Assessing the Effectiveness of AI in Your Organization, Part 1
It is critical that you are able to assess the effectiveness of this technology within your organization.
Over the next three posts, we’re going to explore a critical aspect of your AI maturity.
A continually growing amount of organizations understand the important role the artificial intelligence (AI) and machine learning (ML) can have on an organization. But sometimes investments in data science and AI & ML don’t have an immediate payoff or don’t progress as quickly as they should.
It is critical that you are able to assess the effectiveness of this technology within your organization. Let’s explore several ways that you can assess the effectiveness of artificial intelligence and other data science efforts by asking a few questions.
What is the future of AI in your organization?
Let’s start with the biggest question of all. You should determine what you want AI to do for your organization, both in the present and the future. As with any initiative, you need to visualize what success looks like before you begin the work.
Let’s explore four facets of this below.
Are you intentional about your goals?
It might go without saying, but if you are setting goals for the success of AI, machine learning, and data science applications within your organization, you need to create these with business strategy in mind. While it can be easy to be distracted by opportunities that are less than strategic, setting clear, intentional goals will help you make sure your data science resources aren’t expended on projects and efforts that are not going to make a real impact.
A big reason for this, in addition to the obvious, is that at any organization it is critical to show ROI on data science projects in order to continue to get the funding and support needed to grow an company’s data science maturity. At AdapticAI, we use a model called the Adaptic Acceleration Model™ to assess and accelerate company’s growth. There are several other models in use that provide similar assessments of the stage of growth of an organization’s data science adoption and usage.
Are there practical applications?
This also means that there should be a practical component to your efforts. If your AI initiatives don’t have a real-world application, or if they aren’t closely aligned with strategic business growth or goals, they are simply a novelty that can be discarded when proven to be unprofitable or disconnected from the bottom line.
This idea of a practical application could mean that a theoretical application must be able to implemented in a real customer or business setting, and not simply be an experiment. If an idea sounds great, but is missing key components, integrations, or steps in a process that prevents it from being used in an authentic situation, it’s not going to get out of the R&D phase.
Often, a pilot project that is relatively small in scale can be the best first step to producing a truly practical application of AI for your organization. By starting small, with something that is both meaningful and measurable, you have a better chance of success and have nowhere to go but bigger and better.
Are you leading or following the competition?
While you may not have insights into the inner workings of your closest competitors, there are a lot of things that you can do to assess how they are utilizing data science, AI, and machine learning. By paying attention to what they are doing, you might either get new ideas, learn what to avoid, or at least better understand where your industry is headed.
Another way to better understand this is to assess your organization’s data science maturity. Our Adaptic Acceleration Model™ looks at key factors across 5 stages and 8 dimensions in order to help organizations get a realistic view of both where they are as well as what it will take to move them through the next stages of maturity.
Are you providing your teams with the tools to innovate?
Choosing the right data science platform for your team and their missions is critical, as is the rest of your development tools and infrastructure. Are the tools you provide your teams just enough for today, or will they scale as the data science needs in your organization grow?
In the next article, we will talk about how to determine how well AI is working in your organization.