Hylaine Executive Identifies Key Strategies for Overcoming AI Data Readiness Challenges

Many artificial intelligence initiatives fail due to fundamental data readiness problems, with organizations struggling to access, clean, and govern the data necessary for effective AI deployment. According to Ryan McElroy, Vice President of Technology at Hylaine, the most significant barriers companies face include data access limitations, siloed systems, poor data quality, inadequate governance frameworks, and organizational misalignment between technical and business teams.

McElroy emphasizes that successful AI implementation requires building mature, AI-ready data infrastructure as a foundational first step. This involves investing in data engineering tools and talent while modernizing data architectures to handle the scale and velocity AI demands. Companies that maintain both data warehouses for structured data and data lakes for diverse data types position themselves for success. Establishing data reliability engineering as a core capability ensures ongoing data quality, availability, and observability, which streamlines testing and root cause analysis when errors occur in data movement.

Modern data integration tools play a crucial role in this infrastructure development. Organizations can leverage highly managed ELT tools such as FiveTran or Airbyte, or cloud-native ETL platforms like Azure Data Factory or Databricks to streamline data processing. These tools help overcome the technical challenges of accessing data trapped in legacy systems or incompatible formats.

The experience of industry leaders like American Express and Astra Zeneca demonstrates that robust data architecture forms the foundation for reliable AI systems in regulated environments. American Express built systems capable of analyzing transactions from millions of cardholders in real-time to detect fraud patterns, while Astra Zeneca's strong data foundation enables AI to inform drug discovery and clinical trial design within strict compliance boundaries. These successes highlight that AI achievement in regulated industries depends equally on governance and innovation.

McElroy stresses that governance frameworks should not hinder innovation but rather enable safe scaling of AI capabilities. Effective governance defines clear rules for data use, protects personal information, and prevents unauthorized use of proprietary content. Hylaine recommends creating governance councils with representatives from different business units and IT experts to maintain alignment across the organization. Technical safeguards such as data tokenization and automated PII exposure alerts, supported by tools like Perforce's Delphix, can enable continuous compliance without slowing development.

Organizational challenges represent another critical dimension of AI success. McElroy notes that trust in AI systems emerges from transparency, explainability, and collaboration between IT and business teams. The most successful projects typically involve a trio of champions: an executive sponsor, business process owner, and technical lead who ensure alignment across strategy, outcomes, and execution. For organizations beginning their AI journey, targeting user groups that are already pro-AI can reduce risk and provide clean feedback for subsequent projects.

Addressing the skills gap remains essential for sustained AI success. Many projects falter because data teams lack experience with modern cloud infrastructure, data engineering, and DevOps practices. Companies can close this gap through training, hiring new talent, or creating hybrid teams that pair internal staff with external experts. This approach allows organizations to leverage deep business knowledge while accelerating AI data preparedness.

Ultimately, McElroy emphasizes that technology alone doesn't deliver return on investment—people do. When employees understand both the data and the reasoning behind AI outputs, adoption follows naturally. Nurturing a culture of trust and curiosity around AI, where employees can see how AI supports their work and understand its outputs clearly, drives the sustained ROI that organizations seek. As an MIT study indicates, repeatable and scalable adoption, not one-off successes, generates lasting value from AI investments.