I went to the Aras customer conference for the first time this year. Interesting company, good products, innovative customers. But, sorry, I’m hardly shocked that a survey of 835 “executive level experts” say their companies are not prepared to use Artificial Intelligence. We are all still feeling our way along the path toward discovering if there is a use or not. AR and VR are much farther along the hype curve and still haven’t really found a place.
However, you can check out all the details here.
Aras, a leader in product lifecycle management (PLM) and digital thread solutions, announced today findings from its report, “Spotlight on the Future 2024,” highlighting that nearly 80% of industrial companies lack the knowledge or capacity to successfully use artificial intelligence (AI).
Oh, PLM users seem to be the best positioned to benefit. You can pick up a few ideas from my interview with CTO Rob McAveney.
Despite this unpreparedness, 84% of companies expect AI to provide new or better services, while 82% expect an increase in quality. These findings come from Aras’ recent global industry study in which 835 executive-level experts across the United States, Europe, and Japan were surveyed.
“Adapting and modernizing the existing IT landscape can remove barriers and enable companies to reap the benefits of AI,” said Roque Martin, CEO of Aras. Current gaps in the industry according to Aras’ global study, include capacity bottlenecks 79%, lack of knowledge 77%, reliance on isolated IT applications 75%, and existing data quality concerns 70%.
The findings from the report suggest that augmenting product lifecycle management (PLM) with AI leads to improved effectiveness. Some 75% of respondents noted AI’s influences on their PLM strategy, while 2/3 of respondents said that their current PLM platform and data infrastructure is well-prepared for AI technologies.
Martin added, “Companies that are already using a flexible and modern PLM are much better prepared for the challenges of new, data-intensive technologies, leveraging AI to their benefit.”
Study participants rely primarily on datasets such as product data, quality control data, production data, or customer data. Many survey respondents acknowledge their data quality is not enough to achieve their company’s goals. As a result, 51% of respondents are intensifying their efforts to improve production, while 46% are looking at services data, and 45 percent are paying special attention to research and development datasets. These findings show a growing recognition of the important role that high-quality data plays in driving successful AI use within enterprises.
