Why data is the limiting factor to all AI progress and business success

AI implementation hampered by data challenges: Recent surveys reveal that companies are struggling with data-related issues, hindering their ability to effectively implement artificial intelligence initiatives, particularly generative AI.

• A survey by Presidio of 1,000 IT executives found that 86% reported data-related barriers, such as difficulties in gaining meaningful insights and issues with real-time data access.
• Half of the executives surveyed believe they rushed into generative AI implementation before being fully prepared, with 84% of those who have adopted generative AI experiencing issues with their data sources.
• The survey highlights that readiness for AI adoption goes beyond just implementing the technology; it requires having the right data and infrastructure in place.

Operational integration concerns: The integration of AI into business operations is causing significant apprehension among IT leaders.

• A staggering 92% of IT leaders express concerns about integrating AI into their operations, indicating a widespread hesitation to fully operationalize AI technologies.
• This reluctance suggests that while there’s enthusiasm for AI’s potential, there are substantial reservations about its practical implementation in day-to-day business processes.

Factors contributing to AI project failures: The surveys identify key reasons why AI initiatives often fall short of expectations or fail outright.

• 20% of respondents caution that AI projects fail due to rushing into implementations too quickly, highlighting the importance of thorough planning and preparation.
• Data quality issues were cited by 17% as a primary cause of AI project failures, underscoring the critical role of clean, reliable data in successful AI implementations.
• In the healthcare sector, the problem of hasty adoption is even more pronounced, with 27% of executives pointing to it as a primary cause of failure.

Data governance challenges: The Quest Software and Enterprise Strategy Group survey emphasizes the importance of data governance in achieving AI and data-driven success.

• 33% of respondents cited evolving data and governance to an AI-ready state as a top-three bottleneck impacting their organization’s data value chain.
• Understanding the quality of source data was the most significant challenge, reported by 38% of respondents.
• Finding, identifying, and harvesting data assets was tied with data governance as a major challenge, also at 33%.

AI governance and metadata management: The survey highlights specific areas where organizations are struggling with data management and governance.

• Governing the use of AI models and data to deliver data mapping, lineage, and policies emerged as the most difficult management challenge.
• Metadata management, a crucial component of AI data readiness, saw a 21% increase in importance year over year.
• Other top challenges include data quality monitoring, remediation, profiling, quality scoring, and establishing data policies and controls.

Shift in perception needed: Steve Mitchell, CFO at Redgate Software, emphasizes the need for a change in how organizations view their data and technology investments.

• Many organizations still view technology and databases as cost centers rather than valuable assets.
• Mitchell argues that businesses need to recognize the growth opportunities and value creation potential that effective data management and utilization can bring.
• He suggests that organizations should seek more robust ways to measure the benefits of faster and improved data-focused decision-making, including improved commercial execution, resource efficiency, and team satisfaction.

Implications for future AI adoption: The findings from these surveys have significant implications for the future of AI adoption in businesses.

• Companies may need to slow down their AI implementation plans to ensure they have the necessary data infrastructure and governance in place.
• There’s likely to be an increased focus on data quality, management, and governance as prerequisites for successful AI initiatives.
• Organizations may need to invest more in data readiness and infrastructure before fully committing to large-scale AI projects.

Balancing act required: The surveys reveal a complex landscape where businesses must balance their enthusiasm for AI with the realities of their data capabilities.

• While AI continues to be a priority for IT investment, companies need to address fundamental data issues before they can fully leverage AI’s potential.
• This situation calls for a more measured approach to AI adoption, with a greater emphasis on building strong data foundations and governance frameworks.
• The path forward likely involves a dual focus on improving data management capabilities while cautiously advancing AI initiatives, ensuring that one supports and enhances the other.