The skills gap that is stifling development in artificial intelligence (AI) is well documented, but another aspect stands out: data complexity. According to a new IBM study, the most common barriers to AI success are limited AI skills and knowledge (33%), followed by data complexity (25%).
The majority of companies (58%) that participated in the poll of 8,584 IT professionals said that they have not yet begun to actively adopt AI. At these non-AI-enabled companies, trust and transparency (43%) and data privacy (57%) are the biggest obstacles to generative AI.
Companies using AI typically face data-related challenges. Some are taking initiatives to ensure trustworthy AI, such as tracking data provenance (37%), and reducing bias (27%). Around one-quarter (24%) of businesses are looking to improve their business analytics or intelligence capabilities, which rely on reliable, high-quality data.
However, several industry leaders warn that organisational data may not be ready to support burgeoning AI ambitions. "To remain competitive, CIOs and technology leaders must adapt their data strategies as they integrate gen AI into their technology stacks," notes PwC's US data, analytics, and AI leader, Matt Labovich. "This involves understanding data and preparing for the transformative impact of emerging technologies.”
Head of AI and analytics at Bristlecone Shipra Sharma believes that "data security, AI decision-making ethics, and AI literacy" are issues that tech professionals and their companies need to address. "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."
The vast amount of data that AI demands can be a frustrating aspect of the puzzle. For example, data at the edge is becoming an important source for huge language models and repositories. There will be significant growth of data at the edge as AI continues to evolve and organisations innovate around their digital transformation to grow revenue and profits, stated Bruce Kornfeld, StorMagic's chief marketing and product officer.
At the moment, he says, "there is too much data in too many different formats, which is causing an influx of internal strife as companies struggle to determine what is business-critical versus what can be archived or removed from their data sets.”
According to Osmar Olivo, vice president of product management at Inrupt, a business that Sir Tim Berners-Lee co-founded, training data comes from a range of sources, including both public sources and an organisation's intellectual property.
Between the competitive advantage companies can get by leveraging AI and protecting their most sensitive data," is typically the decision that many organisations must make, Olivo stated. But it doesn't have to be a black-or-white decision. I anticipate that in 2024, creative approaches to data management and privacy will surface, especially when it comes to safeguarding data utilised by AI models.
