Study: Despite the hype, AI adoption has a long way to go
The buzz surrounding artificial intelligence (AI) is intense, but adoption is still in the early or even planning stages.
A majority of organizations are talking about AI, and a few have even begun to implement suitable projects. Despite their optimism about the potential of AI, fewer companies were confident that their organization was ready to exploit that potential, according to the “Enterprise AI Promise Study,” from analytics provider SAS.
A big misnomer about slow adoption is that there is a lack of available technology. That isn’t the case, the report said, as there are many options available. More often, the challenges come from a shortage of data science skills to maximize value from emerging AI technology, as well as barriers caused by deep organizational and societal obstacles.
In fact, 55% of respondents felt that the biggest challenge was the changing scope of human jobs in light of AI's automation and autonomy. This potential effect of AI on jobs includes job losses, but also the development of new jobs requiring new AI-related skills.
Ethical issues were the second-biggest challenge with 41% of respondents questioning whether robots and AI systems should have to work "for the good of humanity" rather than simply for a single company. They also questioned how to look after those who lost jobs to AI systems.
When it comes to organizations' data scientists’ readiness for emerging AI, only 20% felt their data science teams were prepared. Meanwhile, 19% had no data science teams at all. Recruiting data scientists to build organizational skills was the plan for 28% of respondents, while 32% said they would build AI skills in their existing analyst teams through training, conferences and workshops.
Trust also emerged as a major challenge in many organizations. Almost half of respondents (49%) mentioned cultural challenges due to a lack of trust in AI output and more broadly, a lack of trust in the results of so-called "black box" solutions.
The study also evaluated AI readiness in terms of infrastructure required. There was a contrast between those respondents who felt they had the right infrastructure in place for AI (24%), and those who felt they needed to update and adapt their current platform for AI (24%), or had no specific platform in place to address AI (29%).
"We've seen incredible advances in making algorithms perform – with stunning accuracy – tasks that a human could do," said Oliver Schabenberger, executive VP and chief technology officer at SAS.
“We can use knowledge to build systems that solve business problems as well as or better than the static systems in use today,” he added. “We can build systems that learn the rules of business, then learn to play by the rules and are designed to then improve."
A majority of organizations are talking about AI, and a few have even begun to implement suitable projects. Despite their optimism about the potential of AI, fewer companies were confident that their organization was ready to exploit that potential, according to the “Enterprise AI Promise Study,” from analytics provider SAS.
A big misnomer about slow adoption is that there is a lack of available technology. That isn’t the case, the report said, as there are many options available. More often, the challenges come from a shortage of data science skills to maximize value from emerging AI technology, as well as barriers caused by deep organizational and societal obstacles.
In fact, 55% of respondents felt that the biggest challenge was the changing scope of human jobs in light of AI's automation and autonomy. This potential effect of AI on jobs includes job losses, but also the development of new jobs requiring new AI-related skills.
Ethical issues were the second-biggest challenge with 41% of respondents questioning whether robots and AI systems should have to work "for the good of humanity" rather than simply for a single company. They also questioned how to look after those who lost jobs to AI systems.
When it comes to organizations' data scientists’ readiness for emerging AI, only 20% felt their data science teams were prepared. Meanwhile, 19% had no data science teams at all. Recruiting data scientists to build organizational skills was the plan for 28% of respondents, while 32% said they would build AI skills in their existing analyst teams through training, conferences and workshops.
Trust also emerged as a major challenge in many organizations. Almost half of respondents (49%) mentioned cultural challenges due to a lack of trust in AI output and more broadly, a lack of trust in the results of so-called "black box" solutions.
The study also evaluated AI readiness in terms of infrastructure required. There was a contrast between those respondents who felt they had the right infrastructure in place for AI (24%), and those who felt they needed to update and adapt their current platform for AI (24%), or had no specific platform in place to address AI (29%).
"We've seen incredible advances in making algorithms perform – with stunning accuracy – tasks that a human could do," said Oliver Schabenberger, executive VP and chief technology officer at SAS.
“We can use knowledge to build systems that solve business problems as well as or better than the static systems in use today,” he added. “We can build systems that learn the rules of business, then learn to play by the rules and are designed to then improve."