IBM Study Identifies Data Complexity as Key AI Obstacle

A recent IBM study reveals that limited AI skills (33%) and data complexity (25%) are the main hurdles to progress in artificial intelligence (AI).

By Sunil Sonkar
3 Min Read
IBM Study Identifies Data Complexity as Key AI Obstacle

In a recent study by IBM, it is found that making progress in artificial intelligence (AI) is tough because handling complex data is a big challenge. The study shows that not having enough AI skills (mentioned by 33% of people) and dealing with complicated data (mentioned by 25%) are the top hurdles. So, besides not having enough people with the right AI skills, handling tricky data is also a big problem in making AI better.

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Surveying 8,584 IT professionals, the study discloses that 58% of companies are not actively implementing AI. Among these non-AI-enabled companies, data privacy (57%) and concerns about trust and transparency (43%) are the primary inhibitors to embracing generative AI.

Companies that are already using AI face challenges related to data. To ensure AI is trustworthy, some are tracking where the data comes from (37%) and working to reduce biases (27%). Approximately 24% of companies are focused on enhancing their business analytics or intelligence capabilities, which hinge on consistent, high-quality data.

Leaders in the industry warn that companies might not be ready for the growing ambitions in AI. Matt Labovich from PwC emphasizes the need for organizations to adapt their data strategies as generative AI becomes part of their technology stacks.

Dealing with different types of data, including edge data, is a challenge for AI. Bruce Kornfeld from StorMagic notes the challenge companies face in handling data of different formats, complicating the identification of critical information for their business. Urgent solutions are needed to filter unnecessary data and make room for essentials.

Rakesh Jayaprakash, Chief Analytics Evangelist with ManageEngine, emphasizes a data-first approach and centralized data repositories for successful AI adoption. While the future promises generative AI features, businesses are advised to exercise prudence and seamlessly integrate AI capabilities into their platforms.

As organizations prepare for the rise of generative AI, Labovich suggests no-regret moves, such as using AI for critical documentation, customer communications, and knowledge-sharing, to yield immediate benefits in productivity and cost savings.

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