Measuring Generative AI ROI: Key Metrics and Strategies

Generative AI implementation has been a strategic approach to streamlining the operation system, with the market size worldwide intending to gain $45 billion in 2023, according to Statista. Regarding objectives when adopting GenAI, McKinsey reports reveal that a high percentage of high-performer businesses want to increase revenue from core services (27%), create new revenue sources (23%), and increase the value of existing offerings (30%).

Therefore, measuring Generative AI ROI is essential to channel resources properly to gain target revenue. However, evaluating GenAI investment effectiveness encounters challenges in isolating its contribution from other efforts throughout the operation.

This article will discuss the key metrics, KPIs establishing guides, and strategies to maximize the return on investment when implementing Generative AI for businesses.

The Importance of Measuring GenAI ROI

Generative AI’s ROI reflects the businesses’ financial benefits from their investment in AI technologies. The measuring practice benefits enterprises in the following ways:

• Optimizing implementation: By measuring GenAI’s impact in specific operational areas, businesses can leverage their high-performing parts and improve less effective ones.
• Securing funding for investment: Despite the potential benefits of AI technologies, their implementation decisions should be based on successful cases to convince managers and stakeholders to approve the budget and relevant resources.
• Tracking progress and performance: Establishing ROI metrics allows businesses to improve performance with real-time adjustments to adapt to business needs and market conditions.

How to Calculate the ROI of GenAI Projects

Dividing the net gain from an investment by the implementation cost results in the return on investment number.

Regarding GenAI ROI, the calculation follows the formula:

ROI = (Financial gains from GenAI – GenAI implementation cost)/ GenAI implementation cost * 100%

Businesses use tools and software for data-driven analysis to quantify ROI. For example, they manipulate data using Python libraries, visualize data using Tableau, and conduct statistical analysis with R software.

The sequential steps to perform a data-driven analysis include:

• Establishing objectives and KPIs: Define business objectives and crucial KPIs for success measurement.
• Gathering and analyzing data: Analyze data after collecting critical operational metrics, financial records, and customer feedback.
• Optimizing AI strategy based on analyzed results: Evaluate the impact of Generative artificial intelligence on business value by interpreting data insights and planning strategies.

Key Metrics for Measuring GenAI ROI

Measuring Generative AI ROI considers operational, quality, adoption rate, and marketing & sale metrics to optimize implementation cost and achieve long-term objectives.

Operational Metrics

• Development Time Savings: Measure the time saved after implementing GenAI to automate tasks like generating code and content.
• Defect Rate: Compare the amount of technical issues when using GenAI to that of traditional methods.
• Development Cost: Quantify the reduction in operational cost when using GenAI compared to manual labor in specific tasks.

Quality Assurance Metrics

• Test Case Generation: Track the accuracy and effectiveness of AI-generated test cases in improving the operational system compared to manual testing methods.
• Bug Detection Rate: Quantify the number of bugs identified by AI tools during every testing taste compared to traditional methods.
• Software Output Quality: Check the quality of AI-based software to see whether it provides a satisfying customer experience or contains minor errors after launch.
• Test Execution Time: Track the time reduction for fixing a bug by automating test cases with GenAI.
• F1 and BLEU Score: These are two standard metrics for evaluating AI-driven output accuracy. The F1 score evaluates the accuracy of trained AI models regarding the frequency of correct predictions based on the dataset. Meanwhile, the BLEU score helps justify the translation features and measure the resemblance between machine-translated text and reference translations.

Marketing Metrics

• Lead Generation: Calculate the qualified leads gathered through AI-driven marketing campaigns and inbound and outbound content marketing.
• Customer Acquisition Cost: Track whether GenAI implementation reduces the cost of acquiring new customers.
• Website Traffic: Track the impact of AI-generated content in increasing website traffic and engagement.
• Conversion Rate: Measure the number of users who take action after interacting with AI-generated blog posts, banners, advertisements, or videos.

Customer Service and Sales Metrics

• Call Center Volume: Measure how AI-powered chatbots and self-service options can automatically respond to customers’ queries, reducing call center volume.
• First Contact Resolution Rate: Measure the frequency with which GenAI-based support solves customers’ issues in the first contact.
• Customer Satisfaction Score (CSAT): Identify the impact of personalizing and streamlining customer experience on their satisfaction levels.
• Qualified Sales: Track the number of customers qualified from the sale funnel with the support of AI-powered chatbots.
• Sales Cycle Length: Calculate the deal closing time to evaluate the effect of AI-powered sales support tools in accelerating the sales cycle.
• Sales Team Productivity: Track the number of increasing leads a sales agent can serve under the support of AI-driven proposals and other automated tasks.
• Deal Closure Rate: Measure the frequency of successful sales conversions when implementing GenAI solutions.

Adoption and Impact Metrics

• Adoption Rate: Track the percentages of the organization’s employee implementing GenAI applications for their work.
• Use Frequency: Track the difference in user’s frequency of using the Generative AI system to give queries per day, per week, or per month.
• Employee Productivity: Track how much strategic work an employee can do after being released from manually repetitive tasks. Besides, businesses can measure their job satisfaction and engagement levels to justify the impact of GenAI solutions.
• Revenue Growth: Measure the revenue increase from offering AI-driven products and services. Notice the growth in market shares and new customer acquisition to calculate the GenAI ROI correctly.
• Time-to-Market: Track how GenAI can accelerate product development, from creating prototypes and testing to launching them into the market.

How to Select The Right Metrics to Measure AI Tools’ ROI

Choosing appropriate metrics to measure GenAI effectiveness relies on which challenges need addressing and the project’s objectives.

The complete guide to selecting the right metrics to measure GenAI ROI includes:

1. Define the business objectives: Each business objective requires measuring specific metrics to track its effectiveness. For example, if businesses aim to enhance customer satisfaction, customer service metrics should be the priority. Additionally, tracking the mainstream adoption rate, AI development time, and cost metrics is essential if businesses intend to improve the operational system’s productivity.
2. Define types of AI models: Different AI types, such as machine learning and large language models, focus intensely on specific metrics. For instance, businesses should track accuracy and performance metrics to monitor predictive analytics involved in AI projects.
3. Balance leading and lagging indicators: Leading indicator metrics, like the number of customer calls, purchased products, or sales appointments, are associated with future business outcomes. Simultaneously, lagging indicators, such as customer satisfaction score, expense costs, or revenue per employee, reflect past events to generate insights about the overall company performance monthly or annually.
4. Compare selected metrics to industry standards: Evaluate the GenAI project’s performance by comparing chosen metrics to industry standards or benchmarks.

Strategies to Maximize GenAI ROI

According to an IBM study, to maximize GenAI investments, businesses must optimize six core AI capabilities: vision and strategy, AI models, talent and skills, data and technology, AI engineering and operations, and culture and adoption.

Here are the 6 steps businesses can apply in balancing the above capabilities to transform into an AI-powered enterprise:

• Step 1 – Establish an AI Steering Committee: Organize a committee of C-suite executives like the CEO or Chief Technology Officer to develop a strategic vision for GenAI applications that aligns with business goals and objectives. This committee is also responsible for sharing privacy information and approving GenAI programs’ proposals.
• Step 2 – Outsource GenAI Consulting Services: A professional GenAI implementation consulting service can eliminate the gap between technology knowledge and business needs. Based on experience in explaining AI technology terms in measuring business outcomes, GenAI consulting services can convince related stakeholders to invest in GenAI applications.
• Step 3 – Assess GenAI Readiness and Develop a Plan: Before applying GenAI, evaluate the existing infrastructure, employees’ skillsets, tooling, and data availability to identify limitations. Based on the initial assessment of GenAI’s potential, business leaders can strategize a suitable implementation roadmap to increase business value.
• Step 4 – Evaluate GenAI’s Scalable Potential: AI efforts should deliver value for the entire business network using a holistic ecosystem approach.
• Step 5 – Build a Competent Team: Streamline the collaboration between GenAI steering committees and operational teams to ensure relevant execution and achieve results for strategic metrics.
• Step 6 – Embrace GenAI-First Culture: Promoting a high GenAI adoption rate in the business triggers related roles with continuous learning and increased awareness. Indeed, business leaders should explain to employees the benefits of GenAI and their responsibility to adapt to changes in the operation system.      

Measuring Generative AI ROI Case Studies

The International Data Corporation (IDC) survey, sponsored by Microsoft, revealed that, on average, one business receives $3,5 in return for every $1 invested in AI. Meanwhile, 5% of worldwide enterprises witness a higher ROI of 700% ($8 in return for $1 invested).

Real-world examples of GenAI effectiveness with impressive ROI results include:

• Stick Fix (E-commerce industry): The fashion brand integrates GenAI technology to personalize customer clothing recommendations based on their preferences and data analysis. This practice has resulted in higher customer engagements, conversion rates, and online revenue. Moreover, Stick Fix earns more customer loyalty while enhancing their satisfaction with a streamlined shopping experience.
• Paige.AI (Healthcare industry): The Generative AI platform facilitates accurate cancer diagnosis by generating insights from the analysis of digitized pathology slides. The faster and more precise cancer diagnosis supports the development of advanced treatment decisions, reducing healthcare costs and improving patient outcomes.
• Amper Music (Entertainment industry): The company enables content creators to produce original music tailored to their mood and style with Generative AI. The AI-powered features have reduced the cost of making music for creators’ content and attracted more service demands, leading to a higher Return on Investment.

Challenges in Measuring Generative AI ROI

Measuring Generative AI ROI faces different challenges regarding data management and business environment matters. Moreover, implementing artificial intelligence technology must employ ethical uses to avoid violating moral standards.

Common challenges in calculating Generative AI projects’ ROI encompass:

• Data complexity: A large volume of low-quality data negatively affects the integrity of AI output. Thus, gathering consistent, accurate, accessible data ensures quality training and operating performance.
• Business environment changes: Unpredictable changes in internal and external business environments impact the relevancy of chosen metrics. In fact, the organization’s strategies can be redirected due to changes in management policies, technology advancements, or economic shifts.
• Installation and operational costs: Implementing GenAI applications faces financial considerations with high upfront investments and ongoing maintenance costs.
• Ethical and regulatory considerations: Sensitive customer information and industry regulations challenge most companies to deploy data for specific purposes. Therefore, AI project implementation should follow strict policies to promote responsible use and avoid improper content.

Learn more: Data for Generative AI

Maximize Generative AI ROI with Neurond AI Services

Neurond Generative AI consulting services support drafting an AI implementation roadmap for your business needs. Based on experiences identifying the potential of scaling your businesses, we analyze the low-hanging fruit use cases to maximize implementation efficiency.

Additionally, we offer ongoing support and optimize operational and outcome metrics to measure Generative AI ROI accurately for strategic decisions.