AI/ML Product Success Metrics

When you are building an AI/ML product, it’s paramount that you define clear success metrics from the beginning.

These metrics will help guide the AI product development lifecycle and ensure that your team converges on the right product that solves business problems/user needs.

There are two ways to assess AI/ML product success:

1) Business outcomes

Business outcomes are the most important success metrics for AI products (and AI adoption in general). These are business objectives that result in tangible value created and captured by AI/ML products.

For example, if an AI product helps streamline a process that is currently costing a company $5 million/year in manual labor, that is tangible value.

AI/ML products are never isolated from the company; they are always part of a larger business process. Therefore, it’s not enough to think in terms of the product – you must think about the business impact of the product.

How would you measure the business impact of the AI product?

Compare the business process with the AI product and without it. Since you are improving an existing business process, it’s much easier to assess the added value of the AI/ML product/solution. This is why we always recommend choosing a problem you are already solving when selecting an AI/ML use case (check out my blog post titled, “Lessons From Millionaire AI Entrepreneur” for more information).

It’s important to track and measure every step of the business process that is relevant to the AI/ML product – that way you are always making data-driven decisions for the direction of product development.

When defining clear success metrics, consider the following questions:

  • Is the product improving the company’s bottom line? How much?
  • Is it helping generate revenue? How much?
  • Is it improving operational efficiency? How do we quantify it?
  • Is it contributing to other forms of intangible business value? Explore.

Based on the answers to these questions, establish clear business objectives with associated quantifiable metrics to ensure the AI/ML product is truly creating business value.

2) Model outputs

Machine learning is using data to answer business questions. Machine learning algorithms/models learn to make business predictions after learning from data (this process is called training).

Model outputs are the business predictions made by machine learning models.

Let’s look at two different prediction problems:

  • Fraud detection: Classifying a financial transaction as fraud or not fraud.
  • Spam detection: Classifying an incoming email as spam or not spam.

Both of these use cases are examples of binary classification, which means the machine learning models predict one of two possible values. During training, the models learn what is the correct answer for a given transaction or email (check out my blog post titled, “What Is Supervised Learning?” to learn more).

There are 4 possible ML model outputs here for a given transaction or email. We will first use email spam as an example:

  • True positive: The email is truly spam, and the model classified it as spam.
  • True negative: The email is truly not spam, and the model classified it as not spam.
  • False positive: The email is truly not spam, but the model classified it as spam.
  • False negative: The email is truly spam, but the model classified it as not spam.

True positives and true negatives are great because the models’ predictions match the truth.

False positives and false negatives are mistakes made by machine learning models. These mistakes are expected because ML models can never be 100% accurate.

The distribution of all 4 possible model outputs represents the accuracy/performance of machine learning models.

We have two main accuracy metrics: Precision and recall.

In practice, there is a tradeoff between precision and recall. Increasing precision means decreasing false positives. Increasing recall means decreasing false negatives.

Every AI/ML use case is different. The business impact of false positives/negatives also varies with each use case, and you should choose whether precision or recall is a better metric to optimize for.

In the case of spam detection, false positives are worse than false negatives. If an incoming email is truly not spam but it’s classified as spam (false positive), then we may miss/lose important information. If an incoming email is truly spam but it’s classified as not spam (false negative), we can simply mark it as spam ourselves from our inbox. The latter is a much better situation than the former. Therefore, precision is a good metric to optimize for.

In the case of fraud detection, false negatives are worse than false positives. If a financial transaction is truly fraud but it’s classified as not fraud (false negative), then the customer will have to resolve the issue, waste time, and perhaps struggle with a dispute. If a financial transaction is truly not fraud but it’s classified as fraud (false positive), then the customer can simply receive a notification from the bank to confirm or deny the transaction. The latter is a much better situation than the former. Therefore, recall is a good metric to optimize for.

Clearly, false positives and false negatives have different business impact depending on the AI/ML use case. Decide whether precision or recall is more important and optimize for it.

Ideally, AI/ML products should have a built-in feedback mechanism to flag false positive and false negatives to identify weak spots in model performance and re-train accordingly. Check out my blog post titled, “Bias In Artificial Intelligence” to learn ways to maximize model performance.

If you need help to accelerate your company’s machine learning efforts, or if you need help getting started with enterprise AI adoption, send me a LinkedIn message or email me at info@carloslaraai.com and I will be happy to help you.

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