When Should You Use AI Technology?

The potential applications for artificial intelligence are endless, but how do you know whether AI can be truly helpful to you? It’s often a matter of scale and speed. You want to use AI when you want to do something faster than any other technology can or when you’re dealing with an insurmountable volume of data.

Those may be the most obvious examples of when to use AI, but there are additional use cases in which AI technology is uniquely powerful. I’ve listed a few major ones below.

1. When your issue is complex, use AI technology

Complexity comes from many different factors (both scale and speed can add complexity), but for the sake of this article, let’s look at complexity from the perspective of nuanced subject matter and issues that have many different moving parts. Traditional technology like text mining and analytics software has a lot of trouble dealing with complexity because it can’t be programmed to understand context.

It’s also a problem of many different interconnected Features contributing to meaning. For example, if you look at a prominent issue like sexual harassment…it’s not as easy as finding a single high-pressure email because a complex issue like this is about finding a pattern of behavior.

For technology to be useful in helping to understand complex issues, we need to look beyond language Features (e.g. word frequency, sentence structure) and connect the dots between language, emotional sentiment and behavioral Features. This is where AI technology shines, because AI can simultaneously weigh all these different factors and understand the relationships between them.

2. When you don’t know where to start, use AI technology

One of the problems we solve a lot at NexLP is the “cold start” problem. When you first start looking at new data, it can be overwhelming. Where do you start looking? How much of this data is even relevant to what you’re looking for?

Artificial intelligence technology solves this problem with unprecedented efficiency. It can look at a large data set and automatically identify factors that signal hotspots in your data. For example, Story Engine™ can filter based on emotional sentiment, behavior as well as keywords – in fact, all these Features can be used together to hone in on very specific moments in a massive data set.

AI users start seeing results immediately because they don’t have to spend any time looking around in the dark.

Interested in reading more about how AI solves this problem? Read about how Story Engine’s AI identified relevant people before users even started looking at the content of a massive email data set.

3. When you want to make your unstructured data actually useful, use AI technology

Organizations are creating vast amounts of data, but very few are actively using it. For structured data, this is because data is analyzed and used for one specific purpose before being stored in a silo and without context.

The challenge with unstructured data like email or other text is that it takes a lot of effort, time and money to make it usable if you’re leveraging older technology. This is one of the most significant advantages of using AI:

With artificial intelligence technology, you can understand unstructured data in seconds.

Because AI can quickly add structure to unstructured data, it can drastically enhance your other analytics tools; suddenly, the dark data in your organization becomes usable.

With our AI solution, we wanted to go a step further. As you explore your data with Story Engine™, it learns from you and creates an AI model specifically tailored for your business and your data. This AI model can then be deployed for any new data set, meaning all that data you explore with it starts to actively create continuous value for you.

Want to know more about how you could start building your own AI models immediately? Contact us to schedule a Story Engine™ demo and ask about the AI Model Library.

4. When you want to unite unstructured and structured data, use AI technology

One of the other advantages of bringing structure to unstructured data is that AI can also then use it together with your structured data to create a comprehensive view of the concepts, people and facts. This solves the problem of having disparate data and no way to use it with a unified view.

Although we work with a variety of unstructured data, email is a perfect example of this. Artificial intelligence and NLP technology can look at an email, see a name mentioned within the body of the email and link that name to its equivalent entry in the organization’s database. 

Artificial intelligence will bring unprecedented scale and speed to organizations that use it effectively, but it will also shine a light on the dark data that is currently inaccessible or unusable because analytics software can't make sense of it.

Jay Leib

Jay Leib is the CEO of NexLP, Inc. Jay has successfully founded and led software start-ups throughout his career. Most recently, Mr. Leib was at Chicago-based Relativity (formerly kCura) as the Chief Strategy Officer. Relatiivity is a Chicago based company focused on the eDiscovery vertical.