Although more and more businesses are starting to integrate artificial intelligence (AI), overall adoption is still slow. But, according to nearly half of Chief Information Officers (CIOs) that may be about to change.
Earlier this week Gartner released the results of a new survey pertaining to how many business executives are looking to adopt AI in the near future. The survey revealed that around 4% of CIOs have already adopted AI, while another 46% have plans in place to do so.
Overall, AI adoption is still pretty slow, which is a shame as the possibilities for AI are endless. Both AI and cognitive computing can enhance a business’ services in many areas including social media, security, and IoT.
Big companies such as Microsoft, Google, MapR, and SAP are all investing heavily in AI right now. But even with these giant corporations jumping into the game, others are still slow on the uptake when it comes to AI. One of the reasons for this is that early adopters tend to want an immediate return, but with AI it’s going to be a gradual process.
“Leave behind notions of vast teams of infinitely duplicable ‘smart agents’ able to execute tasks just like humans,” said research vice president at Gartner, Whit Andrews. “It will be far more productive to engage with workers on the front line and get them excited and engaged with the idea that AI-powered decision support can enhance and elevate the work they do every day.”
However, it seems the biggest problem in adopting AI into businesses seems to be the companies’ limited ability to mine and exploit data, and this has to take precedence. Gartner has estimated that by 2020 a fifth of all companies will have dedicated AI staff on board to monitor and control neural networks. Neural networking is an integral part of AI that’s based on biological neural networks. And it’s the key to be able to identify patterns and define relationships based on a variety of data.
Of course, not all AI projects will succeed. Garter suggests that as many as 85% of AI projects fail due to poor team management, biased data, or algorithms that are unsuitable. “Whether an AI system produces the right answer is not the only concern,” said Andrews. “Executives need to understand why it is effective, and offer insights into its reasoning when its not.”