI projects. This approach removes guesswork or fake speculations and ensures that the decisions are based on real outcomes. The measured results helps understand what works and what doesn’t, allowing one to make informed adjustments to the AI strategies.