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.
What is the most important element that will determine the success or failure of an AI/ML project?
Most people, including technical professionals in the field, would think it’s the datasets: Quality, quantity, and a data engineering pipeline to produce them. This is because machine learning algorithms perform only as good as the data used to train them.
However, business leaders are quickly realizing that the most important element of AI adoption is actually defining the business problem(s) correctly.
How do you build products that leverage machine learning?
Machine learning is using data to answer valuable business questions.
Answering these business questions should lead to the creation of tangible business value. This could be increased revenue, decreased costs, increased retention rate, increased operational efficiency, etc.
Therefore, always focus on the business impact of artificial intelligence when building AI/ML products.
“Should we build everything from scratch, use third-party machine learning services, or a combination of both?”
When you identify AI/ML use cases for your company (or your company’s clients) that will lead to business value and ROI, you need to know the various implementation options.
Every company is different: Each has its own business objectives, customers, AI adoption roadmap, data strategy, AI monetization plan, timeline, and resources.
It’s important that you and your team choose the right tools for each use case. There is no absolute right or wrong answer, and you must be open to change if the initial tool selected turned out to be inappropriate for a given use case.