You've probably seen the studies that say Generative AI rarely goes beyond pilots and does not deliver RoI. Our own experience at GoML has been quite different. Because of our accelerated AI adoption approach, our own clients have achieved great results with generative AI in enhancing efficiency, creativity, and profitability. In this article, however, I will discuss the challenge: how do organizations measure the return on investment (ROI) of their Generative AI initiatives?
Behind every pilot and deployment, executives need a clear answer to - will generative AI actually pay off for us? And if so, how do you measure it?
This article explores the practical realities of ROI on AI for enterprises, decodes the basic formula that you need to know and illustrates how organizations from healthcare to finance are translating LLMs into true enterprise value.
Why Generative AI ROI Matters
Return on Investment (ROI) is about one thing: what you get back for what you put in. Whether you’re investing in AI, new machinery or a marketing campaign, the fundamental question is the same: was it worth it?
The good news is that the ROI formula is not intimidating. It’s simply:
ROI = (Gains from Investment (minus) Cost of Investment) (divided by) Cost of Investment
In any business context, ROI is the only metric to understand the effectiveness of investments.
It allows companies to assess the value derived from their expenditures, ensuring that resources are allocated effectively. In the realm of Generative AI, measuring ROI becomes even more critical due to the technology's potential for widespread disruption and innovation.
If an AI-powered chatbot saves your support team $500,000 in labor costs over a year, and the total cost of implementing and running it was $200,000, your ROI on AI is:
ROI = (Gain – Cost) / Cost
ROI = ($500,000 – $200,000) / $200,000 = 150%
or, as a percentage, 150% ROI on your AI investment.
While the equation is simple, projecting ROI, or even calculating it, is difficult becase the industry is changing fast. There are newer and cheaper models available week. And, when something breaks, your costs can spiral without the right engineering expertise.
Key Metrics for Measuring Generative AI ROI
To measure the ROI of Generative AI effectively, organizations need to focus on several key metrics. These metrics can be categorized into quantitative and qualitative measures.
1. Cost Savings
One of the most straightforward ways to measure ROI is by evaluating cost savings achieved through Generative AI initiatives. These savings can stem from various sources:
- Reduced Labor Costs: Generative AI can automate tasks that previously required human intervention. For example, in content creation, AI tools can generate articles, social media posts, and marketing copy, reducing the need for human writers.
- Improved Efficiency: By streamlining processes, Generative AI can significantly reduce the time taken to complete tasks. This increased efficiency can lead to cost savings in project timelines and resource allocation.
- Minimized Errors: Automation reduces the likelihood of human errors, which can be costly. Generative AI's precision can help organizations avoid costly mistakes, contributing to overall cost savings.
2. Revenue Growth
Another critical aspect of ROI measurement is assessing the impact of Generative AI on revenue growth. Organizations can look at various factors, such as:
- New Revenue Streams: Generative AI can facilitate the development of innovative products and services that drive new revenue streams. For instance, AI-generated designs in fashion or architecture can open new markets and opportunities.
- Enhanced Customer Experiences: By personalizing interactions and improving service delivery, Generative AI can lead to higher customer satisfaction and retention, ultimately boosting revenue.
- Faster Time-to-Market: Generative AI can accelerate the product development cycle, allowing organizations to bring new offerings to market more quickly, thereby increasing revenue potential.
3. Customer Engagement and Satisfaction
Measuring the impact of Generative AI on customer engagement and satisfaction is crucial for understanding its ROI. Key performance indicators (KPIs) to consider include:
- Net Promoter Score (NPS): This metric gauges customer loyalty and satisfaction. By using Generative AI to enhance customer interactions, organizations can potentially improve their NPS.
- Customer Retention Rates: Increased engagement through personalized experiences can lead to higher retention rates, which directly impacts revenue.
- Engagement Metrics: Tracking metrics like website traffic, social media engagement, and conversion rates can provide insights into how Generative AI initiatives influence customer behavior.
4. Quality of Outputs
The quality of outputs generated by AI models is vital for assessing ROI. High-quality outputs can lead to better customer satisfaction and loyalty. Metrics to consider include:
- Error Rates: Tracking the error rates in AI-generated outputs can help organizations understand the reliability of their models.
- A/B Testing Results: Conducting A/B tests on AI-generated content or designs can provide insights into what resonates better with customers, guiding future AI efforts.
5. Innovation Metrics
Generative AI can drive innovation within an organization, and measuring this impact is essential for ROI assessment. Metrics to consider include:
- Number of New Ideas Generated: Tracking how many new ideas or concepts are generated through AI tools can provide insights into the innovation potential of the technology.
- Speed of Innovation: Assessing the time it takes to move from idea generation to execution can help quantify the innovation impact of Generative AI.
6. Risk Reduction
Fraud detection, quality control, compliance monitoring are all tasks that AI excels at. For insurers, banks and retailers, reducing fraudulent claims or product defects has a very real, quantifiable impact on their bottom line (which can easily be avoided with generative AI solutions).
The common “costs” of AI investments
Capturing the real ROI on AI also means being clear-eyed about the total costs. These typically include:
- Development and deployment: Buying or building the AI technology, integrating it with existing systems, launching proof-of-concepts and scaling successful pilots.
- Data costs: Collecting, cleaning, and managing data, which are often the largest “hidden” cost in AI initiatives.
- Training and change management: Teaching employees and stakeholders to work with AI, updating processes and sometimes hiring new, relevant talent.
- Ongoing operations: Maintaining, updating and monitoring AI models in production.
- Cost of migrations: It is inevitable that you'll need to change models or cloud providers as you assess and optimize for performance.
For a true picture of ROI on AI, all these costs must be included while doing a cost-benefit analysis for the respective project.
Strategies for Measuring Generative AI ROI
While metrics provide a framework for assessing ROI, organizations also need to adopt strategies that enable effective measurement and analysis. Here are some strategies to consider:
1. Set Clear Objectives
Before implementing Generative AI, organizations should define clear objectives for their initiatives. Whether the goal is to enhance efficiency, improve customer engagement, or drive innovation, having specific objectives will guide the measurement process and help determine success.
2. Establish Baselines
To accurately measure the impact of Generative AI, organizations should establish baseline metrics before implementation. This includes gathering data on current performance levels, costs, and revenue. Having these baselines will enable organizations to compare post-implementation results effectively.
3. Use a Combination of Qualitative and Quantitative Metrics
While quantitative metrics provide concrete data, qualitative metrics offer valuable insights into customer perceptions and experiences. Combining both types of metrics will give organizations a more comprehensive view of Generative AI's impact.
4. Implement Feedback Loops
Continuous feedback loops are essential for refining Generative AI initiatives. Organizations should regularly collect feedback from users, customers, and stakeholders to identify areas for improvement and measure success.
5. Conduct Regular Reviews
Periodic reviews of Generative AI projects can help organizations assess progress and measure ROI effectively. These reviews should focus on evaluating metrics, discussing challenges, and making necessary adjustments to optimize performance.
6. Leverage Advanced Analytics
Utilizing advanced analytics tools can enhance the measurement of Generative AI ROI. By analyzing large datasets, organizations can uncover patterns and insights that inform decision-making and optimize AI initiatives.
7. Benchmark Against Industry Standards
To gauge the effectiveness of Generative AI investments, organizations should benchmark their results against industry standards and best practices. This comparison can help identify areas for improvement and provide context for ROI assessments.
Challenges in Measuring Generative AI ROI
While measuring ROI is critical, organizations may encounter challenges in this process:
1. Data Availability and Quality
Accessing high-quality data is essential for accurate ROI measurement. Organizations may struggle with data silos or inadequate data collection methods, hindering their ability to evaluate AI performance effectively.
2. Attribution Difficulties
Determining the direct impact of Generative AI on specific outcomes can be complex. Organizations may find it challenging to attribute improvements in metrics solely to AI initiatives, as multiple factors can influence performance.
3. Technology Changes
The rapidly changing nature of generative AI makes it difficult to establish long-term ROI metrics. Organizations must stay informed about industry trends and adapt their measurement strategies accordingly.
How to Improve Generative AI ROI
The typical company spends 12 - 18 months on running generative AI pilots. Based on our analysis of SMB and enterprise AI projects, they spend $500,000, which includes the cost of infrastructure, development, testing, deployment, and API costs.
Unfortunately, only 5% of these pilots deliver meaningful business impact because many companies fail to account for technical debt, integration challenges, ongoing maintenance complexity.
A fine-tuned implementation that consumes 8% fewer tokens because of an improved schema implementation could well be the difference between positive RoI and a failed pilot. In generative AI, like in other IT investments, the quality of engineering matters.
If you are keen to improve the ROI of your Generative AI initiatives, here is where you should focus on:
Discovery
The easiest use case is probably not the best use case to truly assess whether Generative AI will pay off for you. Enlist the help of an experienced AI consulting company who have already done this for other companies. Frontloading your planning is the best way to begin your gen AI journey.
Timelines
Your best chances of maximizing and proving Generative AI ROI is if you build on an accelerated timeline. Think 4-day POCs, 4-week pilots, and 4-month Gen AI implementations instead of lengthy 12 - 18 month projects. We recommend working with gen AI accelerators and frameworks to speed up your time to production.
Investments
Beyond the lower costs of shorter implementations, aim to keep your costs of discovery, POC, pilot, production, and testing to $50,000 - $250,000+. Remember, at scale, your inferencing costs will rise. Again, we recommend working with a proven AI software development company to ensure the outcome of your Gen AI project is not affected by the lack on internal AI engineering expertise.
Adoption
Adoption is where most gen AI projects fail. The future belongs to organizations that can successfully deploy production-ready AI. This is where your models also need to deliver >90% day 1 accuracy (which is an engineering challenge). And, that also means integrating gen AI in the tools and workflows that your teams already use.
Measuring the ROI of Generative AI is essential if you want to set yourself up for long term wins. By focusing on key metrics such as cost savings, revenue growth, customer engagement, accuracy of outputs, and innovation metrics, businesses can gain valuable insights into the effectiveness of their AI initiatives. Implementing clear strategies for measurement, overcoming challenges, and regularly reviewing performance will enable organizations to optimize their Generative AI investments, ensuring long-term success in an increasingly competitive landscape.
As the adoption of Generative AI continues to rise, organizations that prioritize effective ROI measurement will be better positioned to leverage this technology for sustainable growth and innovation. By understanding and applying the principles outlined in this blog, businesses can make informed decisions, drive strategic initiatives, and ultimately maximize the benefits of Generative AI.
