The reason someone would use AI software is to build an intelligent application from scratch or add machine or deep learning to a pre-existing software application. AI software allows users to implement general machine learning, or more specific deep learning capabilities, such as natural language processing, computer vision, and speech recognition. While this is the primary, and somewhat obvious, reason, there are many motivations behind this rationale, with the following being some of the most common themes:
Automation of mundane tasks
Business may implement machine learning to help automate tedious actions that employees are required to do in their day-to-day. By utilizing AI for these tasks, companies can free up time for employees to concentrate on more important, human-necessary aspects of their jobs. AI software does not offer a way to automate humans out of their jobs, but instead offers a supplemental tool to help improve their performance at work.
Predictive functionality is similar to automation in the sense that it performs a task or provides an outcome that the solutions assumes is correct instead of a human needing to do it manually. This can be as simple as expense management solutions adding an expense to a report on its own. How would a software know to do this? Because it uses AI and machine learning to understand that the user puts the same charge on their report every month. So instead of the employee needing to add it every month, it predicts what will be on the report and automates it for them. This type of predictive capability can be added to applications with AI software.
While you might think that predictive solutions make intelligent decisions, this aspect of AI helps human beings make intelligent decisions instead of the software doing it for them. Machine learning can help take the guesswork out of making critical business decisions by providing analytical proof and predicted outcomes. This functionality not only helps take human error out of decision-making, but can help arm users with the information necessary to defending the decisions that they make.
By using machine learning algorithms, software developers can create a high level of personalization, improving their software products for all users by offering unique experiences. Creating applications that recognize users and their interactions allows for powerful recommendation systems, similar to those used by Amazon to help personalize consumer shopping, or the film recommendation capabilities of Netflix.
Creating conversational interfaces
Given the popularity of consumer conversational AI offerings, such as Amazon’s Alexa, Apple’s Siri, and Google Home, the use of conversational interfaces is broaching into the B2B world. For software companies trying to innovate and keep up with these advancements, AI software is the place to start. Implementing speech recognition into a software can allow users to interact with the application in a streamlined, unique manner.