# Unlocking the Potential of Unstructured Data for AI Innovation

The world of data in today’s digital age is as vast as it is complex. The growth of artificial intelligence (AI) as a business tool has opened up immense possibilities for leveraging data to enhance operations, strategy, and customer experiences. Yet, the reality is stark: less than 1% of enterprise data is presently leveraged by AI. Addressing this shortfall requires not just recognizing unstructured data’s potential but also implementing scalable AI ingestion processes. This is a conviction IBM firmly believes in as it marries data governance with cutting-edge solutions to enhance return on investment (ROI).

## The Data Dilemma: A Personal Perspective

For decades, industries of all sizes have known that data holds immense value, crucial for crafting user experiences and underpinning strategic decisions. However, the accessibility and practicality of AI in recent years have unveiled the deep, untapped potential embedded within the data landscapes most businesses possess.

**Henrique Lemes**, the Americas Data Platform Leader at IBM, perfectly encapsulates this challenge: “Currently, less than 1% of enterprise data is utilized by generative AI, and over 90% of that data is unstructured, which directly affects trust and quality.” This statistic is a jolting reminder of the underutilization and underestimation of unstructured data’s worth.

So, why is unstructured data often left unused, even with AI’s advanced capabilities? The answer lies in the nature of unstructured data itself. Unlike its structured counterparts, which fit neatly into databases, unstructured data spans formats such as emails, social media posts, images, and videos — formats inherently resistant to standard processing methods. This complexity often deters companies from venturing into its integration, despite the wealth of insights it could reveal.

## Bridging the Gap: Lessons in Data Governance and Automation

One of the significant hurdles standing in the way of tapping into AI’s full potential with enterprise data is effective data governance. This process ensures data is complete, reliable, and ethically managed. Trust in data grows when businesses are confident that their information is accurate and securely handled — a key factor Henrique highlights as impacting the decision-makers’ reliance on the data they use.

IBM proposes a systematic approach to overcoming these challenges:

– **Ingestion at Scale**: Automating data ingestion processes is vital. It allows businesses to transition from handling data trickles to a full-blown data cascade, ensuring that both structured and unstructured data get due attention.

– **Curation and Governance**: Without rigorous data governance, it is impossible to maintain the integrity and reliability of data input. This governance must encompass privacy considerations, compliance with regulations, and security measures.

– **Availability for Generative AI**: Data must finally be accessible for AI applications to leverage. “We achieve over 40% of ROI over any conventional RAG use-case,” highlights Henrique. A testament to the strategic advantage good data management can create.

## Embracing Data Complexity for Future Success

The learning moment for many enterprises lies in embracing the complexity of their data environments rather than avoiding it. Businesses must accept that as they scale, their data will diversify in type and volume. Therefore, the AI data ingestion processes must be adaptable and robust enough to grow with the organization.

A common pitfall Henrique mentions is that “[companies] encounter difficulties when scaling because their AI solutions were initially built for specific tasks. When they attempt to broaden their scope, they often aren’t ready.” This emphasizes the importance of a solid foundational AI strategy, capable of evolving with an enterprise’s expanding needs.

By fostering a deep understanding of each client’s unique AI journey, IBM offers a comprehensive, unified strategy. This strategy not only prioritizes data accuracy, compliance with industry regulations but also facilitates scaling across diverse use cases. “We bring together the people, processes, and tools. It’s not inherently simple, but we simplify it by aligning all the essential resources,” shares Henrique. The essence of this approach lies in having a clear, scalable roadmap that maximizes the value of enterprise data.

## Wrapping Up: The Call to Action

As the pace of business continues to accelerate, and data becomes an even more integral asset, the question arises: How can your enterprise transition from merely possessing data to fully leveraging it for competitive advantage?

Engage with this challenge by considering the following:

– **Why wait to harness the potential of your unstructured data?** With robust AI ingestion processes, the insights are within reach.

– **What steps can be taken today to enhance data governance in your organization?** Building trust in your data sets the stage for effective AI solutions.

Through IBM’s strategic insights and technology solutions, enterprises in even the most regulated sectors are finding ways to make AI work for them at any scale. The path to leveraging your entire data ecosystem may not be simple, but with the right partnerships and strategies, the outcomes can be transformative. It’s time to unlock the full potential of your data and elevate your enterprise’s AI capabilities.