Curated News
By: NewsRamp Editorial Staff
October 17, 2025
Hylaine VP Reveals 5 Key Roadblocks to AI Data Readiness
TLDR
- Hylaine's data governance framework gives companies a competitive edge by ensuring AI systems access clean, compliant data for accurate fraud detection and drug discovery.
- Hylaine recommends building AI-ready data infrastructure through data reliability engineering, modern ELT tools, and governance frameworks that monitor KPIs and ensure compliance.
- Proper AI data governance creates trustworthy systems that improve healthcare outcomes, prevent financial fraud, and build employee confidence in technology adoption.
- American Express and Astra Zeneca show how robust data architecture enables AI to analyze millions of transactions and accelerate drug discovery while maintaining compliance.
Impact - Why it Matters
This news matters because it addresses the critical gap between AI ambition and implementation reality that affects nearly every organization pursuing digital transformation. With studies showing up to 95% of generative AI pilots failing, the insights from Hylaine's technology leader provide a practical roadmap for avoiding costly mistakes and achieving sustainable AI success. For businesses across all sectors, understanding how to properly prepare data infrastructure and establish effective governance frameworks can mean the difference between wasted investments and measurable ROI. As AI becomes increasingly integrated into business operations, these data readiness principles will determine competitive advantage, regulatory compliance, and the ability to innovate safely in an AI-driven economy.
Summary
Ryan McElroy, Vice President of Technology at Hylaine, a values-first technology consulting firm, provides expert insights on overcoming the critical challenges companies face when preparing data for artificial intelligence implementation. Drawing from his extensive experience working with Fortune 1000 companies across insurance, healthcare, and manufacturing sectors, McElroy identifies five major roadblocks: data access limitations, siloed systems, poor data quality, inadequate governance frameworks, and organizational human factors. His practical approach emphasizes that successful AI initiatives require robust data infrastructure and strong governance from the outset, rather than rushing into implementation without proper foundations.
McElroy advocates for building mature, AI-ready data infrastructure through investments in data engineering tools and talent, modernizing data architectures to handle the scale AI demands, and establishing data reliability engineering as a core capability. He highlights the importance of governance frameworks that protect businesses while accelerating innovation, recommending companies create governance councils with cross-functional representation. The Hylaine webinar and white paper provide four practical approaches to solve the AI data readiness problem, emphasizing that companies should think about data governance in broader terms to keep data strategy on track through monitoring, auditing, and tracking KPIs including ROI metrics.
Drawing lessons from successful implementations at companies like American Express and Astra Zeneca, McElroy demonstrates how robust data architecture enables reliable, repeatable AI processes that produce positive returns on investment. He stresses that trust in AI systems comes from transparency, explainability, and collaboration between IT and business teams, recommending a trio of champions—executive sponsor, business process owner, and technical lead—to ensure alignment. To sustain long-term AI success, organizations must address skills gaps through training, hiring, or contracting external experts, while nurturing a culture of trust and curiosity around AI adoption.
Source Statement
This curated news summary relied on content disributed by citybiz. Read the original source here, Hylaine VP Reveals 5 Key Roadblocks to AI Data Readiness
