SAS study: AI is still in its infancy
Lack of knowledge
The bottleneck here is not the technology - the majority are convinced that suitable solutions are generally already available. The far greater hurdles for most study participants are the lack of data science expertise, organizational reasons and a lack of social acceptance. For 55% of respondents, the biggest challenge is the change in the world of work - i.e. the potential loss of jobs due to AI on the one hand and new job profiles related to AI on the other. Ethical factors were mentioned second most frequently. There is still a great need for action in terms of know-how within the company: Only 20 percent of the companies surveyed stated that their data science teams are well positioned for AI, while 19 percent still have no such teams at all. Appropriate personnel programmes and organizational measures are planned by 28% of companies, while 32% want to tackle the development of AI knowledge through further training measures. However, companies are not yet very confident about AI. Almost every second respondent (49%) lacks confidence in the results that AI delivers - this also applies to technologies in general, which are a "black box" for most.
Lack of suitable infrastructure
The study also examined whether companies already have the necessary infrastructure in place. Here, a three-way split emerged between those who believe they and their infrastructure are equipped for AI (24%), the group who believe updates and changes are necessary (24%) and those who do not yet have a suitable platform for AI (29%). "AI has made huge progress in recent times. Today, algorithms can perform tasks with impressive precision that were previously the preserve of humans," explains Oliver Schabenberger, Executive Vice President and Chief Technology Officer at SAS. "Previously unthinkable: An algorithm beat the best Go player in the world. We had always thought that the game could not be played by a computer - at least not by human programmers. Now a machine has done it. Once the system had understood the rules, it learned to play - better than the best human player can. We can use this knowledge to solve business tasks better than today's static systems can. We can develop systems that learn business rules and then improve them on their own. This is what we are working on intensively at SAS." For the Study The survey questioned managers from 100 companies from the banking, insurance, manufacturing and retail sectors as well as representatives of public administration throughout Europe.
