The Crucial Role of Data in AI: Understanding and Filling the Gap

The new IBM study has revealed two of the leading obstacles to AI success which include limited AI skills and expertise, and too much data complexity. Around 58% of companies are not yet actively implementing AI. The biggest inhibitors of generative AI at these non-AI-enabled companies include data privacy and trust and transparency.

Among the companies already deploying AI, the key barriers are often related to data, with some organizations taking steps towards trustworthy AI. Some industry leaders are sounding the alarm that organizational data may not be ready to support growing AI ambitions. Organizations looking to make progress in AI must strike a balance and acknowledge the significant role of unstructured data in the advancement of gen AI.

The wide variety of data that AI requires can be a vexing piece of the puzzle. For example, data at the edge is becoming a major source for large language models and repositories. Another consideration is that training data comes from a variety of sources, incorporating both public sources as well as an organization’s intellectual property.

Establishing a data-first approach is critical to the successful adoption of AI adoption for both corporate and internal IT processes. While the future promises features with generative AI at their core, we are still some time away from seeing these capabilities translate into tangible benefits for users. As organizations develop data strategies to accommodate the rise of gen AI, there are some no-regret moves that everyone can take to prepare for the inevitable change brought on by emerging technology.