Breaking Down the Data: Understanding the Power of AI
It has been long recognized that data is a valuable asset for companies of all sizes. The ability to improve user and customer experiences, and develop strategic plans based on empirical evidence, has held significant value. However, the advent of AI has exponentially increased the potential value of this data, despite the challenges it brings in data collection, curation, and preprocessing.
In a recent conversation with Henrique Lemes, Americas Data Platform Leader at IBM, the challenges enterprises face in implementing practical AI were discussed. The complexity of data, particularly between structured and unstructured sources, was highlighted as a key issue in the effective application of AI.
Henrique revealed a striking statistic: currently, less than 1% of enterprise data is utilized by generative AI, and over 90% of that data is unstructured. This unstructured data, which includes diverse formats like emails, social media posts, videos, images, documents, and audio files, directly affects trust and quality.
Concrete Consequences: AI and Your Business
This underutilization of data can have direct consequences on a business. Trust is crucial in decision-making, and if the available data is incomplete, unreliable, or improperly obtained, this trust is undermined. Less than half of available data is being used for AI, with unstructured data often sidelined due to the complexity of processing it.
However, the potential for businesses is enormous. By turning the trickle of easily consumed information into a firehose through automated ingestion, better decisions can be made based on a fuller set of empirical data. But, as Henrique emphasized, governance rules and data policies must still be applied – to unstructured and structured data alike.
As businesses scale and transform, the diversity and volume of their data increase. To keep up, AI data ingestion process must be not only scalable but flexible. Without this, businesses can encounter difficulties when scaling their AI solutions.
Looking Ahead: The Future of AI and Data
For the successful implementation of AI, three processes are crucial: ingestion at scale, curation and data governance, and making the data available for generative AI. According to Henrique, these processes can achieve over 40% of ROI over any conventional RAG use-case.
Companies like IBM are providing unified strategies, rooted in a deep understanding of the enterprise’s AI journey. These strategies enable organizations to transform both structured and unstructured data into AI-ready assets, all within the boundaries of existing governance and compliance frameworks.
As the AI journey continues, it’s essential to remember that it takes time to put the right processes in place, gravitate to the right tools, and have the necessary vision of how any data can be harnessed. The future holds great promise for those companies that can navigate the complexity of data and harness the power of AI effectively.
