The hype surrounding artificial intelligence (AI) is intense. But for most European businesses surveyed in a recent study by SAS, the leader in analytics, AI adoption is still in the early or even planning stages. The good news is, the vast majority of organizations have begun to talk about AI, and a few have even begun to implement suitable projects. There is much optimism about the potential of AI, although fewer were confident that their organization was ready to exploit that potential.
It isn’t so much a lack of available technology slowing AI adoption; most attest that there are many options available. More often, the challenges come from a shortage of data science skills to maximize value from emerging AI technology, and deeper organizational and societal obstacles to AI adoption.
These were some of the findings of the Enterprise AI Promise Study, a phone survey of executives from 100 organizations across Europe in banking, insurance, manufacturing, retail, government and other industries. The SAS study was conducted in August to measure how business leaders felt about AI’s potential, how they use it today and plan to use it in the future, and what challenges they face.
The findings were revealed at the SAS Analytics Experience 2017 conference in Amsterdam this week.
Fifty-five percent of respondents felt that the biggest challenge related to AI 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 cited as the second-biggest challenge, with 41 percent of respondents raising questions about whether robots and AI systems should have to work “for the good of humanity” rather than simply for a single company, and how to look after those who lost jobs to AI systems.
Data science team and organizational readiness
Are organizations’ data scientists ready for the challenge of emerging AI? Only 20 percent felt their data science teams were ready, while 19 percent had no data science teams at all.
Recruiting data scientists to build organizational skills was the plan for 28 percent of respondents, while 32 percent said they would build AI skills in their existing analyst teams through training, conferences and workshops.
Additionally, trust emerged as a major challenge in many organizations. Almost half of respondents (49 percent) 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 sought to assess 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 percent), and those who felt they needed to update and adapt their current platform for AI (24 percent) or had no specific platform in place to address AI (29 percent).
“We’ve seen incredible advances in making algorithms perform – with stunning accuracy – tasks that a human could do,” said Oliver Schabenberger, Executive Vice President and Chief Technoloogy Officer at SAS. “It is remarkable that an algorithm beat the best Go player in the world. We thought that the game of Go could not be computerized – by man. But now a machine did it for us. Once the system knew the rules, it learned to play, and played better than the best of our species can play. We can use this knowledge to build systems that solve business problems as well or better than the static systems in use today. We can build systems that learn the rules of business, then learn to play by the rules and are designed to then improve. That is what SAS is working on.”