AI Success Hinges on Data Readiness and Governance, Says Hylaine Technology VP

The biggest barriers for preparing AI data are structural, technical, and organizational, with challenges consistently appearing in five areas: data access, siloed systems, data quality, governance, and the human factor according to Ryan McElroy, Vice President of Technology at Hylaine. Data access issues often stem from data that exists but can't be used due to legal or security blocks or because it's housed in incompatible formats or legacy systems. Siloed data remains a long-standing problem, especially as enterprises spread operations across multiple cloud platforms. Even when data can be accessed, quality problems like inaccuracies, redundancies, and incomplete records undermine model accuracy and lead to hallucinations or bias.

Tech leaders should begin by building a mature, AI-ready data infrastructure that includes investing in data engineering tools and talent. This means modernizing data architectures to handle additional ways of collecting, processing, and storing data at the scale and velocity AI requires. Companies that have both data warehouses with curated, reliable, and structured data sets and data lakes built to accommodate diverse data types have a head start. In parallel, leaders should establish data reliability engineering as a core capability in the data organization to ensure ongoing data quality, availability, and observability.

Once basic infrastructure and high quality architecture is in place, organizations can adopt modern tools for data integration. These can take the form of highly managed ELT tools such as FiveTran or Airbyte, or cloud-native ETL platforms like Azure Data Factory or Databricks. The lesson from successful implementations in regulated industries is clear: AI success depends on governance as much as innovation. Data must be clean, secure, and traceable, and governance must be built into the architecture from day one. When organizations design their data systems with compliance, transparency, and auditability in mind, they can confidently scale AI and demonstrate measurable business outcomes.

Trust in AI systems comes from transparency, explainability, and collaboration between IT and business teams. The most successful AI projects are led by a trio of champions: an executive sponsor, the business process owner, and a technical lead who ensure alignment across strategy, outcomes, and execution. To balance the pressure for rapid AI adoption with building sustainable infrastructure, organizations should resist the urge to chase short-term wins without a strong foundation. Data reliability engineering provides strategies and processes for ensuring data quality, availability, observability, testing and root cause analysis of errors. As an MIT study shows, it's repeatable and scalable adoption, not one-off successes, that drive sustained ROI from AI.

Strong governance is not a brake on innovation but what allows AI to scale safely. A good governance framework defines clear rules for data use, protects personal data, and prevents unauthorized use of proprietary content or data. At Hylaine, the most success occurs when companies think about data governance for AI in broader terms, setting the rules of the road for the company's data practices as a whole. This keeps the data strategy on track through monitoring, auditing, tracking KPIs including metrics for ROI, and reporting. To speed AI innovation, organizations can implement technical safeguards such as tokenizing real data and automating alerts for PII exposure using tools like Perforce's Delphix to support continuous compliance without slowing development.

Many AI projects falter because data teams lack experience with modern cloud infrastructure, data engineering, and DevOps. To close that gap, companies can train or hire new talent or create hybrid teams that pair internal staff with external experts. When employees understand both the data and the reasoning behind AI outputs, adoption follows naturally. Leaders need to nurture a culture of trust and curiosity around AI, ensuring employees understand how AI supports their work and can see its outputs explained clearly. This approach drives the sustained ROI that most organizations are chasing from their AI investments.