Technology professionals and industry leaders are sounding an alarm that organizational data might not be ready to support growing AI ambitions.
The skills gap that’s holding back progress in artificial intelligence (AI) is well documented, but another factor looms large: data complexity. The two leading obstacles to AI success, a new IBM study reveals, are limited AI skills and expertise (cited by 33% of respondents), followed by too much data complexity (25%).
A majority of companies (58%) to date are not yet actively implementing AI, according to the survey of 8,584 IT professionals. The biggest inhibitors of generative AI at these non-AI-enabled companies include data privacy (57%), and trust and transparency (43%).
Among the companies already deploying AI, the key barriers are often related to data, with some organizations taking steps toward trustworthy AI, such as tracking data provenance (37%) and reducing bias (27%). Around a quarter (242%) of companies are seeking to develop their business analytics or intelligence capabilities, which depends on consistent, high-quality data.
However, some industry leaders are sounding the alarm that organizational data may not be ready to support growing AI ambitions. „To remain competitive, CIOs and technology leaders must adapt their data strategies as they integrate gen AI into their technology stacks,“ says Matt Labovich, US data, analytics and AI leader for PwC. „This involves understanding data and preparing for the transformative impact of emerging technologies.“
Technology professionals and their organizations need to address „data security, AI decision-making ethics, and AI literacy,“ says Shipra Sharma, head of AI and analytics at Bristlecone. „With limited AI education due to the newness of this technology, many individuals are left to figure out how to use it on their own.“
She says actively engaging with the technology, „to educate employees and implementing appropriate safeguards will allow organizations to realize the benefits of generative AI for data management while mitigating the risks.
