What would happen to those who lost their jobs? How would the lag between the introduction of new technology and the necessary changes be handled?
People issues, not lack of available technology, are slowing AI adoption, according to a new survey by SAS.
“Most organisations attest there are many technology options available,” says SAS, which interviewed executives from 100 organisations across Europe in banking, insurance, manufacturing, retail, government and other industries.
Most respondents were aware of and enthusiastic about well-known uses of AI, such as Amazon’s Alexa, and Apple’s Siri, says SAS.
“More often, the challenges come from a shortage of data science skills to maximise value from emerging AI technology, and deeper organisational and societal obstacles to AI adoption.”
The analytics company says more than half (55 per cent) of respondents felt the biggest challenge related to AI was the changing scope of human jobs in light of AI’s automation and autonomy. This potential effect includes job losses but also the development of new jobs requiring new AI-related skills.
SAS says some of the questions raised by respondents were: What would happen to those who lost their jobs? How would regulatory and other systems need to change? How would the lag between the introduction of new technology and the necessary changes be handled?
We’ve seen incredible advances in making algorithms perform – with stunning accuracy – tasks that a human could do.
The second biggest challenges were ethical issues, with 41 per cent 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.
“These are all big questions, and suggest that there is both a need and an appetite for some difficult conversations around the ethics and practical implications of introducing AI,” says SAS, which released the survey results at the Analytics Experience 2017 conference in Amsterdam.
SAS says a positive outcome in the report is that the vast majority of organisations have begun to talk about AI and a few have started implementing suitable projects.
“We’ve seen incredible advances in making algorithms perform – with stunning accuracy – tasks that a human could do,” says Oliver Schabenberger, executive vice president and chief technology 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 computerised – 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,” says Schabenberger.
“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.”
More respondents were optimistic about the potential of AI than their organisation’s readiness to exploit it.
Wanted: Data scientists
The survey finds few respondents could cite own-industry examples, or suggest any of their competitors who were actively engaged in developing AI, even though around two-thirds felt certain AI would have wide-ranging effects within the next 5 to 10 years.
“There is much optimism about the potential of AI, although fewer were confident that their organisation was ready to exploit that potential,” says SAS.
For instance, only 20 per cent of respondents felt their data science teams were ready, while a similar percentage, 19 per cent, had no data science teams at all.
About a quarter (28 per cent) of respondents said they plan to recruit data scientists to build organisational skills, while 32 per cent said they would build these skills in their existing analyst teams through training, conferences and workshops.
In terms of infrastructure needed for AI, the survey finds a contrast between those respondents who felt they had the right infrastructure in place (24 percent), and those who felt they needed to update and adapt their current platform (24 percent) or had no specific platform in place to address AI (29 percent).
Additionally, trust emerged as a major challenge in many organisations, says SAS.
Nearly half of respondents (49 per cent) 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.
This trust needed to be developed both internally and externally, in customer organisations, says SAS.
Respondents discussed the importance of data scientists understanding business issues to improve relationships, and executives being prepared to trust decisions from algorithms, as key to broad uptake of AI.
“These developments might require cultural change, and would therefore take time. This recognition may explain why respondents were more optimistic about the potential of AI than their organisation’s readiness to exploit it,” concludes SAS.
Divina Paredes is attending the Analytics Experience conference in Amsterdam as a guest of SAS.
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