ROI of AI: Key Drivers, KPIs & Challenges

What is the ROI of AI?

ROI is a key metric in determining the success of any investment, including AI.

To calculate ROI, you divide the gain from an investment by its cost. The resulting number can be expressed as a percentage or a ratio. To calculate ROI, you can use the formula below: 

A positive ROI means that the investment generated more revenue than its total cost. On the other hand, a negative ROI indicates that the return was less than the initial investment.

Here are some common metrics used in calculating ROI:

• ROI
• Annualized ROI
• Net Present Value (NPV)

To account for time horizons and the changing value of money, integrate Net Present Value (NPV) and Internal Rate of Return (IRR) into your analysis. For NPV, calculate the present value of future net benefits (cash inflows minus outflows) over a set period:

Where t is the time period, and r is the discount rate. IRR helps determine the discount rate at which NPV equals zero, providing insight into the efficiency of the AI investment over time. 

These metrics complement ROI by showing the time-adjusted profitability of AI projects, helping businesses balance short-term and long-term financial impacts.

So what’s an ideal ROI? Well, each organization is different but let’s see what other sources have to say below.

According to a global IDC study supported by Microsoft, an average of $3.5 in return is produced for every $1. Additionally, an average of $8 in return is produced by 5% of organizations worldwide.

Within 12 months, 92% of AI systems are deployed, and, 40% of organizations of implementation within 6 months

On average, organizations realize this return within 14 months from the initial investment.

Key Drivers of AI ROI in B2B

Why measure the ROI of AI? Here are the key reasons that should come to mind:

1. Cost savings through automation

AI-driven automation offers immense potential for cost savings by simplifying labor-intensive processes, ultimately enhancing operational efficiency. 

According to a McKinsey survey results shown above, respondents showed a large increase in revenue and a significant decrease in costs as a result of the adoption of analytical AI.

For instance, by integrating AI into customer service, procurement, and data management, companies can reduce overhead costs, mitigate manual errors, and reallocate resources toward high-value tasks.

According to McKinsey and Harvard researchers, having AI implementations can save the U.S. up to $360 billion annually if adopted more widely in healthcare.

From predictive maintenance in manufacturing, AI has proven effective in lowering operational expenses while maintaining quality and performance standards.

For example, AI-powered machine learning models can optimize cost strategies in logistics businesses.

2. Revenue growth with AI-powered personalization

To stand out in the highly competitive environment, businesses can consider one solution—personalization.

AI personalization is the use of machine learning algorithms to analyze customer data and deliver tailored content, product recommendations, and offers.

This level of personalization can lead to increased customer satisfaction and loyalty, ultimately driving revenue growth. It is becoming increasingly popular for all companies (be it B2C or B2B), as it allows for targeted and relevant messaging to potential clients.

With the help of AI, companies can gather insights into individual customers' behavior, preferences, and needs in real time. This allows for targeted messaging and offerings that are more likely to resonate with each customer.

Additionally, AI-powered personalization can also improve sales by suggesting personalized upsell or cross-sell opportunities based on past purchasing behavior.

This unique personalization can be used in marketing to:

• Create tailored marketing campaigns
• Optimize lead generation
• Boost conversion rates

For example, Dell saw a 79% increase in conversions by implementing AI-driven personalization in its email campaigns​.

AI also aids in retaining high-value clients by offering customized solutions and real-time assistance through chatbots and virtual assistants. 

This proactive, personalized approach helps companies respond quickly to client needs, building trust, and long-term loyalty that translates into sustained revenue streams.

3. Efficiency and time-to-market

AI can significantly reduce product development cycles by facilitating faster data analysis and empowering research and development teams to make quicker decisions and innovate more efficiently.

With this new-found ability to use AI, businesses can expedite processes that would typically require weeks or months, allowing for a more agile approach to launching new products and services.

Several companies are already benefiting from AI-driven content and product innovation.

Generative AI tools, for example, can create marketing content or prototype designs in a fraction of the time that traditional methods require. This agility not only helps companies remain competitive but also allows them to address market demands promptly.

Measuring the ROI of AI investments

To ensure your investments in AI are performing well, you will require proper implementation of KPIs. Here are some you can start with:

Key performance indicators (KPIs) to track

Tracking the ROI of AI requires a strategic approach, with specific KPIs to monitor:

• Cost savings: Calculate savings achieved from reduced labor, automation, and optimized processes.
• Revenue impact: Measure lead generation, conversion rates, and overall revenue growth tied to AI-powered campaigns.
• Engagement and customer satisfaction: Monitor engagement metrics, such as click-through rates and customer satisfaction scores, which can provide insights into AI’s effectiveness in improving business outcomes.

These KPIs enable B2B companies to gauge the tangible and intangible benefits of their AI investments, providing a clear picture of where AI is driving value.

Long-term vs. short-term ROI

One of the challenges in assessing AI’s ROI is balancing short-term costs with long-term benefits. Implementing AI often requires a significant upfront investment in technology and skilled personnel. 

In a Forbes Technology Council article, deploying a successful AI project from scratch might cost 15 times more than anticipated due to AI infrastructure.

For example, a common role in AI teams is the AI engineer, who has a salary of $145,080 per year in the United States. For an entire team of engineers, this might come up to a pretty large sum.

Although these expenses may initially impact ROI, the potential for substantial, long-term financial returns makes the investment worthwhile.

Therefore, you should approach AI as a strategic, long-term asset that will yield increasing returns as the technology matures and as their teams gain experience in leveraging AI’s capabilities.

Going beyond just measuring the financial ROI of AI, the implementation of AI solutions may bring about several side benefits.

When implementing AI, it's more than just putting a few AI tools together—it doesn’t amount to much. 

To bring about a greater ROI, AI systems have to be built from scratch. This requires strong to better develop solutions unique to your organization. 

In the DataCamp State of Data & AI Literacy 2024 report, 52% of leaders said that developing AI systems from scratch was important, pointing to the need for technical proficiency in AI development within teams. However, almost 1 in 4 leaders surveyed do not offer any online training.

This is where a comprehensive platform for learning data and AI skills with a robust curriculum and tailored learning paths is essential. With DataCamp for Business, you can scale your teams’ learning, whether you have two employees or ten thousand. 

Challenges in Achieving a High ROI With AI

AI has the potential to provide extremely high ROI levels, but every new technology comes with its challenges.

Here are some to look out for:

1. Initial costs and integration complexity

The initial investment required for AI implementation can be a barrier for many companies. Beyond purchasing technology, there is also the need for skilled personnel to integrate and manage AI systems, which can slow ROI in the short term.

In a Deloitte survey, many say their AI implementations are still at the pilot or proof-of-concept stage, with 68% of respondents saying their organization has moved only 30% or fewer AI projects into production. 

According to the State of Data & AI Literacy 2024 report, 35% of leaders stated that the lack of budget for employee training costs was the main challenge.

However, with careful planning and phased deployment, companies can manage these upfront costs and realize positive returns over time. This can be easily achieved through a comprehensive platform that covers all the latest data and AI skills. Our DataCamp for Business offering would fit this pain point perfectly. Worried about cost concerns? Try out our Demo to see whether it’s the right fit.

2. Data quality and availability

High-quality, well-organized data is fundamental to successful AI implementation. Without accurate, relevant data, AI systems cannot deliver the insights and automation needed to drive ROI.

Many organizations collect large amounts of data each day, and savvy executives want to take advantage of this. But how much of this data is actually clean enough to be used?

Before feeding into an AI model, enterprise data should be adequately cleaned to prevent wrong and inaccurate results.

According to a McKinsey article, manufacturing industry leaders who intend to implement AI models are discovering that poor data quality has been a consistent roadblock.

Therefore, to resolve such a challenge, companies must invest in data management practices and infrastructure to ensure data is accessible, organized, and reliable, creating a strong foundation for AI-driven insights.

3. AI skills training

To maximize ROI, companies must ensure that their teams are equipped with the necessary skills to implement and leverage AI technologies.

This particular challenge would be the easiest to resolve through upskilling and reskilling initiatives to foster AI proficiency across existing or new teams.

In our State of Data & AI Literacy 2024 report, as shown in the diagram above, leaders claim an improvement in decision-making in their organizations through data and AI training, with 26% of of them having been completely transformed.

Final Thoughts

The adoption of AI in business environments offers numerous advantages, from cost savings and revenue growth to faster innovation and enhanced customer engagement. However, achieving a high ROI requires a thoughtful approach to implementation, including identifying the right KPIs, managing upfront costs, and ensuring data quality.

While challenges exist, the long-term benefits of AI can be transformative, enabling B2B companies to innovate and compete more effectively.

If you are looking to expand the AI capability of your teams, our DataCamp for Business offers a flexible and scalable approach to AI and data analytics training, helping you close the AI skills gap in your teams. Request a demo to get started today.