Adoption of artificial intelligence is still in its early stages


There is a lot of discussion and debate about artificial intelligence. However, most European companies participating in a recent survey conducted by SAS, the analytics company, indicated that AI adoption is still in its early stages, and arguably still in the planning stage.

Be The positive side is that the vast majority of organizations started talking about artificial intelligence, and some even started implementing appropriate projects. There is a great deal of optimism about the potential of AI, although some are less confident that their organizations will be willing to take advantage of that potential.

The lack of available technology is not the only factor slowing down the pace of AI adoption; Most of the companies participating in the study indicated that there are many technology options available. More often than not, the challenges are a lack of data science skills to maximize the value of emerging AI technology, and deeper institutional and societal obstacles to AI adoption.

These findings emerged in Enterprise AI Pledges, a study A telephone survey of executives from 100 institutions and companies across Europe in the banking, insurance, manufacturing, retail, government and other sectors. The SAS study was conducted in August to measure business people's reaction to the potential of AI, how they use it today, their plans to use it in the future, and what challenges they face.

Against the background of AI automation and autonomy, 55% of the survey respondents considered the biggest challenge to be the changing scope of human employment opportunities. This potential impact of AI on jobs includes job losses and the development of new job opportunities that require new AI skills.

Work ethics was cited as the second biggest challenge, with 41% of respondents asking whether Robots and AI systems should work “for the good of humanity” rather than working for a single company, and ways to take care of those who lost their jobs after the introduction of AI systems.

Are enterprise data scientists prepared for emerging AI challenges? ? Only 20% considered their data science teams ready, 19% of respondents had no data science teams at all.

Hiring data scientists to build organizational skills was the plan adopted by 28% of respondents, while 32% indicated that they would enhance AI skills in their existing analytics teams through training, conferences, and workshops.

In addition, trust emerged as the primary challenge for many organizations. Almost half (49%) of the study participants mentioned the issue of cultural challenges stemming from a lack of trust in AI production and, more broadly, a lack of confidence in the outcomes of so-called “black box” solutions.

The study sought to assess the readiness of intelligence to artificial in terms of the required infrastructure. The study noted a clear discrepancy between respondents who felt they had the right AI infrastructure (24%), and those who felt they needed to update and adapt their existing AI platform (24%) or did not have a specific AI platform ( 29%).

On this occasion, Oliver Schappenberger, Executive Vice President and Chief Technology Officer at SAS, said: “We've seen remarkable progress in driving algorithms toward human-capable tasks, with remarkable accuracy. It is amazing to see that the algorithm outperforms the best “Go” players in the world. We believed that the game of “Go” was incapable of human computing; But the machine did it for us. Once the system understood the rules, it learned how to play, and played better than the best human players. We can use this knowledge to build systems that solve corporate problems, or that outperform the established systems in use today. We can also build systems that learn corporate laws, then learn to play by the rules, and are designed for subsequent improvements. This is what SAS is working on at the moment.

Adoption of artificial intelligence is still in its early stages