Assessing the Effectiveness of AI in Your Organization, Part 3

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Regardless of your company’s maturity level, you should still be paying attention to the tangible benefits that AI does or could provide.

This is the third and last of three posts that explore a critical aspect of your AI maturity, understanding and assessing the results you are getting from your artificial intelligence and data science investments.

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. While many organizations that are just beginning their efforts to grow their AI maturity might have more ad hoc projects and even teams engaged in activities, more mature companies will have a systematic way of measuring, assessing, and improving their ROI from AI.

At AdapticAI, we have created the Adaptic Acceleration Model™ that we use to assess an organization’s data science maturity and its ability to accelerate its growth using artificial intelligence.

Regardless of your company’s maturity level, you should still be paying attention to the tangible benefits that AI does or could provide. We will explore how to assess the results you’re getting from your data science investment 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.

Along with understanding this, you need a way to measure it. While each organization is different, your baseline in order to understand how AI is augmenting your team’s productivity is to have a good understanding of how your current team is performing. This will drastically differ depending on your billing model, employee model, and many more factors, but the commonality is that your starting point is how productive you are/were before you introduced AI into the mix.

Once you have this baseline, you can then set benchmarks that factor in continual improvements to the human workforce, while all the while you will also be continually improving your AI.

How are you measuring results?

We’ve all heard the quote, “you can’t manage what you don’t measure.”  So the question to ask here is this: can you actually measure results from your initial forays into AI? Or, for more mature organizations, how have your operationalized AI and its contributions to your core business objectives?

The ideal scenario is that your AI efforts are directly tied to organizational key performance indicators (KPIs). For those just dipping your toes into the waters of AI, you may have a small data science team that is a bit disconnected from the executive team’s priorities, so this may not be as likely. 

But for those more mature organizations, your projects and efforts will be more aligned with other company priorities. While this puts more pressure on your team to deliver, the benefits are much  more clear: when your team wins, the company wins.  This is the ideal scenario, and the sign of a mature organization where AI is truly being used to accelerate the organization.

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.

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. Until then, may your efforts to assess and improve the contributions that AI makes to your organization be successful!

Greg Kihlstrom