The Metrics of Artificial Intelligence: How to “Measure” the Impact of AI in Business

ai metrics in business

Ok, let’s talk about numbers.

Don’t worry, no math lessons involved.

Instead, we will focus on how to quantify, using proper metrics, the impact of AI on your business.


Numbers and metrics

Each company should know the real status of its processes, activities, and routines.

The right way to know it is to use measurements: metrics.

There are many methods and approaches for quantifying incomes and results, profits and costs, customer satisfaction, eccetera.

But what about AI? How to understand and measure the impact of AI on your business?

If we have to evaluate this impact, we will need some numbers, some metrics.


A practical example

I’ll use an example in the article to make it clearer.

Our example is an abstract “AI Matcher” AI project. The essence of this project is to match two huge databases (several million rows each) with each other and find the best matches.

The owner of the AI ​​Matcher is a hypothetical investment company.

It’s easy to imagine this project in real business: matching goods and customer expectations, matching job offers and candidates, matching signs of illness and possible diagnosis, and so on.

Therefore, this example is quite common for AI projects.


The business metric

Business is money. So … the main business metric is money flow.

Every business task, requirement, goal, and value can be represented as money.

If you have decided to start an AI transformation in your business, an artificial intelligence project should bring you added value.

A good way to understand this is to use measurements. You will need it for at least three good reasons:


1. Project results should be measurable.

At least this will be important for the contract and technical specifications. The project should have clear success and failure criteria.


2. You should know how the project goes.

Is it in the right direction or not? (AI projects can have many right directions). The project development team should know the metric.

A specific feature of AI projects is they can be very unclear for business preliminary results. If you do not have a project metric, you will not be able to understand them and make correct decisions.


3. You should cleverly understand the project value.

This will be extremely important for business planning. One more specific feature of AI projects is that they can be finally tested just on real data in real usage.

So, you need to clearly understand the real impact on your business from AI.


How to specify the metric?

Profit or its money representation will be a very high-level starting point for AI measurement.

How to do it? Simple! Follow these three steps:


1. Choose how to assess the project’s impact on your business.

The impact can be represented as time or resource (materials, money, etc.) savings for employees or manufacturing processes, as an increase in the number of clients, as direct additional profit or savings, and so on.


2. Select the level of impact that you expect to get from the project.

The level should be based on measurements and adjusted for your business.

You can use information about other successful AI projects and you should choose two scenarios for the assessment: pessimistic (low impact level), optimistic (high impact level).

From my experience, the approach with pessimistic and optimistic scenarios is the most accurate and successful, because it is more reliable in case of risk.

For most projects, the difference between these two assessments should be within 20-25%. This limitation will help you to be more careful in your prognosis.


3. Turn your impact levels into cash.

It means you should calculate the profit that