A Brief Guide to measure AI Project Results

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Until now, we discussed why AI in a company is important, then the importance of data, and how to create an artificial intelligence project step by step. Now it is time to talk about why you should care about measuring the results of the project!
Why is it important to measure results? Measurement is a significant part of any project. It lets us know if we are on track towards our goals and if there is any need for change.

We will look at different ways you can measure your AI project’s success and offer advice on how to optimize your project for better performance.

The Importance of Measuring the Results from an AI Project

It is essential to measure the results of an AI project. Otherwise, it would be difficult to find out whether the project was successful or not. How do you measure Artificial Intelligence success?

Usually, you measure projects by looking at four metrics: time, cost, quality and scope.
When it comes to artificial intelligence projects, the best way to start measuring your results is by determining what your objectives and goals are for this particular one.
Also, different strategies and tools claim different outcomes.

With AI projects, there are new metrics that need to be taken into account when measuring success – some of them are data volume, data accuracy and data timeliness.
Also, you should not forget, there are two aspects that you need to consider: quantitative indicators and qualitative indicators. Quality indicators can include both objective measurements like speed or subjective measurements like user experience.

How to measure Your AI Project Results

The challenge with assessing ROI for an AI project is that there are many factors involved in the process, so it’s difficult to evaluate what caused any changes in revenue. Having more variables is surely a great element for your results, but less for your measurements.

There are some general success metrics for AI projects that we can use to determine the success of an AI project. Some of these metrics include:

Accuracy: The accuracy is determined by how closely the AI system is able to predict the correct answer.

Variance: The variance refers to how much variation there is in responses given by different users on a given task. A low variance means that all users give similar responses, while a high variance means that there is a lot of variation in the responses given by different users.

Recall: This measure tells us how successful a system was at retrieving data from storage and presenting it back to the user. A high recall rate indicates that all relevant data has been retrieved, while a low recall rate indicates that many relevant pieces of data have been missed.

What are the Best Metrics for AI Projects?

In order to measure the success of an AI project, it’s important to identify what success looks like for that specific project.. Which metrics should you consider while working on an artificial intelligence project?
The metrics always depend on your goals, as we were saying, like a lead campaign will differ from a sales one in terms of results. The best thing you can do, as we often say, is ask yourself the right questions. There is nothing better than having the right questions in mind to start finding the answers to improve your project. Otherwise, you will have no map to guide you.


Some metrics you may want to use, according to your specific issues, are margins, business impact assessment, and more.
You can have both business and technical metrics to explore, according to the project and to your business.

Not all the questions fit all projects. Some more generic questions may be: 

  1. What was the time taken to make a single output?
  2. How accurate were these outputs?
  3. How much data was processed in order to generate a single output for a given task?
  4. What does it cost to run the project on a monthly/yearly basis?
  5. How many people did it take to build and debug this system?
  6. What is the total amount of time that has been spent on this project over its lifetime?
6 questions to ask yourself after finishing an AI project
Did you ask yourself these questions?

6 Easy Steps to Keep Track of Results from the AI Project

Keeping track of results is not just a way to see if everything went out correctly. It also helps in understanding which variables have had a positive or negative effect on the data being collected, which is essential to improve.
However, let’s get down to business. Here are 6 easy steps to keep track of results from artificial intelligence projects:

1. Define organizational main KPIs. No KPIs, no results! There is nothing worse, as we were saying, than not defining the more urgent elements to measure. 

2. Identify how you’ll measure success. It is exactly what we were saying earlier – you need to know which goals are for you the most valuable. In which case will you consider the project a success? According to which variables and parameters? Pick them, and you will have your answers.

3. What are the available metrics? There is a difference between what you want to measure and the metrics that you can actually measure. So, be sure to know which data and variables you have the chance to see, so you don’t focus attention, time and effort on something that you will never be able to verify.

4. Set up your test. Launch the project on the field and start to collect data about the project. You can use it in a dedicated testing environment first, and then move it in production. Remember: the earlier the system will start to work in production the fastest it will face the exceptions and all those everyday procedures that really put the system in challenge. It will make more mistakes at the beginning, but in the long term you will succeed faster.

5. Develop an A/B testing process. A/B testing is one of the most common and safest ways to see how a project is going. Thanks to artificial intelligence, you can select and review several variables at once, which will give you more control over the results.

6. Evaluate and report back. Now you have all your data, your tests and your results. Time to write them down to evaluate them and create something new from them. Let your artificial intelligence algorithms help you to see how they went, confronting them with other results from previous years. And then, it’s time for new adventures!

6 steps to keep track of results
6 steps to keep track of results

Conclusions

An artificial intelligence project is not something easy and straightforward, but it is a great chance to improve your brand and create something new. It is the reason why measuring your results is crucial to improve and knowing what to do in the future. If you have zero clue on where to start, contact us! We will be happy to support your next AI project with our long term deep expertise.

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