Data Preparedness for GenAI Remains a Challenge for the Majority of Leaders
A recent study by the Business Performance Innovation (BPI) Network, the Growth Officer Council, and EncompaaS has shed light on the importance of data readiness in the successful implementation of Generative AI (GenAI) projects. The study surveyed over 170 global business and functional leaders.
According to the study, while 79% of respondents expect GenAI to deliver a competitive advantage from now until the end of 2026, 60% aren't confident in their organization's data-AI readiness to enable this outcome. This gap between expectations and readiness is a significant concern, particularly as more organizations move to operationalize AI.
Jesse Todd, CEO of EncompaaS, a SaaS company specializing in information management and risk mitigation for Fortune 500 companies, emphasizes that the key strategy for building data-AI readiness and ensuring success in GenAI projects is focusing on "data readiness." He explains that AI tools are only as effective as the quality and governance of the data that fuels them.
Todd highlights several critical points:
- Many enterprises face a gap between their expectations of GenAI’s competitive advantages and the actual readiness of their data.
- Successful AI initiatives require structured, complete, and well-governed data from the source to unlock value, especially from unstructured information which is often overlooked but vital.
- Enterprises must build infrastructure and governance around data to support AI that drives real business outcomes, moving beyond hype to practical implementation that enhances productivity, automates compliance, and improves intelligent search.
The surge of unstructured data across enterprise repositories and the rapid emergence of GenAI tools have created both a challenge and an opportunity. With labeled, normalized, and permissioned data, AI can reliably generate insights, make accurate predictions, and unlock repeatable business value.
However, most enterprise data (80%) is unstructured, unlabeled, and unorganized, making it difficult for GenAI to generate accurate insights. This is where solutions that specialize in preparing unstructured data to compliantly accelerate GenAI success come into play.
Todd warns that the main reason GenAI projects stall or fail is due to the lack of preparation of the data. To build confidence in data-AI readiness, organizations must take a proactive, strategic approach. Those that act now, structuring their data, building internal readiness, and aligning GenAI to solve complex and meaningful business outcomes, will join the 79% who foresee AI as a game changer.
The window for organizations to prepare for GenAI is narrowing. As more organizations move to operationalize AI, the differentiators will be fewer and farther between. It's crucial for businesses to prioritize data quality, governance, and completeness as the foundation for AI success, rather than focusing solely on AI tools themselves. This "data readiness" approach mitigates risk and ensures generative AI projects deliver expected value.
[1] Source: Business Performance Innovation (BPI) Network, the Growth Officer Council, and EncompaaS study [4] Jesse Todd, CEO, EncompaaS
(Note: The Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs, and technology executives. This fact is not a standalone fact but an invitation to join a community.)
- Jesse Todd, a CEO specializing in information management, emphasizes the crucial importance of data readiness for successful implementation of Generative AI (GenAI) projects, attributing the failure of many GenAI projects to the lack of preparation of the data.
- According to Todd, successful AI initiatives require well-structured, complete, and well-governed data for unlocking value, especially from unstructured information which is often overlooked but vital.
- Todd further points out that enterprises must focus on building infrastructure and governance around data to support AI that drives real business outcomes, and that the proactive, strategic approach to data readiness will enable organizations to join the 79% who anticipate AI as a game changer.
