The terms artificial intelligence (AI), machine learning and big data have all become buzzwords of late, and you may be wondering whether you might be able to utilize these innovative technologies for your own benefit. Figuring out which problems in your business would be suitable for AI is a good place to start. Furthermore, determining whether those problems are automation problems or learning problems is equally important.
Automation without machine learning capabilities is appropriate for problems that are relatively straightforward in nature (i.e. automation problems.) These are the tasks and workflows that have a predefined sequence of steps currently being carried out by a human worker, but that could feasibly be transferred over to a software robot. This type of automation is not new. It’s been leveraged for decades.
For the second type of problems traditional automation isn’t sufficient, as these types of problems require learning from data. Machine learning technology uses statistical methods and built-in algorithms to identify patterns of predictability in datasets. This is particularly helpful in determining how certain data features are related to certain outcomes.
So, what business problems are a good fit for AI technology? In general, any problem that either requires prediction as opposed to inference, or is relatively self-contained or insulated from outside influences.
Basic examples of AI problems include predicting the likelihood that a specific type of user will click on a specific type of ad or evaluating how similar a particular piece of text is to previous ones. Examples where AI isn’t yet fully capable would be predicting profits from a brand new and/or revolutionary product or forecasting next year’s sales based on past data when a significant new competitor has recently entered the market.
Once you determine whether your business problem is suitable for AI, the next step is verifying whether you have the appropriate data to solve said problem. This data can come from a variety of places, both internal as well as external resources. If you are obtaining data from an outside source, it’s wise to ask enough probing questions so that you can ascertain what the data’s scope is and whether it’s likely to be a good fit for what you’re trying to achieve.
The good news is, with the right platform, you should be able to tackle both automation problems as well as learning problems. In fact, armed with the right technology, you can conceivably automate almost any task, process or workflow – even ones that are especially complex and require the use of many disparate systems. If you’re unsure where to begin, here are four business processes that are ideal for AI technology. And here are a few tips for adopting AI and automation to get you started.