Artificial intelligence (AI) is something that almost every large business seems desperate to pursue. The number of companies using AI today, and the range of problems AI is being applied to, are both increasing steadily. At the same time, there is another phenomenon taking place that significantly muddies the waters when trying to understand the ways AI is being implemented today: calling a new process “AI-based” when it really isn’t AI-based. These situations usually involve some sort of process automation that, while important and impactful, isn’t tied to AI at all. Let’s explore why this has come to be.
What Exactly Is AI Anyway?
The start of the problem is that AI has been poorly defined, and the scope of what is commonly called AI has been steadily increasing. AI was once primarily seen as covering the realm of algorithms like deep learning models used for image recognition and natural language processing models used to support customer service chat bots. In recent times, many have claimed that any analysis the entails the process “learning” from the data and making decisions is AI. Making the umbrella that large leads to the inclusion of a lot of more traditional predictive, statistical, and machine learning techniques that were not traditionally considered AI.
Automation Versus AI
Many processes today are dubbed AI if there is automation involved that takes humans fully or mostly out of the loop. For example, pricing software that takes account of all recent activity to adjust the pricing of products for a website. Or, a supply chain process that automatically adjusts orders or shipment routes based on the latest data. This type of automation can be incredibly impactful. However, such automation often really doesn’t involve what was traditionally called AI. Because the computers appear to be doing everything on their own, people often think the process is intelligent and attribute it to AI.
Does It Really Matter?
Getting into arguments over what counts as AI and what does not is likely both a losing battle and counterproductive. In the end, if an analytical process is successfully utilizing corporate data to drive value, then celebrate it and adopt it. How you label that process is secondary. It would be silly to hold back on the deployment of a high-impact process just because people misunderstood the degree to which AI is involved. What you really want to focus on getting people to agree to is that a process is working well, that it should be deployed more broadly, and that the automation it will provide is a value driver.
Whatever We Call It, How Do We Do It?
Regardless of the labels applied, any significant automation based on data and analytics requires a clean and complete set of data, governance policies that allow various processes and users to access the automation, and a high level of scalability within the systems enabling all of that. That is no small order!
Luckily, there is a solution today that can help make this happen. A data operating system, such as DataOS from The Modern Data Company, can help manage all of a company’s underlying data repositories from one place. A data operating system catalogs and connects to every piece of data, regardless of where it rests, and then adds a layer of security and governance to ensure that the data is only used appropriately. Then, users can access that robust environment to create analytical processes that make use of any combination of data and push results out to wherever within the ecosystem the results need to go.
Drive Automation – Both with AI and Without!
A data operating system enables the use of AI alongside any traditional analytical technique. You can build scalable, secure processes that enable automation of important corporate tasks more easily than ever before. Whatever mix of approaches you choose, a data operating system will help make your vision a reality. To learn more about how you can drive automation, whether with AI or with other techniques, schedule a quick chat with one of our data experts.
