AI has been an enterprise buzzword for so long that it can be challenging to ascertain what exactly the technology is capable of today and what remains an elusive goal. Marketing hyperbole adds to this challenge, making it hard for companies to evaluate AI solutions and determine whether these products can meet their needs. 

A recent TechRepublic article aims to clear up this confusion by providing key questions to help organizations separate AI hype from reality. Among them: 

How does the AI model learn? 

Most AI technologies get better based on the data they receive. They are designed to test potential future outcomes and improve their accuracy based on those outcomes. Think of game-playing AIs, where they replicate playing thousands of rounds of a game, and their performance continually improves based on the development of each game. 

In the vendor selection process, asking for specifics about how the AI learns and improves is essential. Questions such as, what data does it use? Does it simulate potential scenarios and learn from these simulations? 

How is the AI monitored and adjusted?

It’s essential that AI is monitored and potentially retrained or inputted with additional data over time. Another critical step in separating AI hype from reality is asking potential vendors how the AI will be monitored and adjusted. Should a company claim that no monitoring or adjustment will ever be needed, it’s a safe bet that the technology isn’t accurate AI. Still, a standard algorithm is falsely marketed as containing a degree of intelligence. 

Do you share customer data to train the AI?

Depending upon the nature of the business, sharing customer data to train the AI can be highly beneficial, resulting in a more extensive data set and, in turn, a more effective solution. Conversely, if a company works with unique and precise data, having the AI influenced by other data could negatively impact outcomes. That’s why it’s essential to ask vendors whether or not they share customer data to train the AI.

Head over to TechTarget for more on these and other strategies for cutting through AI hype.