According to a recent Deloitte survey, 82% of early adopters of artificial intelligence are already realizing positive financial results from their investment, with a median ROI of 17%. What’s the biggest differentiator between a successful AI project and an unsuccessful one? The focus of the project itself. Organizations that get the most out of AI tend to focus on the specific business objectives that are to be achieved. They then use those results to prove value and scale up from there. If you’re looking for ways to translate AI into business value, here are a few things to keep in mind.
Target actual problems.
Business leaders may recognize the value of using artificial intelligence, but using AI well is where the true value lies. To do this, you must keep the focus on the concrete business problems that you are trying to solve. For instance, will implementing AI help you accomplish something in a way that is faster or cheaper? Will it help you generate more revenue? Can it be scaled? Adding business value should be at the heart of every AI project.
Understand and acknowledge limitations.
Ask an AI system that is trained on a particular set of data to make predictions based on an entirely different set of data, and chances are you’ll get a response that is completely incorrect. For someone who’s come to rely on those predictions, it can be easy to veer off in the wrong direction. To avoid misleading conclusions and misguided decision-making, make sure employees are trained enough to know which analytics model is the appropriate fit for the corresponding data set.
Listen to stakeholders.
Going back to the first point, successful AI projects keep the focus on actual business problems. Unfortunately, the individuals and teams spearheading these initiatives don’t always have the best insight into what those problems and needs happen to be. That’s why it’s so important to gather feedback and insight from key stakeholders – i.e. those who will be directly impacted by AI. Engage in detailed discussions with all interested parties right from the outset. This will save time and improve the outcome of the project in the long run.
Don’t underestimate the value of real-world testing.
The definitive proof of your AI project’s value will only become evident once it hits the real world. If you’re not prepared for this, your initiative is doomed to fail before it’s even begun. And while real-world testing is important for most new technologies, it’s especially beneficial for artificial intelligence. Why? Because early exposure allows more time for the tool to learn, adapt and improve. The sooner you can bring your project live, the sooner you can increase its ability and, ultimately, your ROI.
Artificial intelligence holds tremendous promise for businesses of every size and industry. Unfortunately, most organizations aren’t adequately positioned to take advantage. As an early adopter, the four factors above should enable you to transform your AI projects into quantifiable and sustainable business value.