34 AI KPIs: The Most Comprehensive List of Success Metrics

34 AI KPIs to Use in Business

The success of your AI initiatives doesn't just come down to model performance. It also depends on a variety of other factors the model’s robustness, accuracy, quality of data, integration abilities and more. The KPIs below will help you measure all of these aspects.

Assessing AI Models - Evaluating Effectiveness and Efficiency

1) Accuracy

Accuracy pertains to how often and correctly an AI model predicts outcomes. It’s an essential KPI for evaluating model performance, ensuring reliable results, minimizing errors, and improving effectiveness.

This KPI can show how well an AI model understands data and how accurate its processing of data is.

2) Precision and recall

Precision KPI helps showcase the AI model’s ability to make true positive predictions while disregarding false positives. This helps ensure higher relevancy and quality of predicted outcomes.

Recall indicator can evaluate the model’s true positive predictions compared to all positive instances in the data. This helps reduce false negatives, improve correctness, and eliminate the possibility of a missing positive instance leading to big consequences for the business.

‍3) F1 score

F1 score is a performance metric that combines precision and recall KPI in one number to provide an easier AI evaluation. This KPI helps balance precision measures against recall measures to give the exact number of actual positives.

This metric is useful for dealing with uneven classes and datasets as it ensures the AI model performs better in identifying relevant cases and minimizes incorrect predictions.

‍4) Area under the ROC curve (AUR-ROC)

AUR-ROC measures the model’s capability to differentiate between positive and negative classes. A higher AUR-ROC value indicates better identification of positive cases while minimizing false alarms.

This is a valuable metric to assess classification models and their accuracy.

Assessing Data Quality - Evaluating Data for Higher Reliability and Performance

5) Completeness

Data completeness shows if all needed data is available in the dataset. High completeness ensures no information is missing, which is crucial for the AI’s accurate training and analysis.

Incomplete data leads to unreliable results which can impact business decisions, which is why we highly recommend determining completeness.

6) Timelines

Outdated data can lead to incorrect insights, which affects AI’s performance. Measuring the timeline indicator helps you understand how up-to-date your data is.

High timelines mean that data reflects the most current information, which leads to better accuracy and relevant decisions. This metric can be especially useful if you’re training an AI model using your data.

7) Uniqueness

Uniqueness measures duplicates within the dataset. High uniqueness ensures that each data point is only recorded once, which helps prevent redundancy and ensures better data integrity.

Duplicate data can distort insights and lead to inaccurate outcomes.

8) Integrity

Integrity indicator measures the accuracy and reliability of data relationship with the dataset.

Higher integrity indicates correct and maintained consistency, which supports a better decision-making process. Poor data integrity, on the other hand, can lead to flawed AI outcomes, errors, and misinterpretations.

9) Accessibility

Data accessibility indicates how accessible data is to users and other systems. High accessibility leads to better data availability for model training and analysis without disruptions.

Limited accessibility can reduce the effectiveness of the AI’s model to promptly make accurate decisions.

10) Error rate

Error rate indicators can measure dataset anomalies. The lower error indicates high-quality and reliable data. Higher error rates can lead to incorrect predictions, flawed insights, and inaccuracies that can negatively impact business decisions.

11) Bias detection

A bias detection indicator is useful in measuring the presence of biases within the dataset. Effective bias detection leads to better data representation, which reduces the risk of distorted outcomes.

Undetected bias can lead to discriminatory decisions and undermine the fairness and reliability of the AI model.

12) Data quality score

A data quality score is a metric that combines various indicators of data quality, like accuracy, completeness, consistency, and timelines, into one score. A higher data quality score ensures that data meets high standards across all quality dimensions, which ensures better accuracy and reliability.

Analyzing Performance - Gaining a Clear Understanding of Model’s Operational Efficiency

13) Response time

Response time helps measure the time it takes the AI model to deliver results after receiving an input. Shorter response times lead to faster and more efficient systems, which is important for real-time apps.

It’s highly recommended to measure this key performance indicator to ensure a lower response time and maintain a smooth user experience, which improves user satisfaction.

14) Throughput

Throughput is an indicator that measures the number of tasks an AI system can process in a specific time frame. Higher throughput indicates better performance and higher efficiency, which means that the AI model can handle more requests simultaneously.

It’s a good idea to track this KPI for AI models that have a direct impact on productivity and user satisfaction, as it can help you understand how responsive your AI model is.

15) Error rate

The error rate is a simple indicator that helps determine the ratio of incorrect outputs produced by the AI model compared to the total number of generated outputs. A lower error rate indicates a much better AI performance with higher accuracy and reliability.

We highly recommend monitoring the error rate indicator to maintain your AI model’s quality and performance levels.

16) Robustness

A robust AI model can maintain consistent performance across a variety of inputs, which includes unexpected data. The robustness indicator will show how adaptable an AI model is to various scenarios without a drop in accuracy and reliability.

This indicator can also give insight into how effective an AI model is in real-world use.

Assessing AI’s Business Impact - Measuring Tangible Business Benefits

17) Cost savings

Cost savings measure the reduction in expenses achieved through AI’s automation efforts. For example, you can track the reduced costs associated with labor, resources, and time-to-decision.

Tracking cost savings can help businesses track the financial impact of their AI investment.

‍18) Time savings

Time savings indicate the reduction in time needed to complete tasks after the implementation of AI solutions. Automation of repetitive tasks and enhancement of decision-making processes help speed up task execution.

Monitoring this indicator can help the organization improve productivity and efficiency, and achieve a better overall resource allocation.

19) Revenue growth

Revenue growth indicators measure growth in sales and new business opportunities generated with AI’s help.

Leveraging AI solutions can help tailor and personalize products and services to improve customer experience, and also lead to the company’s revenue growth by, for example, allowing you to serve more customers in less time.

Tracking the growth helps assess the financial benefits of AI implementation and also refine automation strategy when needed.

20) Customer satisfaction

AI can improve customer experience in many ways and this indicator helps track those improvements after the AI implementation. Monitoring this key indicator can help businesses understand user needs, enhance and customize their product offerings, and eventually improve retention and growth.

21) Employee productivity

Employee productivity measures the increase in output per employee after the AI implementation and automation of repetitive and labor-intensive tasks.

With such automation initiatives, employees get a chance to focus on higher-value work, while companies that track employee productivity can evaluate the impact of AI on the workforce’s productivity and efficiency.

Analyzing User Experience - Improving Interaction Quality

22) Customer effort score

CES measures how easy it is for users to achieve their goals using the AI system. A lower score indicates a user-friendly experience but also reduced effort required for completing tasks. This indicator helps businesses track friction points, but also learn how they can enhance overall user experience.

23) Task success rate

Task success rate is a similar indicator to customer effort score, but it measures the percentage of successfully completed tasks by any end user — including employees. A higher success percentage indicates AI’s effectiveness, which shows how well the AI solution meets the user’s needs.

24) Time on task

Time on task indicators measure the time users take to complete tasks using the AI solution. A shorter time indicates an efficient and user-friendly experience.

By measuring this metric, businesses can evaluate the AI’s useability, streamline processes, and reduce user effort in task completion.

25) User retention rate

The user retention rate indicates the percentage of users who continue using the AI system. Higher retention rates indicate a satisfying user experience and great engagement. This metric helps assess the long-term value of the company’s AI model but also ensures the model meets user needs.

You can also use this metric to evaluate customer satisfaction, as described above.

26) Latency

The latency metric measures the AI’s response time from the user’s perspective, where lower latency indicates faster interactions and better user experience. It’s highly recommended to measure latency for real-time AI systems.
This indicator can help you improve overall system efficiency, ensuring enhanced user experience and less frustration.

27) Helpdesk inquiries

The helpdesk inquiries indicator measures the number of received support requests or questions regarding the AI system. Fewer inquiries indicate a user-friendly experience.

A higher number of helpdesk inquiries can help businesses identify pain points that need work, which eventually leads to better satisfaction and efficiency.

Assessing Compliance and Ethics - Ensuring Legal Compliance

28) Regulatory compliance rate

The regulatory compliance measures a percentage of AI processes and outputs that adhere to relevant regulations, industry standards, and laws. A high compliance rate indicates good AI system operation within legal frameworks, with correct risk mitigation and ethical practices in place.

Relying on this indicator helps maintain trust and accountability for the AI solution.

29) Ethical standards adherence

Ethical standards adherence evaluates to which extent the AI model complies with ethical guidelines. A higher adherence rate can indicate AI’s fairness, transparency, accountability, minimized bias, and responsible use.

30) Data privacy and security scores

This indicator measures the effectiveness of practices that protect sensitive user information and compliance with data protection regulations. Higher data privacy and security scores ensure better security against unauthorized access and breaches.

Monitoring this KPI helps maintain user trust, protects the company’s reputation, and adheres to legal obligations.

31) Audit frequency and results

This KPI measures how often an AI system is audited, as well as the outcomes of those audits. Higher audit frequency means positive results that indicate regular and thorough examination of AI processes.

This ensures the AI model meets compliance, ethical standards, and expected performance. Therefore, measuring this metric can help you maintain transparency while continuously improving the AI model.

Analyzing Return on Investment - Measuring Financial Success, Returns, and Business Growth

32) ROI

ROI measures the return on AI investment relative to its cost. This indicator helps businesses assess progress and the effectiveness of AI projects in generating revenue and cutting costs.

If you need help calculating an AI ROI, check out our AI ROI calculation post or ask our experts to calculate AI ROI for your business.

33) Market share growth

Market share growth represents the growth a company experiences after implementing AI. This indicator reflects competitive positioning, customer acquisition, and retention. A higher number always indicates successful AI strategies in the expansion of a company’s presence.

34) Scalability and flexibility

Scalability measures the AI’s ability to expand its capabilities without compromising its performance. The flexibility indicator assesses the AI’s ability to adapt to new tasks or changes, where a higher score ensures that AI can grow with the business.

Calculate AI KPIs With Multimodal

Do you need help calculating AI ROI or implementing AI into your workflow? Please schedule a 30-minute call with our experts. We can discuss your needs and demo our AI solutions, which seamlessly integrate with your existing systems.

FAQs

How do you measure the performance of Gen AI?

Measuring Gen AI’s performance ensures better effectiveness and optimization of its use in various case scenarios. Key performance indicators are crucial as they help evaluate the outputs across various integrations.
This is what measuring the performance of Gen AI looks like:

1. Defining business objectives and use cases
2. Selecting appropriate KPIs to measure performance
3. Collecting the model’s input and output data
4. Evaluating collected data according to chosen KPIs
5. Comparing results with projections
6. Analyzing results and continuously monitoring and improving

Identifying goals and setting use cases early on in the measurement process is important. This will help you choose relevant KPIs you can take measures against.

After you have measured the performance for a variety of KPIs, you can compare the results with projections made before the AI implementation.

Such a comparison will help you measure AI’s performance metrics like accuracy, efficiency, performance, its impact on your business, and additional metrics.

Can I use these KPIs for test automation?

Yes, you can use KPIs for test automation to gain insights into the effectiveness of the test automation efforts, which can also help you identify areas for improvement.

Some of the best KPIs for test automation include:

• Execution time
• Automation rate
• Time to feedback
• Return on investment
• Cost savings
• Test maintenance effort

Should I opt for manual or automated testing?

Choosing between manual and automated testing depends on the size of the project, complexity, budget, and goals. Here’s a quick guide to help you make the right choice for your needs.

Test automation KPIs are ideal if you’re dealing with:

• Many repetitive tasks
• Large test cases
• Continuous integration
• Performance testing
• Testing across multiple platforms

Automation testing is also ideal if you want to achieve better long-term cost efficiency after the testing process. If you’re dealing with complex projects, automation testing teams can help simplify the process.

However, if you’re dealing with the following cases, you might want to opt in for manual testing:

• Exploratory testing
• Short-term projects
• Frequent changes
• Complex user interaction
• User experience testing

Keep in mind that you can combine both manual and automated tests for a balanced approach that leads to high-quality results.

What are the benefits of using AI KPIs?

Using AI KPIs helps:

• Improve the automation testing process
• Improve testing skills and testing efforts
• Measure performance
• Make data-driven decisions with higher accuracy
• Drive continuous improvement
• Ensure transparency and accountability
• Allocate resources better
• Manage risk early in the AI development process
• Improve user satisfaction

There are many more benefits, depending on the way you implement AI in your business and which KPIs you choose.