Your Roadmap to AI and ML Deployments
Don’t adopt artificial intelligence and machine learning without having a real business problem to solve. Here are some ways to help build the right use cases.
As the hype around artificial intelligence hits an all-time high, it is more important now than ever before for enterprises to find practical use cases for AI and machine learning deployments. But unfolding the ever-expanding map of possibilities posed by these technological advances can get tiresome, and many organizations lack the expertise to truly understand when AI and ML can add value — and when they can’t. Despite how excited your team may be to leverage the power these tools offer, in both product development and for business decision-making, a lack of comprehension will serve as the greatest roadblock to benefiting from that power.
This is your roadmap for building the right use case for AI/ML within your enterprise.
The first seemingly easy step is identifying what you hope to accomplish by integrating and deploying AI or ML. The litmus test for whether AI/ML solutions are the right tools for the job is the degree of variability and uncertainty in the question or problem you are trying to solve.
Variability allows your models and algorithms to leverage different information from different attributes to make better, more specific predictions. The presence of uncertainty lends meaning and value to the predictions ML makes.
For example, predicting next year’s sales numbers is a very different problem than migrating data to the cloud. There’s definite uncertainty in predicting sales numbers because no one really knows the future — just look at how quickly the COVID-19 outbreak upended businesses of all stripes. What is more, sales tend to be affected by many factors, such as the availability of your products, price, and a plethora of other external circumstances that are beyond the company’s control. That is variability.
The true value of AI and ML is their ability to apply logic and order to the chaos of variability and uncertainty. Unless your use case has both, you are likely dealing with a business intelligence or data engineering problem, making AI and ML overkill.
Once you have your use case locked and loaded, it is time to check on the data that you will be feeding into your model. To produce good results, your models might require a long data history and a variety of related attributes. When data for an important attribute is missing, it may be possible to use other variables as a proxy. Proxy variables essentially capture the same information as the missing variable. Think about what the attribute represents and how the essence of the numerical relationship might be captured or what could be correlated.
