Assessing the Effectiveness of Artificial Intelligence in Your Organization, Part 3
After assessing how well AI is currently being utilized and understood within your organization, it’s time to start assessing the results that you are achieving.
What results is your organization getting from AI?
After assessing how well AI is currently being utilized and understood within your organization, it’s time to start assessing the results that you are achieving.
We will explore this in three aspects, each discussed below.
How does AI augment your team’s efforts?
Let’s remember that the fundamental purpose of artificial intelligence in its broadest sense, and more specifically machine learning, is to augment teams of humans trying to complete a task, or ultimately accomplish a goal.
Thus, the best applications of AI are those which take a challenge that teams are working on or regularly tasked with doing and build on those to accelerate their efforts so that results are gained more quickly. Understanding when and where to use this requires strategic focus and the ability to prioritize and understand the best applications of AI and ML. They’re not a one-size-fits-all solution, and sometimes not the best fit.
Are the results of your work with AI repeatable and testable?
While this might seem like a no-brainer, just like any other piece of enterprise software, your AI and machine learning solutions need to pass similar quality control standards. This does present unique challenges for testing machine learning solutions which, unlike more traditional software, are built to modify themselves as they take on new data. This evolution may be novel, but it doesn’t prevent testing from occurring.
That being said, just like with any other software application, a testing plan and proper quality assurance (QA) processes should be outlined and adopted with any AI initiative.
In addition to repeatability and reliability, there are several other factors that require testing. Security, compliance, data security, and auditing needs may vary by industry, but any organization employing this (or any other) software needs to keep these requirements in mind. In fact, by solving for reliability and repeatability, your ability to meet your compliance requirements will be greatly helped.
Conclusion
Wherever you sit in an organization, your investments in data science have never been more critical. The people, processes, and technology utilized affect so many factors and everything must work together in order to achieve the highest levels of success.
Over the next few months, we will explore each of the areas in this post in more depth, with some practical ideas and examples.