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Why data is the limiting factor to all AI progress and business success
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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.
Why data is the Achilles Heel of AI (and most every other business plan)

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