The 4 great trends that mark the immediate future of artificial intelligence and its use by companies

The future for a responsible artificial intelligence, which is capable of eliminating bias

 

From being something almost science fiction, artificial intelligence has become one of those key pieces of corporate innovation and digital transformation. Companies need to catch up, apply AI as efficiently as possible, and be very clear about how they can take advantage of it. It is almost, so to speak, the key to the future.

But artificial intelligence is also something alive, as is technology, so it is always starring in adjustments and changes. Those responsible for companies must take this into account.

Where is AI going? Gartner’s latest analysis of the issue makes it clear that innovation in artificial intelligence is happening at a “fast pace” and that it needs to be followed fast. It also indicates the four major trends that will mark the agenda and the execution that companies make of things.

Responsible artificial intelligence

Although at first AI was seen as a kind of promise of how to do everything better and faster, critical voices soon began. Artificial intelligence had fallen into biases and was no more equanimous than people themselves: since it had to feed on information, it had adopted the same biases as the information used.

So it is not surprising that one of the trends Gartner sees for the future is responsible artificial intelligence. In 2023, they hope that all AI training and development workers will be required to have knowledge of responsible AI. Responsible artificial intelligence is that improvement in trust, transparency and justice to be more fair.

The new types of data

The future of data goes through a leap to small and wide data, new ways of dealing with data and that, according to Gartner, allows to create more robust analyzes. Thus, companies depend less on big data and are able to recognize the environment in a more complex and rich way. By 2025, 75% of companies will be transitioning from big data to small and wide data.

“Small data is the application of analytical techniques that require less data but still offer useful insights, while wide data allows analysis and synergy of a variety of data sources,” explains Svetlana Sicular, vice president of research at Gartner.

The operationalization of AI platforms

Since artificial intelligence has become a critical element for business transformation, the need for the operationalization of AI platforms has also increased, they explain from the analysis firm.

Artificial intelligence cannot be in the concept phase: it has to go into production and must now solve corporate problems. Or what is the same: the time has come to abandon the pilot projects and get serious.

An efficient use of resources

The demands of AI are many and the environment they generate is very complex. Therefore, to continue innovating, companies must be efficient in how they use the resources they have.

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